Risk Predictions Using Panel Count Data with Informative Observation Times
Open AccessAccurate prediction of risks of disease and its precursors during screening are essentialfor optimal scheduling of screening exams. In epidemiology studies of screeningdetected disease, researchers often face screening data in the form of interval censored panel count data. Furthermore, observation times are usually informative about the disease risks, i.e, the screening frequency and timing is correlated with the risks of the medical event of interest. We analyze the Study of Colonoscopy Utilization within the PLCO (prostate, lung, colorectal, and ovarian) Cancer Screening trial, which followed patients for up to 15 years on colorectal cancer screening results including both cancer and its non-advanced/advanced adenoma precursors. Screening times strongly depend on past screening results. Furthermore, the recurrent adenoma processes are reset to zero at each screening because colonoscopy removes all detected adenomas. We model the recurrent times to screening and recurrent adenoma at each screening jointly. Correlations between the screening and adenoma processes are modeled by subject specific frailty terms. The baseline intensity function and regression coefficients for the recurrent adenoma processes are estimated using estimating equations for interval censored panel count data under the piecewise baseline intensity assumption. Probabilities of advanced adenoma at the next fixed or expected screening time are predicted. Performance of the risk prediction is examined through extensive simulation studies, and illustrated on the PLCO clinical trial data.
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AlHusseini_gwu_0075A_16039.pdf | 2022-10-04 | Open Access |
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