Adaptive sample size determination for the development of clinical prediction models.

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

SUBMITTER: Christodoulou E 

PROVIDER: S-EPMC7983402 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature