RWE ready for reimbursement? A round up of developments in real-world evidence relating to health technology assessment: part 16.

In this update, we discuss recent US FDA guidance offering more specific guidelines on appropriate study design and analysis to support causal inference for non-interventional studies and the launch of the European Medicines Agency (EMA) and the Heads of Medicines Agencies (HMA) public electronic catalogues. We also highlight an article recommending assessing data quality and suitability prior to protocol finalization and a Journal of the American Medical Association-endorsed framework for using causal language when publishing real-world evidence studies. Finally, we explore the potential of large language models to automate the development of health economic models.

Compatibility in Missing Data Handling Across the Prediction Model Pipeline: A Simulation Study.

Careful handling of missing data is crucial to ensure that clinical prediction models are developed, validated, and implemented in a robust manner. We determined the bias in estimating predictive performance of different combinations of approaches for handling missing data across validation and implementation. We found four strategies that are compatible across the model pipeline and have provided recommendations for handling missing data between model validation and implementation under different missingness mechanisms.

Missing data was handled inconsistently in UK prediction models: a review of method used.

OBJECTIVES: No clear guidance exists on handling missing data at each stage of developing, validating and implementing a clinical prediction model (CPM). We aimed to review the approaches to handling missing data that underly the CPMs currently recommended for use in UK healthcare. STUDY DESIGN AND SETTING: A descriptive cross-sectional meta-epidemiological study aiming to identify CPMs recommended by the National Institute for Health and Care Excellence (NICE), which summarized how missing data is handled across their pipelines. RESULTS: A total of 23 CPMs were included through "sampling strategy." Six missing data strategies were identified: complete case analysis (CCA), multiple imputation, imputation of mean values, k-nearest neighbours imputation, using an additional category for missingness, considering missing values as risk-factor-absent. 52% of the development articles and 48% of the validation articles did not report how missing data were handled. CCA was the most common approach used for development (40%) and validation (44%). At implementation, 57% of the CPMs required complete data entry, whilst 43% allowed missing values. Three CPMs had consistent paths in their pipelines. CONCLUSION: A broad variety of methods for handling missing data underly the CPMs currently recommended for use in UK healthcare. Missing data handling strategies were generally inconsistent. Better quality assurance of CPMs needs greater clarity and consistency in handling of missing data.