A simple sample size formula for analysis of covariance in randomized clinical trials.

OBJECTIVE Randomized clinical trials that compare two treatments on a continuous outcome can be analyzed using analysis of covariance (ANCOVA) or a t-test approach. We present a method for the sample size calculation when ANCOVA is used. STUDY DESIGN AND SETTING We derived an approximate sample size formula. Simulations were used to verify the accuracy of the formula and to improve the approximation for small trials. The sample size calculations are illustrated in a clinical trial in rheumatoid arthritis. RESULTS If the correlation between the outcome measured at baseline and at follow-up is rho, ANCOVA comparing groups of (1-rho(2))n subjects has the same power as t-test comparing groups of n subjects. When on the same data, ANCOVA is used instead of t-test, the precision of the treatment estimate is increased, and the length of the confidence interval is reduced by a factor 1-rho(2). CONCLUSION ANCOVA may considerably reduce the number of patients required for a trial.

[1]  D. Altman,et al.  Analysing controlled trials with baseline and follow up measurements , 2001, BMJ : British Medical Journal.

[2]  M. Prevoo,et al.  Modified disease activity scores that include twenty-eight-joint counts. Development and validation in a prospective longitudinal study of patients with rheumatoid arthritis. , 1995, Arthritis and rheumatism.

[3]  Karin Proper,et al.  Evaluation of the results of a randomized controlled trial: how to define changes between baseline and follow-up. , 2004, Journal of clinical epidemiology.

[4]  Sample size in guidelines trials. , 2000, Family practice.

[5]  P. Armitage,et al.  Statistical methods in medical research. , 1972 .

[6]  P. V. van Riel,et al.  The Nijmegen inception cohort of early rheumatoid arthritis. , 2004, The Journal of rheumatology. Supplement.

[7]  P. van Riel,et al.  Validation of rheumatoid arthritis improvement criteria that include simplified joint counts. , 1998, Arthritis and rheumatism.

[8]  Scott E Maxwell,et al.  Power in randomized group comparisons: the value of adding a single intermediate time point to a traditional pretest-posttest design. , 2002, Psychological methods.

[9]  Franklin A. Graybill,et al.  Introduction to The theory , 1974 .

[10]  J. Wolfowitz,et al.  Introduction to the Theory of Statistics. , 1951 .

[11]  M. Dougados,et al.  Efficacy and safety of adalimumab as monotherapy in patients with rheumatoid arthritis for whom previous disease modifying antirheumatic drug treatment has failed , 2004, Annals of the rheumatic diseases.

[12]  Ewout W Steyerberg,et al.  Covariate adjustment in randomized controlled trials with dichotomous outcomes increases statistical power and reduces sample size requirements. , 2004, Journal of clinical epidemiology.

[13]  J. Bland,et al.  Sample size in guideline trials , 2000 .

[14]  G M Raab,et al.  How to select covariates to include in the analysis of a clinical trial. , 2000, Controlled clinical trials.

[15]  Ewout W Steyerberg,et al.  Randomized controlled trials with time-to-event outcomes: how much does prespecified covariate adjustment increase power? , 2006, Annals of epidemiology.

[16]  M. Dougados,et al.  When a DMARD fails, should patients switch to sulfasalazine or add sulfasalazine to continuing leflunomide? , 2004, Annals of the rheumatic diseases.

[17]  G. Breukelen ANCOVA versus change from baseline had more power in randomized studies and more bias in nonrandomized studies , 2006 .