131 results on '"Riley, Richard D."'
Search Results
2. Using temporal recalibration to improve the calibration of risk prediction models in competing risk settings when there are trends in survival over time
3. Two‐stage or not two‐stage? That is the question for IPD meta‐analysis projects
4. Stability of clinical prediction models developed using statistical or machine learning methods
5. 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
6. Developing prediction models to estimate the risk of two survival outcomes both occurring: A comparison of techniques
7. Calculating the power to examine treatment‐covariate interactions when planning an individual participant data meta‐analysis of randomized trials with a binary outcome
8. MA‐cont:pre/post effect size: An interactive tool for the meta‐analysis of continuous outcomes using R Shiny
9. A refined method for multivariate meta‐analysis and meta‐regression
10. Systematic Reviews of Prediction Models
11. Systematic Reviews of Prognostic Factor Studies
12. Combining Results Using Meta‐Analysis
13. Minimum sample size calculations for external validation of a clinical prediction model with a time‐to‐event outcome
14. Applied meta‐analysis with R and Stata. Ding‐GengChen, Karl E.Peace (2021). Boca Raton, FL, Chapman and Hall/CRC Press, 2nd ed., 456 pages. ISBN 9780367183837
15. A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint
16. Dealing with Missing Data in an IPD Meta‐Analysis
17. Minimum sample size for external validation of a clinical prediction model with a binary outcome
18. Prognostic models for predicting relapse or recurrence of major depressive disorder in adults
19. Developing more generalizable prediction models from pooled studies and large clustered data sets
20. The One‐stage Approach to IPD Meta‐Analysis
21. Rationale for Embarking on an IPD Meta‐Analysis Project
22. Examining the Potential for Bias in IPD Meta‐Analysis Results
23. The Two‐stage Approach to IPD Meta‐Analysis
24. Multivariate Meta‐Analysis Using IPD
25. IPD Meta‐Analysis for Clinical Prediction Model Research
26. IPD Meta‐Analysis for Test Accuracy Research
27. Power Calculations for Planning an IPD Meta‐Analysis
28. Planning and Initiating an IPD Meta‐Analysis Project
29. Individual Participant Data Meta‐Analysis for Healthcare Research
30. IPD Meta‐Analysis for Prognostic Factor Research
31. Using IPD Meta‐Analysis to Examine Interactions between Treatment Effect and Participant‐level Covariates
32. Reporting and Dissemination of IPD Meta‐Analyses
33. Network Meta‐Analysis Using IPD
34. A Tool for the Critical Appraisal of IPD Meta‐Analysis Projects (CheckMAP)
35. One‐stage versus Two‐stage Approach to IPD Meta‐Analysis
36. Running an IPD Meta‐Analysis Project
37. Dealing with Missing Data in an IPD Meta‐Analysis
38. Individual participant data meta‐analysis for external validation, recalibration, and updating of a flexible parametric prognostic model
39. Authors' reply to Sabour and Ghajari “Clinical prediction models to predict the risk of multiple binary outcomes: Methodological issues”
40. A note on estimating theCox‐Snell R 2from a reported C statistic ( AUROC ) to inform sample size calculations for developing a prediction model with a binary outcome
41. Minimum sample size for external validation of a clinical prediction model with a continuous outcome
42. Clinical prediction models to predict the risk of multiple binary outcomes: a comparison of approaches
43. Meta‐analysis of continuous outcomes: Using pseudo IPD created from aggregate data to adjust for baseline imbalance and assess treatment‐by‐baseline modification
44. Doug Altman: Driving critical appraisal and improvements in the quality of methodological and medical research
45. One‐stage individual participant data meta‐analysis models for continuous and binary outcomes: Comparison of treatment coding options and estimation methods
46. Individual participant data meta‐analysis to examine interactions between treatment effect and participant‐level covariates: Statistical recommendations for conduct and planning
47. Individual participant data meta‐analysis of intervention studies with time‐to‐event outcomes: A review of the methodology and an applied example
48. Prognostic models for predicting relapse or recurrence of depression
49. A matrix-based method of moments for fitting multivariate network meta-analysis models with multiple outcomes and random inconsistency effects
50. Individual recovery expectations and prognosis of outcomes in non-specific low back pain: prognostic factor review
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