Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review
Background Clinical prediction models are often not evaluated properly in specific settings
or updated, for instance, with information from new markers. These key steps are needed …
or updated, for instance, with information from new markers. These key steps are needed …
[HTML][HTML] Risk-stratified and stepped models of care for back pain and osteoarthritis: are we heading towards a common model?
1. Background Musculoskeletal pain conditions are the largest causes of disability
worldwide. 30 Among the most disabling are knee and hip osteoarthritis (OA) and low back …
worldwide. 30 Among the most disabling are knee and hip osteoarthritis (OA) and low back …
Continual updating and monitoring of clinical prediction models: time for dynamic prediction systems?
Clinical prediction models (CPMs) have become fundamental for risk stratification across
healthcare. The CPM pipeline (development, validation, deployment, and impact …
healthcare. The CPM pipeline (development, validation, deployment, and impact …
Targeted validation: validating clinical prediction models in their intended population and setting
Clinical prediction models must be appropriately validated before they can be used. While
validation studies are sometimes carefully designed to match an intended population/setting …
validation studies are sometimes carefully designed to match an intended population/setting …
[HTML][HTML] Detection of calibration drift in clinical prediction models to inform model updating
Abstract Model calibration, critical to the success and safety of clinical prediction models,
deteriorates over time in response to the dynamic nature of clinical environments. To support …
deteriorates over time in response to the dynamic nature of clinical environments. To support …
A nonparametric updating method to correct clinical prediction model drift
Objective Clinical prediction models require updating as performance deteriorates over time.
We developed a testing procedure to select updating methods that minimizes overfitting …
We developed a testing procedure to select updating methods that minimizes overfitting …
Missing data should be handled differently for prediction than for description or causal explanation
Missing data are much studied in epidemiology and statistics. Theoretical development and
application of methods for handling missing data have mostly been conducted in the context …
application of methods for handling missing data have mostly been conducted in the context …
[HTML][HTML] Prediction models for covid-19 outcomes
Robust models that predict the prognosis of coronavirus 2019 (covid-19) are urgently
needed to support decisions about shielding, hospital admission, treatment, and population …
needed to support decisions about shielding, hospital admission, treatment, and population …
External validation of models for predicting cumulative live birth over multiple complete cycles of IVF treatment
STUDY QUESTION Can two prediction models developed using data from 1999 to 2009
accurately predict the cumulative probability of live birth per woman over multiple complete …
accurately predict the cumulative probability of live birth per woman over multiple complete …
Continuous learning in model‐informed precision dosing: a case study in pediatric dosing of vancomycin
Model‐informed precision dosing (MIPD) leverages pharmacokinetic (PK) models to tailor
dosing to an individual patient's needs, improving attainment of therapeutic drug exposure …
dosing to an individual patient's needs, improving attainment of therapeutic drug exposure …