In injury biomechanics, there are currently no general a priori estimates of how few specimens are necessary to obtain sufficiently accurate injury risk curves for a given underlying distribution. Further, several methods are available for constructing these curves, and recent methods include Bayesian survival analysis.

This study used statistical simulations to evaluate the fidelity of different injury risk methods using limited sample sizes across four different underlying distributions. Five risk curve techniques were evaluated, including Bayesian techniques. For the Bayesian analyses, various prior distributions were assessed, each incorporating more accurate information. Simulated subject injury and biomechanical input values were randomly sampled from each underlying distribution, and injury status was determined by comparing these values. Injury risk curves were developed for this data using each technique for various small sample sizes;​ for each, analyses on 2000 simulated data sets were performed. Resulting median predicted risk values and confidence intervals were compared with the underlying distributions.

Across conditions, the standard and Bayesian survival analyses better represented the underlying distributions included in this study, especially for extreme (1, 10, and 90%) risk. This study demonstrates that the value of the Bayesian analysis is the use of informed priors. As the mean of the prior approaches the actual value, the sample size necessary for good reproduction of the underlying distribution with small confidence intervals can be as small as 2. This study provides estimates of confidence intervals and number of samples to allow the selection of the most appropriate sample sizes given known information.