Abstract
Background: We suggest an adaptive sample size calculation method for developing clinical prediction models, in which model performance is monitored sequentially as new data comes in. Methods: We illustrate the approach using data for the diagnosis of ovarian cancer (n=5914, 33% event fraction) and obstructive coronary artery disease (CAD; n=4888, 44% event fraction). We used logistic regression to develop a prediction model consisting only of a-priori selected predictors and assumed linear relations for continuous predictors. We mimicked prospective patient recruitment by developing the model on 100 randomly selected patients, and we used bootstrapping to internally validate the model. We sequentially added 50 random new patients until we reached a sample size of 3000, and re-estimated model performance at each step. We examined the required sample size for satisfying the following stopping rule: obtaining a calibration slope ≥0.9 and optimism in the c-statistic (ΔAUC) <=0.02 at two consecutive sample sizes. This procedure was repeated 500 times. We also investigated the impact of alternative modeling strategies: modeling nonlinear relations for continuous predictors, and applying Firth’s bias correction.Results: Better discrimination was achieved in the ovarian cancer data (c-statistic 0.9 with 7 predictors) than in the CAD data (c-statistic 0.7 with 11 predictors). Adequate calibration and limited optimism in discrimination was achieved after a median of 450 patients (interquartile range 450-500) for the ovarian cancer data (22 events per parameter (EPP), 20-24), and 750 patients (700-800) for the CAD data (30 EPP, 28-33). A stricter criterion, requiring ΔAUC <=0.01, was met with a median of 500 (23 EPP) and 1350 (54 EPP) patients, respectively. These sample sizes were much higher than the well-known 10 EPP rule of thumb and slightly higher than a recently published fixed sample size calculation method by Riley et al. Higher sample sizes were required when nonlinear relationships were modeled, and lower sample sizes when Firth’s correction was used. Conclusions: Adaptive sample size determination can be a useful supplement to a priori sample size calculations, because it allows to further tailor the sample size to the specific prediction modeling context in a dynamic fashion.
Randomized test-treatment studies with an outlook on adaptive designs
