A Note on Sample Size Determination for Akaike Information Criterion (AIC) Approach to Clinical Data Analysis

ABSTRACT

Because of its flexibility and usefulness, Akaike Information Criterion (AIC) has been widely used for clinical data analysis. In general, however, AIC is used without paying much attention to sample size. If sample sizes are not large enough, it is possible that the AIC approach does not lead us to the conclusions which we seek. This article focuses on the sample size determination for AIC approach to clinical data analysis. We consider a situation in which outcome variables are dichotomous and propose a method for sample size determination under this situation. The basic idea is also applicable to the situations in which outcome variables have more than two categories or outcome variables are continuous. We present simulation studies and an application to an actual clinical trial.

Keywords:

• Clinical significance
• Clinical trial
• Dichotomous outcome
• Minimum AIC procedure

Mathematics Subject Classification:

• Primary 62B10
• Secondary 62P10

Acknowledgment

The present study was carried out under the ISM Cooperative Research Programs (2002-ISM·CRP-1003, 2003-ISM·CRP-1002).

Notes

*Number of patients with response.

^†Total number of patients.