Power and sample size calculations for studies involving linear regression

Control Clin Trials. 1998 Dec;19(6):589-601. doi: 10.1016/s0197-2456(98)00037-3.


This article presents methods for sample size and power calculations for studies involving linear regression. These approaches are applicable to clinical trials designed to detect a regression slope of a given magnitude or to studies that test whether the slopes or intercepts of two independent regression lines differ by a given amount. The investigator may either specify the values of the independent (x) variable(s) of the regression line(s) or determine them observationally when the study is performed. In the latter case, the investigator must estimate the standard deviation(s) of the independent variable(s). This study gives examples using this method for both experimental and observational study designs. Cohen's method of power calculations for multiple linear regression models is also discussed and contrasted with the methods of this study. We have posted a computer program to perform these and other sample size calculations on the Internet (see http://www.mc.vanderbilt.edu/prevmed/psintro+ ++.htm). This program can determine the sample size needed to detect a specified alternative hypothesis with the required power, the power with which a specific alternative hypothesis can be detected with a given sample size, or the specific alternative hypotheses that can be detected with a given power and sample size. Context-specific help messages available on request make the use of this software largely self-explanatory.

Publication types

  • Comparative Study
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Bacterial Vaccines / therapeutic use
  • Cadmium / adverse effects
  • Clinical Trials as Topic / statistics & numerical data*
  • Diet
  • Female
  • Humans
  • Linear Models*
  • Occupational Exposure
  • Pneumococcal Vaccines
  • Sample Size*
  • Software


  • Bacterial Vaccines
  • Pneumococcal Vaccines
  • Cadmium