A simple approach to power and sample size calculations in logistic regression and Cox regression models Export

@article{2004_VathSkovland, abstract = {For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright {\copyright} 2004 John Wiley \& Sons, Ltd.}, address = {Department of Biostatistics, University of Aarhus, Vennelyst Boulevard 6, DK-8000 Arhus C, Denmark; Section of Medical Statistics, University of Oslo, P.O.Box 1122 Blindern, N-0317 Oslo, Norway}, author = {V{\ae}th, Michael and Skovlund, Eva}, citeulike-article-id = {4075999}, citeulike-linkout-0 = {http://dx.doi.org/10.1002/sim.1753}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/15160408}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=15160408}, citeulike-linkout-3 = {http://www3.interscience.wiley.com/cgi-bin/abstract/108565621/ABSTRACT}, day = {15}, doi = {10.1002/sim.1753}, issn = {1097-0258}, journal = {Statistics in Medicine}, keywords = {logistic-regression, lrs, power, sample-size, statistics}, month = {June}, number = {11}, pages = {1781--1792}, posted-at = {2009-11-03 16:06:05}, priority = {0}, title = {A simple approach to power and sample size calculations in logistic regression and Cox regression models}, url = {http://dx.doi.org/10.1002/sim.1753}, volume = {23}, year = {2004} }

TY - JOUR ID - 2004_VathSkovland L3 - citeulike-article-id:4075999 N2 - For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright © 2004 John Wiley & Sons, Ltd. IS - 11 JF - Statistics in Medicine SN - 1097-0258 AD - Department of Biostatistics, University of Aarhus, Vennelyst Boulevard 6, DK-8000 Arhus C, Denmark; Section of Medical Statistics, University of Oslo, P.O.Box 1122 Blindern, N-0317 Oslo, Norway EP - 1792 TI - A simple approach to power and sample size calculations in logistic regression and Cox regression models VL - 23 SP - 1781 KW - logistic-regression KW - lrs KW - power KW - sample-size KW - statistics AU - Væth, Michael AU - Skovlund, Eva PY - 2004/06/15/ UR - http://dx.doi.org/10.1002/sim.1753 ER -