Jonathan Bartlett

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Professor in Medical Statistics

London School of Hygiene & Tropical Medicine

United Kingdom

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Methods for handling missing data and covariate measurement error in individual participant data meta-analysis

Open Date: 2013-09-30

Close Date: 2016-09-29

Articles (11)

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2024

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2023

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2023

Year:

2023

Year:

2023

Year:

2023

For simulation studies that evaluate methods of handling missing data, we argue that generating partially observed data by fixing the complete data and repeatedly simulating the missingness indicators is a superficially attractive idea but only rarely appropriate to use.

Year:

2023

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2022

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2020

Year:

2020

Year:

2020

Collaborators (5)

Rachael Hughes

University of Bristol

UNITED KINGDOM

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Nancy Flournoy

University of Missouri

UNITED STATES

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Tra My Pham

UNITED KINGDOM

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Rhian Daniel

Professor of Statistics

Cardiff University

UNITED KINGDOM

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John Macleod

University of Bristol

UNITED KINGDOM

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Jonathan Bartlett

Research Interests

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Recent Grants

Articles (11)

Rejoinder to the letter: “Standard and reference‐based conditional mean imputation: Regulators and trial statisticians be aware!”

Software tools for quantitative bias analysis on measurement error or misclassification (Web of Science Core Collection and four other databases hosted on the same platform).

Complete case logistic regression with a dichotomised continuous outcome led to biased estimates

Reference-Based Multiple Imputation—What is the Right Variance and How to Estimate It

Estimands and their Estimators for Clinical Trials Impacted by the COVID-19 Pandemic: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

Hypothetical Estimands in Clinical Trials: A Unification of Causal Inference and Missing Data Methods

Comment on Oberman & Vink: Should we fix or simulate the complete data in simulation studies evaluating missing data methods?

Multiple imputation of partially observed covariates in discrete-time survival analysis

Bootstrap inference for multiple imputation under uncongeniality and misspecification

The Hazards of Period Specific and Weighted Hazard Ratios

Robustness of ANCOVA in randomized trials with unequal randomization

Collaborators (5)

Rachael Hughes

Nancy Flournoy

Tra My Pham

Rhian Daniel

John Macleod

Make your next move.

Jonathan Bartlett

Research Interests

Explore related searches

Recent Grants

Articles (11)

Rejoinder to the letter: “Standard and reference‐based conditional mean imputation: Regulators and trial statisticians be aware!”

Software tools for quantitative bias analysis on measurement error or misclassification (Web of Science Core Collection and four other databases hosted on the same platform).

Complete case logistic regression with a dichotomised continuous outcome led to biased estimates

Reference-Based Multiple Imputation—What is the Right Variance and How to Estimate It

Estimands and their Estimators for Clinical Trials Impacted by the COVID-19 Pandemic: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

Hypothetical Estimands in Clinical Trials: A Unification of Causal Inference and Missing Data Methods

Comment on Oberman &amp; Vink: Should we fix or simulate the complete data in simulation studies evaluating missing data methods?

Multiple imputation of partially observed covariates in discrete-time survival analysis

Bootstrap inference for multiple imputation under uncongeniality and misspecification

The Hazards of Period Specific and Weighted Hazard Ratios

Robustness of ANCOVA in randomized trials with unequal randomization

Collaborators (5)

Rachael Hughes

Nancy Flournoy

Tra My Pham

Rhian Daniel

John Macleod

Make your next move.

Comment on Oberman & Vink: Should we fix or simulate the complete data in simulation studies evaluating missing data methods?