Minimum sample size for developing a multivariable prediction model: Part I  – Continuous outcomes

In the medical literature, hundreds of prediction models are being developed to predict health outcomes in individuals. For continuous outcomes, typically a linear regression model is developed to predict an individual's outcome value conditional on values of multiple predictors (covariates). To improve model development and reduce the potential for overfitting, a suitable sample size is required in terms of the number of subjects (n) relative to the number of predictor parameters (p) for potential inclusion. We propose that the minimum value ofn should meet the following four key criteria: (i) small optimism in predictor effect estimates as defined by a global shrinkage factor of≥0.9; (ii) small absolute difference of≤ 0.05 in the apparent and adjustedR2; (iii) precise estimation (a margin of error≤ 10% of the true value) of the model's residual standard deviation; and similarly, (iv) precise estimation of the mean predicted outcome value (model intercept). The criteria require prespecification of the user's chosenp and the model's anticipatedR2 as informed by previous studies. The value ofn that meets all four criteria provides the minimum sample size required for model development. In an applied example, a new model to predict lung function in African ‐American women using 25 predictor parameters requires at least 918 subjects to meet all criteria, corresponding to at least 36.7 subjects per predictor parameter. Even larger sample sizes may be needed to additionall...

Source: Statistics in Medicine - October 22, 2018 Category: Statistics Authors: Richard D. Riley, Kym I.E. Snell, Joie Ensor, Danielle L. Burke, Frank E. Harrell, Karel G.M. Moons, Gary S. Collins Tags: RESEARCH ARTICLE Source Type: research