Evaluation of changes in prediction modelling in biomedicine using systematic reviews.

Abstract

The number of prediction models proposed in the biomedical literature has been growing year on year. In the last few years there has been an increasing attention to the changes occurring in the prediction modeling landscape. It is suggested that machine learning techniques are becoming more popular to develop prediction models to exploit complex data structures, higher-dimensional predictor spaces, very large number of participants, heterogeneous subgroups, with the ability to capture higher-order interactions. We examine the changes in modelling practices by investigating a selection of systematic reviews on prediction models published in the biomedical literature. We selected systematic reviews published between 2020 and 2022 which included at least 50 prediction models. Information was extracted guided by the CHARMS checklist. Time trends were explored using the models published since 2005. We identified 8 reviews, which included 1448 prediction models published in 887 papers. The average number of study participants and outcome events increased considerably between 2015 and 2019 but remained stable afterwards. The number of candidate and final predictors did not noticeably increase over the study period, with a few recent studies using very large numbers of predictors. Internal validation and reporting of discrimination measures became more common, but assessing calibration and carrying out external validation were less common. Information about missing values was not reported in about half of the papers, however the use of imputation methods increased. There was no sign of an increase in using of machine learning methods. Overall, most of the findings were heterogeneous across reviews. Our findings indicate that changes in the prediction modeling landscape in biomedicine are smaller than expected and that poor reporting is still common; adherence to well established best practice recommendations from the traditional biostatistics literature is still needed. For machine learning best practice recommendations are still missing, whereas such recommendations are available in the traditional biostatistics literature, but adherence is still inadequate.