Sample Size for Survival Analysis

 

Menu location: Analysis_Sample Size_Survival Times.

 

This function gives you the minimum number of subjects that you require to detect a true ratio of median survival times (hr) with power POWER and two sided type I error probability ALPHA (Dupont, 1990; Schoenfeld and Richter, 1982).

 

The method used here is suitable for calculating sample sizes for studies that will be analysed by the log-rank test.

 

Information required

• POWER: probability of detecting a real effect.
• ALPHA: probability of detecting a false effect (two sided: double this if you need one sided).
• A: accrual time during which subjects are recruited to the study.
• F: additional follow-up time after the end of recruitment.
• *: input either (C and r) or (C and E), where r=E/C.
• C: median survival time for control group.
• E: median survival time for experimental group.
• r: hazard ratio or ratio of median survival times.
• M: number of controls per experimental subject.

 

Practical issues

• Usual values for POWER are 80%, 85% and 90%; try several in order to explore/scope.
• 5% is the usual choice for ALPHA.
• C is usually estimated from previous studies.
• If possible, choose a range of hazard ratios that you want have the statistical power to detect.

 

Technical validation

The estimated sample size per group n is calculated as:

- where α = alpha, β = 1 - power and z_p is the standard normal deviate for probability p. n is rounded up to the closest integer. (1+1/m)/p is equivalent to 2/p in the first equation if the experimental and control group sizes are unequal.