Analysis of survival data with missing measurements of a time-dependent binary covariate.
The objective of this study was to investigate the influence of the number and timing of a binary time-dependent covariate on the bias using the last-observation carried forward in the proportional hazards model. Under various assumptions of censoring rates, transition probabilities of the time-dependent covariate, sample size, and the log hazard-ratio for the covariate, we empirically examined the impact that the number and timing have on the bias of the estimator of the covariate. An example from the Systolic Hypertension in the Elderly Program was used. Inference on the effect of systolic blood pressure on survival is strongly affected by the number and timing of systolic blood measurements.
Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina 27710, USA. susan.halabi@duke.edu
Susan
S
Halabi
Chuan-Chuan
CC
Wun
Barry R
BR
Davis
D006801:Humans; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic / Q000379:methods / Q000706:statistics & numerical data*; D016019:Survival Analysis; D013997:Time Factors