|Title||Sensitivity analysis for missing outcomes in time-to-event data with covariate adjustment.|
|Publication Type||Journal Article|
|Year of Publication||2016|
|Authors||Zhao, Yue, Benjamin R. Saville, Haibo Zhou, and Gary G. Koch|
|Journal||J Biopharm Stat|
|Keywords||Computer Simulation, Data Interpretation, Statistical, Humans, Kaplan-Meier Estimate, Outcome Assessment, Health Care, Proportional Hazards Models, Randomized Controlled Trials as Topic|
Covariate-adjusted sensitivity analyses is proposed for missing time-to-event outcomes. The method invokes multiple imputation (MI) for the missing failure times under a variety of specifications regarding the post-withdrawal tendency for having the event of interest. With a clinical trial example, we compared methods of covariance analyses for time-to-event data, i.e., the multivariable Cox proportional hazards (PH) model and nonparametric analysis of covariance, and then illustrated how to incorporate these methods into the proposed sensitivity analysis for covariate adjustment. The MI methods considered are Kaplan-Meier multiple imputation and covariate-adjusted and unadjusted PH multiple imputation. The assumptions, statistical issues, and features for these methods are discussed.
|Alternate Journal||J Biopharm Stat|
|Original Publication||Sensitivity analysis for missing outcomes in time-to-event data with covariate adjustment.|
|PubMed Central ID||PMC4821499|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 ES021900 / ES / NIEHS NIH HHS / United States
Sensitivity analysis for missing outcomes in time-to-event data with covariate adjustment.