-------------
      name:  <unnamed>
       log:  Z:\Social supp-LD_29Nov18_TA.smcl
  log type:  smcl
 opened on:  29 Nov 2018, 20:24:58

. do "C:\Users\tahmed\AppData\Local\Temp\68\STD1f58_000000.tmp"

. misstable summarize Pot_Liv_Donor Living_Donor ldktreadgrp_3 support_tertile education2 income3 marital_status2 ethnicity2 sdidistress_grouped days_since_dialaysis_Cat
                                                               Obs<.
                                                +------------------------------
               |                                | Unique
      Variable |     Obs=.     Obs>.     Obs<.  | values        Min         Max
  -------------+--------------------------------+------------------------------
  Pot_Liv_Do~r |        32                 230  |      2          0           1
  Living_Donor |        19                 243  |      2          0           1
  ldktreadgr~3 |        41                 221  |      3          0           2
  support_te~e |        58                 204  |      3          1           3
    education2 |        20                 242  |      2          0           1
       income3 |        58                 204  |      2          0           1
  marital_st~2 |        15                 247  |      3          1           3
    ethnicity2 |        21                 241  |      5          1           5
  sdidistres~d |        65                 197  |      2          0           1
  days_since~t |       110                 152  |      3          1           3
  -------------+--------------------------------+------------------------------

. capture mi extract 0, clear

. mi set wide

. mi register imputed support_received_Cat support_tertile education2 income3 marital_status2 days_since_dialaysis_Cat sdidistress_grouped Living_Donor Pot_Liv_Donor ldktreadgrp

. mi register regular age sex ethnicity2

. mi impute chained (logit) Living_Donor Pot_Liv_Donor education2 income3 sdidistress_grouped (mlogit) ldktreadgrp_3 support_tertile support_received_Cat days_since_dialaysis_Cat marital_status2  =  age sex ethnicity2, add(5) rseed (99) augment force

Conditional models:


marital_stat~2: mlogit marital_status2 i.Living_Donor i.education2 i.support_received_Cat i.Pot_Liv_Donor i.ldktreadgrp_3 i.income3 i.support_tertile i.sdidistress_grouped i.days_since_dialaysis_Cat age sex ethnicity2 , augment


Living_Donor: logit Living_Donor i.marital_status2 i.education2 i.support_received_Cat i.Pot_Liv_Donor i.ldktreadgrp_3 i.income3 i.support_tertile i.sdidistress_grouped i.days_since_dialaysis_Cat age sex ethnicity2 , augment


education2: logit education2 i.marital_status2 i.Living_Donor i.support_received_Cat i.Pot_Liv_Donor i.ldktreadgrp_3 i.income3 i.support_tertile i.sdidistress_grouped i.days_since_dialaysis_Cat age sex ethnicity2 , augment


support_rece~t: mlogit support_received_Cat i.marital_status2 i.Living_Donor i.education2 i.Pot_Liv_Donor i.ldktreadgrp_3 i.income3 i.support_tertile i.sdidistress_grouped i.days_since_dialaysis_Cat age sex ethnicity2 , augment


Pot_Liv_Donor: logit Pot_Liv_Donor i.marital_status2 i.Living_Donor i.education2 i.support_received_Cat i.ldktreadgrp_3 i.income3 i.support_tertile i.sdidistress_grouped i.days_since_dialaysis_Cat age sex ethnicity2 , augment


ldktreadgrp_3: mlogit ldktreadgrp_3 i.marital_status2 i.Living_Donor i.education2 i.support_received_Cat i.Pot_Liv_Donor i.income3 i.support_tertile i.sdidistress_grouped i.days_since_dialaysis_Cat age sex ethnicity2 , augment


income3: logit income3 i.marital_status2 i.Living_Donor i.education2 i.support_received_Cat i.Pot_Liv_Donor i.ldktreadgrp_3 i.support_tertile i.sdidistress_grouped i.days_since_dialaysis_Cat age sex ethnicity2 , augment


support_tert~e: mlogit support_tertile i.marital_status2 i.Living_Donor i.education2 i.support_received_Cat i.Pot_Liv_Donor i.ldktreadgrp_3 i.income3 i.sdidistress_grouped i.days_since_dialaysis_Cat age sex ethnicity2 , augment


sdidistress_~d: logit sdidistress_grouped i.marital_status2 i.Living_Donor i.education2 i.support_received_Cat i.Pot_Liv_Donor i.ldktreadgrp_3 i.income3 i.support_tertile i.days_since_dialaysis_Cat age sex ethnicity2 , augment


days_since_d~t: mlogit days_since_dialaysis_Cat i.marital_status2 i.Living_Donor i.education2 i.support_received_Cat i.Pot_Liv_Donor i.ldktreadgrp_3 i.income3 i.support_tertile i.sdidistress_grouped age sex ethnicity2 , augment


Performing chained iterations ...

Multivariate imputation                     Imputations   =        5
Chained equations                           added   =        5
Imputed: m=1 through m=5                    updated   =        0

Initialization: monotone                    Iterations   =       50
                                            burn-in   =       10


Living_Donor: logistic regression


Pot_Liv_Donor: logistic regression


education2: logistic regression


income3: logistic regression


sdidistress_~d: logistic regression


ldktreadgrp_3: multinomial logistic regression


support_tert~e: multinomial logistic regression


support_rece~t: multinomial logistic regression


days_since_d~t: multinomial logistic regression


marital_stat~2: multinomial logistic regression


-------------------+----------------------------------------------
                   |  Observations per m
                   +-----------------------------------+----------
          Variable |Complete   Incomplete   Imputed   |Total
-------------------+-----------------------------------+----------
Living_Donor   |        243           19        15 |       262
Pot_Liv_Donor   |        230           32        21 |       262
education2   |        242           20        10 |       262
income3   |        204           58        46 |       262
sdidistress_~d   |        197           65        55 |       262
ldktreadgrp_3   |        221           41        37 |       262
support_tert~e   |        204           58        49 |       262
support_rece~t   |        239           23        14 |       262
days_since_d~t   |        152          110        97 |       262
marital_stat~2   |        247           15         6 |       262
-------------------+-----------------------------------+----------


(complete + incomplete = total; imputed is the minimum across m
 of the number of filled-in observations.)


Note: Right-hand-side variables (or weights) have missing values; model parameters estimated using listwise deletion.


.
end of do-file

. do "C:\Users\tahmed\AppData\Local\Temp\68\STD1f58_000000.tmp"

. mi estimate, or: logistic Pot_Liv_Donor ib1.support_received_Cat

Multiple-imputation estimates                   Imputations       =          5
Logistic regression                             Number of obs     =        251
                                                Average RVI       =     0.0703
                                                Largest FMI       =     0.1144
DF adjustment: Large sample                     DF:     min       =     335.95
                                                        avg       =   1,859.63
                                                        max       =   3,626.34
Model F test: Equal FMI                         F(   2,  315.5)   =       9.02
Within VCE type: OIM                            Prob > F          =     0.0002

-------------+----------------------------------------------------------------
 Pot_Liv_Do~r | Odds Ratio    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
support_re~t |
Little/Some  |    1.358295    .4327443      0.96    0.337      .7273037     2.536718
A Great D..  |    4.317594    1.571422      4.02    0.000      2.110184      8.83412
             |
       _cons |    .7464395    .1762109     -1.24    0.216      .4697882     1.186006
-------------+----------------------------------------------------------------


Note: _cons estimates baseline odds.


. mi estimate, or: logistic Pot_Liv_Donor ib1.support_received_Cat age i.sex i.ethnicity2

Multiple-imputation estimates                   Imputations       =          5
Logistic regression                             Number of obs     =        241
                                                Average RVI       =     0.1118
                                                Largest FMI       =     0.1661
DF adjustment: Large sample                     DF:     min       =     164.39
                                                        avg       =     669.04
                                                        max       =   1,494.86
Model F test: Equal FMI                         F(   8, 2086.9)   =       3.99
Within VCE type: OIM                            Prob > F          =     0.0001

-------------+----------------------------------------------------------------
 Pot_Liv_Do~r | Odds Ratio    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
support_re~t |
Little/Some  |    1.209292    .4281518      0.54    0.592      .6038342     2.421836
A Great D..  |    4.036879    1.615863      3.49    0.001      1.837541     8.868589
             |
         age |    .9564103    .0126429     -3.37    0.001      .9318316     .9816374
             |
         sex |
     Female  |    1.052888    .3499441      0.16    0.877      .5483773     2.021553
             |
  ethnicity2 |
South Asian  |    .5721055      .38934     -0.82    0.413      .1492774     2.192593
      Black  |    .4967518    .3166581     -1.10    0.273      .1421388     1.736067
      Other  |    1.832075    1.568638      0.71    0.480      .3413713     9.832399
      White  |    1.457408    .8318481      0.66    0.509      .4754452     4.467472
             |
       _cons |    9.600876    9.612512      2.26    0.025      1.329711     69.32092
-------------+----------------------------------------------------------------


Note: _cons estimates baseline odds.


. mi estimate, or: logistic Pot_Liv_Donor ib1.support_received_Cat age i.sex i.ethnicity2 i.marital_status2 i.income3 i.education2

Multiple-imputation estimates                   Imputations       =          5
Logistic regression                             Number of obs     =        241
                                                Average RVI       =     0.1464
                                                Largest FMI       =     0.2868
DF adjustment: Large sample                     DF:     min       =      58.00
                                                        avg       =     304.14
                                                        max       =     741.10
Model F test: Equal FMI                         F(  12, 2248.9)   =       3.16
Within VCE type: OIM                            Prob > F          =     0.0002

-------------+----------------------------------------------------------------
 Pot_Liv_Do~r | Odds Ratio    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
support_re~t |
Little/Some  |    1.229771    .4697068      0.54    0.588       .581004     2.602973
A Great D..  |    3.778261    1.622298      3.10    0.002      1.621746     8.802401
             |
         age |    .9494317    .0140172     -3.51    0.001      .9222555     .9774088
             |
         sex |
     Female  |    1.168247    .4289477      0.42    0.672      .5671398     2.406463
             |
  ethnicity2 |
South Asian  |    .5177253    .3761118     -0.91    0.366      .1230453     2.178381
      Black  |    .5812095     .397873     -0.79    0.428      .1513485     2.231965
      Other  |     2.26938    2.124517      0.88    0.382       .360354     14.29174
      White  |    1.439901    .8877613      0.59    0.555      .4286752     4.836564
             |
marital_st~2 |
Married/C~w  |    2.892409    1.418724      2.17    0.032      1.098865     7.613341
Divorced/~d  |    1.327446    .7102073      0.53    0.597      .4638707     3.798717
             |
     income3 |
       >30k  |    1.847587       .6501      1.74    0.083        .92148     3.704452
             |
  education2 |
More than..  |    1.056152     .413206      0.14    0.889      .4843878      2.30282
       _cons |    4.646469    5.309502      1.34    0.184      .4717761     45.76254
-------------+----------------------------------------------------------------


Note: _cons estimates baseline odds.


. mi estimate, or: logistic Pot_Liv_Donor ib1.support_received_Cat age i.sex i.ethnicity2 i.marital_status2 i.income3 i.education2 i.days_since_dialaysis_Cat

Multiple-imputation estimates                   Imputations       =          5
Logistic regression                             Number of obs     =        241
                                                Average RVI       =     0.1986
                                                Largest FMI       =     0.3312
DF adjustment: Large sample                     DF:     min       =      44.01
                                                        avg       =     293.01
                                                        max       =   1,106.39
Model F test: Equal FMI                         F(  14, 1637.3)   =       2.74
Within VCE type: OIM                            Prob > F          =     0.0005

-------------+----------------------------------------------------------------
 Pot_Liv_Do~r | Odds Ratio    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
support_re~t |
Little/Some  |    1.067251    .4194603      0.17    0.869      .4935752     2.307701
A Great D..  |    3.564303    1.584342      2.86    0.005      1.481339     8.576197
             |
         age |    .9607959    .0156366     -2.46    0.015      .9303874     .9921983
             |
         sex |
     Female  |    .9143041    .3590998     -0.23    0.820      .4215682     1.982957
             |
  ethnicity2 |
South Asian  |    .4307893    .3326436     -1.09    0.278      .0929911      1.99567
      Black  |    .5007877    .3485705     -0.99    0.321      .1277656     1.962877
      Other  |     2.01041    1.883825      0.75    0.456      .3193408     12.65654
      White  |    1.308583    .8460933      0.42    0.678      .3661339     4.676945
             |
marital_st~2 |
Married/C~w  |    2.443999     1.30333      1.68    0.098      .8458643      7.06157
Divorced/~d  |    1.160711     .641812      0.27    0.788      .3912911     3.443089
             |
     income3 |
       >30k  |    1.549361     .562506      1.21    0.230      .7562248     3.174346
             |
  education2 |
More than..  |    1.085078    .4572052      0.19    0.847      .4641581     2.536621
             |
days_since~t |
      1-3yr  |    .3832528     .175124     -2.10    0.039      .1541912     .9526007
     >3 yrs  |    .3542971     .204071     -1.80    0.078      .1111643     1.129197
             |
       _cons |    7.576708    9.131698      1.68    0.099      .6770797     84.78544
-------------+----------------------------------------------------------------


Note: _cons estimates baseline odds.


.
end of do-file

. do "C:\Users\tahmed\AppData\Local\Temp\68\STD1f58_000000.tmp"

. capture mi extract 0, clear

. mi set wide

. mi register imputed support_received_Cat support_tertile education2 income3 marital_status2 days_since_dialaysis_Cat sdidistress_grouped Living_Donor Pot_Liv_Donor ldktreadgrp_3

. mi register regular age sex ethnicity2

. mi impute chained (logit) Living_Donor Pot_Liv_Donor education2 income3 sdidistress_grouped (mlogit) ldktreadgrp_3 support_tertile support_received_Cat days_since_dialaysis_Cat marital_status2  =  age sex ethnicity2, add(5) rseed (99) augment force

Conditional models:


marital_stat~2: mlogit marital_status2 i.Living_Donor i.education2 i.support_received_Cat i.Pot_Liv_Donor i.ldktreadgrp_3 i.income3 i.support_tertile i.sdidistress_grouped i.days_since_dialaysis_Cat age sex ethnicity2 , augment


Living_Donor: logit Living_Donor i.marital_status2 i.education2 i.support_received_Cat i.Pot_Liv_Donor i.ldktreadgrp_3 i.income3 i.support_tertile i.sdidistress_grouped i.days_since_dialaysis_Cat age sex ethnicity2 , augment


education2: logit education2 i.marital_status2 i.Living_Donor i.support_received_Cat i.Pot_Liv_Donor i.ldktreadgrp_3 i.income3 i.support_tertile i.sdidistress_grouped i.days_since_dialaysis_Cat age sex ethnicity2 , augment


support_rece~t: mlogit support_received_Cat i.marital_status2 i.Living_Donor i.education2 i.Pot_Liv_Donor i.ldktreadgrp_3 i.income3 i.support_tertile i.sdidistress_grouped i.days_since_dialaysis_Cat age sex ethnicity2 , augment


Pot_Liv_Donor: logit Pot_Liv_Donor i.marital_status2 i.Living_Donor i.education2 i.support_received_Cat i.ldktreadgrp_3 i.income3 i.support_tertile i.sdidistress_grouped i.days_since_dialaysis_Cat age sex ethnicity2 , augment


ldktreadgrp_3: mlogit ldktreadgrp_3 i.marital_status2 i.Living_Donor i.education2 i.support_received_Cat i.Pot_Liv_Donor i.income3 i.support_tertile i.sdidistress_grouped i.days_since_dialaysis_Cat age sex ethnicity2 , augment


income3: logit income3 i.marital_status2 i.Living_Donor i.education2 i.support_received_Cat i.Pot_Liv_Donor i.ldktreadgrp_3 i.support_tertile i.sdidistress_grouped i.days_since_dialaysis_Cat age sex ethnicity2 , augment


support_tert~e: mlogit support_tertile i.marital_status2 i.Living_Donor i.education2 i.support_received_Cat i.Pot_Liv_Donor i.ldktreadgrp_3 i.income3 i.sdidistress_grouped i.days_since_dialaysis_Cat age sex ethnicity2 , augment


sdidistress_~d: logit sdidistress_grouped i.marital_status2 i.Living_Donor i.education2 i.support_received_Cat i.Pot_Liv_Donor i.ldktreadgrp_3 i.income3 i.support_tertile i.days_since_dialaysis_Cat age sex ethnicity2 , augment


days_since_d~t: mlogit days_since_dialaysis_Cat i.marital_status2 i.Living_Donor i.education2 i.support_received_Cat i.Pot_Liv_Donor i.ldktreadgrp_3 i.income3 i.support_tertile i.sdidistress_grouped age sex ethnicity2 , augment


Performing chained iterations ...

Multivariate imputation                     Imputations   =        5
Chained equations                           added   =        5
Imputed: m=1 through m=5                    updated   =        0

Initialization: monotone                    Iterations   =       50
                                            burn-in   =       10


Living_Donor: logistic regression


Pot_Liv_Donor: logistic regression


education2: logistic regression


income3: logistic regression


sdidistress_~d: logistic regression


ldktreadgrp_3: multinomial logistic regression


support_tert~e: multinomial logistic regression


support_rece~t: multinomial logistic regression


days_since_d~t: multinomial logistic regression


marital_stat~2: multinomial logistic regression


-------------------+----------------------------------------------
                   |  Observations per m
                   +-----------------------------------+----------
          Variable |Complete   Incomplete   Imputed   |Total
-------------------+-----------------------------------+----------
Living_Donor   |        243           19        15 |       262
Pot_Liv_Donor   |        230           32        21 |       262
education2   |        242           20        10 |       262
income3   |        204           58        46 |       262
sdidistress_~d   |        197           65        55 |       262
ldktreadgrp_3   |        221           41        37 |       262
support_tert~e   |        204           58        49 |       262
support_rece~t   |        239           23        14 |       262
days_since_d~t   |        152          110        97 |       262
marital_stat~2   |        247           15         6 |       262
-------------------+-----------------------------------+----------


(complete + incomplete = total; imputed is the minimum across m
 of the number of filled-in observations.)


Note: Right-hand-side variables (or weights) have missing values; model parameters estimated using listwise deletion.


.
end of do-file

. do "C:\Users\tahmed\AppData\Local\Temp\68\STD1f58_000000.tmp"

. mi estimate, rrr: mlogit ldktreadgrp_3 ib1.support_received_Cat, b(1)

Multiple-imputation estimates                   Imputations       =          5
Multinomial logistic regression                 Number of obs     =        252
                                                Average RVI       =     0.1796
                                                Largest FMI       =     0.3494
DF adjustment: Large sample                     DF:     min       =      39.70
                                                        avg       =     134.59
                                                        max       =     398.57
Model F test: Equal FMI                         F(   4,  372.2)   =       3.27
Within VCE type: OIM                            Prob > F          =     0.0118

-------------+----------------------------------------------------------------
 ldktreadgr~3 |        RRR    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
0            |
support_re~t |
Little/Some  |    1.145371    .5658726      0.27    0.785      .4236306     3.096741
A Great D..  |    1.655704    .8654698      0.96    0.336      .5898162     4.647813
             |
       _cons |    .6117382    .2194723     -1.37    0.178       .296377     1.262661
-------------+----------------------------------------------------------------
 1            |  (base outcome)
-------------+----------------------------------------------------------------
2            |
support_re~t |
Little/Some  |    1.345151    .5808799      0.69    0.496      .5618828     3.220302
A Great D..  |    4.092742    1.704576      3.38    0.001      1.804761     9.281304
             |
       _cons |    .9946696    .2850421     -0.02    0.985      .5639731     1.754282
-------------+----------------------------------------------------------------

. mi estimate, rrr: mlogit ldktreadgrp_3 ib1.support_received_Cat age i.sex i.ethnicity2, b(1)

Multiple-imputation estimates                   Imputations       =          5
Multinomial logistic regression                 Number of obs     =        241
                                                Average RVI       =     0.1907
                                                Largest FMI       =     0.4050
DF adjustment: Large sample                     DF:     min       =      29.80
                                                        avg       =     520.91
                                                        max       =   2,852.99
Model F test: Equal FMI                         F(  16, 2186.4)   =       1.70
Within VCE type: OIM                            Prob > F          =     0.0408

-------------+----------------------------------------------------------------
 ldktreadgr~3 |        RRR    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
0            |
support_re~t |
Little/Some  |    1.096773    .5645774      0.18    0.858      .3925391     3.064437
A Great D..  |    1.591482    .8702549      0.85    0.396      .5416847     4.675812
             |
         age |    1.011198    .0190719      0.59    0.557      .9737705     1.050063
             |
         sex |
     Female  |    2.408878    1.158746      1.83    0.072      .9231734     6.285595
             |
  ethnicity2 |
South Asian  |    .6132636    .5365904     -0.56    0.576      .1102938     3.409914
      Black  |     .612582    .5110268     -0.59    0.557      .1191694     3.148934
      Other  |    4.271944    5.418914      1.14    0.254      .3498283     52.16704
      White  |     .968832    .7249336     -0.04    0.966      .2233818     4.201934
             |
       _cons |    .2941368    .3938758     -0.91    0.362      .0210064     4.118576
-------------+----------------------------------------------------------------
 1            |  (base outcome)
-------------+----------------------------------------------------------------
2            |
support_re~t |
Little/Some  |    1.146132    .5568828      0.28    0.781      .4247769     3.092491
A Great D..  |    3.125887    1.427524      2.50    0.013      1.270846     7.688712
             |
         age |    .9680395    .0142655     -2.20    0.029      .9402129     .9966897
             |
         sex |
     Female  |    1.402387    .5486657      0.86    0.388      .6498912     3.026183
             |
  ethnicity2 |
South Asian  |    .9306102    .6985868     -0.10    0.924      .2119482      4.08607
      Black  |     .547436    .3936976     -0.84    0.403      .1331716     2.250376
      Other  |    1.824589    2.324415      0.47    0.638      .1446204     23.01976
      White  |    1.055005    .6985303      0.08    0.936      .2867719      3.88126
             |
       _cons |    6.904798     7.50669      1.78    0.077      .8107836     58.80266
-------------+----------------------------------------------------------------

. mi estimate, rrr: mlogit ldktreadgrp_3 ib1.support_received_Cat age i.sex i.ethnicity2 i.marital_status2 i.income3 i.education2, b(1)

Multiple-imputation estimates                   Imputations       =          5
Multinomial logistic regression                 Number of obs     =        241
                                                Average RVI       =     0.2344
                                                Largest FMI       =     0.4501
DF adjustment: Large sample                     DF:     min       =      24.21
                                                        avg       =     518.99
                                                        max       =   4,494.81
Model F test: Equal FMI                         F(  24, 2399.0)   =       1.76
Within VCE type: OIM                            Prob > F          =     0.0129

-------------+----------------------------------------------------------------
 ldktreadgr~3 |        RRR    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
0            |
support_re~t |
Little/Some  |    1.091619    .5987507      0.16    0.873      .3657839     3.257748
A Great D..  |    1.969144    1.202109      1.11    0.270      .5861197     6.615588
             |
         age |     1.01588    .0208333      0.77    0.444      .9753454     1.058099
             |
         sex |
     Female  |    2.316836     1.23539      1.58    0.123      .7900298     6.794334
             |
  ethnicity2 |
South Asian  |    .5549108    .5116971     -0.64    0.523      .0909702     3.384912
      Black  |    .6935109    .6064092     -0.42    0.676      .1247651     3.854904
      Other  |    6.457375    8.579056      1.40    0.162      .4720348     88.33606
      White  |    1.320191    1.037974      0.35    0.724      .2826285     6.166767
             |
marital_st~2 |
Married/C~w  |    .7326478    .4803859     -0.47    0.637      .1983437     2.706276
Divorced/~d  |    .5975515    .4190884     -0.73    0.465      .1474328     2.421901
             |
     income3 |
       >30k  |    .2752731    .1565636     -2.27    0.033      .0851518     .8898846
             |
  education2 |
More than..  |    2.018792    .9650449      1.47    0.142      .7903045     5.156901
       _cons |    .2895595    .4161204     -0.86    0.389      .0171385     4.892172
-------------+----------------------------------------------------------------
 1            |  (base outcome)
-------------+----------------------------------------------------------------
2            |
support_re~t |
Little/Some  |    1.130653    .5319954      0.26    0.795      .4404785     2.902245
A Great D..  |    3.322978    1.565705      2.55    0.011      1.316025     8.390558
             |
         age |      .96146    .0149732     -2.52    0.012      .9324879     .9913322
             |
         sex |
     Female  |    1.502978    .6381308      0.96    0.339      .6500145     3.475219
             |
  ethnicity2 |
South Asian  |     .819662    .6463578     -0.25    0.801      .1719424     3.907389
      Black  |      .66627    .4939302     -0.55    0.584      .1550658     2.862757
      Other  |    2.370066     3.11303      0.66    0.513      .1735262     32.37096
      White  |    1.188253    .8069489      0.25    0.800      .3122616     4.521676
             |
marital_st~2 |
Married/C~w  |    3.265969    1.819516      2.12    0.035      1.085018     9.830762
Divorced/~d  |    1.272822    .7875834      0.39    0.697      .3762783     4.305524
             |
     income3 |
       >30k  |     .633803    .2598828     -1.11    0.268      .2818191     1.425405
             |
  education2 |
More than..  |    1.420572    .5576349      0.89    0.372      .6557783     3.077298
       _cons |    4.293627    4.898301      1.28    0.203      .4537956     40.62453
-------------+----------------------------------------------------------------

. mi estimate, rrr: mlogit ldktreadgrp_3 ib1.support_received_Cat age i.sex i.ethnicity2 i.marital_status2 i.income3 i.education2 i.days_since_dialaysis_Cat, b(1)

Multiple-imputation estimates                   Imputations       =          5
Multinomial logistic regression                 Number of obs     =        241
                                                Average RVI       =     0.3828
                                                Largest FMI       =     0.7337
DF adjustment: Large sample                     DF:     min       =       8.68
                                                        avg       =     406.49
                                                        max       =   5,434.11
Model F test: Equal FMI                         F(  28, 1298.4)   =       1.60
Within VCE type: OIM                            Prob > F          =     0.0245

-------------+----------------------------------------------------------------
 ldktreadgr~3 |        RRR    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
0            |
support_re~t |
Little/Some  |    .8912413     .587315     -0.17    0.863      .2302317     3.450051
A Great D..  |    1.911228    1.281914      0.97    0.339      .4956193     7.370157
             |
         age |    1.029113    .0242728      1.22    0.228       .981872     1.078627
             |
         sex |
     Female  |    2.295231    1.373766      1.39    0.175      .6761055     7.791809
             |
  ethnicity2 |
South Asian  |    .7068031     .716456     -0.34    0.732      .0963754     5.183589
      Black  |    .9696098    .9202141     -0.03    0.974      .1504544     6.248692
      Other  |    8.680096    12.25198      1.53    0.128      .5351692     140.7855
      White  |    1.727061    1.565102      0.60    0.547        .28888     10.32519
             |
marital_st~2 |
Married/C~w  |    .7624073    .5418755     -0.38    0.704      .1845344     3.149899
Divorced/~d  |    .6193135    .4756873     -0.62    0.535      .1325639     2.893315
             |
     income3 |
       >30k  |    .2161052    .1326386     -2.50    0.019      .0611351     .7639051
             |
  education2 |
More than..  |    2.248991      1.1577      1.57    0.117      .8160259     6.198283
             |
days_since~t |
      1-3yr  |    .2215256    .2318738     -1.44    0.185      .0204805     2.396116
     >3 yrs  |    1.007557    .8470415      0.01    0.993      .1745011     5.817569
             |
       _cons |    .2279501    .3416356     -0.99    0.324      .0120735     4.303733
-------------+----------------------------------------------------------------
 1            |  (base outcome)
-------------+----------------------------------------------------------------
2            |
support_re~t |
Little/Some  |    1.035569    .5121862      0.07    0.944      .3833485     2.797464
A Great D..  |    3.176549    1.539488      2.38    0.018      1.223586     8.246636
             |
         age |    .9719447    .0175104     -1.58    0.118      .9377778     1.007356
             |
         sex |
     Female  |    1.196246    .5272946      0.41    0.685      .5013772     2.854145
             |
  ethnicity2 |
South Asian  |     .702911    .5923705     -0.42    0.677      .1307574      3.77863
      Black  |    .6010864     .481502     -0.64    0.526      .1229892     2.937696
      Other  |    2.460473    3.322195      0.67    0.507      .1655498     36.56861
      White  |    1.111703    .7861637      0.15    0.881      .2758997     4.479465
             |
marital_st~2 |
Married/C~w  |    2.788716    1.573555      1.82    0.071      .9170084     8.480772
Divorced/~d  |    1.133137    .7210896      0.20    0.844      .3234678     3.969484
             |
     income3 |
       >30k  |     .505619    .2466663     -1.40    0.172      .1873234     1.364755
             |
  education2 |
More than..  |    1.491865    .6350961      0.94    0.350      .6407174     3.473702
             |
days_since~t |
      1-3yr  |    .4083866    .2890232     -1.27    0.232      .0857845     1.944168
     >3 yrs  |    .3206876     .230186     -1.58    0.125       .073623     1.396853
             |
       _cons |    7.043737    7.911427      1.74    0.082      .7786752     63.71621
-------------+----------------------------------------------------------------

.
end of do-file

. log close
      name:  <unnamed>
       log:  Z:\Social supp-LD_29Nov18_TA.smcl
  log type:  smcl
 closed on:  29 Nov 2018, 20:31:53
-------------