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Introduction to Double Robust Methods for Incomplete Data

Statistical science, 2018-05, Vol.33 (2), p.184-197

2018

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Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model

Statistical methods in medical research, 2015-08, Vol.24 (4), p.462-487

2015

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Correcting for Optimistic Prediction in Small Data Sets

American journal of epidemiology, 2014-08, Vol.180 (3), p.318-324

2014

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Combining Multiple Imputation and Inverse‐Probability Weighting

Biometrics, 2012-03, Vol.68 (1), p.129-137

2012

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Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods

BMC medical research methodology, 2012-04, Vol.12 (1), p.46-46, Article 46

2012

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An evaluation of sample size requirements for developing risk prediction models with binary outcomes

BMC medical research methodology, 2024-07, Vol.24 (1), p.1-13, Article 146

2024

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Prediction of five-year mortality after COPD diagnosis using primary care records

PloS one, 2020-07, Vol.15 (7), p.e0236011-e0236011

2020

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Handling missing data in matched case‐control studies using multiple imputation

Biometrics, 2015-12, Vol.71 (4), p.1150-1159

2015

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Review of inverse probability weighting for dealing with missing data

Statistical methods in medical research, 2013-06, Vol.22 (3), p.278-295

2013

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How to develop a more accurate risk prediction model when there are few events

BMJ (Online), 2015-08, Vol.351, p.h3868

2015

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Comparative analysis of the risks of hospitalisation and death associated with SARS-CoV-2 omicron (B.1.1.529) and delta (B.1.617.2) variants in England: a cohort study

The Lancet (British edition), 2022-04, Vol.399 (10332), p.1303-1312

2022

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Risk of hospital admission for patients with SARS-CoV-2 variant B.1.1.7: cohort analysis

BMJ (Online), 2021-06, Vol.373, p.n1412

2021

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Hospital admission and emergency care attendance risk for SARS-CoV-2 delta (B.1.617.2) compared with alpha (B.1.1.7) variants of concern: a cohort study

The Lancet infectious diseases, 2022-01, Vol.22 (1), p.35-42

2022

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Semi-Parametric Methods of Handling Missing Data in Mortal Cohorts under Non-Ignorable Missingness

Biometrics, 2018-12, Vol.74 (4), p.1427-1437

2018

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Propensity score analysis with partially observed covariates: How should multiple imputation be used?

Statistical methods in medical research, 2019-01, Vol.28 (1), p.3-19

2019

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Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models

Statistics in medicine, 2023-06, Vol.42 (13), p.2191-2225

2023

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A general method for elicitation, imputation, and sensitivity analysis for incomplete repeated binary data

Statistics in medicine, 2020-09, Vol.39 (22), p.2921-2935

2020

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Using generalized linear models to implement g‐estimation for survival data with time‐varying confounding

Statistics in medicine, 2021-07, Vol.40 (16), p.3779-3790

2021

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Estimating a time-to-event distribution from right-truncated data in an epidemic: A review of methods

Statistical methods in medical research, 2022-09, Vol.31 (9), p.1641-1655

2022

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Nowcasting COVID‐19 deaths in England by age and region

Journal of the Royal Statistical Society Series C: Applied Statistics, 2022-11, Vol.71 (5), p.1266-1281

2022

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Assessing the Causal Effect of Binary Interventions from Observational Panel Data with Few Treated Units

Statistical science, 2019-08, Vol.34 (3), p.486-503

2019

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Sensitivity analysis for calibrated inverse probability-of-censoring weighted estimators under non-ignorable dropout

Statistical methods in medical research, 2022-07, Vol.31 (7), p.1374-1391

2022

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Dynamic Prediction of Survival in Cystic Fibrosis: A Landmarking Analysis Using UK Patient Registry Data

Epidemiology (Cambridge, Mass.), 2019-01, Vol.30 (1), p.29

2019

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Simulating longitudinal data from marginal structural models using the additive hazard model

Biometrical journal, 2021-10, Vol.63 (7), p.1526-1541

2021

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Methods for observed-cluster inference when cluster size is informative: A review and clarifications

Biometrics, 2014-06, Vol.70 (2), p.449-456

2014

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Introduction to Double Robust Methods for Incomplete Data

Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model

Correcting for Optimistic Prediction in Small Data Sets

Combining Multiple Imputation and Inverse‐Probability Weighting

Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods

An evaluation of sample size requirements for developing risk prediction models with binary outcomes

Prediction of five-year mortality after COPD diagnosis using primary care records

Handling missing data in matched case‐control studies using multiple imputation

Review of inverse probability weighting for dealing with missing data

How to develop a more accurate risk prediction model when there are few events

Comparative analysis of the risks of hospitalisation and death associated with SARS-CoV-2 omicron (B.1.1.529) and delta (B.1.617.2) variants in England: a cohort study

Risk of hospital admission for patients with SARS-CoV-2 variant B.1.1.7: cohort analysis

Hospital admission and emergency care attendance risk for SARS-CoV-2 delta (B.1.617.2) compared with alpha (B.1.1.7) variants of concern: a cohort study

Semi-Parametric Methods of Handling Missing Data in Mortal Cohorts under Non-Ignorable Missingness

Propensity score analysis with partially observed covariates: How should multiple imputation be used?

Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models

A general method for elicitation, imputation, and sensitivity analysis for incomplete repeated binary data

Using generalized linear models to implement g‐estimation for survival data with time‐varying confounding

Estimating a time-to-event distribution from right-truncated data in an epidemic: A review of methods

Nowcasting COVID‐19 deaths in England by age and region

Assessing the Causal Effect of Binary Interventions from Observational Panel Data with Few Treated Units

Sensitivity analysis for calibrated inverse probability-of-censoring weighted estimators under non-ignorable dropout

Dynamic Prediction of Survival in Cystic Fibrosis: A Landmarking Analysis Using UK Patient Registry Data

Simulating longitudinal data from marginal structural models using the additive hazard model

Methods for observed-cluster inference when cluster size is informative: A review and clarifications

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