David B Richardson1. 1. Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-8050, USA. david_richardson@unc.edu
Abstract
BACKGROUND: Power calculations can be a useful step in the design of epidemiologic studies. For occupational and environmental cohort studies, however, the calculation of statistical power has been difficult because researchers are often interested in situations where exposure assignment is time-dependent, and in research questions that pertain to cumulative exposure-mortality trends evaluated with statistical methods for survival analysis. These conditions are not easily accommodated by available software or published formulas for power calculation. METHODS: Monte Carlo methods can be used to estimate statistical power for survival analyses. Simple computer programs are presented to illustrate this approach. RESULTS: We show that, for the simple case of a randomized clinical trial involving a dichotomous exposure, the results of power calculations derived via this Monte Carlo approach conform to values derived using a previously published formula. We then illustrate how the Monte Carlo approach may be extended to obtain estimates of statistical power for analyses of cumulative exposure-mortality trends under conditions more typical of occupational cohort studies. CONCLUSIONS: The Monte Carlo approach provides a way to perform power calculations for a wide range of study conditions. The approach illustrated in this study should simplify the task of calculating power for survival analyses, particularly in epidemiologic research on occupational cohorts. Copyright 2003 Wiley-Liss, Inc.
BACKGROUND: Power calculations can be a useful step in the design of epidemiologic studies. For occupational and environmental cohort studies, however, the calculation of statistical power has been difficult because researchers are often interested in situations where exposure assignment is time-dependent, and in research questions that pertain to cumulative exposure-mortality trends evaluated with statistical methods for survival analysis. These conditions are not easily accommodated by available software or published formulas for power calculation. METHODS: Monte Carlo methods can be used to estimate statistical power for survival analyses. Simple computer programs are presented to illustrate this approach. RESULTS: We show that, for the simple case of a randomized clinical trial involving a dichotomous exposure, the results of power calculations derived via this Monte Carlo approach conform to values derived using a previously published formula. We then illustrate how the Monte Carlo approach may be extended to obtain estimates of statistical power for analyses of cumulative exposure-mortality trends under conditions more typical of occupational cohort studies. CONCLUSIONS: The Monte Carlo approach provides a way to perform power calculations for a wide range of study conditions. The approach illustrated in this study should simplify the task of calculating power for survival analyses, particularly in epidemiologic research on occupational cohorts. Copyright 2003 Wiley-Liss, Inc.