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Power calculations for survival analyses via Monte Carlo estimation
Author(s) -
Richardson David B.
Publication year - 2003
Publication title -
american journal of industrial medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.7
H-Index - 104
eISSN - 1097-0274
pISSN - 0271-3586
DOI - 10.1002/ajim.10310
Subject(s) - monte carlo method , statistical power , medicine , power (physics) , statistics , range (aeronautics) , reliability engineering , computer science , econometrics , mathematics , engineering , physics , quantum mechanics , aerospace engineering
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. Am. J. Ind. Med. 44:532–539, 2003. © 2003 Wiley‐Liss, Inc.