Description Usage Arguments Details Value References See Also Examples

The function fits a generalized proportional hazards model as proposed in Tang and Wahed (2011).

1 |

`data` |
a data frame (X, TR, R, Z, U, delta, ...) representing the data from a two-stage randomization design with therapies A1 and A2 available at the first stage, and B1 and B2 available at the second stage. |

`covar` |
covariate(s) to be adjusted in the model. The default (covar=NULL) fits a model without any covariates |

In sequentially randomized designs, there could be more than two therapies available at each stage. For simplicity, and to maintain similarity to the most common sequentially randomized clinical trials, a two-stage randomization design allowing two treatment options at each stage is used in the current version of the package. In detail, patients are initially randomized to either A1 or A2 at the first stage. Based on their response status, they are then randomized to either B1 or B2 at the second stage. Therefore, there are a total of four DTRs: A1B1, A1B2, A2B1, and A2B2.

The function returns an object of class `coxph`

. See `coxph.object`

for details.

Tang X, Wahed AS: Comparison of treatment regimes with adjustment for auxiliary variables. Journal of Applied Statistics 38(12):2925-2938, 2011

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```
Call:
"coxph(Surv(U, delta) ~ X + R + XR + RZ + XRZ + V)"
n= 573, number of events= 187
coef exp(coef) se(coef) z Pr(>|z|)
X -0.4613 0.6305 0.1869 -2.468 0.01357 *
R -0.7918 0.4530 0.3266 -2.425 0.01532 *
XR 0.6769 1.9679 0.4263 1.588 0.11229
RZ 1.2743 3.5761 0.3557 3.583 0.00034 ***
XRZ -1.4178 0.2423 0.5105 -2.777 0.00548 **
V -0.2520 0.7772 0.1539 -1.638 0.10152
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
X 0.6305 1.5862 0.43709 0.9094
R 0.4530 2.2074 0.23886 0.8592
XR 1.9679 0.5082 0.85335 4.5379
RZ 3.5761 0.2796 1.78102 7.1803
XRZ 0.2423 4.1279 0.08907 0.6589
V 0.7772 1.2866 0.57487 1.0509
Concordance= 0.601 (se = 0.023 )
Rsquare= 0.048 (max possible= 0.968 )
Likelihood ratio test= 28.2 on 6 df, p=8.627e-05
Wald test = 29.91 on 6 df, p=4.082e-05
Score (logrank) test = 31.72 on 6 df, p=1.845e-05
```

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