|Title||A global logrank test for adaptive treatment strategies based on observational studies.|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Li, Zhiguo, Marcia Valenstein, Paul Pfeiffer, and Dara Ganoczy|
|Date Published||2014 Feb 28|
|Keywords||Antidepressive Agents, Computer Simulation, Depression, Humans, Observational Studies as Topic, Patient Compliance, Probability, Proportional Hazards Models, Research Design, United States|
In studying adaptive treatment strategies, a natural question that is of paramount interest is whether there is any significant difference among all possible treatment strategies. When the outcome variable of interest is time-to-event, we propose an inverse probability weighted logrank test for testing the equivalence of a fixed set of pre-specified adaptive treatment strategies based on data from an observational study. The weights take into account both the possible selection bias in an observational study and the fact that the same subject may be consistent with more than one treatment strategy. The asymptotic distribution of the weighted logrank statistic under the null hypothesis is obtained. We show that, in an observational study where the treatment selection probabilities need to be estimated, the estimation of these probabilities does not have an effect on the asymptotic distribution of the weighted logrank statistic, as long as the estimation of the parameters in the models for these probabilities is n-consistent. Finite sample performance of the test is assessed via a simulation study. We also show in the simulation that the test can be pretty robust to misspecification of the models for the probabilities of treatment selection. The method is applied to analyze data on antidepressant adherence time from an observational database maintained at the Department of Veterans Affairs' Serious Mental Illness Treatment Research and Evaluation Center.
|Alternate Journal||Stat Med|
|Original Publication||A global logrank test for adaptive treatment strategies based on observational studies.|
|PubMed Central ID||PMC3961568|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States|
A global logrank test for adaptive treatment strategies based on observational studies.