Table 4: Model one multivariate logistic regression.
|
Treat_outcome_4 |
Coef. |
Std. Err. |
z |
P > |z| |
[95% Conf. Interval] |
|
Treatment - Success
|
(Base outcome) |
|
|
|
|
|
FailedSexMF-1 M BMI-Cat3NutritionSupport-1Age_registrn_2 |
0.0001125 -0.0516885 -0.0270661 0.0371438 |
0.1782375 0.1471804 0.0096194 0.1150748 |
0.00 0.725 -2.81 0.32 |
0.999 -0.35 0.005 0.747 |
-0.3492265 0.3494515 -0.3401569 0.2367798 -0.0459197 -0.0082124 -0.1883986 0.2626862
|
|
HIVStatus-1 Pos ARTYN-1 Intens_phse_regimen_1cont-phse-regimen-1 --cons |
3.652657 -1.457199 0.0032203 -0.0103776 0.0467406 |
1.248597 0.6310172 0.0029832 0.0079737 0.8357014 |
2.93 -2.31 1.08 -1.30 0.06 |
0.003 0.021 0.280 0.193 0.955 |
1.205453 6.099861 -2.69397 -0.2204283 -.0026266 0.0090672 -.0260059 0.0052506 -1.591204 1.684685 |
|
Ongoing-Meds SexMF-1 M BMI-Cat3 NutritionSupport-1 Age-registrn-2
|
0.2482052 -0.0181541 0.0121478 -0.1271593 |
0.2896057 0.2245291 0.0147266 0.1805295 |
0.86 -0.08 0.82 -0.70 |
0.391 0.936 0.409 0.481
|
-0.3194115 0.8158219 -0.458223 0.4219148 -0.0167158 0.0410114 -0.4809906 0.2266719
|
|
HIVStatus-1 Pos ARTYN-1 Intens-phse-regimen-1 cont-phse-regimen-1 -cons |
1.711165 -0.7891827 -0.0261607 0.0498746 0.6121316 |
2.626775 1.325283 0.0030932 0.0104255 1.507744 |
0.65 -0.60 -8.46 4.78 0.41 |
0.515 0.552 0.000 0.000 0.685 |
-3.43722 6.859551 -3.38669 1.808325 -0.0322233 -0.0200981 0.0294409 0.0703082 -2.342992 3.567255
|
Table showing Multinomial logistic regression of drug-resistant tuberculosis treatment outcome as treatment success as the base outcome and ongoing treatment and fail on treatment as the comparative levels mlogit Treat_outcome_4 i.SexMF_1 BMI_Cat3 NutritionSupport_1Age_registrn_2 i. HIVStatus_1 ARTYN_1 Intens_phse_regimen_1 cont_phse_regimen_ > 1
Iteration 0:log likelihood = -755.61234; Iteration 1: log likelihood = -695.87899;Iteration 2: log likelihood = -654.42391; Iteration 3: log likelihood = -649.4599; Iteration 4: log likelihood = -649.44322; Iteration 5: log likelihood = -649.44322
Multinomial logistic regression: Number of obs = 927; LR chi2 (16) = 212.34; Prob > chi2 = 0.0000;
Log likelihood = -649.44322; Pseudo R2 = 0.1405