Thomas P. A. Debray
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View article: How to conduct an individual participant data meta-analysis in response to an emerging pathogen: Lessons learned from Zika and COVID-19
How to conduct an individual participant data meta-analysis in response to an emerging pathogen: Lessons learned from Zika and COVID-19 Open
Sharing, harmonizing, and analyzing participant-level data is of central importance in the rapid research response to emerging pathogens. Individual participant data meta-analyses (IPD-MAs), which synthesize participant-level data from rel…
View article: Risk prediction models for cancer therapy related cardiac dysfunction in patients with cancer and cancer survivors: systematic review and meta-analysis
Risk prediction models for cancer therapy related cardiac dysfunction in patients with cancer and cancer survivors: systematic review and meta-analysis Open
Objectives To systematically review all prediction models developed or validated for cancer therapy related cardiac dysfunction (CTRCD) and to quantitatively analyse their performance. Design Systematic review and meta-analysis. Data sourc…
View article: Deriving calibration plots in large clustered datasets
Deriving calibration plots in large clustered datasets Open
Background It is widely recommended to assess the discrimination and calibration performance of clinical prediction models (CPMs) before their implementation in clinical practice. Calibration is preferably assessed visually, by plotting th…
View article: POS0584 OVERALL SAFETY PROFILE AND LABORATORY PARAMETERS IN PATIENTS WITH RHEUMATOID ARTHRITIS TREATED WITH FILGOTINIB FOR UP TO 2 YEARS: REAL-WORLD EVIDENCE FROM FILOSOPHY AND PARROTFISH
POS0584 OVERALL SAFETY PROFILE AND LABORATORY PARAMETERS IN PATIENTS WITH RHEUMATOID ARTHRITIS TREATED WITH FILGOTINIB FOR UP TO 2 YEARS: REAL-WORLD EVIDENCE FROM FILOSOPHY AND PARROTFISH Open
View article: Applying the Principal Stratum Strategy in Equivalence Trials: A Case Study
Applying the Principal Stratum Strategy in Equivalence Trials: A Case Study Open
The estimand framework, introduced in the ICH E9 (R1) Addendum, provides a structured approach for defining precise research questions in randomised clinical trials. It suggests five strategies for addressing intercurrent events (ICE). Thi…
View article: Adverse fetal and perinatal outcomes associated with Zika virus infection during pregnancy: an individual participant data meta-analysis
Adverse fetal and perinatal outcomes associated with Zika virus infection during pregnancy: an individual participant data meta-analysis Open
View article: Developing a multivariable prediction model to support personalized selection among five major empirically-supported treatments for adult depression. Study protocol of a systematic review and individual participant data network meta-analysis
Developing a multivariable prediction model to support personalized selection among five major empirically-supported treatments for adult depression. Study protocol of a systematic review and individual participant data network meta-analysis Open
Background Various treatments are recommended as first-line options in practice guidelines for depression, but it is unclear which is most efficacious for a given person. Accurate individualized predictions of relative treatment effects ar…
View article: P110 Baseline characteristics, disease activity, patient-reported outcomes and safety in patients with rheumatoid arthritis treated with filgotinib in the UK: up to 1-year interim results from FILOSOPHY
P110 Baseline characteristics, disease activity, patient-reported outcomes and safety in patients with rheumatoid arthritis treated with filgotinib in the UK: up to 1-year interim results from FILOSOPHY Open
Background/Aims FILOSOPHY (NCT04871919) is an ongoing, prospective, observational real-world European study of filgotinib for RA. We report up to 1-year interim data from UK patients. Methods Patients with moderate to severe active RA are …
View article: Measuring the Performance of Survival Models to Personalize Treatment Choices
Measuring the Performance of Survival Models to Personalize Treatment Choices Open
Various statistical and machine learning algorithms can be used to predict treatment effects at the patient level using data from randomized clinical trials (RCTs). Such predictions can facilitate individualized treatment decisions. Recent…
View article: Developing clinical prediction models: a step-by-step guide
Developing clinical prediction models: a step-by-step guide Open
Predicting future outcomes of patients is essential to clinical practice, with many prediction models published each year. Empirical evidence suggests that published studies often have severe methodological limitations, which undermine the…
View article: The use of imputation in clinical decision support systems: a cardiovascular risk management pilot vignette study among clinicians
The use of imputation in clinical decision support systems: a cardiovascular risk management pilot vignette study among clinicians Open
Aims A major challenge of the use of prediction models in clinical care is missing data. Real-time imputation may alleviate this. However, to what extent clinicians accept this solution remains unknown. We aimed to assess acceptance of rea…
View article: Evaluating individualized treatment effect predictions: A model‐based perspective on discrimination and calibration assessment
Evaluating individualized treatment effect predictions: A model‐based perspective on discrimination and calibration assessment Open
In recent years, there has been a growing interest in the prediction of individualized treatment effects. While there is a rapidly growing literature on the development of such models, there is little literature on the evaluation of their …
View article: Challenges in the Assessment of a Disease Model in the NICE Single Technology Appraisal of Tirzepatide for Treating Type 2 Diabetes: An External Assessment Group Perspective
Challenges in the Assessment of a Disease Model in the NICE Single Technology Appraisal of Tirzepatide for Treating Type 2 Diabetes: An External Assessment Group Perspective Open
View article: Application of causal inference methods in individual-participant data meta-analyses in medicine: addressing data handling and reporting gaps with new proposed reporting guidelines
Application of causal inference methods in individual-participant data meta-analyses in medicine: addressing data handling and reporting gaps with new proposed reporting guidelines Open
Observational data provide invaluable real-world information in medicine, but certain methodological considerations are required to derive causal estimates. In this systematic review, we evaluated the methodology and reporting quality of i…
View article: Visualizing the target estimand in comparative effectiveness studies with multiple treatments
Visualizing the target estimand in comparative effectiveness studies with multiple treatments Open
Aim: Comparative effectiveness research using real-world data often involves pairwise propensity score matching to adjust for confounding bias. We show that corresponding treatment effect estimates may have limited external validity, and p…
View article: Evaluation of clinical prediction models (part 3): calculating the sample size required for an external validation study
Evaluation of clinical prediction models (part 3): calculating the sample size required for an external validation study Open
An external validation study evaluates the performance of a prediction model in new data, but many of these studies are too small to provide reliable answers. In the third article of their series on model evaluation, Riley and colleagues d…
View article: The potential benefit of statin prescription based on prediction of treatment responsiveness in older individuals: an application to the PROSPER randomized controlled trial
The potential benefit of statin prescription based on prediction of treatment responsiveness in older individuals: an application to the PROSPER randomized controlled trial Open
Aims Clinical guidelines often recommend treating individuals based on their cardiovascular risk. We revisit this paradigm and quantify the efficacy of three treatment strategies: (i) overall prescription, i.e. treatment to all individuals…
View article: Multiple imputation of incomplete multilevel data using Heckman selection models
Multiple imputation of incomplete multilevel data using Heckman selection models Open
Missing data is a common problem in medical research, and is commonly addressed using multiple imputation. Although traditional imputation methods allow for valid statistical inference when data are missing at random (MAR), their implement…
View article: Risk of bias assessments in individual participant data meta-analyses of test accuracy and prediction models: a review shows improvements are needed
Risk of bias assessments in individual participant data meta-analyses of test accuracy and prediction models: a review shows improvements are needed Open
View article: Developing a multivariable prediction model to support personalized selection among five major empirically-supported treatments for adult depression. Study protocol of a systematic review and individual participant data network meta-analysis.
Developing a multivariable prediction model to support personalized selection among five major empirically-supported treatments for adult depression. Study protocol of a systematic review and individual participant data network meta-analysis. Open
Background: Various treatments are recommended as first-line options in practice guidelines for depression, but it is unclear which is most efficacious for a given person. Accurate individualized predictions of relative treatment effects a…
View article: Confounder Adjustment Using the Disease Risk Score: A Proposal for Weighting Methods
Confounder Adjustment Using the Disease Risk Score: A Proposal for Weighting Methods Open
Propensity score analysis is a common approach to addressing confounding in nonrandomized studies. Its implementation, however, requires important assumptions (e.g., positivity). The disease risk score (DRS) is an alternative confounding s…
View article: Regularized parametric survival modeling to improve risk prediction models
Regularized parametric survival modeling to improve risk prediction models Open
We propose to combine the benefits of flexible parametric survival modeling and regularization to improve risk prediction modeling in the context of time‐to‐event data. Thereto, we introduce ridge, lasso, elastic net, and group lasso penal…
View article: Application of Causal Inference Methods to Pooled Longitudinal Non- Randomized Studies: A Methodological Systematic Review
Application of Causal Inference Methods to Pooled Longitudinal Non- Randomized Studies: A Methodological Systematic Review Open
View article: Handling related publications reporting real-world evidence in network meta-analysis: a case study in multiple sclerosis
Handling related publications reporting real-world evidence in network meta-analysis: a case study in multiple sclerosis Open
Aim: The presence of two or more publications that report on overlapping patient cohorts poses a challenge for quantitatively synthesizing real-world evidence (RWE) studies. Thus, we evaluated eight approaches for handling such related pub…
View article: Propensity‐based standardization to enhance the validation and interpretation of prediction model discrimination for a target population
Propensity‐based standardization to enhance the validation and interpretation of prediction model discrimination for a target population Open
External validation of the discriminative ability of prediction models is of key importance. However, the interpretation of such evaluations is challenging, as the ability to discriminate depends on both the sample characteristics (ie, cas…
View article: Methods for comparative effectiveness based on time to confirmed disability progression with irregular observations in multiple sclerosis
Methods for comparative effectiveness based on time to confirmed disability progression with irregular observations in multiple sclerosis Open
Real-world data sources offer opportunities to compare the effectiveness of treatments in practical clinical settings. However, relevant outcomes are often recorded selectively and collected at irregular measurement times. It is therefore …
View article: Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA)
Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA) Open
Most clinical specialties have a plethora of studies that develop or validate one or more prediction models, for example, to inform diagnosis or prognosis. Having many prediction model studies in a particular clinical field motivates the n…
View article: Development of treatment-decision algorithms for children evaluated for pulmonary tuberculosis: an individual participant data meta-analysis
Development of treatment-decision algorithms for children evaluated for pulmonary tuberculosis: an individual participant data meta-analysis Open
View article: Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration
Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration Open
The TRIPOD-Cluster (transparent reporting of multivariable prediction models developed or validated using clustered data) statement comprises a 19 item checklist, which aims to improve the reporting of studies developing or validating a pr…
View article: Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-Cluster checklist
Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-Cluster checklist Open
The increasing availability of large combined datasets (or big data), such as those from electronic health records and from individual participant data meta-analyses, provides new opportunities and challenges for researchers developing and…