|Title||Estimating treatment effects with treatment switching via semicompeting risks models: an application to a colorectal cancer study.|
|Publication Type||Journal Article|
|Year of Publication||2012|
|Authors||Zeng, Donglin, Qingxia Chen, Ming-Hui Chen, and Joseph G. Ibrahim|
|Corporate Authors||AMGEN RESEARCH GROUP|
|Date Published||2012 Mar|
Treatment switching is a frequent occurrence in clinical trials, where, during the course of the trial, patients who fail on the control treatment may change to the experimental treatment. Analysing the data without accounting for switching yields highly biased and inefficient estimates of the treatment effect. In this paper, we propose a novel class of semiparametric semicompeting risks transition survival models to accommodate treatment switches. Theoretical properties of the proposed model are examined and an efficient expectation-maximization algorithm is derived for obtaining the maximum likelihood estimates. Simulation studies are conducted to demonstrate the superiority of the model compared with the intent-to-treat analysis and other methods proposed in the literature. The proposed method is applied to data from a colorectal cancer clinical trial.
|Original Publication||Estimating treatment effects with treatment switching via semicompeting risks models: an application to a colorectal cancer study.|
|PubMed Central ID||PMC3412606|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 CA082659 / CA / NCI NIH HHS / United States
R21 HL097334 / HL / NHLBI NIH HHS / United States
R37 GM047845 / GM / NIGMS NIH HHS / United States
Estimating treatment effects with treatment switching via semicompeting risks models: an application to a colorectal cancer study.