Abstract

BackgroundVolunteering participants in disease studies tend to be healthier than the general population partially due to specific enrollment criteria. Using modeling to accurately predict outcomes of cohort studies enrolling volunteers requires adjusting for the bias introduced in this way. Here we propose a new method to account for the effect of a specific form of healthy volunteer bias resulting from imposing disease status-related eligibility criteria, on disease-specific mortality, by explicitly modeling the length of the time interval between the moment when the subject becomes ineligible for the study, and the outcome.MethodsUsing survival time data from 1190 newly diagnosed lung cancer patients at MD Anderson Cancer Center, we model the time from clinical lung cancer diagnosis to death using an exponential distribution to approximate the length of this interval for a study where lung cancer death serves as the outcome. Incorporating this interval into our previously developed lung cancer risk model, we adjust for the effect of disease status-related eligibility criteria in predicting the number of lung cancer deaths in the control arm of CARET. The effect of the adjustment using the MD Anderson-derived approximation is compared to that based on SEER data.ResultsUsing the adjustment developed in conjunction with our existing lung cancer model, we are able to accurately predict the number of lung cancer deaths observed in the control arm of CARET.ConclusionsThe resulting adjustment was accurate in predicting the lower rates of disease observed in the early years while still maintaining reasonable prediction ability in the later years of the trial. This method could be used to adjust for, or predict the duration and relative effect of any possible biases related to disease-specific eligibility criteria in modeling studies of volunteer-based cohorts.

Highlights

  • Volunteering participants in disease studies tend to be healthier than the general population partially due to specific enrollment criteria

  • Using survival time data from newly diagnosed lung cancer patients treated at MD Anderson Cancer Center, we estimate the distribution of this time interval for lung cancer

  • In order to estimate the distribution of the time interval between clinical lung cancer diagnosis and death from lung cancer, we examined data on lung cancer survival times from 1,190 newly diagnosed LC patients at the University of Texas MD Anderson Cancer Center (MDA)

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Summary

Introduction

Volunteering participants in disease studies tend to be healthier than the general population partially due to specific enrollment criteria. We propose a new method to account for the effect of a specific form of healthy volunteer bias resulting from imposing disease status-related eligibility criteria, on disease-specific mortality, by explicitly modeling the length of the time interval between the moment when the subject becomes ineligible for the study, and the outcome. In order to adjust for the eligibility-related bias, we assume that for each individual there is a time interval prior to LC death in which an individual would not be eligible or likely to volunteer for a study. For our study, this interval is modeled as the time between clinical diagnosis and death from LC. We use an exponential distribution to approximate the empirical distribution of this interval

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