Ignorability and bias in clinical trials.
Patient non-compliance and drop-out can bias analyses of clinical trial data. I describe a parametric model for treatment cross-over and drop-out and demonstrate how the concept of ignorability, originally defined for incomplete-data problems, can elucidate sources of bias in clinical trials. I discuss some implications of the theory and present simulation examples that illustrate the potential effects of non-ignorable cross-over and drop-out on bias and power.
Heitjan
D F
DF
Division of Biostatistics, Columbia University School of Public Health, 600 W. 168th Street, New York, NY 10032, USA. dfh5@columbia.edu
D000959:Antihypertensive Agents / Q000494:pharmacology / Q000627:therapeutic use; D015982:Bias; D001794:Blood Pressure / Q000187:drug effects; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D010352:Patient Dropouts; D016032:Randomized Controlled Trials as Topic; D016312:Treatment Refusal
D000959:Antihypertensive Agents