Many prognostic models for cancer use biomarkers that have utility in early detection. For example, in prostate cancer, models predicting disease-specific survival use serum prostate-specific antigen (PSA) levels. These models are typically interpreted as indicating that detecting disease at a lower threshold of the biomarker is likely to generate a survival benefit. However, lowering the threshold of the biomarker is tantamount to early detection. It is not known whether the existing prognostic models imply a survival benefit under early detection once lead time has been accounted for. In the first half of this talk, we investigate survival benefit implied by prognostic models that utilize a biomarker. We show that the benefit depends not only on the parameters of the prognostic model, but also on the rate of biomarker change and the lead time and that early detection does not necessarily imply survival extension.

Next, we examine modeling of disease progression in active surveillance studies (AS). Specifically, prostate cancer grade, assessed with the Gleason score, describes how abnormal the tumor tissue and cells appear, and it is an important prognostic indicator of disease progression. Whether prostate tumors change grade is a question that has important implications for screening and treatment. Empirical longitudinal data on tumor grade are available from men biopsied regularly as part of AS programs, but subject to missclassification. We thus develop models that allow for estimation of the time of grade change while accounting for the misclassification error from biopsy grade. The proposed models are assessed with a simulation study and applied to data from the Johns Hopkins AS cohort. We conclude that knowledge of rates of grade misclassification allows for determination of true grade progression rates among men with serial biopsies on AS. Although our results are sensitive to prior specifications, they indicate that in a nontrivial fraction of the patient population, tumor grade can progress.