Screening for chronic diseases: optimizing lead time through balancing prescribed frequency and individual adherence

John D. Rice, Brent A. Johnson and Robert L. Strawderman ()
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John D. Rice: University of Colorado
Brent A. Johnson: University of Rochester
Robert L. Strawderman: University of Rochester

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2022, vol. 28, issue 4, No 5, 605-636

Abstract: Abstract Screening for chronic diseases, such as cancer, is an important public health priority, but traditionally only the frequency or rate of screening has received attention. In this work, we study the importance of adhering to recommended screening policies and develop new methodology to better optimize screening policies when adherence is imperfect. We consider a progressive disease model with four states (healthy, undetectable preclinical, detectable preclinical, clinical), and overlay this with a stochastic screening–behavior model using the theory of renewal processes that allows us to capture imperfect adherence to screening programs in a transparent way. We show that decreased adherence leads to reduced efficacy of screening programs, quantified here using elements of the lead time distribution (i.e., the time between screening diagnosis and when diagnosis would have occurred clinically in the absence of screening). Under the assumption of an inverse relationship between prescribed screening frequency and individual adherence, we show that the optimal screening frequency generally decreases with increasing levels of non-adherence. We apply this model to an example in breast cancer screening, demonstrating how accounting for imperfect adherence affects the recommended screening frequency.

Keywords: Forward recurrence time; Periodic screening; Renewal processes; Semi-Markov models (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10985-022-09563-7

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