The accuracy of the clinical prediction model determines its extrapolation and application value. When the prediction model is applied to a new setting, the differences between the new population and the initially modeled population in terms of study time, population characteristics, region, and other factors could lead to a reduction in its predictive performance. Calibrating or updating the prediction model with appropriate statistical methods is important to improve the accuracy of the prediction model in new populations. The model updating methods mainly include regression coefficients updating, meta-model updating and dynamic model updating. However, due to the limitations of meta-model updating and dynamic model updating in practical applications, the regression coefficient updating method is still the most common method in model updating. This paper introducd several types of model updating methods, the regression coefficients updating methods for two common clinical prediction models based on Logistic regression and Cox regression, and provide corresponding R codes for reference of researchers.
The calculation of sample size is a critical component in the design phase of clinical trials incorporating health economic evaluations. A reasonable sample size is essential to ensure the scientific validity and accuracy of trial results. This paper summarizes the sample size calculation methods in the frequentist framework based on two health economic evaluation metrics: incremental cost-effectiveness ratio (ICER) and net benefit. It focuses on their applicable conditions, advantages, and limitations. The ICER method derives the sample size calculation formula by computing the ratio of incremental cost to incremental effect, while the net benefit method determines the economic viability of interventions by calculating incremental net benefit, subsequently leading to the formulation of the sample size calculation. Furthermore, this paper briefly discusses other sample size calculation methods, such as the classical Bayesian approach and the value of information analysis, providing a reference for calculating sample size in clinical trials with integrated health economic evaluations.