Estimation with Cox models: cause-specific survival analysis with misclassified cause of failure.
While epidemiologic and clinical research often aims to analyze predictors of specific endpoints, time-to-the-specific-event analysis can be hampered by problems with cause ascertainment. Under typical assumptions of competing risks analysis (and missing-data settings), we correct the cause-specific proportional hazards analysis when information on the reliability of diagnosis is available. Our method avoids bias in effect estimates at low cost in variance, thus offering a perspective for better-informed decision making. The ratio of different cause-specific hazards can be estimated flexibly for this purpose. It thus complements an all-cause analysis. In a sensitivity analysis, this approach can reveal the likely extent and direction of the bias of a standard cause-specific analysis when the diagnosis is suspect. These 2 uses are illustrated in a randomized vaccine trial and an epidemiologic cohort study, respectively.
Department of Applied Mathematics and Computer Science, Ghent University, Ghent, Belgium. bart.vanrompaye@ugent.be
Bart
B
Van Rompaye
Shabbar
S
Jaffar
Els
E
Goetghebeur
D015982:Bias; D002423:Cause of Death; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D006801:Humans; D015233:Models, Statistical; D009026:Mortality; D016016:Proportional Hazards Models; D016019:Survival Analysis