Correcting the Results of the Wrong Model: Treatment Effects Under Early Detection of Cancer
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Early detection of cancer leads to variability of the point of diagnosis advanced by the amount of the so-called lead time, a random variable. Estimated treatment effects by the proportional hazards (PH) model may be biased if this variability is ignored. We study how true and PH-estimated treatment effects differ in screened versus unscreened populations and offer an approximate correction for the reported PH-based estimate that does not require raw data, targeting a meta-analysis-type application. We rely on a joint cancer incidence and survival model of prostate cancer to furnish key information for the correction. The procedure is applied to a series of prostate cancer data analyses using the PH models reported in the literature. Simulations are used for assessing the quality of the method and sensitivity analyses.
KeywordsBias Early detection Lead time Misspecified model Proportional hazards
AMS Subject Classification62A01 62P10
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