Statistics in Medicine

Estimating sample size for epidemiologic studies: the impact of ignoring exposure measurement uncertainty

Journal Article

Abstract

Sample size requirements for epidemiologic studies are usually determined on the basis of the desired level of statistical power. Suppose, however, that one is planning a study in which the participants' true exposure levels are unobservable. Instead, the analysis will be based on an imprecise surrogate measure that differs from true exposure by some non‐negligible amount of measurement error. Sample size estimates for tests of association between the surrogate exposure measure and the outcome of interest may be misleading if they are based solely on the anticipated characteristics of the distribution of surrogate measures in the study population. We examine the accuracy of sample size estimates for cohort studies in which a continuous surrogate exposure measure is subject to either classical or Berkson measurement error. In particular, we evaluate the consequences of not adjusting the sample size estimation procedure for tests based on imprecise exposure measurements to account for anticipated differences between the distributions of the true exposure and the surrogate measure in the study population. As expected, failure to adjust for classical measurement error can lead to underestimation of the required sample size. Disregard of Berkson measurement error, however, can result in sample size estimates that exceed the actual number of participants required for tests of association between the outcome and the surrogate exposure measure. We illustrate this Berkson error effect by estimating sample size for a hypothetical cohort study that examines an association between childhood exposure to radioiodine and the development of thyroid neoplasms. © 1998 John Wiley & Sons, Ltd.

Related Topics

Related Publications

Related Content

Site Footer

Address:

This website is provided by John Wiley & Sons Limited, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ (Company No: 00641132, VAT No: 376766987)

Published features on StatisticsViews.com are checked for statistical accuracy by a panel from the European Network for Business and Industrial Statistics (ENBIS)   to whom Wiley and StatisticsViews.com express their gratitude. This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis.