{smcl} {com}{sf}{ul off}{txt}{.-} name: {res} {txt}log: {res}/Users/Sumaya/Desktop/ELPAAT 2019/elpat_nov18_fatigue_final.smcl {txt}log type: {res}smcl {txt}opened on: {res}18 Nov 2018, 16:44:27 {com}. **Patient Population is KTR and Dialysis patients ** . . keep if researchstudy2==8 {txt}(1,131 observations deleted) {com}. keep if real_enrollment_status == 3 {txt}(157 observations deleted) {com}. drop if rrt_simplified==3 {txt}(20 observations deleted) {com}. drop if rrt_simplified==. {txt}(9 observations deleted) {com}. . **Variables to generate** . *cci . gen crf_score_Cat = 0 if charlson_comorbidity_index < 3 {txt}(213 missing values generated) {com}. replace crf_score_Cat = 1 if charlson_comorbidity_index >= 3 & !mi(charlson_comorbidity_index) {txt}(200 real changes made) {com}. label variable crf_score_Cat "Categorised comorbidity crf_score >=3" . label define crf_score_Catlbl 0 "< 3" 1 ">= 3" . label values crf_score_Cat crf_score_Catlbl . . **binary RRT variable** . gen rrt_simplified2 = 0 if rrt_simplified == 1 {txt}(158 missing values generated) {com}. replace rrt_simplified2 = 1 if rrt_simplified == 2 {txt}(158 real changes made) {com}. label define rrt_simplifiedlbl2 0 "dialysis" 1 "tx" . label values rrt_simplified2 rrt_simplifiedlbl2 . . **A. PROMIS CONTINOUS VARIABLE** . graph bar fat_promis, over( rrt_simplified2) {res} {com}. summarize fat_promis if rrt_simplified2==0, detail {txt}Fatigue 8a- PROMIS-57 SF v1.0 {hline 61} Percentiles Smallest 1% {res} 33.1 33.1 {txt} 5% {res} 33.1 33.1 {txt}10% {res} 33.1 33.1 {txt}Obs {res} 163 {txt}25% {res} 48.1 33.1 {txt}Sum of Wgt. {res} 163 {txt}50% {res} 54.6 {txt}Mean {res} 54.18957 {txt}Largest Std. Dev. {res} 11.61938 {txt}75% {res} 63.3 74.2 {txt}90% {res} 68.6 77.8 {txt}Variance {res} 135.0101 {txt}95% {res} 71 77.8 {txt}Skewness {res}-.2535429 {txt}99% {res} 77.8 77.8 {txt}Kurtosis {res} 2.371687 {com}. summarize fat_promis if rrt_simplified2==1, detail {txt}Fatigue 8a- PROMIS-57 SF v1.0 {hline 61} Percentiles Smallest 1% {res} 33.1 33.1 {txt} 5% {res} 33.1 33.1 {txt}10% {res} 33.1 33.1 {txt}Obs {res} 157 {txt}25% {res} 41 33.1 {txt}Sum of Wgt. {res} 157 {txt}50% {res} 49.2 {txt}Mean {res} 49.23822 {txt}Largest Std. Dev. {res} 10.65636 {txt}75% {res} 56.6 72.4 {txt}90% {res} 63.3 72.4 {txt}Variance {res} 113.558 {txt}95% {res} 67.5 72.4 {txt}Skewness {res} .2094036 {txt}99% {res} 72.4 77.8 {txt}Kurtosis {res} 2.412715 {com}. ranksum fat_promis if rrt_simplified2 == 0 | rrt_simplified2 == 1, by(rrt_simplified2) {txt}Two-sample Wilcoxon rank-sum (Mann-Whitney) test rrt_simpli~2 {c |} obs rank sum expected {hline 13}{c +}{hline 33} dialysis {c |}{res}{col 17} 163{col 26} 29576{col 38} 26161.5 {txt}tx {c |}{res}{col 17} 157{col 26} 21784{col 38} 25198.5 {txt}{hline 13}{c +}{hline 33} combined {c |}{res}{col 17} 320{col 26} 51360{col 38} 51360 {txt}unadjusted variance{col 22}{res} 684559.25 {txt}adjustment for ties{col 22}{res} -2490.79 {txt}{col 22}{hline 10} adjusted variance{col 22}{res} 682068.46 {txt}Ho: fat_pr~s(rrt_si~2==dialysis) = fat_pr~s(rrt_si~2==tx) {col 14}z = {res} 4.134 {txt}{col 5}Prob > |z| = {res} 0.0000 {com}. **TABLE 1 Patient characteristics** . . tab rrt_simplified2 {txt}rrt_simplif {c |} ied2 {c |} Freq. Percent Cum. {hline 12}{c +}{hline 35} dialysis {c |}{res} 163 50.78 50.78 {txt} tx {c |}{res} 158 49.22 100.00 {txt}{hline 12}{c +}{hline 35} Total {c |}{res} 321 100.00 {com}. summarize age if rrt_simplified2==0, detail {txt}Age based on DOB-DOE {hline 61} Percentiles Smallest 1% {res} 23 18 {txt} 5% {res} 40 23 {txt}10% {res} 46 24 {txt}Obs {res} 163 {txt}25% {res} 54 27 {txt}Sum of Wgt. {res} 163 {txt}50% {res} 65 {txt}Mean {res} 63.66871 {txt}Largest Std. Dev. {res} 14.43677 {txt}75% {res} 75 86 {txt}90% {res} 81 88 {txt}Variance {res} 208.4204 {txt}95% {res} 83 90 {txt}Skewness {res}-.5978325 {txt}99% {res} 90 90 {txt}Kurtosis {res} 3.145726 {com}. summarize age if rrt_simplified2==1, detail {txt}Age based on DOB-DOE {hline 61} Percentiles Smallest 1% {res} 18 18 {txt} 5% {res} 20 18 {txt}10% {res} 22 19 {txt}Obs {res} 158 {txt}25% {res} 35 19 {txt}Sum of Wgt. {res} 158 {txt}50% {res} 53 {txt}Mean {res} 49.63924 {txt}Largest Std. Dev. {res} 16.84558 {txt}75% {res} 64 75 {txt}90% {res} 70 77 {txt}Variance {res} 283.7735 {txt}95% {res} 73 78 {txt}Skewness {res}-.3710831 {txt}99% {res} 78 84 {txt}Kurtosis {res} 2.096935 {com}. ranksum age if rrt_simplified2 == 0 | rrt_simplified2 == 1, by(rrt_simplified2) {txt}Two-sample Wilcoxon rank-sum (Mann-Whitney) test rrt_simpli~2 {c |} obs rank sum expected {hline 13}{c +}{hline 33} dialysis {c |}{res}{col 17} 163{col 26} 32183{col 38} 26243 {txt}tx {c |}{res}{col 17} 158{col 26} 19498{col 38} 25438 {txt}{hline 13}{c +}{hline 33} combined {c |}{res}{col 17} 321{col 26} 51681{col 38} 51681 {txt}unadjusted variance{col 22}{res} 691065.67 {txt}adjustment for ties{col 22}{res} -330.83 {txt}{col 22}{hline 10} adjusted variance{col 22}{res} 690734.84 {txt}Ho: age(rrt_si~2==dialysis) = age(rrt_si~2==tx) {col 14}z = {res} 7.147 {txt}{col 5}Prob > |z| = {res} 0.0000 {com}. . tab sex if rrt_simplified2==0 {txt}Sex - patient {c |} reported {c |} Freq. Percent Cum. {hline 21}{c +}{hline 35} Male {c |}{res} 94 57.67 57.67 {txt} Female {c |}{res} 69 42.33 100.00 {txt}{hline 21}{c +}{hline 35} Total {c |}{res} 163 100.00 {com}. tab sex if rrt_simplified2==1 {txt}Sex - patient {c |} reported {c |} Freq. Percent Cum. {hline 21}{c +}{hline 35} Male {c |}{res} 88 55.70 55.70 {txt} Female {c |}{res} 70 44.30 100.00 {txt}{hline 21}{c +}{hline 35} Total {c |}{res} 158 100.00 {com}. ranksum sex if rrt_simplified2 == 0 | rrt_simplified2 == 1, by(rrt_simplified2) {txt}Two-sample Wilcoxon rank-sum (Mann-Whitney) test rrt_simpli~2 {c |} obs rank sum expected {hline 13}{c +}{hline 33} dialysis {c |}{res}{col 17} 163{col 26} 25989{col 38} 26243 {txt}tx {c |}{res}{col 17} 158{col 26} 25692{col 38} 25438 {txt}{hline 13}{c +}{hline 33} combined {c |}{res}{col 17} 321{col 26} 51681{col 38} 51681 {txt}unadjusted variance{col 22}{res} 691065.67 {txt}adjustment for ties{col 22}{res}-182062.00 {txt}{col 22}{hline 10} adjusted variance{col 22}{res} 509003.67 {txt}Ho: sex(rrt_si~2==dialysis) = sex(rrt_si~2==tx) {col 14}z = {res} -0.356 {txt}{col 5}Prob > |z| = {res} 0.7218 {com}. . summarize alb if rrt_simplified2==0 {txt} Variable {c |} Obs Mean Std. Dev. Min Max {hline 13}{c +}{hline 57} {space 9}alb {c |}{res} 154 36.52597 3.729757 23 45 {com}. summarize alb if rrt_simplified2==1 {txt} Variable {c |} Obs Mean Std. Dev. Min Max {hline 13}{c +}{hline 57} {space 9}alb {c |}{res} 158 41.75316 3.07794 32 50 {com}. ranksum alb if rrt_simplified2 == 0 | rrt_simplified2 == 1, by(rrt_simplified2) {txt}Two-sample Wilcoxon rank-sum (Mann-Whitney) test rrt_simpli~2 {c |} obs rank sum expected {hline 13}{c +}{hline 33} dialysis {c |}{res}{col 17} 154{col 26} 15167.5{col 38} 24101 {txt}tx {c |}{res}{col 17} 158{col 26} 33660.5{col 38} 24727 {txt}{hline 13}{c +}{hline 33} combined {c |}{res}{col 17} 312{col 26} 48828{col 38} 48828 {txt}unadjusted variance{col 22}{res} 634659.67 {txt}adjustment for ties{col 22}{res} -3460.27 {txt}{col 22}{hline 10} adjusted variance{col 22}{res} 631199.39 {txt}Ho: alb(rrt_si~2==dialysis) = alb(rrt_si~2==tx) {col 14}z = {res}-11.244 {txt}{col 5}Prob > |z| = {res} 0.0000 {com}. . summarize hgb if rrt_simplified2==0 {txt} Variable {c |} Obs Mean Std. Dev. Min Max {hline 13}{c +}{hline 57} {space 9}hgb {c |}{res} 155 107.3516 14.73358 12.5 152 {com}. summarize hgb if rrt_simplified2==1 {txt} Variable {c |} Obs Mean Std. Dev. Min Max {hline 13}{c +}{hline 57} {space 9}hgb {c |}{res} 158 126.0443 16.50722 84 160 {com}. ranksum hgb if rrt_simplified2 == 0 | rrt_simplified2 == 1, by(rrt_simplified2) {txt}Two-sample Wilcoxon rank-sum (Mann-Whitney) test rrt_simpli~2 {c |} obs rank sum expected {hline 13}{c +}{hline 33} dialysis {c |}{res}{col 17} 155{col 26} 16685{col 38} 24335 {txt}tx {c |}{res}{col 17} 158{col 26} 32456{col 38} 24806 {txt}{hline 13}{c +}{hline 33} combined {c |}{res}{col 17} 313{col 26} 49141{col 38} 49141 {txt}unadjusted variance{col 22}{res} 640821.67 {txt}adjustment for ties{col 22}{res} -303.44 {txt}{col 22}{hline 10} adjusted variance{col 22}{res} 640518.22 {txt}Ho: hgb(rrt_si~2==dialysis) = hgb(rrt_si~2==tx) {col 14}z = {res} -9.559 {txt}{col 5}Prob > |z| = {res} 0.0000 {com}. . tabulate marital_status2 rrt_simplified2, row column chi2 {txt} {c TLC}{hline 19}{c TRC} {c |} Key{col 21}{c |} {c LT}{hline 19}{c RT} {c |}{space 5}{it:frequency}{col 21}{c |} {c |}{space 2}{it:row percentage}{col 21}{c |} {c |}{space 1}{it:column percentage}{col 21}{c |} {c BLC}{hline 19}{c BRC} Grouped {c |} rrt_simplified2 marital_status {c |} dialysis tx {c |} Total {hline 22}{c +}{hline 22}{c +}{hline 10} Single {c |}{res} 35 42 {txt}{c |}{res} 77 {txt}{c |}{res} 45.45 54.55 {txt}{c |}{res} 100.00 {txt}{c |}{res} 21.60 26.92 {txt}{c |}{res} 24.21 {txt}{hline 22}{c +}{hline 22}{c +}{hline 10} Married/Common-Law {c |}{res} 63 95 {txt}{c |}{res} 158 {txt}{c |}{res} 39.87 60.13 {txt}{c |}{res} 100.00 {txt}{c |}{res} 38.89 60.90 {txt}{c |}{res} 49.69 {txt}{hline 22}{c +}{hline 22}{c +}{hline 10} Divorced/Separated/Wi {c |}{res} 64 19 {txt}{c |}{res} 83 {txt}{c |}{res} 77.11 22.89 {txt}{c |}{res} 100.00 {txt}{c |}{res} 39.51 12.18 {txt}{c |}{res} 26.10 {txt}{hline 22}{c +}{hline 22}{c +}{hline 10} Total {c |}{res} 162 156 {txt}{c |}{res} 318 {txt}{c |}{res} 50.94 49.06 {txt}{c |}{res} 100.00 {txt}{c |}{res} 100.00 100.00 {txt}{c |}{res} 100.00 {txt} Pearson chi2({res}2{txt}) = {res} 31.4129 {txt} Pr = {res}0.000 {com}. tabulate income3 rrt_simplified2, row column chi2 {txt} {c TLC}{hline 19}{c TRC} {c |} Key{col 21}{c |} {c LT}{hline 19}{c RT} {c |}{space 5}{it:frequency}{col 21}{c |} {c |}{space 2}{it:row percentage}{col 21}{c |} {c |}{space 1}{it:column percentage}{col 21}{c |} {c BLC}{hline 19}{c BRC} Subgrouped {c |} 2 cat {c |} Income <30 {c |} rrt_simplified2 >30 {c |} dialysis tx {c |} Total {hline 11}{c +}{hline 22}{c +}{hline 10} <30k {c |}{res} 68 17 {txt}{c |}{res} 85 {txt}{c |}{res} 80.00 20.00 {txt}{c |}{res} 100.00 {txt}{c |}{res} 65.38 14.29 {txt}{c |}{res} 38.12 {txt}{hline 11}{c +}{hline 22}{c +}{hline 10} >30k {c |}{res} 36 102 {txt}{c |}{res} 138 {txt}{c |}{res} 26.09 73.91 {txt}{c |}{res} 100.00 {txt}{c |}{res} 34.62 85.71 {txt}{c |}{res} 61.88 {txt}{hline 11}{c +}{hline 22}{c +}{hline 10} Total {c |}{res} 104 119 {txt}{c |}{res} 223 {txt}{c |}{res} 46.64 53.36 {txt}{c |}{res} 100.00 {txt}{c |}{res} 100.00 100.00 {txt}{c |}{res} 100.00 {txt} Pearson chi2({res}1{txt}) = {res} 61.4342 {txt} Pr = {res}0.000 {com}. tabulate crf_score_Cat rrt_simplified2, row column chi2 {txt} {c TLC}{hline 19}{c TRC} {c |} Key{col 21}{c |} {c LT}{hline 19}{c RT} {c |}{space 5}{it:frequency}{col 21}{c |} {c |}{space 2}{it:row percentage}{col 21}{c |} {c |}{space 1}{it:column percentage}{col 21}{c |} {c BLC}{hline 19}{c BRC} Categorise {c |} d {c |} comorbidit {c |} y {c |} crf_score {c |} rrt_simplified2 >=3 {c |} dialysis tx {c |} Total {hline 11}{c +}{hline 22}{c +}{hline 10} < 3 {c |}{res} 39 69 {txt}{c |}{res} 108 {txt}{c |}{res} 36.11 63.89 {txt}{c |}{res} 100.00 {txt}{c |}{res} 26.00 43.67 {txt}{c |}{res} 35.06 {txt}{hline 11}{c +}{hline 22}{c +}{hline 10} >= 3 {c |}{res} 111 89 {txt}{c |}{res} 200 {txt}{c |}{res} 55.50 44.50 {txt}{c |}{res} 100.00 {txt}{c |}{res} 74.00 56.33 {txt}{c |}{res} 64.94 {txt}{hline 11}{c +}{hline 22}{c +}{hline 10} Total {c |}{res} 150 158 {txt}{c |}{res} 308 {txt}{c |}{res} 48.70 51.30 {txt}{c |}{res} 100.00 {txt}{c |}{res} 100.00 100.00 {txt}{c |}{res} 100.00 {txt} Pearson chi2({res}1{txt}) = {res} 10.5527 {txt} Pr = {res}0.001 {com}. tabulate whiteNonW rrt_simplified2, row column chi2 {txt} {c TLC}{hline 19}{c TRC} {c |} Key{col 21}{c |} {c LT}{hline 19}{c RT} {c |}{space 5}{it:frequency}{col 21}{c |} {c |}{space 2}{it:row percentage}{col 21}{c |} {c |}{space 1}{it:column percentage}{col 21}{c |} {c BLC}{hline 19}{c BRC} Ethnicity2 {c |} rrt_simplified2 == 5 {c |} dialysis tx {c |} Total {hline 11}{c +}{hline 22}{c +}{hline 10} Not White {c |}{res} 90 59 {txt}{c |}{res} 149 {txt}{c |}{res} 60.40 39.60 {txt}{c |}{res} 100.00 {txt}{c |}{res} 59.21 39.86 {txt}{c |}{res} 49.67 {txt}{hline 11}{c +}{hline 22}{c +}{hline 10} White {c |}{res} 62 89 {txt}{c |}{res} 151 {txt}{c |}{res} 41.06 58.94 {txt}{c |}{res} 100.00 {txt}{c |}{res} 40.79 60.14 {txt}{c |}{res} 50.33 {txt}{hline 11}{c +}{hline 22}{c +}{hline 10} Total {c |}{res} 152 148 {txt}{c |}{res} 300 {txt}{c |}{res} 50.67 49.33 {txt}{c |}{res} 100.00 {txt}{c |}{res} 100.00 100.00 {txt}{c |}{res} 100.00 {txt} Pearson chi2({res}1{txt}) = {res} 11.2261 {txt} Pr = {res}0.001 {com}. **model 1: fat_promis and RRT . regress fat_promis i0.rrt_simplified2 {txt} Source {c |} SS df MS Number of obs ={res} 320 {txt}{hline 13}{c +}{hline 34} F(1, 318) = {res} 15.75 {txt} Model {c |} {res} 1960.58325 1 1960.58325 {txt}Prob > F ={res} 0.0001 {txt} Residual {c |} {res} 39586.6875 318 124.486439 {txt}R-squared ={res} 0.0472 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.0442 {txt} Total {c |} {res} 41547.2708 319 130.242228 {txt}Root MSE = {res} 11.157 {txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fat_promis{col 17}{c |} Coef.{col 29} Std. Err.{col 41} t{col 49} P>|t|{col 57} [95% Con{col 70}f. Interval] {hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} rrt_simplified2 {c |} {space 6}dialysis {c |}{col 17}{res}{space 2} 4.951354{col 29}{space 2} 1.247649{col 40}{space 1} 3.97{col 49}{space 3}0.000{col 57}{space 4} 2.496665{col 70}{space 3} 7.406044 {txt}{space 10}_cons {c |}{col 17}{res}{space 2} 49.23822{col 29}{space 2} .8904534{col 40}{space 1} 55.30{col 49}{space 3}0.000{col 57}{space 4} 47.48629{col 70}{space 3} 50.99014 {txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {com}. . **model 2: MODEL 1+ Clinical variables ([alb] hgb, comorbidity) . regress fat_promis i0.rrt_simplified2 i.crf_score_Cat alb hgb {txt} Source {c |} SS df MS Number of obs ={res} 306 {txt}{hline 13}{c +}{hline 34} F(4, 301) = {res} 6.89 {txt} Model {c |} {res} 3297.20672 4 824.30168 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 36035.0828 301 119.717883 {txt}R-squared ={res} 0.0838 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.0717 {txt} Total {c |} {res} 39332.2895 305 128.958326 {txt}Root MSE = {res} 10.942 {txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fat_promis{col 17}{c |} Coef.{col 29} Std. Err.{col 41} t{col 49} P>|t|{col 57} [95% Con{col 70}f. Interval] {hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} rrt_simplified2 {c |} {space 6}dialysis {c |}{col 17}{res}{space 2} 1.985559{col 29}{space 2} 1.693378{col 40}{space 1} 1.17{col 49}{space 3}0.242{col 57}{space 4}-1.346799{col 70}{space 3} 5.317917 {txt}{space 15} {c |} {space 2}crf_score_Cat {c |} {space 10}>= 3 {c |}{col 17}{res}{space 2} 1.214366{col 29}{space 2} 1.337311{col 40}{space 1} 0.91{col 49}{space 3}0.365{col 57}{space 4}-1.417297{col 70}{space 3} 3.846029 {txt}{space 12}alb {c |}{col 17}{res}{space 2}-.1588251{col 29}{space 2} .1920697{col 40}{space 1} -0.83{col 49}{space 3}0.409{col 57}{space 4}-.5367946{col 70}{space 3} .2191444 {txt}{space 12}hgb {c |}{col 17}{res}{space 2}-.1102644{col 29}{space 2} .0416954{col 40}{space 1} -2.64{col 49}{space 3}0.009{col 57}{space 4}-.1923159{col 70}{space 3}-.0282129 {txt}{space 10}_cons {c |}{col 17}{res}{space 2} 69.0812{col 29}{space 2} 8.550451{col 40}{space 1} 8.08{col 49}{space 3}0.000{col 57}{space 4} 52.25497{col 70}{space 3} 85.90743 {txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {com}. regress fat_promis i0.rrt_simplified2 i.crf_score_Cat hgb {txt} Source {c |} SS df MS Number of obs ={res} 307 {txt}{hline 13}{c +}{hline 34} F(3, 303) = {res} 8.73 {txt} Model {c |} {res} 3158.00279 3 1052.6676 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 36521.0006 303 120.531355 {txt}R-squared ={res} 0.0796 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.0705 {txt} Total {c |} {res} 39679.0034 306 129.669946 {txt}Root MSE = {res} 10.979 {txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fat_promis{col 17}{c |} Coef.{col 29} Std. Err.{col 41} t{col 49} P>|t|{col 57} [95% Con{col 70}f. Interval] {hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} rrt_simplified2 {c |} {space 6}dialysis {c |}{col 17}{res}{space 2} 2.476504{col 29}{space 2} 1.485283{col 40}{space 1} 1.67{col 49}{space 3}0.096{col 57}{space 4}-.4462703{col 70}{space 3} 5.399279 {txt}{space 15} {c |} {space 2}crf_score_Cat {c |} {space 10}>= 3 {c |}{col 17}{res}{space 2} 1.223284{col 29}{space 2} 1.336629{col 40}{space 1} 0.92{col 49}{space 3}0.361{col 57}{space 4}-1.406968{col 70}{space 3} 3.853536 {txt}{space 12}hgb {c |}{col 17}{res}{space 2}-.1223322{col 29}{space 2} .0404424{col 40}{space 1} -3.02{col 49}{space 3}0.003{col 57}{space 4}-.2019156{col 70}{space 3}-.0427487 {txt}{space 10}_cons {c |}{col 17}{res}{space 2} 63.96095{col 29}{space 2} 5.246269{col 40}{space 1} 12.19{col 49}{space 3}0.000{col 57}{space 4} 53.63722{col 70}{space 3} 74.28469 {txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {com}. . **model 3: MODEL 2+ soci- factors (sex, age , ethnicity ,marital status, ) . regress fat_promis i0.rrt_simplified2 i.crf_score_Cat alb hgb i.gender2 age ib1.ethnicity_4cat i.marital_status2 {txt} Source {c |} SS df MS Number of obs ={res} 303 {txt}{hline 13}{c +}{hline 34} F(11, 291) = {res} 4.11 {txt} Model {c |} {res} 5244.59814 11 476.781649 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 33794.0287 291 116.130683 {txt}R-squared ={res} 0.1343 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.1016 {txt} Total {c |} {res} 39038.6268 302 129.266976 {txt}Root MSE = {res} 10.776 {txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fat_promis{col 29}{c |} Coef.{col 41} Std. Err.{col 53} t{col 61} P>|t|{col 69} [95% Con{col 82}f. Interval] {hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 12}rrt_simplified2 {c |} {space 18}dialysis {c |}{col 29}{res}{space 2} 3.98378{col 41}{space 2} 1.877288{col 52}{space 1} 2.12{col 61}{space 3}0.035{col 69}{space 4} .2889961{col 82}{space 3} 7.678564 {txt}{space 27} {c |} {space 14}crf_score_Cat {c |} {space 22}>= 3 {c |}{col 29}{res}{space 2} 2.100176{col 41}{space 2} 1.400321{col 52}{space 1} 1.50{col 61}{space 3}0.135{col 69}{space 4}-.6558646{col 82}{space 3} 4.856217 {txt}{space 24}alb {c |}{col 29}{res}{space 2} -.184835{col 41}{space 2} .1963179{col 52}{space 1} -0.94{col 61}{space 3}0.347{col 69}{space 4} -.571218{col 82}{space 3} .201548 {txt}{space 24}hgb {c |}{col 29}{res}{space 2}-.0876384{col 41}{space 2} .0433253{col 52}{space 1} -2.02{col 61}{space 3}0.044{col 69}{space 4}-.1729091{col 82}{space 3}-.0023678 {txt}{space 27} {c |} {space 20}gender2 {c |} {space 18} Female {c |}{col 29}{res}{space 2} 2.472934{col 41}{space 2} 1.337657{col 52}{space 1} 1.85{col 61}{space 3}0.066{col 69}{space 4}-.1597747{col 82}{space 3} 5.105643 {txt}{space 24}age {c |}{col 29}{res}{space 2}-.0598872{col 41}{space 2} .0489549{col 52}{space 1} -1.22{col 61}{space 3}0.222{col 69}{space 4}-.1562377{col 82}{space 3} .0364634 {txt}{space 27} {c |} {space 13}ethnicity_4cat {c |} {space 21}Asian {c |}{col 29}{res}{space 2} 2.942397{col 41}{space 2} 1.800378{col 52}{space 1} 1.63{col 61}{space 3}0.103{col 69}{space 4}-.6010157{col 82}{space 3} 6.48581 {txt}{space 21}Black {c |}{col 29}{res}{space 2}-2.841796{col 41}{space 2} 1.616505{col 52}{space 1} -1.76{col 61}{space 3}0.080{col 69}{space 4}-6.023321{col 82}{space 3} .3397278 {txt}{space 21}Other {c |}{col 29}{res}{space 2} 4.387389{col 41}{space 2} 2.141528{col 52}{space 1} 2.05{col 61}{space 3}0.041{col 69}{space 4} .1725425{col 82}{space 3} 8.602236 {txt}{space 27} {c |} {space 12}marital_status2 {c |} {space 8}Married/Common-Law {c |}{col 29}{res}{space 2} .7384644{col 41}{space 2} 1.791146{col 52}{space 1} 0.41{col 61}{space 3}0.680{col 69}{space 4}-2.786779{col 82}{space 3} 4.263708 {txt}Divorced/Separated/Widowed {c |}{col 29}{res}{space 2} .7244395{col 41}{space 2} 2.038064{col 52}{space 1} 0.36{col 61}{space 3}0.723{col 69}{space 4}-3.286775{col 82}{space 3} 4.735655 {txt}{space 27} {c |} {space 22}_cons {c |}{col 29}{res}{space 2} 67.44745{col 41}{space 2} 9.194262{col 52}{space 1} 7.34{col 61}{space 3}0.000{col 69}{space 4} 49.35177{col 82}{space 3} 85.54313 {txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {com}. regress fat_promis i0.rrt_simplified2 i.crf_score_Cat hgb i.gender2 age ib1.ethnicity_4cat i.marital_status2 {txt} Source {c |} SS df MS Number of obs ={res} 304 {txt}{hline 13}{c +}{hline 34} F(10, 293) = {res} 4.39 {txt} Model {c |} {res} 5128.36857 10 512.836857 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 34256.6729 293 116.916972 {txt}R-squared ={res} 0.1302 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.1005 {txt} Total {c |} {res} 39385.0415 303 129.983635 {txt}Root MSE = {res} 10.813 {txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fat_promis{col 29}{c |} Coef.{col 41} Std. Err.{col 53} t{col 61} P>|t|{col 69} [95% Con{col 82}f. Interval] {hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 12}rrt_simplified2 {c |} {space 18}dialysis {c |}{col 29}{res}{space 2} 4.435141{col 41}{space 2} 1.753687{col 52}{space 1} 2.53{col 61}{space 3}0.012{col 69}{space 4} .9837206{col 82}{space 3} 7.886561 {txt}{space 27} {c |} {space 14}crf_score_Cat {c |} {space 22}>= 3 {c |}{col 29}{res}{space 2} 2.002752{col 41}{space 2} 1.403165{col 52}{space 1} 1.43{col 61}{space 3}0.155{col 69}{space 4}-.7588083{col 82}{space 3} 4.764311 {txt}{space 24}hgb {c |}{col 29}{res}{space 2}-.1013781{col 41}{space 2} .0418107{col 52}{space 1} -2.42{col 61}{space 3}0.016{col 69}{space 4}-.1836654{col 82}{space 3}-.0190907 {txt}{space 27} {c |} {space 20}gender2 {c |} {space 18} Female {c |}{col 29}{res}{space 2} 2.510715{col 41}{space 2} 1.341707{col 52}{space 1} 1.87{col 61}{space 3}0.062{col 69}{space 4}-.1298903{col 82}{space 3} 5.151321 {txt}{space 24}age {c |}{col 29}{res}{space 2}-.0550182{col 41}{space 2} .0482586{col 52}{space 1} -1.14{col 61}{space 3}0.255{col 69}{space 4}-.1499956{col 82}{space 3} .0399592 {txt}{space 27} {c |} {space 13}ethnicity_4cat {c |} {space 21}Asian {c |}{col 29}{res}{space 2} 2.908125{col 41}{space 2} 1.801001{col 52}{space 1} 1.61{col 61}{space 3}0.107{col 69}{space 4}-.6364129{col 82}{space 3} 6.452662 {txt}{space 21}Black {c |}{col 29}{res}{space 2}-2.759611{col 41}{space 2} 1.615094{col 52}{space 1} -1.71{col 61}{space 3}0.089{col 69}{space 4}-5.938268{col 82}{space 3} .4190452 {txt}{space 21}Other {c |}{col 29}{res}{space 2} 4.445408{col 41}{space 2} 2.146751{col 52}{space 1} 2.07{col 61}{space 3}0.039{col 69}{space 4} .2204012{col 82}{space 3} 8.670415 {txt}{space 27} {c |} {space 12}marital_status2 {c |} {space 8}Married/Common-Law {c |}{col 29}{res}{space 2} 1.030023{col 41}{space 2} 1.783073{col 52}{space 1} 0.58{col 61}{space 3}0.564{col 69}{space 4}-2.479232{col 82}{space 3} 4.539277 {txt}Divorced/Separated/Widowed {c |}{col 29}{res}{space 2} 1.178927{col 41}{space 2} 2.030713{col 52}{space 1} 0.58{col 61}{space 3}0.562{col 69}{space 4}-2.817706{col 82}{space 3} 5.17556 {txt}{space 27} {c |} {space 22}_cons {c |}{col 29}{res}{space 2} 61.011{col 41}{space 2} 5.888637{col 52}{space 1} 10.36{col 61}{space 3}0.000{col 69}{space 4} 49.42161{col 82}{space 3} 72.60039 {txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {com}. . **model 4: MODEL 3+ economic- factors (education, income ) . regress fat_promis i0.rrt_simplified2 i.crf_score_Cat alb hgb i.gender2 age ib1.ethnicity_4cat i.marital_status2 education2 i.income3 {txt} Source {c |} SS df MS Number of obs ={res} 205 {txt}{hline 13}{c +}{hline 34} F(13, 191) = {res} 3.61 {txt} Model {c |} {res} 4782.54238 13 367.887875 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 19462.3605 191 101.897176 {txt}R-squared ={res} 0.1973 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.1426 {txt} Total {c |} {res} 24244.9029 204 118.847563 {txt}Root MSE = {res} 10.094 {txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fat_promis{col 29}{c |} Coef.{col 41} Std. Err.{col 53} t{col 61} P>|t|{col 69} [95% Con{col 82}f. Interval] {hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 12}rrt_simplified2 {c |} {space 18}dialysis {c |}{col 29}{res}{space 2} 2.453433{col 41}{space 2} 2.265843{col 52}{space 1} 1.08{col 61}{space 3}0.280{col 69}{space 4}-2.015857{col 82}{space 3} 6.922722 {txt}{space 27} {c |} {space 14}crf_score_Cat {c |} {space 22}>= 3 {c |}{col 29}{res}{space 2} 1.168316{col 41}{space 2} 1.576868{col 52}{space 1} 0.74{col 61}{space 3}0.460{col 69}{space 4}-1.941996{col 82}{space 3} 4.278629 {txt}{space 24}alb {c |}{col 29}{res}{space 2}-.5020455{col 41}{space 2} .2555081{col 52}{space 1} -1.96{col 61}{space 3}0.051{col 69}{space 4}-1.006026{col 82}{space 3} .0019346 {txt}{space 24}hgb {c |}{col 29}{res}{space 2} -.071213{col 41}{space 2} .0484789{col 52}{space 1} -1.47{col 61}{space 3}0.143{col 69}{space 4}-.1668358{col 82}{space 3} .0244099 {txt}{space 27} {c |} {space 20}gender2 {c |} {space 18} Female {c |}{col 29}{res}{space 2} 1.835513{col 41}{space 2} 1.562404{col 52}{space 1} 1.17{col 61}{space 3}0.242{col 69}{space 4}-1.246271{col 82}{space 3} 4.917296 {txt}{space 24}age {c |}{col 29}{res}{space 2} -.079865{col 41}{space 2} .0564703{col 52}{space 1} -1.41{col 61}{space 3}0.159{col 69}{space 4}-.1912505{col 82}{space 3} .0315205 {txt}{space 27} {c |} {space 13}ethnicity_4cat {c |} {space 21}Asian {c |}{col 29}{res}{space 2} 5.559984{col 41}{space 2} 2.051018{col 52}{space 1} 2.71{col 61}{space 3}0.007{col 69}{space 4} 1.514429{col 82}{space 3} 9.60554 {txt}{space 21}Black {c |}{col 29}{res}{space 2} .5701855{col 41}{space 2} 2.005715{col 52}{space 1} 0.28{col 61}{space 3}0.777{col 69}{space 4}-3.386012{col 82}{space 3} 4.526383 {txt}{space 21}Other {c |}{col 29}{res}{space 2} 8.516103{col 41}{space 2} 2.58348{col 52}{space 1} 3.30{col 61}{space 3}0.001{col 69}{space 4} 3.420288{col 82}{space 3} 13.61192 {txt}{space 27} {c |} {space 12}marital_status2 {c |} {space 8}Married/Common-Law {c |}{col 29}{res}{space 2}-1.184369{col 41}{space 2} 2.132076{col 52}{space 1} -0.56{col 61}{space 3}0.579{col 69}{space 4}-5.389808{col 82}{space 3} 3.02107 {txt}Divorced/Separated/Widowed {c |}{col 29}{res}{space 2}-.3822967{col 41}{space 2} 2.339654{col 52}{space 1} -0.16{col 61}{space 3}0.870{col 69}{space 4}-4.997175{col 82}{space 3} 4.232581 {txt}{space 27} {c |} {space 17}education2 {c |}{col 29}{res}{space 2}-2.332509{col 41}{space 2} 1.77636{col 52}{space 1} -1.31{col 61}{space 3}0.191{col 69}{space 4}-5.836312{col 82}{space 3} 1.171294 {txt}{space 27} {c |} {space 20}income3 {c |} {space 22}>30k {c |}{col 29}{res}{space 2} 4.304829{col 41}{space 2} 2.009857{col 52}{space 1} 2.14{col 61}{space 3}0.033{col 69}{space 4} .340463{col 82}{space 3} 8.269196 {txt}{space 22}_cons {c |}{col 29}{res}{space 2} 79.0659{col 41}{space 2} 11.77416{col 52}{space 1} 6.72{col 61}{space 3}0.000{col 69}{space 4} 55.84181{col 82}{space 3} 102.29 {txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {com}. regress fat_promis i0.rrt_simplified2 i.crf_score_Cat hgb i.gender2 age ib1.ethnicity_4cat i.marital_status2 education2 i.income3 {txt} Source {c |} SS df MS Number of obs ={res} 206 {txt}{hline 13}{c +}{hline 34} F(12, 193) = {res} 3.50 {txt} Model {c |} {res} 4394.30766 12 366.192305 {txt}Prob > F ={res} 0.0001 {txt} Residual {c |} {res} 20216.3468 193 104.747911 {txt}R-squared ={res} 0.1786 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.1275 {txt} Total {c |} {res} 24610.6545 205 120.051973 {txt}Root MSE = {res} 10.235 {txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fat_promis{col 29}{c |} Coef.{col 41} Std. Err.{col 53} t{col 61} P>|t|{col 69} [95% Con{col 82}f. Interval] {hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 12}rrt_simplified2 {c |} {space 18}dialysis {c |}{col 29}{res}{space 2} 3.856692{col 41}{space 2} 2.054605{col 52}{space 1} 1.88{col 61}{space 3}0.062{col 69}{space 4}-.1956702{col 82}{space 3} 7.909055 {txt}{space 27} {c |} {space 14}crf_score_Cat {c |} {space 22}>= 3 {c |}{col 29}{res}{space 2} 1.103883{col 41}{space 2} 1.591935{col 52}{space 1} 0.69{col 61}{space 3}0.489{col 69}{space 4}-2.035942{col 82}{space 3} 4.243708 {txt}{space 24}hgb {c |}{col 29}{res}{space 2}-.1017744{col 41}{space 2} .0468067{col 52}{space 1} -2.17{col 61}{space 3}0.031{col 69}{space 4}-.1940928{col 82}{space 3} -.009456 {txt}{space 27} {c |} {space 20}gender2 {c |} {space 18} Female {c |}{col 29}{res}{space 2} 2.007711{col 41}{space 2} 1.581984{col 52}{space 1} 1.27{col 61}{space 3}0.206{col 69}{space 4}-1.112487{col 82}{space 3} 5.127909 {txt}{space 24}age {c |}{col 29}{res}{space 2}-.0639926{col 41}{space 2} .0560898{col 52}{space 1} -1.14{col 61}{space 3}0.255{col 69}{space 4}-.1746204{col 82}{space 3} .0466351 {txt}{space 27} {c |} {space 13}ethnicity_4cat {c |} {space 21}Asian {c |}{col 29}{res}{space 2} 5.506962{col 41}{space 2} 2.05948{col 52}{space 1} 2.67{col 61}{space 3}0.008{col 69}{space 4} 1.444985{col 82}{space 3} 9.568939 {txt}{space 21}Black {c |}{col 29}{res}{space 2} .6281129{col 41}{space 2} 2.032061{col 52}{space 1} 0.31{col 61}{space 3}0.758{col 69}{space 4}-3.379785{col 82}{space 3} 4.636011 {txt}{space 21}Other {c |}{col 29}{res}{space 2} 8.656546{col 41}{space 2} 2.618801{col 52}{space 1} 3.31{col 61}{space 3}0.001{col 69}{space 4} 3.491403{col 82}{space 3} 13.82169 {txt}{space 27} {c |} {space 12}marital_status2 {c |} {space 8}Married/Common-Law {c |}{col 29}{res}{space 2} -.554692{col 41}{space 2} 2.124664{col 52}{space 1} -0.26{col 61}{space 3}0.794{col 69}{space 4}-4.745234{col 82}{space 3} 3.63585 {txt}Divorced/Separated/Widowed {c |}{col 29}{res}{space 2} .2970475{col 41}{space 2} 2.343509{col 52}{space 1} 0.13{col 61}{space 3}0.899{col 69}{space 4}-4.325129{col 82}{space 3} 4.919224 {txt}{space 27} {c |} {space 17}education2 {c |}{col 29}{res}{space 2}-2.377426{col 41}{space 2} 1.773055{col 52}{space 1} -1.34{col 61}{space 3}0.182{col 69}{space 4}-5.874478{col 82}{space 3} 1.119626 {txt}{space 27} {c |} {space 20}income3 {c |} {space 22}>30k {c |}{col 29}{res}{space 2} 4.223306{col 41}{space 2} 2.009478{col 52}{space 1} 2.10{col 61}{space 3}0.037{col 69}{space 4} .2599492{col 82}{space 3} 8.186663 {txt}{space 22}_cons {c |}{col 29}{res}{space 2} 60.76233{col 41}{space 2} 6.965269{col 52}{space 1} 8.72{col 61}{space 3}0.000{col 69}{space 4} 47.02451{col 82}{space 3} 74.50015 {txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {com}. . **model 5: MODEL 4+ Psycological factors (anx and dep from promis) . . regress fat_promis i0.rrt_simplified2 i.crf_score_Cat alb hgb i.gender2 age ib1.ethnicity_4cat i.marital_status2 education2 i.income3 anx_promis dep_promis {txt} Source {c |} SS df MS Number of obs ={res} 205 {txt}{hline 13}{c +}{hline 34} F(15, 189) = {res} 13.58 {txt} Model {c |} {res} 12575.8765 15 838.391767 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 11669.0264 189 61.7408805 {txt}R-squared ={res} 0.5187 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.4805 {txt} Total {c |} {res} 24244.9029 204 118.847563 {txt}Root MSE = {res} 7.8575 {txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fat_promis{col 29}{c |} Coef.{col 41} Std. Err.{col 53} t{col 61} P>|t|{col 69} [95% Con{col 82}f. Interval] {hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 12}rrt_simplified2 {c |} {space 18}dialysis {c |}{col 29}{res}{space 2} 1.777889{col 41}{space 2} 1.765087{col 52}{space 1} 1.01{col 61}{space 3}0.315{col 69}{space 4}-1.703913{col 82}{space 3} 5.259691 {txt}{space 27} {c |} {space 14}crf_score_Cat {c |} {space 22}>= 3 {c |}{col 29}{res}{space 2} .4681965{col 41}{space 2} 1.231187{col 52}{space 1} 0.38{col 61}{space 3}0.704{col 69}{space 4}-1.960436{col 82}{space 3} 2.896829 {txt}{space 24}alb {c |}{col 29}{res}{space 2}-.4135913{col 41}{space 2} .1993677{col 52}{space 1} -2.07{col 61}{space 3}0.039{col 69}{space 4} -.806863{col 82}{space 3}-.0203196 {txt}{space 24}hgb {c |}{col 29}{res}{space 2}-.0348914{col 41}{space 2} .0379231{col 52}{space 1} -0.92{col 61}{space 3}0.359{col 69}{space 4}-.1096984{col 82}{space 3} .0399156 {txt}{space 27} {c |} {space 20}gender2 {c |} {space 18} Female {c |}{col 29}{res}{space 2}-.1559264{col 41}{space 2} 1.236634{col 52}{space 1} -0.13{col 61}{space 3}0.900{col 69}{space 4}-2.595304{col 82}{space 3} 2.283451 {txt}{space 24}age {c |}{col 29}{res}{space 2}-.0165221{col 41}{space 2} .0443187{col 52}{space 1} -0.37{col 61}{space 3}0.710{col 69}{space 4}-.1039449{col 82}{space 3} .0709007 {txt}{space 27} {c |} {space 13}ethnicity_4cat {c |} {space 21}Asian {c |}{col 29}{res}{space 2} 3.209306{col 41}{space 2} 1.615993{col 52}{space 1} 1.99{col 61}{space 3}0.048{col 69}{space 4} .0216058{col 82}{space 3} 6.397007 {txt}{space 21}Black {c |}{col 29}{res}{space 2} -.572823{col 41}{space 2} 1.565638{col 52}{space 1} -0.37{col 61}{space 3}0.715{col 69}{space 4}-3.661193{col 82}{space 3} 2.515548 {txt}{space 21}Other {c |}{col 29}{res}{space 2} 4.33424{col 41}{space 2} 2.045511{col 52}{space 1} 2.12{col 61}{space 3}0.035{col 69}{space 4} .2992745{col 82}{space 3} 8.369205 {txt}{space 27} {c |} {space 12}marital_status2 {c |} {space 8}Married/Common-Law {c |}{col 29}{res}{space 2} 1.44295{col 41}{space 2} 1.679463{col 52}{space 1} 0.86{col 61}{space 3}0.391{col 69}{space 4} -1.86995{col 82}{space 3} 4.75585 {txt}Divorced/Separated/Widowed {c |}{col 29}{res}{space 2} 1.405291{col 41}{space 2} 1.832117{col 52}{space 1} 0.77{col 61}{space 3}0.444{col 69}{space 4}-2.208734{col 82}{space 3} 5.019315 {txt}{space 27} {c |} {space 17}education2 {c |}{col 29}{res}{space 2}-1.562018{col 41}{space 2} 1.385013{col 52}{space 1} -1.13{col 61}{space 3}0.261{col 69}{space 4}-4.294088{col 82}{space 3} 1.170052 {txt}{space 27} {c |} {space 20}income3 {c |} {space 22}>30k {c |}{col 29}{res}{space 2} 4.635065{col 41}{space 2} 1.566521{col 52}{space 1} 2.96{col 61}{space 3}0.003{col 69}{space 4} 1.544953{col 82}{space 3} 7.725177 {txt}{space 17}anx_promis {c |}{col 29}{res}{space 2} .2603256{col 41}{space 2} .087844{col 52}{space 1} 2.96{col 61}{space 3}0.003{col 69}{space 4} .0870449{col 82}{space 3} .4336063 {txt}{space 17}dep_promis {c |}{col 29}{res}{space 2} .454998{col 41}{space 2} .0914788{col 52}{space 1} 4.97{col 61}{space 3}0.000{col 69}{space 4} .2745473{col 82}{space 3} .6354486 {txt}{space 22}_cons {c |}{col 29}{res}{space 2} 32.48089{col 41}{space 2} 10.07182{col 52}{space 1} 3.22{col 61}{space 3}0.001{col 69}{space 4} 12.61327{col 82}{space 3} 52.3485 {txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {com}. . regress fat_promis i0.rrt_simplified2 i.crf_score_Cat hgb i.gender2 age ib1.ethnicity_4cat i.marital_status2 education2 i.income3 anx_promis dep_promis {txt} Source {c |} SS df MS Number of obs ={res} 206 {txt}{hline 13}{c +}{hline 34} F(14, 191) = {res} 14.12 {txt} Model {c |} {res} 12515.598 14 893.971287 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 12095.0565 191 63.324903 {txt}R-squared ={res} 0.5085 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.4725 {txt} Total {c |} {res} 24610.6545 205 120.051973 {txt}Root MSE = {res} 7.9577 {txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fat_promis{col 29}{c |} Coef.{col 41} Std. Err.{col 53} t{col 61} P>|t|{col 69} [95% Con{col 82}f. Interval] {hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 12}rrt_simplified2 {c |} {space 18}dialysis {c |}{col 29}{res}{space 2} 2.993596{col 41}{space 2} 1.601599{col 52}{space 1} 1.87{col 61}{space 3}0.063{col 69}{space 4}-.1654971{col 82}{space 3} 6.152689 {txt}{space 27} {c |} {space 14}crf_score_Cat {c |} {space 22}>= 3 {c |}{col 29}{res}{space 2} .4424658{col 41}{space 2} 1.240948{col 52}{space 1} 0.36{col 61}{space 3}0.722{col 69}{space 4}-2.005258{col 82}{space 3} 2.890189 {txt}{space 24}hgb {c |}{col 29}{res}{space 2}-.0591761{col 41}{space 2} .0366111{col 52}{space 1} -1.62{col 61}{space 3}0.108{col 69}{space 4} -.13139{col 82}{space 3} .0130379 {txt}{space 27} {c |} {space 20}gender2 {c |} {space 18} Female {c |}{col 29}{res}{space 2}-.0809597{col 41}{space 2} 1.251156{col 52}{space 1} -0.06{col 61}{space 3}0.948{col 69}{space 4}-2.548817{col 82}{space 3} 2.386898 {txt}{space 24}age {c |}{col 29}{res}{space 2}-.0015187{col 41}{space 2} .0439592{col 52}{space 1} -0.03{col 61}{space 3}0.972{col 69}{space 4}-.0882266{col 82}{space 3} .0851892 {txt}{space 27} {c |} {space 13}ethnicity_4cat {c |} {space 21}Asian {c |}{col 29}{res}{space 2} 3.061134{col 41}{space 2} 1.620431{col 52}{space 1} 1.89{col 61}{space 3}0.060{col 69}{space 4}-.1351033{col 82}{space 3} 6.257372 {txt}{space 21}Black {c |}{col 29}{res}{space 2}-.5686512{col 41}{space 2} 1.584442{col 52}{space 1} -0.36{col 61}{space 3}0.720{col 69}{space 4}-3.693902{col 82}{space 3} 2.5566 {txt}{space 21}Other {c |}{col 29}{res}{space 2} 4.371403{col 41}{space 2} 2.071417{col 52}{space 1} 2.11{col 61}{space 3}0.036{col 69}{space 4} .2856129{col 82}{space 3} 8.457194 {txt}{space 27} {c |} {space 12}marital_status2 {c |} {space 8}Married/Common-Law {c |}{col 29}{res}{space 2} 1.885074{col 41}{space 2} 1.668739{col 52}{space 1} 1.13{col 61}{space 3}0.260{col 69}{space 4}-1.406451{col 82}{space 3} 5.176598 {txt}Divorced/Separated/Widowed {c |}{col 29}{res}{space 2} 1.894723{col 41}{space 2} 1.832344{col 52}{space 1} 1.03{col 61}{space 3}0.302{col 69}{space 4}-1.719506{col 82}{space 3} 5.508953 {txt}{space 27} {c |} {space 17}education2 {c |}{col 29}{res}{space 2}-1.518933{col 41}{space 2} 1.381857{col 52}{space 1} -1.10{col 61}{space 3}0.273{col 69}{space 4}-4.244593{col 82}{space 3} 1.206728 {txt}{space 27} {c |} {space 20}income3 {c |} {space 22}>30k {c |}{col 29}{res}{space 2} 4.654329{col 41}{space 2} 1.563959{col 52}{space 1} 2.98{col 61}{space 3}0.003{col 69}{space 4} 1.569479{col 82}{space 3} 7.739179 {txt}{space 17}anx_promis {c |}{col 29}{res}{space 2} .2698504{col 41}{space 2} .0887612{col 52}{space 1} 3.04{col 61}{space 3}0.003{col 69}{space 4} .0947723{col 82}{space 3} .4449284 {txt}{space 17}dep_promis {c |}{col 29}{res}{space 2} .458115{col 41}{space 2} .0924006{col 52}{space 1} 4.96{col 61}{space 3}0.000{col 69}{space 4} .2758583{col 82}{space 3} .6403717 {txt}{space 22}_cons {c |}{col 29}{res}{space 2} 16.56638{col 41}{space 2} 6.675523{col 52}{space 1} 2.48{col 61}{space 3}0.014{col 69}{space 4} 3.399169{col 82}{space 3} 29.7336 {txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {com}. . capture mi extract 0, clear . . mdesc fat_promis income3 education2 marital_status2 crf_score_Cat anx_promis dep_promis alb hgb gender2 age ethnicity_4cat rrt_simplified2 whiteNonW {err}command {bf}mdesc{sf} is unrecognized {txt}{search r(199):r(199);} {com}. . . . . mi set wide . . **evaluating missingness** 64% missingness . mi misstable summarize marital_status2 education2 ethnicity_4cat income3 rrt_simplified2 sex crf_score_Cat age fat_promis anx_promis dep_promis alb hgb {txt}{col 64}Obs<. {col 49}{c TLC}{hline 30} {col 16}{c |}{col 49}{c |} Unique {col 7}Variable {c |}{col 22}Obs=.{col 32}Obs>.{col 42}Obs<.{col 49}{c |} values{col 65}Min{col 77}Max {hline 13}{c +}{hline 32}{c +}{hline 30} marital_st~2 {c |}{res} 3{txt}{space 10}{res} 318{txt} {c |} 3 1 3 education2 {c |}{res} 8{txt}{space 10}{res} 313{txt} {c |} 2 0 1 income3 {c |}{res} 98{txt}{space 10}{res} 223{txt} {c |} 2 0 1 crf_score_~t {c |}{res} 13{txt}{space 10}{res} 308{txt} {c |} 2 0 1 fat_promis {c |}{res} 1{txt}{space 10}{res} 320{txt} {c |} 33 33.1 77.8 anx_promis {c |}{res} 1{txt}{space 10}{res} 320{txt} {c |} 30 37.1 78.2 dep_promis {c |}{res} 2{txt}{space 10}{res} 319{txt} {c |} 29 38.2 81.3 alb {c |}{res} 9{txt}{space 10}{res} 312{txt} {c |} 23 23 50 hgb {c |}{res} 8{txt}{space 10}{res} 313{txt} {c |} 72 12.5 160 {hline 13}{c BT}{hline 32}{c BT}{hline 30} {com}. mi misstable patterns marital_status2 education2 ethnicity_4cat income3 rrt_simplified2 sex crf_score_Cat age fat_promis anx_promis dep_promis alb hgb {res} {txt}Missing-value patterns (1 means complete) {res}{txt}{space 2} {c |} Pattern {space 2} Percent {c |} 1 2 3 4 5 6 7 8 9 {space 2}{hline 12}{c +}{hline 32} {space 2} {res: 64}% {c |} 1 1 1 1 1 1 1 1 1 {space 2}{space 12}{c |} {space 2} {res: 28} {c |} 1 1 1 1 1 1 1 1 0 {space 2} {res: 2} {c |} 1 1 1 1 1 0 0 0 1 {space 2} {res: 2} {c |} 1 1 1 1 1 1 1 0 1 {space 2} {res: 1} {c |} 1 1 1 1 0 1 1 1 0 {space 2} <1 {c |} 1 1 1 1 0 1 1 1 1 {space 2} <1 {c |} 1 1 1 0 1 1 1 1 1 {space 2} <1 {c |} 1 1 1 1 1 0 0 0 0 {space 2} <1 {c |} 0 1 0 1 1 1 1 1 0 {space 2} <1 {c |} 1 0 0 1 1 1 1 1 1 {space 2} <1 {c |} 1 1 1 0 0 1 1 1 0 {space 2} <1 {c |} 1 1 1 1 1 1 0 1 1 {space 2}{hline 12}{c +}{hline 32} {space 2} {res:100}% {c |} {p 2 16 2} Variables are {bind: (1) {res:anx_promis}} {bind: (2) {res:fat_promis}} {bind: (3) {res:dep_promis}} {bind: (4) {res:marital_status2}} {bind: (5) {res:education2}} {bind: (6) {res:hgb}} {bind: (7) {res:alb}} {bind: (8) {res:crf_score_Cat}} {bind: (9) {res:income3}} {p_end} {com}. . **performing imputation-** . mi register imputed fat_promis income3 education2 marital_status2 crf_score_Cat anx_promis dep_promis alb hgb whiteNonW {res} {com}. mi register regular gender2 age rrt_simplified2 . . mi misstable summarize marital_status2 education2 ethnicity_4cat income3 rrt_simplified2 sex crf_score_Cat age fat_promis anx_promis dep_promis alb hgb whiteNonW {txt}{col 64}Obs<. {col 49}{c TLC}{hline 30} {col 16}{c |}{col 49}{c |} Unique {col 7}Variable {c |}{col 22}Obs=.{col 32}Obs>.{col 42}Obs<.{col 49}{c |} values{col 65}Min{col 77}Max {hline 13}{c +}{hline 32}{c +}{hline 30} marital_st~2 {c |}{res} 3{txt}{space 10}{res} 318{txt} {c |} 3 1 3 education2 {c |}{res} 8{txt}{space 10}{res} 313{txt} {c |} 2 0 1 income3 {c |}{res} 98{txt}{space 10}{res} 223{txt} {c |} 2 0 1 crf_score_~t {c |}{res} 13{txt}{space 10}{res} 308{txt} {c |} 2 0 1 fat_promis {c |}{res} 1{txt}{space 10}{res} 320{txt} {c |} 33 33.1 77.8 anx_promis {c |}{res} 1{txt}{space 10}{res} 320{txt} {c |} 30 37.1 78.2 dep_promis {c |}{res} 2{txt}{space 10}{res} 319{txt} {c |} 29 38.2 81.3 alb {c |}{res} 9{txt}{space 10}{res} 312{txt} {c |} 23 23 50 hgb {c |}{res} 8{txt}{space 10}{res} 313{txt} {c |} 72 12.5 160 whiteNonW {c |}{res} 21{txt}{space 10}{res} 300{txt} {c |} 2 0 1 {hline 13}{c BT}{hline 32}{c BT}{hline 30} {com}. mi impute chained (regress)fat_promis anx_promis dep_promis alb hgb (logit) education2 income3 whiteNonW crf_score_Cat (mlogit) marital_status2 = gender2 age rrt_simplified2, add(5) rseed(33) augment {res} {txt}Conditional models: {p 8 21 2} {bf:fat_promis}: regress fat_promis anx_promis dep_promis i.marital_status2 hgb i.education2 alb i.crf_score_Cat i.whiteNonW i.income3 gender2 age rrt_simplified2 {p_end} {p 8 21 2} {bf:anx_promis}: regress anx_promis fat_promis dep_promis i.marital_status2 hgb i.education2 alb i.crf_score_Cat i.whiteNonW i.income3 gender2 age rrt_simplified2 {p_end} {p 8 21 2} {bf:dep_promis}: regress dep_promis fat_promis anx_promis i.marital_status2 hgb i.education2 alb i.crf_score_Cat i.whiteNonW i.income3 gender2 age rrt_simplified2 {p_end} {p 4 21 2} {bf:marital_stat~2}: mlogit marital_status2 fat_promis anx_promis dep_promis hgb i.education2 alb i.crf_score_Cat i.whiteNonW i.income3 gender2 age rrt_simplified2 , augment {p_end} {p 15 21 2} {bf:hgb}: regress hgb fat_promis anx_promis dep_promis i.marital_status2 i.education2 alb i.crf_score_Cat i.whiteNonW i.income3 gender2 age rrt_simplified2 {p_end} {p 8 21 2} {bf:education2}: logit education2 fat_promis anx_promis dep_promis i.marital_status2 hgb alb i.crf_score_Cat i.whiteNonW i.income3 gender2 age rrt_simplified2 , augment {p_end} {p 15 21 2} {bf:alb}: regress alb fat_promis anx_promis dep_promis i.marital_status2 hgb i.education2 i.crf_score_Cat i.whiteNonW i.income3 gender2 age rrt_simplified2 {p_end} {p 5 21 2} {bf:crf_score_Cat}: logit crf_score_Cat fat_promis anx_promis dep_promis i.marital_status2 hgb i.education2 alb i.whiteNonW i.income3 gender2 age rrt_simplified2 , augment {p_end} {p 9 21 2} {bf:whiteNonW}: logit whiteNonW fat_promis anx_promis dep_promis i.marital_status2 hgb i.education2 alb i.crf_score_Cat i.income3 gender2 age rrt_simplified2 , augment {p_end} {p 11 21 2} {bf:income3}: logit income3 fat_promis anx_promis dep_promis i.marital_status2 hgb i.education2 alb i.crf_score_Cat i.whiteNonW gender2 age rrt_simplified2 , augment {p_end} {res}{txt}Performing chained iterations ... {res}{txt} Multivariate imputation{txt}{col 45}{ralign 12:Imputations }= {res} 5 {txt}Chained equations{txt}{col 45}{ralign 12:added }= {res} 5 {txt}Imputed: {it:m}=1 through {it:m}=5{txt}{col 45}{ralign 12:updated }= {res} 0 {txt}Initialization: {res}monotone{txt}{col 45}{ralign 12:Iterations }= {res} 50 {txt}{col 45}{ralign 12:burn-in }= {res} 10 {txt}{p 8 15 2}{bf:fat_promis}: linear regression{p_end} {txt}{p 8 15 2}{bf:anx_promis}: linear regression{p_end} {txt}{p 8 15 2}{bf:dep_promis}: linear regression{p_end} {txt}{p 15 15 2}{bf:alb}: linear regression{p_end} {txt}{p 15 15 2}{bf:hgb}: linear regression{p_end} {txt}{p 8 15 2}{bf:education2}: logistic regression{p_end} {txt}{p 11 15 2}{bf:income3}: logistic regression{p_end} {txt}{p 9 15 2}{bf:whiteNonW}: logistic regression{p_end} {txt}{p 5 15 2}{bf:crf_score_Cat}: logistic regression{p_end} {txt}{p 4 15 2}{bf:marital_stat~2}: multinomial logistic regression{p_end} {txt}{hline 19}{c TT}{hline 35}{hline 11} {txt}{col 20}{c |}{center 46: Observations per {it:m}} {txt}{col 20}{c LT}{hline 35}{c TT}{hline 10} {txt}{col 11}Variable {c |}{ralign 12:Complete }{ralign 13:Incomplete }{ralign 10:Imputed }{c |}{ralign 10:Total} {hline 19}{c +}{hline 35}{c +}{hline 10} {txt}{ralign 19:fat_promis }{c |}{res} 320 1 1 {txt}{c |}{res} 321 {txt}{ralign 19:anx_promis }{c |}{res} 320 1 1 {txt}{c |}{res} 321 {txt}{ralign 19:dep_promis }{c |}{res} 319 2 2 {txt}{c |}{res} 321 {txt}{ralign 19:alb }{c |}{res} 312 9 9 {txt}{c |}{res} 321 {txt}{ralign 19:hgb }{c |}{res} 313 8 8 {txt}{c |}{res} 321 {txt}{ralign 19:education2 }{c |}{res} 313 8 8 {txt}{c |}{res} 321 {txt}{ralign 19:income3 }{c |}{res} 223 98 98 {txt}{c |}{res} 321 {txt}{ralign 19:whiteNonW }{c |}{res} 300 21 21 {txt}{c |}{res} 321 {txt}{ralign 19:crf_score_Cat }{c |}{res} 308 13 13 {txt}{c |}{res} 321 {txt}{ralign 19:marital_stat~2 }{c |}{res} 318 3 3 {txt}{c |}{res} 321 {txt}{hline 19}{c BT}{hline 35}{c BT}{hline 10} {p 0 1 1 66}(complete + incomplete = total; imputed is the minimum across {it:m} of the number of filled-in observations.){p_end} {res} {com}. . . **model 1: fat_promis and RRT . mi estimate: regress fat_promis ib1.rrt_simplified2 {res} {txt}Multiple-imputation estimates{col 49}Imputations{col 67}= {res} 5 {txt}Linear regression{col 49}Number of obs{col 67}= {res} 321 {txt}{col 49}Average RVI{col 67}= {res} 0.0026 {txt}{col 49}Largest FMI{col 67}= {res} 0.0052 {txt}{col 49}Complete DF{col 67}= {res} 319 {txt}DF adjustment:{ralign 15: {res:Small sample}}{col 49}DF: min{col 67}= {res} 314.75 {txt}{col 49} avg{col 67}= {res} 315.39 {txt}{col 49} max{col 67}= {res} 316.02 {txt}Model F test:{ralign 16: {res:Equal FMI}}{col 49}F({res} 1{txt},{res} 316.0{txt}){col 67}= {res} 16.05 {txt}Within VCE type: {ralign 12:{res:OLS}}{col 49}Prob > F{col 67}= {res} 0.0001 {txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fat_promis{col 17}{c |} Coef.{col 29} Std. Err.{col 41} t{col 49} P>|t|{col 57} [95% Con{col 70}f. Interval] {hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} rrt_simplified2 {c |} {space 6}dialysis {c |}{col 17}{res}{space 2} 4.99638{col 29}{space 2} 1.247148{col 40}{space 1} 4.01{col 49}{space 3}0.000{col 57}{space 4} 2.542618{col 70}{space 3} 7.450143 {txt}{space 10}_cons {c |}{col 17}{res}{space 2} 49.19319{col 29}{space 2} .8898293{col 40}{space 1} 55.28{col 49}{space 3}0.000{col 57}{space 4} 47.44242{col 70}{space 3} 50.94396 {txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {com}. . **model 2: MODEL 1+ Clinical variables (, hgb, comorbidity) . mi estimate: regress fat_promis ib1.rrt_simplified2 i.crf_score_Cat hgb {res} {txt}Multiple-imputation estimates{col 49}Imputations{col 67}= {res} 5 {txt}Linear regression{col 49}Number of obs{col 67}= {res} 321 {txt}{col 49}Average RVI{col 67}= {res} 0.0351 {txt}{col 49}Largest FMI{col 67}= {res} 0.0998 {txt}{col 49}Complete DF{col 67}= {res} 317 {txt}DF adjustment:{ralign 15: {res:Small sample}}{col 49}DF: min{col 67}= {res} 173.30 {txt}{col 49} avg{col 67}= {res} 264.21 {txt}{col 49} max{col 67}= {res} 312.45 {txt}Model F test:{ralign 16: {res:Equal FMI}}{col 49}F({res} 3{txt},{res} 284.1{txt}){col 67}= {res} 8.01 {txt}Within VCE type: {ralign 12:{res:OLS}}{col 49}Prob > F{col 67}= {res} 0.0000 {txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fat_promis{col 17}{c |} Coef.{col 29} Std. Err.{col 41} t{col 49} P>|t|{col 57} [95% Con{col 70}f. Interval] {hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} rrt_simplified2 {c |} {space 6}dialysis {c |}{col 17}{res}{space 2} 2.706416{col 29}{space 2} 1.464072{col 40}{space 1} 1.85{col 49}{space 3}0.065{col 57}{space 4}-.1742718{col 70}{space 3} 5.587104 {txt}{space 15} {c |} {space 2}crf_score_Cat {c |} {space 10}>= 3 {c |}{col 17}{res}{space 2} 1.428617{col 29}{space 2} 1.384408{col 40}{space 1} 1.03{col 49}{space 3}0.304{col 57}{space 4}-1.303854{col 70}{space 3} 4.161088 {txt}{space 12}hgb {c |}{col 17}{res}{space 2}-.1071909{col 29}{space 2} .0400595{col 40}{space 1} -2.68{col 49}{space 3}0.008{col 57}{space 4}-.1860382{col 70}{space 3}-.0283435 {txt}{space 10}_cons {c |}{col 17}{res}{space 2} 61.89926{col 29}{space 2} 5.203297{col 40}{space 1} 11.90{col 49}{space 3}0.000{col 57}{space 4} 51.65731{col 70}{space 3} 72.14122 {txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {com}. . **model 3: MODEL 2+ soci- factors (sex, age , ethnicity ,marital status, ) . mi estimate: regress fat_promis ib1.rrt_simplified2 i.crf_score_Cat hgb i.gender2 age i.whiteNonW i.marital_status2 {res} {txt}Multiple-imputation estimates{col 49}Imputations{col 67}= {res} 5 {txt}Linear regression{col 49}Number of obs{col 67}= {res} 321 {txt}{col 49}Average RVI{col 67}= {res} 0.0217 {txt}{col 49}Largest FMI{col 67}= {res} 0.1017 {txt}{col 49}Complete DF{col 67}= {res} 312 {txt}DF adjustment:{ralign 15: {res:Small sample}}{col 49}DF: min{col 67}= {res} 169.00 {txt}{col 49} avg{col 67}= {res} 282.07 {txt}{col 49} max{col 67}= {res} 309.96 {txt}Model F test:{ralign 16: {res:Equal FMI}}{col 49}F({res} 8{txt},{res} 307.8{txt}){col 67}= {res} 3.84 {txt}Within VCE type: {ralign 12:{res:OLS}}{col 49}Prob > F{col 67}= {res} 0.0002 {txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fat_promis{col 29}{c |} Coef.{col 41} Std. Err.{col 53} t{col 61} P>|t|{col 69} [95% Con{col 82}f. Interval] {hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 12}rrt_simplified2 {c |} {space 18}dialysis {c |}{col 29}{res}{space 2} 3.198022{col 41}{space 2} 1.71373{col 52}{space 1} 1.87{col 61}{space 3}0.063{col 69}{space 4} -.174216{col 82}{space 3} 6.570261 {txt}{space 27} {c |} {space 14}crf_score_Cat {c |} {space 22}>= 3 {c |}{col 29}{res}{space 2} 1.771358{col 41}{space 2} 1.451741{col 52}{space 1} 1.22{col 61}{space 3}0.224{col 69}{space 4}-1.094524{col 82}{space 3} 4.637241 {txt}{space 24}hgb {c |}{col 29}{res}{space 2}-.0796974{col 41}{space 2} .0417241{col 52}{space 1} -1.91{col 61}{space 3}0.057{col 69}{space 4}-.1618219{col 82}{space 3} .0024271 {txt}{space 27} {c |} {space 20}gender2 {c |} {space 18} Female {c |}{col 29}{res}{space 2} 2.779265{col 41}{space 2} 1.333407{col 52}{space 1} 2.08{col 61}{space 3}0.038{col 69}{space 4} .155485{col 82}{space 3} 5.403045 {txt}{space 24}age {c |}{col 29}{res}{space 2}-.0204837{col 41}{space 2} .0473887{col 52}{space 1} -0.43{col 61}{space 3}0.666{col 69}{space 4}-.1137333{col 82}{space 3} .072766 {txt}{space 27} {c |} {space 18}whiteNonW {c |} {space 21}White {c |}{col 29}{res}{space 2}-1.128109{col 41}{space 2} 1.314745{col 52}{space 1} -0.86{col 61}{space 3}0.392{col 69}{space 4}-3.716882{col 82}{space 3} 1.460663 {txt}{space 27} {c |} {space 12}marital_status2 {c |} {space 8}Married/Common-Law {c |}{col 29}{res}{space 2} .6121748{col 41}{space 2} 1.743209{col 52}{space 1} 0.35{col 61}{space 3}0.726{col 69}{space 4}-2.817897{col 82}{space 3} 4.042247 {txt}Divorced/Separated/Widowed {c |}{col 29}{res}{space 2} .8301007{col 41}{space 2} 1.984698{col 52}{space 1} 0.42{col 61}{space 3}0.676{col 69}{space 4}-3.075084{col 82}{space 3} 4.735285 {txt}{space 27} {c |} {space 22}_cons {c |}{col 29}{res}{space 2} 58.23783{col 41}{space 2} 5.779309{col 52}{space 1} 10.08{col 61}{space 3}0.000{col 69}{space 4} 46.8622{col 82}{space 3} 69.61347 {txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {com}. . **model 4: MODEL 3+ economic- factors (education, income ) . mi estimate: regress fat_promis ib1.rrt_simplified2 i.crf_score_Cat hgb i.gender2 age i.whiteNonW i.marital_status2 i.education2 i.income3 {res} {txt}Multiple-imputation estimates{col 49}Imputations{col 67}= {res} 5 {txt}Linear regression{col 49}Number of obs{col 67}= {res} 321 {txt}{col 49}Average RVI{col 67}= {res} 0.0962 {txt}{col 49}Largest FMI{col 67}= {res} 0.4908 {txt}{col 49}Complete DF{col 67}= {res} 310 {txt}DF adjustment:{ralign 15: {res:Small sample}}{col 49}DF: min{col 67}= {res} 18.32 {txt}{col 49} avg{col 67}= {res} 227.46 {txt}{col 49} max{col 67}= {res} 307.74 {txt}Model F test:{ralign 16: {res:Equal FMI}}{col 49}F({res} 10{txt},{res} 282.9{txt}){col 67}= {res} 3.74 {txt}Within VCE type: {ralign 12:{res:OLS}}{col 49}Prob > F{col 67}= {res} 0.0001 {txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fat_promis{col 29}{c |} Coef.{col 41} Std. Err.{col 53} t{col 61} P>|t|{col 69} [95% Con{col 82}f. Interval] {hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 12}rrt_simplified2 {c |} {space 18}dialysis {c |}{col 29}{res}{space 2} 5.062284{col 41}{space 2} 1.875696{col 52}{space 1} 2.70{col 61}{space 3}0.008{col 69}{space 4} 1.363428{col 82}{space 3} 8.76114 {txt}{space 27} {c |} {space 14}crf_score_Cat {c |} {space 22}>= 3 {c |}{col 29}{res}{space 2} 2.07159{col 41}{space 2} 1.445086{col 52}{space 1} 1.43{col 61}{space 3}0.154{col 69}{space 4} -.782662{col 82}{space 3} 4.925842 {txt}{space 24}hgb {c |}{col 29}{res}{space 2}-.0763126{col 41}{space 2} .0417902{col 52}{space 1} -1.83{col 61}{space 3}0.069{col 69}{space 4}-.1586188{col 82}{space 3} .0059935 {txt}{space 27} {c |} {space 20}gender2 {c |} {space 18} Female {c |}{col 29}{res}{space 2} 2.600541{col 41}{space 2} 1.342757{col 52}{space 1} 1.94{col 61}{space 3}0.054{col 69}{space 4}-.0434573{col 82}{space 3} 5.244539 {txt}{space 24}age {c |}{col 29}{res}{space 2}-.0021249{col 41}{space 2} .0477533{col 52}{space 1} -0.04{col 61}{space 3}0.965{col 69}{space 4}-.0961084{col 82}{space 3} .0918586 {txt}{space 27} {c |} {space 18}whiteNonW {c |} {space 21}White {c |}{col 29}{res}{space 2}-1.740866{col 41}{space 2} 1.314643{col 52}{space 1} -1.32{col 61}{space 3}0.187{col 69}{space 4}-4.328866{col 82}{space 3} .8471332 {txt}{space 27} {c |} {space 12}marital_status2 {c |} {space 8}Married/Common-Law {c |}{col 29}{res}{space 2}-.6656755{col 41}{space 2} 1.829609{col 52}{space 1} -0.36{col 61}{space 3}0.716{col 69}{space 4}-4.270721{col 82}{space 3} 2.93937 {txt}Divorced/Separated/Widowed {c |}{col 29}{res}{space 2} .7092564{col 41}{space 2} 1.961669{col 52}{space 1} 0.36{col 61}{space 3}0.718{col 69}{space 4}-3.150725{col 82}{space 3} 4.569238 {txt}{space 27} {c |} {space 17}education2 {c |} {space 11}More than 12yrs {c |}{col 29}{res}{space 2}-.3153768{col 41}{space 2} 1.55313{col 52}{space 1} -0.20{col 61}{space 3}0.839{col 69}{space 4}-3.373947{col 82}{space 3} 2.743193 {txt}{space 27} {c |} {space 20}income3 {c |} {space 22}>30k {c |}{col 29}{res}{space 2} 4.756454{col 41}{space 2} 2.11679{col 52}{space 1} 2.25{col 61}{space 3}0.037{col 69}{space 4} .3147763{col 82}{space 3} 9.198131 {txt}{space 22}_cons {c |}{col 29}{res}{space 2} 54.05933{col 41}{space 2} 5.940717{col 52}{space 1} 9.10{col 61}{space 3}0.000{col 69}{space 4} 42.36044{col 82}{space 3} 65.75822 {txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {com}. . **model 5: MODEL 4+ Psycological factors (anx and dep from promis) . . mi estimate: regress fat_promis ib1.rrt_simplified2 i.crf_score_Cat hgb i.gender2 age i.whiteNonW i.marital_status2 i.education2 i.income3 anx_promis dep_promis {res} {txt}Multiple-imputation estimates{col 49}Imputations{col 67}= {res} 5 {txt}Linear regression{col 49}Number of obs{col 67}= {res} 321 {txt}{col 49}Average RVI{col 67}= {res} 0.1156 {txt}{col 49}Largest FMI{col 67}= {res} 0.5252 {txt}{col 49}Complete DF{col 67}= {res} 308 {txt}DF adjustment:{ralign 15: {res:Small sample}}{col 49}DF: min{col 67}= {res} 16.09 {txt}{col 49} avg{col 67}= {res} 193.44 {txt}{col 49} max{col 67}= {res} 293.79 {txt}Model F test:{ralign 16: {res:Equal FMI}}{col 49}F({res} 12{txt},{res} 279.1{txt}){col 67}= {res} 21.37 {txt}Within VCE type: {ralign 12:{res:OLS}}{col 49}Prob > F{col 67}= {res} 0.0000 {txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fat_promis{col 29}{c |} Coef.{col 41} Std. Err.{col 53} t{col 61} P>|t|{col 69} [95% Con{col 82}f. Interval] {hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 12}rrt_simplified2 {c |} {space 18}dialysis {c |}{col 29}{res}{space 2} 3.740695{col 41}{space 2} 1.507716{col 52}{space 1} 2.48{col 61}{space 3}0.015{col 69}{space 4} .7496558{col 82}{space 3} 6.731734 {txt}{space 27} {c |} {space 14}crf_score_Cat {c |} {space 22}>= 3 {c |}{col 29}{res}{space 2} 1.401168{col 41}{space 2} 1.143147{col 52}{space 1} 1.23{col 61}{space 3}0.223{col 69}{space 4} -.865743{col 82}{space 3} 3.668078 {txt}{space 24}hgb {c |}{col 29}{res}{space 2} -.044099{col 41}{space 2} .0336733{col 52}{space 1} -1.31{col 61}{space 3}0.193{col 69}{space 4}-.1107532{col 82}{space 3} .0225552 {txt}{space 27} {c |} {space 20}gender2 {c |} {space 18} Female {c |}{col 29}{res}{space 2}-.3038086{col 41}{space 2} 1.069713{col 52}{space 1} -0.28{col 61}{space 3}0.777{col 69}{space 4}-2.411379{col 82}{space 3} 1.803762 {txt}{space 24}age {c |}{col 29}{res}{space 2} .0302083{col 41}{space 2} .0373924{col 52}{space 1} 0.81{col 61}{space 3}0.420{col 69}{space 4}-.0434551{col 82}{space 3} .1038718 {txt}{space 27} {c |} {space 18}whiteNonW {c |} {space 21}White {c |}{col 29}{res}{space 2}-.4571688{col 41}{space 2} 1.008479{col 52}{space 1} -0.45{col 61}{space 3}0.651{col 69}{space 4}-2.442046{col 82}{space 3} 1.527708 {txt}{space 27} {c |} {space 12}marital_status2 {c |} {space 8}Married/Common-Law {c |}{col 29}{res}{space 2} .5872033{col 41}{space 2} 1.422542{col 52}{space 1} 0.41{col 61}{space 3}0.680{col 69}{space 4}-2.218517{col 82}{space 3} 3.392924 {txt}Divorced/Separated/Widowed {c |}{col 29}{res}{space 2} 1.930812{col 41}{space 2} 1.522914{col 52}{space 1} 1.27{col 61}{space 3}0.206{col 69}{space 4}-1.066391{col 82}{space 3} 4.928016 {txt}{space 27} {c |} {space 17}education2 {c |} {space 11}More than 12yrs {c |}{col 29}{res}{space 2} .352146{col 41}{space 2} 1.194472{col 52}{space 1} 0.29{col 61}{space 3}0.768{col 69}{space 4}-1.999981{col 82}{space 3} 2.704273 {txt}{space 27} {c |} {space 20}income3 {c |} {space 22}>30k {c |}{col 29}{res}{space 2} 5.707308{col 41}{space 2} 1.676195{col 52}{space 1} 3.40{col 61}{space 3}0.004{col 69}{space 4} 2.155578{col 82}{space 3} 9.259039 {txt}{space 17}anx_promis {c |}{col 29}{res}{space 2} .4057831{col 41}{space 2} .0733617{col 52}{space 1} 5.53{col 61}{space 3}0.000{col 69}{space 4} .261357{col 82}{space 3} .5502091 {txt}{space 17}dep_promis {c |}{col 29}{res}{space 2} .3787676{col 41}{space 2} .078869{col 52}{space 1} 4.80{col 61}{space 3}0.000{col 69}{space 4} .2234547{col 82}{space 3} .5340805 {txt}{space 22}_cons {c |}{col 29}{res}{space 2} 9.5431{col 41}{space 2} 5.744533{col 52}{space 1} 1.66{col 61}{space 3}0.099{col 69}{space 4}-1.816884{col 82}{space 3} 20.90308 {txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {com}. . . mi extract 0, clear . . **B.1 GENERATE CATEGORIXAL PROMIS VARIABLE . **binary cat** . gen fat_promis_cat =. {txt}(321 missing values generated) {com}. replace fat_promis_cat = 0 if fat_promis < 55 {txt}(194 real changes made) {com}. replace fat_promis_cat = 1 if fat_promis >= 55 & !mi(fat_promis) {txt}(126 real changes made) {com}. label define fat_promis_catlbl 0 " No Fatigue" 1 " Fatigue" . label values fat_promis_cat fat_promis_catlbl . . . **B.2 Descriptives of RRT using PROMIS CAT** . tab fat_promis_cat rrt_simplified2, row column chi {txt} {c TLC}{hline 19}{c TRC} {c |} Key{col 21}{c |} {c LT}{hline 19}{c RT} {c |}{space 5}{it:frequency}{col 21}{c |} {c |}{space 2}{it:row percentage}{col 21}{c |} {c |}{space 1}{it:column percentage}{col 21}{c |} {c BLC}{hline 19}{c BRC} fat_promis_ {c |} rrt_simplified2 cat {c |} dialysis tx {c |} Total {hline 12}{c +}{hline 22}{c +}{hline 10} No Fatigue {c |}{res} 84 110 {txt}{c |}{res} 194 {txt}{c |}{res} 43.30 56.70 {txt}{c |}{res} 100.00 {txt}{c |}{res} 51.53 70.06 {txt}{c |}{res} 60.62 {txt}{hline 12}{c +}{hline 22}{c +}{hline 10} Fatigue {c |}{res} 79 47 {txt}{c |}{res} 126 {txt}{c |}{res} 62.70 37.30 {txt}{c |}{res} 100.00 {txt}{c |}{res} 48.47 29.94 {txt}{c |}{res} 39.38 {txt}{hline 12}{c +}{hline 22}{c +}{hline 10} Total {c |}{res} 163 157 {txt}{c |}{res} 320 {txt}{c |}{res} 50.94 49.06 {txt}{c |}{res} 100.00 {txt}{c |}{res} 100.00 100.00 {txt}{c |}{res} 100.00 {txt} Pearson chi2({res}1{txt}) = {res} 11.5031 {txt} Pr = {res}0.001 {com}. . . **B.3 Logistic Regression not adjusted** . **model 1: fat_promis and RRT . logit fat_promis i0.rrt_simplified2,or {txt}outcome does not vary; remember: 0 = negative outcome, all other nonmissing values = positive outcome {search r(2000):r(2000);} {com}. . **model 2: MODEL 1+ Clinical variables ( hgb, comorbidity) . logit fat_promis i0.rrt_simplified2 i.crf_score_Cat hgb,or {txt}outcome does not vary; remember: 0 = negative outcome, all other nonmissing values = positive outcome {search r(2000):r(2000);} {com}. . **model 3: MODEL 2+ soci- factors (sex, age , ethnicity ,marital status, ) . logit fat_promis i0.rrt_simplified2 i.crf_score_Cat hgb i.gender2 age ib1.ethnicity_4cat i.marital_status2,or {txt}outcome does not vary; remember: 0 = negative outcome, all other nonmissing values = positive outcome {search r(2000):r(2000);} {com}. . **model 4: MODEL 3+ economic- factors (education, income ) . logit fat_promis i0.rrt_simplified2 i.crf_score_Cat hgb i.gender2 age ib1.ethnicity_4cat i.marital_status2 education2 i.income3,or {txt}outcome does not vary; remember: 0 = negative outcome, all other nonmissing values = positive outcome {search r(2000):r(2000);} {com}. . **model 5: MODEL 4+ Psycological factors (anx and dep from promis) . . logit fat_promis i0.rrt_simplified2 i.crf_score_Cat hgb i.gender2 age ib1.ethnicity_4cat i.marital_status2 education2 i.income3 anx_promis dep_promis,or {txt}outcome does not vary; remember: 0 = negative outcome, all other nonmissing values = positive outcome {search r(2000):r(2000);} {com}. . . . . . **B.4 Logistic Regression Adjusted** . . . capture mi extract 0, clear . mi set wide . mi register imputed fat_promis_cat income3 education2 marital_status2 crf_score_Cat anx_promis dep_promis alb hgb whiteNonW {res} {com}. mi register regular gender2 age rrt_simplified2 . . mi impute chained (logit) whiteNonW income3 education2 crf_score_Cat fat_promis_cat (regress) anx_promis dep_promis alb hgb (mlogit) marital_status2 = gender2 age rrt_simplified2, add(5) rseed(33) augment {res} {txt}Conditional models: {p 4 21 2} {bf:fat_promis_cat}: logit fat_promis_cat anx_promis dep_promis i.marital_status2 i.education2 hgb alb i.crf_score_Cat i.whiteNonW i.income3 gender2 age rrt_simplified2 , augment {p_end} {p 8 21 2} {bf:anx_promis}: regress anx_promis i.fat_promis_cat dep_promis i.marital_status2 i.education2 hgb alb i.crf_score_Cat i.whiteNonW i.income3 gender2 age rrt_simplified2 {p_end} {p 8 21 2} {bf:dep_promis}: regress dep_promis i.fat_promis_cat anx_promis i.marital_status2 i.education2 hgb alb i.crf_score_Cat i.whiteNonW i.income3 gender2 age rrt_simplified2 {p_end} {p 4 21 2} {bf:marital_stat~2}: mlogit marital_status2 i.fat_promis_cat anx_promis dep_promis i.education2 hgb alb i.crf_score_Cat i.whiteNonW i.income3 gender2 age rrt_simplified2 , augment {p_end} {p 8 21 2} {bf:education2}: logit education2 i.fat_promis_cat anx_promis dep_promis i.marital_status2 hgb alb i.crf_score_Cat i.whiteNonW i.income3 gender2 age rrt_simplified2 , augment {p_end} {p 15 21 2} {bf:hgb}: regress hgb i.fat_promis_cat anx_promis dep_promis i.marital_status2 i.education2 alb i.crf_score_Cat i.whiteNonW i.income3 gender2 age rrt_simplified2 {p_end} {p 15 21 2} {bf:alb}: regress alb i.fat_promis_cat anx_promis dep_promis i.marital_status2 i.education2 hgb i.crf_score_Cat i.whiteNonW i.income3 gender2 age rrt_simplified2 {p_end} {p 5 21 2} {bf:crf_score_Cat}: logit crf_score_Cat i.fat_promis_cat anx_promis dep_promis i.marital_status2 i.education2 hgb alb i.whiteNonW i.income3 gender2 age rrt_simplified2 , augment {p_end} {p 9 21 2} {bf:whiteNonW}: logit whiteNonW i.fat_promis_cat anx_promis dep_promis i.marital_status2 i.education2 hgb alb i.crf_score_Cat i.income3 gender2 age rrt_simplified2 , augment {p_end} {p 11 21 2} {bf:income3}: logit income3 i.fat_promis_cat anx_promis dep_promis i.marital_status2 i.education2 hgb alb i.crf_score_Cat i.whiteNonW gender2 age rrt_simplified2 , augment {p_end} {res}{txt}Performing chained iterations ... {res}{txt} Multivariate imputation{txt}{col 45}{ralign 12:Imputations }= {res} 5 {txt}Chained equations{txt}{col 45}{ralign 12:added }= {res} 5 {txt}Imputed: {it:m}=1 through {it:m}=5{txt}{col 45}{ralign 12:updated }= {res} 0 {txt}Initialization: {res}monotone{txt}{col 45}{ralign 12:Iterations }= {res} 50 {txt}{col 45}{ralign 12:burn-in }= {res} 10 {txt}{p 9 15 2}{bf:whiteNonW}: logistic regression{p_end} {txt}{p 11 15 2}{bf:income3}: logistic regression{p_end} {txt}{p 8 15 2}{bf:education2}: logistic regression{p_end} {txt}{p 5 15 2}{bf:crf_score_Cat}: logistic regression{p_end} {txt}{p 4 15 2}{bf:fat_promis_cat}: logistic regression{p_end} {txt}{p 8 15 2}{bf:anx_promis}: linear regression{p_end} {txt}{p 8 15 2}{bf:dep_promis}: linear regression{p_end} {txt}{p 15 15 2}{bf:alb}: linear regression{p_end} {txt}{p 15 15 2}{bf:hgb}: linear regression{p_end} {txt}{p 4 15 2}{bf:marital_stat~2}: multinomial logistic regression{p_end} {txt}{hline 19}{c TT}{hline 35}{hline 11} {txt}{col 20}{c |}{center 46: Observations per {it:m}} {txt}{col 20}{c LT}{hline 35}{c TT}{hline 10} {txt}{col 11}Variable {c |}{ralign 12:Complete }{ralign 13:Incomplete }{ralign 10:Imputed }{c |}{ralign 10:Total} {hline 19}{c +}{hline 35}{c +}{hline 10} {txt}{ralign 19:whiteNonW }{c |}{res} 300 21 21 {txt}{c |}{res} 321 {txt}{ralign 19:income3 }{c |}{res} 223 98 98 {txt}{c |}{res} 321 {txt}{ralign 19:education2 }{c |}{res} 313 8 8 {txt}{c |}{res} 321 {txt}{ralign 19:crf_score_Cat }{c |}{res} 308 13 13 {txt}{c |}{res} 321 {txt}{ralign 19:fat_promis_cat }{c |}{res} 320 1 1 {txt}{c |}{res} 321 {txt}{ralign 19:anx_promis }{c |}{res} 320 1 1 {txt}{c |}{res} 321 {txt}{ralign 19:dep_promis }{c |}{res} 319 2 2 {txt}{c |}{res} 321 {txt}{ralign 19:alb }{c |}{res} 312 9 9 {txt}{c |}{res} 321 {txt}{ralign 19:hgb }{c |}{res} 313 8 8 {txt}{c |}{res} 321 {txt}{ralign 19:marital_stat~2 }{c |}{res} 318 3 3 {txt}{c |}{res} 321 {txt}{hline 19}{c BT}{hline 35}{c BT}{hline 10} {p 0 1 1 66}(complete + incomplete = total; imputed is the minimum across {it:m} of the number of filled-in observations.){p_end} {res} {com}. . . **model 1: fat_promis and RRT . mi estimate, or: logit fat_promis_cat ib1.rrt_simplified2 {res} {txt}Multiple-imputation estimates{col 49}Imputations{col 67}= {res} 5 {txt}Logistic regression{col 49}Number of obs{col 67}= {res} 321 {txt}{col 49}Average RVI{col 67}= {res} 0.0000 {txt}{col 49}Largest FMI{col 67}= {res} 0.0000 {txt}DF adjustment:{ralign 15: {res:Large sample}}{col 49}{help mi_missingdf##|_new:DF}: min{col 67}= {res} . {txt}{col 49} avg{col 67}= {res} . {txt}{col 49} max{col 67}= {res} . {txt}Model F test:{ralign 16: {res:Equal FMI}}{col 49}{help mi_missingdf##|_new:F( 1, .)}{col 67}= {res} 11.61 {txt}Within VCE type: {ralign 12:{res:OIM}}{col 49}Prob > F{col 67}= {res} 0.0007 {txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fat_promis_cat{col 17}{c |} Odds Ratio{col 29} Std. Err.{col 41} t{col 49} P>|t|{col 57} [95% Con{col 70}f. Interval] {hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} rrt_simplified2 {c |} {space 6}dialysis {c |}{col 17}{res}{space 2} 2.221125{col 29}{space 2} .5201824{col 40}{space 1} 3.41{col 49}{space 3}0.001{col 57}{space 4} 1.403536{col 70}{space 3} 3.514977 {txt}{space 10}_cons {c |}{col 17}{res}{space 2} .4234234{col 29}{space 2} .0736874{col 40}{space 1} -4.94{col 49}{space 3}0.000{col 57}{space 4} .3010526{col 70}{space 3} .5955352 {txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {com}. . **model 2: MODEL 1+ clinical variables (hgb, comorbidity) . mi estimate, or: logit fat_promis_cat ib1.rrt_simplified2 i.crf_score_Cat hgb {res} {txt}Multiple-imputation estimates{col 49}Imputations{col 67}= {res} 5 {txt}Logistic regression{col 49}Number of obs{col 67}= {res} 321 {txt}{col 49}Average RVI{col 67}= {res} 0.0109 {txt}{col 49}Largest FMI{col 67}= {res} 0.0273 {txt}DF adjustment:{ralign 15: {res:Large sample}}{col 49}DF: min{col 67}= {res} 5,523.69 {txt}{col 49} avg{col 67}= {res} 46,689.89 {txt}{col 49} max{col 67}= {res}106,473.95 {txt}Model F test:{ralign 16: {res:Equal FMI}}{col 49}F({res} 3{txt},{res}29204.9{txt}){col 67}= {res} 6.38 {txt}Within VCE type: {ralign 12:{res:OIM}}{col 49}Prob > F{col 67}= {res} 0.0003 {txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fat_promis_cat{col 17}{c |} Odds Ratio{col 29} Std. Err.{col 41} t{col 49} P>|t|{col 57} [95% Con{col 70}f. Interval] {hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} rrt_simplified2 {c |} {space 6}dialysis {c |}{col 17}{res}{space 2} 1.452464{col 29}{space 2} .399752{col 40}{space 1} 1.36{col 49}{space 3}0.175{col 57}{space 4} .8468997{col 70}{space 3} 2.491027 {txt}{space 15} {c |} {space 2}crf_score_Cat {c |} {space 10}>= 3 {c |}{col 17}{res}{space 2} 1.465486{col 29}{space 2} .3801725{col 40}{space 1} 1.47{col 49}{space 3}0.141{col 57}{space 4} .8812862{col 70}{space 3} 2.43695 {txt}{space 12}hgb {c |}{col 17}{res}{space 2} .9794796{col 29}{space 2} .0079271{col 40}{space 1} -2.56{col 49}{space 3}0.010{col 57}{space 4} .9640645{col 70}{space 3} .9951411 {txt}{space 10}_cons {c |}{col 17}{res}{space 2} 4.517861{col 29}{space 2} 4.653161{col 40}{space 1} 1.46{col 49}{space 3}0.143{col 57}{space 4} .600093{col 70}{space 3} 34.01318 {txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {com}. . **model 3: MODEL 2+ socio factors (sex, age , ethnicity , marital status ) . mi estimate, or: logit fat_promis_cat ib1.rrt_simplified2 i.crf_score_Cat hgb i.gender2 age i.whiteNonW i.marital_status2 {res} {txt}Multiple-imputation estimates{col 49}Imputations{col 67}= {res} 5 {txt}Logistic regression{col 49}Number of obs{col 67}= {res} 321 {txt}{col 49}Average RVI{col 67}= {res} 0.0158 {txt}{col 49}Largest FMI{col 67}= {res} 0.0813 {txt}DF adjustment:{ralign 15: {res:Large sample}}{col 49}DF: min{col 67}= {res} 648.77 {txt}{col 49} avg{col 67}= {res}711,058.63 {txt}{col 49} max{col 67}= {res}4302869.96 {txt}Model F test:{ralign 16: {res:Equal FMI}}{col 49}F({res} 8{txt},{res}82302.9{txt}){col 67}= {res} 2.90 {txt}Within VCE type: {ralign 12:{res:OIM}}{col 49}Prob > F{col 67}= {res} 0.0031 {txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fat_promis_cat{col 29}{c |} Odds Ratio{col 41} Std. Err.{col 53} t{col 61} P>|t|{col 69} [95% Con{col 82}f. Interval] {hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 12}rrt_simplified2 {c |} {space 18}dialysis {c |}{col 29}{res}{space 2} 1.704509{col 41}{space 2} .5587647{col 52}{space 1} 1.63{col 61}{space 3}0.104{col 69}{space 4} .8965028{col 82}{space 3} 3.24076 {txt}{space 27} {c |} {space 14}crf_score_Cat {c |} {space 22}>= 3 {c |}{col 29}{res}{space 2} 1.555233{col 41}{space 2} .4262076{col 52}{space 1} 1.61{col 61}{space 3}0.107{col 69}{space 4} .9088787{col 82}{space 3} 2.661246 {txt}{space 24}hgb {c |}{col 29}{res}{space 2} .9836378{col 41}{space 2} .0083164{col 52}{space 1} -1.95{col 61}{space 3}0.051{col 69}{space 4} .9674718{col 82}{space 3} 1.000074 {txt}{space 27} {c |} {space 20}gender2 {c |} {space 18} Female {c |}{col 29}{res}{space 2} 1.588524{col 41}{space 2} .4035009{col 52}{space 1} 1.82{col 61}{space 3}0.068{col 69}{space 4} .9655615{col 82}{space 3} 2.61341 {txt}{space 24}age {c |}{col 29}{res}{space 2} .9923114{col 41}{space 2} .009164{col 52}{space 1} -0.84{col 61}{space 3}0.403{col 69}{space 4} .9745118{col 82}{space 3} 1.010436 {txt}{space 27} {c |} {space 18}whiteNonW {c |} {space 21}White {c |}{col 29}{res}{space 2} 1.039378{col 41}{space 2} .2688461{col 52}{space 1} 0.15{col 61}{space 3}0.881{col 69}{space 4} .6254462{col 82}{space 3} 1.727259 {txt}{space 27} {c |} {space 12}marital_status2 {c |} {space 8}Married/Common-Law {c |}{col 29}{res}{space 2} 1.13924{col 41}{space 2} .3856784{col 52}{space 1} 0.39{col 61}{space 3}0.700{col 69}{space 4} .5867447{col 82}{space 3} 2.211981 {txt}Divorced/Separated/Widowed {c |}{col 29}{res}{space 2} 1.304081{col 41}{space 2} .4971193{col 52}{space 1} 0.70{col 61}{space 3}0.486{col 69}{space 4} .6177564{col 82}{space 3} 2.75291 {txt}{space 27} {c |} {space 22}_cons {c |}{col 29}{res}{space 2} 2.642931{col 41}{space 2} 3.039446{col 52}{space 1} 0.85{col 61}{space 3}0.398{col 69}{space 4} .2774374{col 82}{space 3} 25.17716 {txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {com}. . **model 4: MODEL 3+ economic- factors (education, income ) . mi estimate, or: logit fat_promis_cat ib1.rrt_simplified2 i.crf_score_Cat hgb i.gender2 age i.whiteNonW i.marital_status2 education2 i.income3 {res} {txt}Multiple-imputation estimates{col 49}Imputations{col 67}= {res} 5 {txt}Logistic regression{col 49}Number of obs{col 67}= {res} 321 {txt}{col 49}Average RVI{col 67}= {res} 0.0275 {txt}{col 49}Largest FMI{col 67}= {res} 0.0828 {txt}DF adjustment:{ralign 15: {res:Large sample}}{col 49}DF: min{col 67}= {res} 626.28 {txt}{col 49} avg{col 67}= {res} 96,085.66 {txt}{col 49} max{col 67}= {res}790,367.87 {txt}Model F test:{ralign 16: {res:Equal FMI}}{col 49}F({res} 10{txt},{res}38371.8{txt}){col 67}= {res} 3.34 {txt}Within VCE type: {ralign 12:{res:OIM}}{col 49}Prob > F{col 67}= {res} 0.0002 {txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fat_promis_cat{col 29}{c |} Odds Ratio{col 41} Std. Err.{col 53} t{col 61} P>|t|{col 69} [95% Con{col 82}f. Interval] {hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 12}rrt_simplified2 {c |} {space 18}dialysis {c |}{col 29}{res}{space 2} 2.664922{col 41}{space 2} .971804{col 52}{space 1} 2.69{col 61}{space 3}0.007{col 69}{space 4} 1.304003{col 82}{space 3} 5.44616 {txt}{space 27} {c |} {space 14}crf_score_Cat {c |} {space 22}>= 3 {c |}{col 29}{res}{space 2} 1.776797{col 41}{space 2} .5052152{col 52}{space 1} 2.02{col 61}{space 3}0.043{col 69}{space 4} 1.01764{col 82}{space 3} 3.102285 {txt}{space 24}hgb {c |}{col 29}{res}{space 2} .9846732{col 41}{space 2} .0083694{col 52}{space 1} -1.82{col 61}{space 3}0.069{col 69}{space 4} .9684054{col 82}{space 3} 1.001214 {txt}{space 27} {c |} {space 20}gender2 {c |} {space 18} Female {c |}{col 29}{res}{space 2} 1.558842{col 41}{space 2} .4103778{col 52}{space 1} 1.69{col 61}{space 3}0.092{col 69}{space 4} .9303885{col 82}{space 3} 2.611798 {txt}{space 24}age {c |}{col 29}{res}{space 2} .9983276{col 41}{space 2} .0097517{col 52}{space 1} -0.17{col 61}{space 3}0.864{col 69}{space 4} .9793941{col 82}{space 3} 1.017627 {txt}{space 27} {c |} {space 18}whiteNonW {c |} {space 21}White {c |}{col 29}{res}{space 2} .8551424{col 41}{space 2} .2322619{col 52}{space 1} -0.58{col 61}{space 3}0.565{col 69}{space 4} .5017531{col 82}{space 3} 1.457427 {txt}{space 27} {c |} {space 12}marital_status2 {c |} {space 8}Married/Common-Law {c |}{col 29}{res}{space 2} .7945278{col 41}{space 2} .2895681{col 52}{space 1} -0.63{col 61}{space 3}0.528{col 69}{space 4} .3889178{col 82}{space 3} 1.623157 {txt}Divorced/Separated/Widowed {c |}{col 29}{res}{space 2} 1.271578{col 41}{space 2} .5026904{col 52}{space 1} 0.61{col 61}{space 3}0.543{col 69}{space 4} .5858483{col 82}{space 3} 2.759947 {txt}{space 27} {c |} {space 17}education2 {c |}{col 29}{res}{space 2} .7600436{col 41}{space 2} .2402186{col 52}{space 1} -0.87{col 61}{space 3}0.385{col 69}{space 4} .4090656{col 82}{space 3} 1.412161 {txt}{space 27} {c |} {space 20}income3 {c |} {space 22}>30k {c |}{col 29}{res}{space 2} 3.538882{col 41}{space 2} 1.267608{col 52}{space 1} 3.53{col 61}{space 3}0.000{col 69}{space 4} 1.751378{col 82}{space 3} 7.150761 {txt}{space 22}_cons {c |}{col 29}{res}{space 2} .7977471{col 41}{space 2} .9646414{col 52}{space 1} -0.19{col 61}{space 3}0.852{col 69}{space 4} .0745663{col 82}{space 3} 8.534688 {txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {com}. . **model 5: MODEL 4+ Psycological factors (anx and dep from promis) . mi estimate, or: logit fat_promis_cat ib1.rrt_simplified2 i.crf_score_Cat hgb i.gender2 age i.whiteNonW i.marital_status2 education2 i.income3 anx_promis dep_promis {res} {txt}Multiple-imputation estimates{col 49}Imputations{col 67}= {res} 5 {txt}Logistic regression{col 49}Number of obs{col 67}= {res} 321 {txt}{col 49}Average RVI{col 67}= {res} 0.0414 {txt}{col 49}Largest FMI{col 67}= {res} 0.0999 {txt}DF adjustment:{ralign 15: {res:Large sample}}{col 49}DF: min{col 67}= {res} 436.35 {txt}{col 49} avg{col 67}= {res} 8,874.02 {txt}{col 49} max{col 67}= {res} 54,896.18 {txt}Model F test:{ralign 16: {res:Equal FMI}}{col 49}F({res} 12{txt},{res}24292.1{txt}){col 67}= {res} 6.64 {txt}Within VCE type: {ralign 12:{res:OIM}}{col 49}Prob > F{col 67}= {res} 0.0000 {txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fat_promis_cat{col 29}{c |} Odds Ratio{col 41} Std. Err.{col 53} t{col 61} P>|t|{col 69} [95% Con{col 82}f. Interval] {hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 12}rrt_simplified2 {c |} {space 18}dialysis {c |}{col 29}{res}{space 2} 2.586333{col 41}{space 2} 1.175825{col 52}{space 1} 2.09{col 61}{space 3}0.037{col 69}{space 4} 1.060781{col 82}{space 3} 6.305843 {txt}{space 27} {c |} {space 14}crf_score_Cat {c |} {space 22}>= 3 {c |}{col 29}{res}{space 2} 1.902302{col 41}{space 2} .6814812{col 52}{space 1} 1.80{col 61}{space 3}0.073{col 69}{space 4} .9415564{col 82}{space 3} 3.843374 {txt}{space 24}hgb {c |}{col 29}{res}{space 2} .986133{col 41}{space 2} .0104359{col 52}{space 1} -1.32{col 61}{space 3}0.187{col 69}{space 4} .9658836{col 82}{space 3} 1.006807 {txt}{space 27} {c |} {space 20}gender2 {c |} {space 18} Female {c |}{col 29}{res}{space 2} .969788{col 41}{space 2} .3218467{col 52}{space 1} -0.09{col 61}{space 3}0.926{col 69}{space 4} .5059107{col 82}{space 3} 1.859002 {txt}{space 24}age {c |}{col 29}{res}{space 2} 1.004116{col 41}{space 2} .0116973{col 52}{space 1} 0.35{col 61}{space 3}0.724{col 69}{space 4} .9814457{col 82}{space 3} 1.02731 {txt}{space 27} {c |} {space 18}whiteNonW {c |} {space 21}White {c |}{col 29}{res}{space 2} 1.034907{col 41}{space 2} .3495838{col 52}{space 1} 0.10{col 61}{space 3}0.919{col 69}{space 4} .5328099{col 82}{space 3} 2.01016 {txt}{space 27} {c |} {space 12}marital_status2 {c |} {space 8}Married/Common-Law {c |}{col 29}{res}{space 2} 1.072295{col 41}{space 2} .4678392{col 52}{space 1} 0.16{col 61}{space 3}0.873{col 69}{space 4} .455963{col 82}{space 3} 2.521734 {txt}Divorced/Separated/Widowed {c |}{col 29}{res}{space 2} 1.989776{col 41}{space 2} .9631966{col 52}{space 1} 1.42{col 61}{space 3}0.155{col 69}{space 4} .7700885{col 82}{space 3} 5.141239 {txt}{space 27} {c |} {space 17}education2 {c |}{col 29}{res}{space 2} .8897938{col 41}{space 2} .3454947{col 52}{space 1} -0.30{col 61}{space 3}0.764{col 69}{space 4} .4156794{col 82}{space 3} 1.904672 {txt}{space 27} {c |} {space 20}income3 {c |} {space 22}>30k {c |}{col 29}{res}{space 2} 5.93139{col 41}{space 2} 2.651438{col 52}{space 1} 3.98{col 61}{space 3}0.000{col 69}{space 4} 2.46493{col 82}{space 3} 14.27277 {txt}{space 17}anx_promis {c |}{col 29}{res}{space 2} 1.091494{col 41}{space 2} .0260235{col 52}{space 1} 3.67{col 61}{space 3}0.000{col 69}{space 4} 1.041656{col 82}{space 3} 1.143716 {txt}{space 17}dep_promis {c |}{col 29}{res}{space 2} 1.097678{col 41}{space 2} .027113{col 52}{space 1} 3.77{col 61}{space 3}0.000{col 69}{space 4} 1.045794{col 82}{space 3} 1.152136 {txt}{space 22}_cons {c |}{col 29}{res}{space 2} .0000308{col 41}{space 2} .0000613{col 52}{space 1} -5.22{col 61}{space 3}0.000{col 69}{space 4} 6.20e-07{col 82}{space 3} .0015309 {txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {com}. mi estimate, or mcerror {txt}{p 0 6 0}note: Monte Carlo error estimates are not available; {bf:mcerror} ignored{p_end} {res} {txt}Multiple-imputation estimates{col 49}Imputations{col 67}= {res} 5 {txt}Logistic regression{col 49}Number of obs{col 67}= {res} 321 {txt}{col 49}Average RVI{col 67}= {res} 0.0414 {txt}{col 49}Largest FMI{col 67}= {res} 0.0999 {txt}DF adjustment:{ralign 15: {res:Large sample}}{col 49}DF: min{col 67}= {res} 436.35 {txt}{col 49} avg{col 67}= {res} 8,874.02 {txt}{col 49} max{col 67}= {res} 54,896.18 {txt}Model F test:{ralign 16: {res:Equal FMI}}{col 49}F({res} 12{txt},{res}24292.1{txt}){col 67}= {res} 6.64 {txt}Within VCE type: {ralign 12:{res:OIM}}{col 49}Prob > F{col 67}= {res} 0.0000 {txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fat_promis_cat{col 29}{c |} Odds Ratio{col 41} Std. Err.{col 53} t{col 61} P>|t|{col 69} [95% Con{col 82}f. Interval] {hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 12}rrt_simplified2 {c |} {space 18}dialysis {c |}{col 29}{res}{space 2} 2.586333{col 41}{space 2} 1.175825{col 52}{space 1} 2.09{col 61}{space 3}0.037{col 69}{space 4} 1.060781{col 82}{space 3} 6.305843 {txt}{space 27} {c |} {space 14}crf_score_Cat {c |} {space 22}>= 3 {c |}{col 29}{res}{space 2} 1.902302{col 41}{space 2} .6814812{col 52}{space 1} 1.80{col 61}{space 3}0.073{col 69}{space 4} .9415564{col 82}{space 3} 3.843374 {txt}{space 24}hgb {c |}{col 29}{res}{space 2} .986133{col 41}{space 2} .0104359{col 52}{space 1} -1.32{col 61}{space 3}0.187{col 69}{space 4} .9658836{col 82}{space 3} 1.006807 {txt}{space 27} {c |} {space 20}gender2 {c |} {space 18} Female {c |}{col 29}{res}{space 2} .969788{col 41}{space 2} .3218467{col 52}{space 1} -0.09{col 61}{space 3}0.926{col 69}{space 4} .5059107{col 82}{space 3} 1.859002 {txt}{space 24}age {c |}{col 29}{res}{space 2} 1.004116{col 41}{space 2} .0116973{col 52}{space 1} 0.35{col 61}{space 3}0.724{col 69}{space 4} .9814457{col 82}{space 3} 1.02731 {txt}{space 27} {c |} {space 18}whiteNonW {c |} {space 21}White {c |}{col 29}{res}{space 2} 1.034907{col 41}{space 2} .3495838{col 52}{space 1} 0.10{col 61}{space 3}0.919{col 69}{space 4} .5328099{col 82}{space 3} 2.01016 {txt}{space 27} {c |} {space 12}marital_status2 {c |} {space 8}Married/Common-Law {c |}{col 29}{res}{space 2} 1.072295{col 41}{space 2} .4678392{col 52}{space 1} 0.16{col 61}{space 3}0.873{col 69}{space 4} .455963{col 82}{space 3} 2.521734 {txt}Divorced/Separated/Widowed {c |}{col 29}{res}{space 2} 1.989776{col 41}{space 2} .9631966{col 52}{space 1} 1.42{col 61}{space 3}0.155{col 69}{space 4} .7700885{col 82}{space 3} 5.141239 {txt}{space 27} {c |} {space 17}education2 {c |}{col 29}{res}{space 2} .8897938{col 41}{space 2} .3454947{col 52}{space 1} -0.30{col 61}{space 3}0.764{col 69}{space 4} .4156794{col 82}{space 3} 1.904672 {txt}{space 27} {c |} {space 20}income3 {c |} {space 22}>30k {c |}{col 29}{res}{space 2} 5.93139{col 41}{space 2} 2.651438{col 52}{space 1} 3.98{col 61}{space 3}0.000{col 69}{space 4} 2.46493{col 82}{space 3} 14.27277 {txt}{space 17}anx_promis {c |}{col 29}{res}{space 2} 1.091494{col 41}{space 2} .0260235{col 52}{space 1} 3.67{col 61}{space 3}0.000{col 69}{space 4} 1.041656{col 82}{space 3} 1.143716 {txt}{space 17}dep_promis {c |}{col 29}{res}{space 2} 1.097678{col 41}{space 2} .027113{col 52}{space 1} 3.77{col 61}{space 3}0.000{col 69}{space 4} 1.045794{col 82}{space 3} 1.152136 {txt}{space 22}_cons {c |}{col 29}{res}{space 2} .0000308{col 41}{space 2} .0000613{col 52}{space 1} -5.22{col 61}{space 3}0.000{col 69}{space 4} 6.20e-07{col 82}{space 3} .0015309 {txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {p 0 6 0 94}{help mi_mcerroreform:Note}: Monte Carlo error estimates are not available for exponentiated coefficients.{p_end} {res} {com}. . . **nov 13 dataset used** . log close {txt}name: {res} {txt}log: {res}/Users/Sumaya/Desktop/ELPAAT 2019/elpat_nov18_fatigue_final.smcl {txt}log type: {res}smcl {txt}closed on: {res}18 Nov 2018, 16:53:05 {txt}{.-} {smcl} {txt}{sf}{ul off}