1.4k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University College LondonGB16 papers
- London School of Hygiene & Tropical MedicineGB4 papers
- MRC Biostatistics UnitGB4 papers
- University of CambridgeGB3 papers
- University of PennsylvaniaUS3 papers
- AstraZeneca (United Kingdom)GB2 papers
- Medical Research CouncilGB2 papers
- Medical University of ViennaAT2 papers
- The University of MelbourneAU2 papers
- University of LeicesterGB2 papers
- Yale UniversityUS2 papers
- Zimmer Biomet (Germany)DE2 papers
15 papers · 1 filter
Introducing precision-weighted bias as a performance measure to inform the inclusion of adaptive designs in meta-analysis
Martin Law, David S. Robertson, Sofia S. Villar +4
We propose a novel, intuitive measure of statistical performance: precision-weighted bias. Precision-weighted bias is defined as the unconditional bias of an estimator weighted by…
What is estimated in cluster randomized crossover trials with informative sizes? -- A survey of estimands and common estimators
Kenneth M. Lee, Andrew B. Forbes, Jessica Kasza +5
In the analysis of cluster randomized trials (CRTs), previous work has defined two meaningful estimands: the individual-average treatment effect (iATE) and cluster-average treatmen…
Analysis of cohort stepped wedge cluster-randomized trials with non-ignorable dropout via joint modeling
Alessandro Gasparini, Michael J. Crowther, Emiel O. Hoogendijk +2
Stepped wedge cluster-randomized trial (CRTs) designs randomize clusters of individuals to intervention sequences, ensuring that every cluster eventually transitions from a control…
Phases of methodological research in biostatistics - building the evidence base for new methods
Georg Heinze, Anne-Laure Boulesteix, Michael Kammer +2
Although the biostatistical scientific literature publishes new methods at a very high rate, many of these developments are not trustworthy enough to be adopted by the scientific c…
Using modified intention-to-treat as a principal stratum estimator for failure to initiate treatment
Brennan C Kahan, Ian R White, Mark Edwards +1
Background: A common intercurrent event affecting many trials is when some participants do not begin their assigned treatment. Many trials use a modified intention-to-treat (mITT)…
Handling missing data when estimating causal effects with Targeted Maximum Likelihood Estimation
S. Ghazaleh Dashti, Katherine J. Lee, Julie A. Simpson +3
Targeted Maximum Likelihood Estimation (TMLE) is increasingly used for doubly robust causal inference, but how missing data should be handled when using TMLE with data-adaptive app…
Prediction meets causal inference: the role of treatment in clinical prediction models
Nan van Geloven, Sonja Swanson, Chava Ramspek +7
In this paper we study approaches for dealing with treatment when developing a clinical prediction model. Analogous to the estimand framework recently proposed by the European Medi…
State-of-the-art in selection of variables and functional forms in multivariable analysis -- outstanding issues
Willi Sauerbrei, Aris Perperoglou, Matthias Schmid +7
How to select variables and identify functional forms for continuous variables is a key concern when creating a multivariable model. Ad hoc 'traditional' approaches to variable sel…
Bivariate network meta-analysis for surrogate endpoint evaluation
Sylwia Bujkiewicz, Dan Jackson, John R Thompson +3
Surrogate endpoints are very important in regulatory decision-making in healthcare, in particular if they can be measured early compared to the long-term final clinical outcome and…
Population-calibrated multiple imputation for a binary/categorical covariate in categorical regression models
Tra My Pham, James R Carpenter, Tim P Morris +2
Multiple imputation (MI) has become popular for analyses with missing data in medical research. The standard implementation of MI is based on the assumption of data being missing a…
Using simulation studies to evaluate statistical methods
Tim P Morris, Ian R White, Michael J Crowther
Simulation studies are computer experiments that involve creating data by pseudorandom sampling. The key strength of simulation studies is the ability to understand the behaviour o…
Multiple imputation in Cox regression when there are time-varying effects of exposures
Ruth H. Keogh, Tim P. Morris
In Cox regression it is sometimes of interest to study time-varying effects (TVE) of exposures and to test the proportional hazards assumption. TVEs can be investigated with log ha…
A matrix-based method of moments for fitting multivariate network meta-analysis models with multiple outcomes and random inconsistency effects
Dan Jackson, Sylwia Bujkiewicz, Martin Law +2
Random-effects meta-analyses are very commonly used in medical statistics. Recent methodological developments include multivariate (multiple outcomes) and network (multiple treatme…
A mean score method for sensitivity analysis to departures from the missing at random assumption in randomised trials
Ian R. White, James Carpenter, Nicholas J. Horton
Most analyses of randomised trials with incomplete outcomes make untestable assumptions and should therefore be subjected to sensitivity analyses. However, methods for sensitivity…
Multiple imputation of covariates by fully conditional specification: accommodating the substantive model
Jonathan W. Bartlett, Shaun R. Seaman, Ian R. White +1
Missing covariate data commonly occur in epidemiological and clinical research, and are often dealt with using multiple imputation (MI). Imputation of partially observed covariates…