Cumulative Hazard Ratio Estimation for Treatment Regimes in Sequentially
Randomized Clinical Trials.
Author(s): Tang X(1), Wahed AS(2).
Affiliation(s): Author information:
(1)Tang Biostatistics Program, Department of Pediatrics, University of Arkansas
for Medical Sciences, Little Rock, AR 72202, USA. (2)Department of Biostatistics,
University of Pittsburgh, Pittsburgh, PA 15261, USA.
Publication date & source: 2015, Stat Biosci. , 7(1):1-18
The proportional hazards model is widely used in survival analysis to allow
adjustment for baseline covariates. The proportional hazard assumption may not be
valid for treatment regimes that depend on intermediate responses to prior
treatments received, and it is not clear how such a model can be adapted to
clinical trials employing more than one randomization. Besides, since treatment
is modified post-baseline, the hazards are unlikely to be proportional across
treatment regimes. Although Lokhnygina and Helterbrand (Biometrics 63: 422-428,
2007) introduced the Cox regression method for two-stage randomization designs,
their method can only be applied to test the equality of two treatment regimes
that share the same maintenance therapy. Moreover, their method does not allow
auxiliary variables to be included in the model nor does it account for treatment
effects that are not constant over time. In this article, we propose a model that
assumes proportionality across covariates within each treatment regime but not
across treatment regimes. Comparisons among treatment regimes are performed by
testing the log ratio of the estimated cumulative hazards. The ratio of the
cumulative hazard across treatment regimes is estimated using a weighted
Breslow-type statistic. A simulation study was conducted to evaluate the
performance of the estimators and proposed tests.
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