Richard David Riley
0000-0001-8699-0735
Keele University
25 papers found
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TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
Factors affecting the implementation of calcium supplementation strategies during pregnancy to prevent pre-eclampsia: a mixed-methods systematic review
Predictors of outcome in sciatica patients following an epidural steroid injection: the POiSE prospective observational cohort study protocol
Regularized parametric survival modeling to improve risk prediction models
Two‐stage or not two‐stage? That is the question for IPD meta‐analysis projects
Calculating the power of a planned individual participant data meta‐analysis of randomised trials to examine a treatment‐covariate interaction with a time‐to‐event outcome
Propensity‐based standardization to enhance the validation and interpretation of prediction model discrimination for a target population
Calcium supplementation to prevent pre-eclampsia: protocol for an individual participant data meta-analysis, network meta-analysis and health economic evaluation
Maternal and child outcomes for pregnant women with pre-existing multiple long-term conditions: protocol for an observational study in the UK
Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models
Association of pregnancy complications/risk factors with the development of future long-term health conditions in women: overarching protocol for umbrella reviews
Calculating the power to examine treatment‐covariate interactions when planning an individual participant data meta‐analysis of randomized trials with a binary outcome
Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review
Case-finding and improving patient outcomes for chronic obstructive pulmonary disease in primary care: the BLISS research programme including cluster RCT
Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review
A prognostic model, including quantitative fetal fibronectin, to predict preterm labour: the QUIDS meta-analysis and prospective cohort study
A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint
Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence
Developing more generalizable prediction models from pooled studies and large clustered data sets
Development and validation of a clinical prediction rule for development of diabetic foot ulceration: an analysis of data from five cohort studies
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