Power and sample size for cost-effectiveness analysis: fFN neonatal screening

Contemp Clin Trials. 2011 Nov;32(6):893-901. doi: 10.1016/j.cct.2011.07.007. Epub 2011 Jul 18.


Randomised controlled trials (RCTs) which involve cost-effectiveness evaluations rarely use health economic input when undertaking sample size calculations for the trial design; however, in studies undertaken with cost-effectiveness as the primary outcome, sample size calculations should be directly related to the cost-effectiveness result rather than to the effectiveness outcome alone. This paper reports on a case in which a clinical trial design sample size and power calculations were determined with regard to cost-effectiveness using the net monetary benefit (NMB) approach to demonstrate the feasibility of sample size calculation for cost-effectiveness in a real life setting. The proposed RCT of fetal fibronectin screening (fFN) for women with threatened pre-term labour is discussed, followed by the design of a preliminary model to inform the trial design calculation. The predictions from this pre-trial indicate potential cost-savings, but with a marginal detrimental impact on the effectiveness endpoint, neonatal morbidity. The NMB approach for cost-effectiveness is discussed and used to calculate the required sample sizes for different powers. The sample size calculations are then recalculated using a non-inferiority margin, to ensure that the NMB sample size for the trial was also sufficient to demonstrate non-inferiority for the effectiveness endpoint. Finally, a probabilistic analysis explored uncertainty in the model parameters and the impact on sample size. Considerations of economic assessments alongside clinical trials can and should be used to guide conventional trial design. This paper demonstrates the feasibility of such calculations, whilst simultaneously highlighting limitations and demonstrating the role for economic considerations to guide non-inferiority margins.

Publication types

  • Review

MeSH terms

  • Cost-Benefit Analysis
  • Humans
  • Infant, Newborn
  • Neonatal Screening / economics*
  • Obesity / prevention & control*
  • Primary Health Care*
  • Public Health / economics*
  • Research Design*
  • Sample Size