Prediction of personalised prognosis in patients with amyotrophic lateral sclerosis: development and validation of a prediction model. Lancet Neurology 17 (pp. 423-33).
Predicition models for delayed graft function: external validation on The Dutch Prospective Renal Transplantation Registry. Nephrology Dialysis Transplantation 0 (pp. ).
Exacerbations in adults with asthma: A systematic review and external validation of prediction models. J Allergy Clin Immunol Pract 0 (pp. ).
Validation of an imaging based cardiovascular risk score in a Scottish population. Eur J Radiol 98 (pp. 143-49).
Detecting small-study effects and funnel plot asymmetry in meta-analysis of survival data: a comparison of new and existing tests. Res Synth Methods 9 (pp. 41-50).
An overview of methods for network meta-analysis using individual participant data: when do benefits arise?. Stat Methods Med Res 7 (pp. 1351 - 1364).
Multiple imputation for multilevel data with continuous and binary variables. Stat Sci 33 (pp. 160-83).
The development of CHAMP: a checklist for the appraisal of moderators and predictors. BMC Med Res Methodol 173 (pp. ).
Practical Implications of Using Real-World Evidence in Comparative Effectiveness Research: Learnings from IMI-GetReal. J Comp Eff Res 6 (pp. 485-490).
Reporting of Bayesian analysis in epidemiologic research should become more transparent. J Clin Epidemiol 86 (pp. 51-58).
Predictive performance of the CHA2DS2-VASc rule in atrial fibrillation: a systematic review and meta-analysis. J Thromb Haemost 15 (pp. 1-13).
Combining randomized and non-randomized evidence in network meta-analysis. Stat Med 36 (pp. 1210-1226).
A guide to systematic review and meta-analysis of prediction model performance. BMJ 356 (pp. i6460).
Practicalities Of Using Real-World Evidence (RWE) In Comparative Effectiveness Research (CER). Value in Health 9 (pp. A692).
Methodological guidance, recommendations and illustrative case studies for (network) meta-analysis and modelling to predict real-world effectiveness using individual participant and/or aggregate data. 0 (pp. ).
A closed testing procedure to select an appropriate method for updating prediction models. Stat Med 36 (pp. 4529-4539).
GetReal in mathematical modelling: a review of studies predicting drug effectiveness in the real world. Res Synth Methods 7 (pp. 264-77).
Explicit inclusion of treatment in prognostic modeling was recommended in observational and randomized settings. J Clin Epidemiol 78 (pp. 90-100).
GetReal in network meta-analysis: a review of the methodology. Res Synth Methods 7 (pp. 236-63).
Individual participant data (IPD) meta-analyses of diagnostic and prognostic modeling studies: guidance on their use. PLoS Med 12 (pp. e1001886).
Get real in individual participant data (IPD) meta-analysis: a review of the methodology. Res Synth Methods 6 (pp. 293-309).
Summarising and validating test accuracy results across multiple studies for use in clinical practice. Stat Med 34 (pp. 2081-103).
A new framework to enhance the interpretation of external validation studies of clinical prediction models. J Clin Epidemiol 68 (pp. 279-89).
Meta-analysis and aggregation of multiple published prediction models. Stat Med 33 (pp. 2341-62).
Developing and validating risk prediction models in an individual participant data meta-analysis. BMC Med Res Methodol 14 (pp. 3).
Clinical prediction models for bronchopulmonary dysplasia: a systematic review and external validation study. BMC Pediatr 13 (pp. 207).
Individual participant data meta-analyses should not ignore clustering. J Clin Epidemiol 66 (pp. 865-873).
Individual participant data meta-analysis for a binary outcome: one-stage or two-stage?. PLoS One 8 (pp. e60650).
Incorporating published univariable associations in diagnostic and prognostic modeling. BMC Med Res Methodol 12 (pp. 121).
Aggregating published prediction models with individual patient data: a comparison of different approaches. Stat Med 31 (pp. 2697-712).
Development and validation of clinical prediction models: marginal differences between logistic regression, penalized maximum likelihood estimation, and genetic programming. J Clin Epidemiol 65 (pp. 404-12).