Cohort study design for illness-death processes with disease status under intermittent observation

Abstract

Cohort studies are routinely conducted to learn about the incidence or progression rates of chronic diseases. The illness-death model offers a natural framework for joint consideration of non-fatal events in the semi-competing risks setting. We consider the design of prospective cohort studies where the goal is to estimate the effect of a marker on the risk of a non-fatal event which is subject to interval-censoring due to an intermittent observation scheme. The sample size is shown to depend on the effect of interest, the number of assessments, and the duration of follow-up. Minimum-cost designs are also developed to account for the different costs of recruitment and follow-up examination. We also consider the setting where the event status of individuals is observed subject to misclassification; the consequent need to increase the sample size to account for this error is illustrated through asymptotic calculations.

Keywords:

• Censored data
• illness-death model
• intermittent assessment
• sample size
• study design

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Nathalie C. Moon http://orcid.org/0000-0001-7789-205X

Leilei Zeng http://orcid.org/0000-0002-2336-7249

Richard J. Cook http://orcid.org/0000-0002-1414-4908

Additional information

Funding

This research was supported by an Alexandar Graham Bell Canada Graduate Scholarship and an Ontario Graduate Scholarship to N. Moon, Discovery Grants from the Natural Science and Engineering Research Council of Canada to L. Zeng [grant number RGPIN 115928] and R.J. Cook [grant number RGPIN 155849] and from the Canadian Institutes for Health Research to R.J. Cook [grant number FRN 13887]. This work was conducted while R.J. Cook held a Canada Research Chair in Statistical Methods for Health Research.

Notes on contributors

Nathalie C. Moon

Nathalie C. Moon is an Assistant Professor, Teaching Stream in the Department of Statistical Sciences at the University of Toronto

Leilei Zeng

Leilei Zeng is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo.

Richard J. Cook

Richard J. Cook is a Professor in the Department of Statistics and Actuarial Science at the University of Waterloo and a Faculty Research Chair.