Including multiple imputation in a sensitivity analysis for clinical trials with treatment failures.
When treatment failures occur during the course of a clinical trial, the treatment regimen following failure may be changed. This change in therapy complicates comparisons among the original treatment arms. As in some clinical trials with dropouts, intent-to-treat analysis can yield a large bias. We examine the use of multiple imputation to replace observations after treatment failure has occurred. As a sensitivity analysis, this approach is compared to existing methods for handling treatment failures - removing treatment failure subjects, removing data after the onset of treatment failure, and imputation the last observation prior to treatment failure for all subsequent observations - in addition to an analysis of all collected data based on randomized treatment assignment. A data set from the Asthma Clinical Research Network is used to demonstrate the methods.
Shaffer
Michele L
ML
Penn State College of Medicine, Department of Health Evaluation Sciences, 600 Centerview Drive, Suite 2200, Hershey, PA 17033, USA. mshaffer@hes.hmc.psu.edu
Chinchilli
Vernon M
VM
D000465:Algorithms; D000893:Anti-Inflammatory Agents / Q000627:therapeutic use*; D001249:Asthma / Q000188:drug therapy*; D001993:Bronchodilator Agents / Q000627:therapeutic use*; D002986:Clinical Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data; D004359:Drug Therapy, Combination; D006801:Humans; D008390:Markov Chains; D009010:Monte Carlo Method; D010352:Patient Dropouts / Q000706:statistics & numerical data*; D016032:Randomized Controlled Trials as Topic / Q000379:methods; D012044:Regression Analysis; D012107:Research Design; D017211:Treatment Failure
D000893:Anti-Inflammatory Agents; D001993:Bronchodilator Agents