Calculating the power to examine treatment-covariate interactions when planning an individual participant data meta-analysis of randomized trials with a binary outcome

Calculating the power to examine treatment-covariate interactions when planning an individual participant data meta-analysis of randomized trials with a binary outcome

Before embarking on an individual participant data meta-analysis (IPDMA) project, researchers and funders need assurance it is worth their time and cost. This should include consideration of how many studies are promising their IPD and, given the characteristics of these studies, the power of an IPD...

Bibliografske podrobnosti
Main Authors:	Riley, RD, Hattle, M, Collins, GS, Whittle, R, Ensor, J
Format:	Journal article
Jezik:	English
Izdano:	Wiley 2022

Podobne knjige/članki

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
od: Riley, RD, et al.
Izdano: (2023)

Calculating the power of a planned individual participant data meta‐analysis to examine prognostic factor effects for a binary outcome
od: Whittle, R, et al.
Izdano: (2024)

Simulation-based power calculations for planning a two-stage individual participant data meta-analysis
od: Joie Ensor, et al.
Izdano: (2018-05-01)

Risk of bias assessments in individual participant data meta-analyses of test accuracy and prediction models: a review shows improvements are needed
od: Levis, B, et al.
Izdano: (2023)

Minimum sample size for external validation of a clinical prediction model with a binary outcome
od: Riley, RD, et al.
Izdano: (2021)

A note on estimating the Cox-Snell R2 from a reported C statistic (AUROC) to inform sample size calculations for developing a prediction model with a binary outcome
od: Riley, RD, et al.
Izdano: (2020)

External validation of clinical prediction models: simulation-based sample size calculations were more reliable than rules-of-thumb
od: Snell, KIE, et al.
Izdano: (2021)

Penalization and shrinkage methods produced unreliable clinical prediction models especially when sample size was small
od: Riley, RD, et al.
Izdano: (2020)

Minimum sample size calculations for external validation of a clinical prediction model with a time-to-event outcome
od: Riley, RD, et al.
Izdano: (2021)

Minimum sample size for external validation of a clinical prediction model with a continuous outcome
od: Archer, L, et al.
Izdano: (2020)

Diagnostics of fitted binary logistic regression model based on individual subjects and covariate patterns
od: Sarkar, S. K., et al.
Izdano: (2010)

Individual participant data meta-analysis for a binary outcome: one-stage or two-stage?
od: Thomas P A Debray, et al.
Izdano: (2013-01-01)

Developing clinical prediction models when adhering to minimum sample size recommendations: the importance of quantifying bootstrap variability in tuning parameters and predictive performance
od: Martin, GP, et al.
Izdano: (2021)

Predictors of the effects of treatment for shoulder pain: protocol of an individual participant data meta-analysis
od: van der Windt, DA, et al.
Izdano: (2019)

Calculating the sample size required for developing a clinical prediction model
od: Riley, RD, et al.
Izdano: (2020)

Sample size requirements are not being considered in studies developing prediction models for binary outcomes: a systematic review
od: Dhiman, P, et al.
Izdano: (2023)

Stability of clinical prediction models developed using statistical or machine learning methods
od: Riley, RD, et al.
Izdano: (2023)

On the calculation of covariant expressions for Dirac bilinears
od: M. A. Olpak, et al.
Izdano: (2021-09-01)

Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes
od: Riley, R, et al.
Izdano: (2018)

The impact of adjustment for covariates on meta-analysis of randomised intervention studies for binary outcome
od: Yu, L
Izdano: (2015)

Minimum sample size for developing a multivariable prediction model using multinomial logistic regression
od: Pate, A, et al.
Izdano: (2023)

Evaluation of clinical prediction models (part 2): how to undertake an external validation study
od: Riley, RD, et al.
Izdano: (2024)

Effects of mechanical interventions in the management of knee osteoarthritis: protocol for an OA Trial Bank systematic review and individual participant data meta-analysis
od: Erin M Macri, et al.
Izdano: (2021-02-01)

Planning a method for covariate adjustment in individually-randomised trials: a practical guide
od: Morris, TP, et al.
Izdano: (2022)

Planning a method for covariate adjustment in individually randomised trials: a practical guide
od: Tim P. Morris, et al.
Izdano: (2022-04-01)

Expression variation and covariation impair analog and enable binary signaling control
od: Kyle M Kovary, et al.
Izdano: (2018-05-01)

A Binary Choice Model with Sample Selection and Covariate-Related Misclassification
od: Jorge González Chapela
Izdano: (2022-03-01)

Estimation of covariance components between one continuous and one binary trait
od: Schaeffer LR, et al.
Izdano: (1989-11-01)

Open science practices need substantial improvement in prognostic model studies in oncology using machine learning
od: Collins, GS, et al.
Izdano: (2023)

Application of nuclear data covariance matrices to representativity calculations
od: Grimaldi Federico, et al.
Izdano: (2024-01-01)

The dissociative subtype of PTSD in trauma-exposed individuals: a latent class analysis and examination of clinical covariates
od: Aljosha Deen, et al.
Izdano: (2022-07-01)

Predictors of the effects of treatment for shoulder pain: protocol of an individual participant data meta-analysis
od: Danielle A. van der Windt, et al.
Izdano: (2019-08-01)

COVID-19 prediction models should adhere to methodological and reporting standards
od: Collins, GS, et al.
Izdano: (2020)

Flaws in the development and validation of a covid-19 prediction model
od: Collins, GS, et al.
Izdano: (2020)

Methodology of calculation of time reserves when planning the duration of NPP construction
od: A. S. Pavlov, et al.
Izdano: (2024-10-01)

Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis.
od: Groenwold, R, et al.
Izdano: (2012)

Comparison of methods for handling covariate missingness in propensity score estimation with a binary exposure
od: Donna L. Coffman, et al.
Izdano: (2020-06-01)

The quandary of covarying: A brief review and empirical examination of covariate use in structural neuroimaging studies on psychological variables
od: Courtland S. Hyatt, et al.
Izdano: (2020-01-01)

Targeted validation: validating clinical prediction models in their intended population and setting
od: Sperrin, M, et al.
Izdano: (2022)

Calculated covariance matrices for fission product yields using BeoH
od: Lovell A. E., et al.
Izdano: (2023-01-01)