Minimum sample size for developing a multivariable prediction model: Part I – Continuous outcomes

Minimum sample size for developing a multivariable prediction model: Part I – Continuous outcomes

In the medical literature, hundreds of prediction models are being developed to predict health outcomes in individuals. For continuous outcomes, typically a linear regression model is developed to predict an individual's outcome value conditional on values of multiple predictors (covariates). T...

書目詳細資料
Main Authors:	Riley, R, Snell, K, Ensor, J, Burke, D, Harrell, F, Moons, K, Collins, G
格式:	Journal article
語言:	English
出版:	Wiley 2018

相似書籍

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

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

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

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

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

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

External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges
由: Riley, R, et al.
出版: (2016)

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

Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-Cluster checklist
由: Debray, TPA, et al.
出版: (2023)

Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration
由: Debray, TPA, et al.
出版: (2023)

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

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

A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes
由: Debray, T, et al.
出版: (2018)

A guide to systematic review and meta-analysis of prognostic factor studies
由: Riley, R, et al.
出版: (2019)

Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA)
由: Snell, KIE, et al.
出版: (2023)

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

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement.
由: Collins, G, et al.
出版: (2015)

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement
由: Collins, G, et al.
出版: (2015)

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement
由: Collins, G, et al.
出版: (2015)

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement.
由: Collins, G, et al.
出版: (2014)

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) : the TRIPOD statement
由: Collins, G, et al.
出版: (2015)

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) : the TRIPOD statement
由: Collins, G, et al.
出版: (2015)

Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement.
由: Collins, G, et al.
出版: (2015)

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement.
由: Collins, G, et al.
出版: (2015)

Impact of measurement error and sample size on the performance of multivariable risk prediction models: a simulation study
由: Vazquez Montes, M, et al.
出版: (2017)

Sample size for binary logistic prediction models: Beyond events per variable criteria
由: Van Smeden, M, et al.
出版: (2018)

A solution to minimum sample size for regressions.
由: David G Jenkins, et al.
出版: (2020-01-01)

Practical Minimum Sample Size for Road Crash Time-Series Prediction Models
由: Fady M. A. Hassouna, et al.
出版: (2020-01-01)

Multivariate Statistical Analysis and Required Sample Size
由: Ömer AKBULUT, et al.
出版: (2022-06-01)

Requirements for Minimum Sample Size for Sensitivity and Specificity Analysis
由: Mohamad Adam Bujang, et al.
出版: (2016-10-01)

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

Sample size considerations for the external validation of a multivariable prognostic model: a resampling study
由: Collins, GS, et al.
出版: (2015)

Identifiability of the multivariate normal by the maximum and the minimum
由: Arunava Mukherjea
出版: (2010-11-01)

Sample size requirements are not being considered in studies developing prediction models for binary outcomes: a systematic review
由: Paula Dhiman, et al.
出版: (2023-08-01)

Multivariate process variability monitoring for general sample size /
由: Revathi Sagadavan, 1986-, author 530660, et al.
出版: (2016)

Multivariate process variability monitoring for general sample size /
由: Revathi Sagadavan, 1986-, author 530660, et al.
出版: (2016)

Evaluation of clinical prediction models (part 1): from development to external validation
由: Collins, GS, et al.
出版: (2024)

Minimum Sample Size Estimate for Classifying Invasive Lung Adenocarcinoma
由: Chenchen Ma, et al.
出版: (2022-08-01)

Minimum important difference is minimally important in sample size calculations
由: Hubert Wong
出版: (2023-01-01)

PUMP: Estimating Power, Minimum Detectable Effect Size, and Sample Size When Adjusting for Multiple Outcomes in Multi-Level Experiments
由: Kristen B. Hunter, et al.
出版: (2024-03-01)