Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

<p style="text-align:justify;"> <b>Background:</b> There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data tech...

Full description

Bibliographic Details
Main Authors:	Marshall, A, Altman, D, Royston, P, Holder, R
Format:	Journal article
Language:	English
Published:	BioMed Central 2010

Similar Items

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study
by: Royston Patrick, et al.
Published: (2010-01-01)

Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study.
by: Marshall, A, et al.
Published: (2010)

Missing covariate data within cancer prognostic studies: a review of current reporting and proposed guidelines.
by: Burton, A, et al.
Published: (2004)

Missing covariate data in the construction of prognostic models: A review of current approaches
by: Burton, A, et al.
Published: (2003)

Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines
by: Marshall, A, et al.
Published: (2009)

How are missing data in covariates handled in observational time-to-event studies in oncology? A systematic review
by: Orlagh U. Carroll, et al.
Published: (2020-05-01)

Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines
by: Holder Roger L, et al.
Published: (2009-07-01)

Computing within-study covariances, data visualization, and missing data solutions for multivariate meta-analysis with metavcov
by: Min Lu
Published: (2023-06-01)

Handling missing covariates in observational studies: an illustration with the assessment of prognostic factors of survival outcomes in soft-tissue or visceral sarcomas in irradiated fields (SIF)
by: Noémie Huchet, et al.
Published: (2024-01-01)

The design of simulation studies in medical statistics.
by: Burton, A, et al.
Published: (2006)

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

Causal effect on a target population: A sensitivity analysis to handle missing covariates
by: Colnet Bénédicte, et al.
Published: (2022-11-01)

Developing a prognostic model in the presence of missing data: an ovarian cancer case study.
by: Clark, T, et al.
Published: (2003)

Evaluation of multiple imputation approaches for handling missing covariate information in a case-cohort study with a binary outcome
by: Melissa Middleton, et al.
Published: (2022-04-01)

Handling Planned and Unplanned Missing Data in a Longitudinal Study
by: Caron-Diotte, Mathieu, et al.
Published: (2023-06-01)

Handling of missing data in medical questionnaires : a comparitive study
by: Malhotra, Parul.
Published: (2013)

Handling missing data in medical questionnaires : a comparative study
by: Woon, Eric Sing Yong.
Published: (2012)

Software for handling and replacement of missing data
by: Mayer, Benjamin, et al.
Published: (2009-10-01)

The effect of missing values for covariates.
by: Whitlock, G, et al.
Published: (2002)

A Comparative Study of Various Methods for Handling Missing Data in UNSODA
by: Yingpeng Fu, et al.
Published: (2021-07-01)

Development and validation of a prediction model with missing predictor data: a practical approach.
by: Vergouwe, Y, et al.
Published: (2010)

missMDA: A Package for Handling Missing Values in Multivariate Data Analysis
by: Julie Josse, et al.
Published: (2016-04-01)

Handling missing data in a composite outcome with partially observed components: simulation study based on clustered paediatric routine data
by: Gachau, S, et al.
Published: (2021)

Enhanced mechanism to handle missing data of Hadith classifier
by: Aldhlan, Kawther A., et al.
Published: (2011)

Approaches to handling missing or “problematic” pharmacology data: Pharmacokinetics
by: Donald J. Irby, et al.
Published: (2021-04-01)

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

Missing data.
by: Altman, D, et al.
Published: (2007)

Reporting methods in studies developing prognostic models in cancer: a review.
by: Mallett, S, et al.
Published: (2010)

What do we mean by validating a prognostic model?
by: Altman, D, et al.
Published: (2000)

External validation of a Cox prognostic model: principles and methods.
by: Royston, P, et al.
Published: (2013)

Methods of handling missing data with reference to rainfall in Peninsular Malaysia /
by: Ho, Ming Kang, 1986-, et al.
Published: (2014)

Methods of handling missing data with reference to rainfall in Peninsular Malaysia
by: Ho, Ming Kang
Published: (2014)

A framework for handling missing accelerometer outcome data in trials
by: Mia S. Tackney, et al.
Published: (2021-06-01)

Class center-based firefly algorithm for handling missing data
by: Heru Nugroho, et al.
Published: (2021-02-01)

A hierarchical Bayesian approach for handling missing classification data
by: Alison C. Ketz, et al.
Published: (2019-03-01)

New insights into handling missing values in environmental epidemiological studies.
by: Célina Roda, et al.
Published: (2014-01-01)

Prognosis and prognostic research: Developing a prognostic model.
by: Royston, P, et al.
Published: (2009)

Prognosis and prognostic research: Validating a prognostic model
by: Altman, D, et al.
Published: (2009)

Prognosis and prognostic research: Developing a prognostic model
by: Royston, P, et al.
Published: (2009)

Prognosis and prognostic research: validating a prognostic model.
by: Altman, D, et al.
Published: (2009)