Table 6: Multivariate logistic regression.
|
Treat_outcome_4 |
Coef. |
Std. Err. |
z |
P > |z| |
[95% Conf. Interval] |
|
Treatment - Success
|
(Base outcome) |
|
|
|
|
|
Failed Quarter 2 3 4
|
0.0550254 0.2671141 0.3475711 |
0.1410267 0.1473716 0.1412895 |
0.39 1.81 2.46 |
0.696 0.070 0.014 |
-0.2213819 0.3314327 -0.021729 0.5559572 0.0706488 0.6244934 |
|
County_1 HealthFacility_1 |
0.0141287 0.0000922 |
0.0044185 0.0001701 |
0.001 0.54 |
3.20 0.588 |
0.0054687 0.0227888--0.0002412 0.0004255
|
|
Sector_1 Prisons Private Public |
0.6115028 0.0671998 0.3733351 |
0.6218228 0.432512 0.409629 |
0.98 0.16 0.91 |
0.325 0.877 0.362 |
-0.6072474 1.830253 -0.7805082 0.9149079 -0.4295229 1.176193
|
|
ModelOfCare_1 FB I -cons |
0.0255268 2.332626 -1.748054 |
0.1145843 0.5016934 0.4418323 |
0.22 4.65 -3.96 |
0.824 0.000 0.000
|
-0.1990542 0.2501078 1.349325 3.315927 -2.61403 -0.8820788
|
|
Ongoing_Meds Quarter 2 3 4 |
-0.1591966 0.3783635 -0.154734 |
0.1238194 0.1238157 0.1313156 |
-1.29 3.06 -1.18 |
0.199 0.002 0.239 |
-0.4018781 0.0834848 0.1356891 0.6210379 -0.412108 0.1026399 |
|
County_1 HealthFacility_1 |
0.0182387 0.0000865 |
0.0039221 0.0001522 |
4.65 0.57 |
0.000 0.570 |
0.0105515 0.0259258 -0.0002117 0.0003848
|
|
Sector_1 Prisons Private Public |
0.2758945 -0.8135085 -0.2305065
|
0.5009495 0.3223696 0.2934007 |
0.55 -2.52 -0.79 |
0.582 0.012 0.432 |
-0.7059485 1.257737 -1.445341 -0.1816757 -0.8055612 0.3445483 |
|
Model of Care_1 FB I -cons |
-0.1277871 0.9465254 -0.662439 |
0.1037235 0.5563221 0.3269804 |
-1.23 1.70 -2.03 |
0.218 0.089 0.043
|
-0.3310814 0.0755072 -0.143846 2.036897 -1.303309 -0.0215692
|
mlogit Treat_outcome_4 i.Quarter County_1 HealthFacility_1 i.Sector_1 i.ModelOfCare_1
Iteration 0:log likelihood = -2753.7174 ; Iteration 1:log likelihood = -2693.7795; Iteration 2:log likelihood = -2691.7733; Iteration 3: log likelihood = -2691.6071; Iteration 4:log likelihood = -2691.6069; Iteration 5:log likelihood = -2691.6069
Multinomial logistic regression: Number of obs = 2648; LR chi2(20) = 124.22; Prob > chi2 = 0.0000; Log likelihood = -2691.6069; Pseudo R2 = 0.0226