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Using simulation studies to evaluate statistical methods

Statistics in medicine, 2019-05, Vol.38 (11), p.2074-2102

2019

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Parametric multistate survival models: Flexible modelling allowing transition‐specific distributions with application to estimating clinically useful measures of effect differences

Statistics in medicine, 2017-12, Vol.36 (29), p.4719-4742

2017

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A flexible parametric accelerated failure time model and the extension to time-dependent acceleration factors

Biostatistics (Oxford, England), 2023-07, Vol.24 (3), p.811-831

2023

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The burgeoning role of MR1-restricted T-cells in infection, cancer and autoimmune disease

Current opinion in immunology, 2021-04, Vol.69, p.10-17

2021

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Neoantigen-reactive CD8+ T cells affect clinical outcome of adoptive cell therapy with tumor-infiltrating lymphocytes in melanoma

The Journal of clinical investigation, 2022-01, Vol.132 (2)

2022

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Simulating biologically plausible complex survival data

Statistics in medicine, 2013-10, Vol.32 (23), p.4118-4134

2013

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One‐stage individual participant data meta‐analysis models: estimation of treatment‐covariate interactions must avoid ecological bias by separating out within‐trial and across‐trial information

Statistics in medicine, 2017-02, Vol.36 (5), p.772-789

2017

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Genome-wide CRISPR-Cas9 screening reveals ubiquitous T cell cancer targeting via the monomorphic MHC class I-related protein MR1

Nature immunology, 2020-02, Vol.21 (2), p.178-185

2020

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A general framework for parametric survival analysis

Statistics in medicine, 2014-12, Vol.33 (30), p.5280-5297

2014

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Functional role of T-cell receptor nanoclusters in signal initiation and antigen discrimination

Proceedings of the National Academy of Sciences - PNAS, 2016-09, Vol.113 (37), p.E5454-E5463

2016

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Measuring and Analyzing Length of Stay in Critical Care Trials

Medical care, 2019-09, Vol.57 (9), p.e53-e59

2019

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Dynamic assessment of venous thromboembolism risk in patients with cancer by longitudinal D‐Dimer analysis: A prospective study

Journal of thrombosis and haemostasis, 2020-06, Vol.18 (6), p.1348-1356

2020

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A scoping methodological review of simulation studies comparing statistical and machine learning approaches to risk prediction for time-to-event data

Diagnostic and prognostic research, 2022-06, Vol.6 (1), p.10-10, Article 10

2022

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Multiple imputation strategies for missing event times in a multi‐state model analysis

Statistics in medicine, 2024-03, Vol.43 (6), p.1238-1255

2024

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Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modeling working group

Statistics in medicine, 2015-06, Vol.34 (14), p.2181-2195

2015

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Modeling the multi‐state natural history of rare diseases with heterogeneous individual patient data: A simulation study

Statistics in medicine, 2024-01, Vol.43 (1), p.184-200

2024

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Individual patient data meta-analysis of survival data using Poisson regression models

BMC medical research methodology, 2012-03, Vol.12 (1), p.34-34, Article 34

2012

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Relaxing the assumption of constant transition rates in a multi-state model in hospital epidemiology

BMC medical research methodology, 2021-01, Vol.21 (1), p.16-16, Article 16

2021

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Multilevel mixed effects parametric survival models using adaptive Gauss-Hermite quadrature with application to recurrent events and individual participant data meta-analysis

Statistics in medicine, 2014-09, Vol.33 (22), p.3844-3858

2014

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Setting benchmark revision rates for total hip replacement: analysis of registry evidence

BMJ (Online), 2015-03, Vol.350 (mar09 1), p.h756-h756

2015

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Exploring different research questions via complex multi-state models when using registry-based repeated prescriptions of antidepressants in women with breast cancer and a matched population comparison group

BMC medical research methodology, 2023-04, Vol.23 (1), p.87-87, Article 87

2023

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Flexible parametric modelling of the cause‐specific cumulative incidence function

Statistics in medicine, 2017-04, Vol.36 (9), p.1429-1446

2017

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Impact of model misspecification in shared frailty survival models

Statistics in medicine, 2019-10, Vol.38 (23), p.4477-4502

2019

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Development of a dynamic interactive web tool to enhance understanding of multi-state model analyses: MSMplus

BMC medical research methodology, 2021-11, Vol.21 (1), p.262-262, Article 262

2021

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Using simulation studies to evaluate statistical methods

Parametric multistate survival models: Flexible modelling allowing transition‐specific distributions with application to estimating clinically useful measures of effect differences

A flexible parametric accelerated failure time model and the extension to time-dependent acceleration factors

The burgeoning role of MR1-restricted T-cells in infection, cancer and autoimmune disease

Neoantigen-reactive CD8+ T cells affect clinical outcome of adoptive cell therapy with tumor-infiltrating lymphocytes in melanoma

Simulating biologically plausible complex survival data

One‐stage individual participant data meta‐analysis models: estimation of treatment‐covariate interactions must avoid ecological bias by separating out within‐trial and across‐trial information

Genome-wide CRISPR-Cas9 screening reveals ubiquitous T cell cancer targeting via the monomorphic MHC class I-related protein MR1

A general framework for parametric survival analysis

Functional role of T-cell receptor nanoclusters in signal initiation and antigen discrimination

Measuring and Analyzing Length of Stay in Critical Care Trials

Dynamic assessment of venous thromboembolism risk in patients with cancer by longitudinal D‐Dimer analysis: A prospective study

A scoping methodological review of simulation studies comparing statistical and machine learning approaches to risk prediction for time-to-event data

Multiple imputation strategies for missing event times in a multi‐state model analysis

Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modeling working group

Modeling the multi‐state natural history of rare diseases with heterogeneous individual patient data: A simulation study

Individual patient data meta-analysis of survival data using Poisson regression models

Targeting of multiple tumor-associated antigens by individual T cell receptors during successful cancer immunotherapy

Relaxing the assumption of constant transition rates in a multi-state model in hospital epidemiology

Multilevel mixed effects parametric survival models using adaptive Gauss-Hermite quadrature with application to recurrent events and individual participant data meta-analysis

Setting benchmark revision rates for total hip replacement: analysis of registry evidence

Exploring different research questions via complex multi-state models when using registry-based repeated prescriptions of antidepressants in women with breast cancer and a matched population comparison group

Flexible parametric modelling of the cause‐specific cumulative incidence function

Impact of model misspecification in shared frailty survival models

Development of a dynamic interactive web tool to enhance understanding of multi-state model analyses: MSMplus

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