Thomas Debray | University Medical Center Utrecht (original) (raw)

Papers by Thomas Debray

Research paper thumbnail of Real-time imputation of missing predictor values in clinical practice

European Heart Journal - Digital Health

Aims Use of prediction models is widely recommended by clinical guidelines, but usually requires ... more Aims Use of prediction models is widely recommended by clinical guidelines, but usually requires complete information on all predictors, which is not always available in daily practice. We aim to describe two methods for real-time handling of missing predictor values when using prediction models in practice. Methods and results We compare the widely used method of mean imputation (M-imp) to a method that personalizes the imputations by taking advantage of the observed patient characteristics. These characteristics may include both prediction model variables and other characteristics (auxiliary variables). The method was implemented using imputation from a joint multivariate normal model of the patient characteristics (joint modelling imputation; JMI). Data from two different cardiovascular cohorts with cardiovascular predictors and outcome were used to evaluate the real-time imputation methods. We quantified the prediction model’s overall performance [mean squared error (MSE) of lin...

Research paper thumbnail of Internal-external cross-validation helped to evaluate the generalizability of prediction models in large clustered datasets

Journal of Clinical Epidemiology

Research paper thumbnail of Real-time imputation of missing predictor values improved the application of prediction models in daily practice

Journal of Clinical Epidemiology

Research paper thumbnail of Prognostic models for chronic kidney disease: a systematic review and external validation

Nephrology Dialysis Transplantation

Background Accurate risk prediction is needed in order to provide personalized healthcare for chr... more Background Accurate risk prediction is needed in order to provide personalized healthcare for chronic kidney disease (CKD) patients. An overload of prognosis studies is being published, ranging from individual biomarker studies to full prediction studies. We aim to systematically appraise published prognosis studies investigating multiple biomarkers and their role in risk predictions. Our primary objective was to investigate if the prognostic models that are reported in the literature were of sufficient quality and to externally validate them. Methods We undertook a systematic review and appraised the quality of studies reporting multivariable prognosis models for end-stage renal disease (ESRD), cardiovascular (CV) events and mortality in CKD patients. We subsequently externally validated these models in a randomized trial that included patients from a broad CKD population. Results We identified 91 papers describing 36 multivariable models for prognosis of ESRD, 50 for CV events, 46...

Research paper thumbnail of Handling missing predictor values when validating and applying a prediction model to new patients

Research paper thumbnail of Individual participant data meta‐analysis to examine interactions between treatment effect and participant‐level covariates: Statistical recommendations for conduct and planning

Research paper thumbnail of Can personalized treatment prediction improve the outcomes, compared with the group average approach, in a randomized trial? Developing and validating a multivariable prediction model in a pragmatic megatrial of acute treatment for major depression

Journal of Affective Disorders

Research paper thumbnail of On the aggregation of published prognostic scores for causal inference in observational studies

Statistics in Medicine

As real world evidence on drug efficacy involves nonrandomized studies, statistical methods adjus... more As real world evidence on drug efficacy involves nonrandomized studies, statistical methods adjusting for confounding are needed. In this context, prognostic score (PGS) analysis has recently been proposed as a method for causal inference. It aims to restore balance across the different treatment groups by identifying subjects with a similar prognosis for a given reference exposure ("control"). This requires the development of a multivariable prognostic model in the control arm of the study sample, which is then extrapolated to the different treatment arms. Unfortunately, large cohorts for developing prognostic models are not always available. Prognostic models are therefore subject to a dilemma between overfitting and parsimony; the latter being prone to a violation of the assumption of no unmeasured confounders when important covariates are ignored. Although it is possible to limit overfitting by using penalization strategies, an alternative approach is to adopt evidence synthesis. Aggregating previously published prognostic models may improve the generalizability of PGS, while taking account of a large set of covariates-even when limited individual participant data are available. In this article, we extend a method for prediction model aggregation to PGS analysis in nonrandomized studies. We conduct extensive simulations to assess the validity of model aggregation, compared with other methods of PGS analysis for estimating marginal treatment effects. We show that aggregating existing PGS into a "meta-score" is robust to misspecification, even when elementary scores wrongfully omit confounders or focus on different outcomes. We illustrate our methods in a setting of treatments for asthma.

Research paper thumbnail of Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection

Objective To review and critically appraise published and preprint reports of models that aim to ... more Objective To review and critically appraise published and preprint reports of models that aim to predict either (i) presence of existing COVID-19 infection, (ii) future complications in individuals already diagnosed with COVID-19, or (iii) models to identify individuals at high risk for COVID-19 in the general population. Design Rapid systematic review and critical appraisal of prediction models for diagnosis or prognosis of COVID-19 infection. Data sources PubMed, EMBASE via Ovid, Arxiv, medRxiv and bioRxiv until 24th March 2020. Study selection Studies that developed or validated a multivariable COVID-19 related prediction model. Two authors independently screened titles, abstracts and full text. Data extraction Data from included studies were extracted independently by at least two authors based on the CHARMS checklist, and risk of bias was assessed using PROBAST. Data were extracted on various domains including the participants, predictors, outcomes, data analysis, and predictio...

Research paper thumbnail of Individual participant data meta‐analysis of intervention studies with time‐to‐event outcomes: A review of the methodology and an applied example

Research Synthesis Methods

Research paper thumbnail of Predicting disability progression in multiple sclerosis: Insights from advanced statistical modeling

Multiple Sclerosis Journal

Background: There is an unmet need for precise methods estimating disease prognosis in multiple s... more Background: There is an unmet need for precise methods estimating disease prognosis in multiple sclerosis (MS). Objective: Using advanced statistical modeling, we assessed the prognostic value of various clinical measures for disability progression. Methods: Advanced models to assess baseline prognostic factors for disability progression over 2 years were applied to a pooled sample of patients from placebo arms in four different phase III clinical trials. least absolute shrinkage and selection operator (LASSO) and ridge regression, elastic nets, support vector machines, and unconditional and conditional random forests were applied to model time to clinical disability progression confirmed at 24 weeks. Sensitivity analyses for different definitions of a combined endpoint were carried out, and bootstrap was used to assess prediction model performance. Results: A total of 1582 patients were included, of which 434 (27.4%) had disability progression in a combined endpoint over 2 years. O...

Research paper thumbnail of Empirical evidence of the impact of study characteristics on the performance of prediction models: a meta-epidemiological study

BMJ Open

ObjectivesTo empirically assess the relation between study characteristics and prognostic model p... more ObjectivesTo empirically assess the relation between study characteristics and prognostic model performance in external validation studies of multivariable prognostic models.DesignMeta-epidemiological study.Data sources and study selectionOn 16 October 2018, we searched electronic databases for systematic reviews of prognostic models. Reviews from non-overlapping clinical fields were selected if they reported common performance measures (either the concordance (c)-statistic or the ratio of observed over expected number of events (OE ratio)) from 10 or more validations of the same prognostic model.Data extraction and analysesStudy design features, population characteristics, methods of predictor and outcome assessment, and the aforementioned performance measures were extracted from the included external validation studies. Random effects meta-regression was used to quantify the association between the study characteristics and model performance.ResultsWe included 10 systematic review...

Research paper thumbnail of Systematic review and network meta-analysis with individual participant data on Cord Management at Preterm Birth (iCOMP): study protocol

Introduction: Timing of cord clamping and other cord management strategies may improve outcomes a... more Introduction: Timing of cord clamping and other cord management strategies may improve outcomes at preterm birth. However, it is unclear whether benefits apply to all preterm subgroups such as those who usually receive immediate neonatal care. Previous and current trials compare various policies, including immediate cord clamping, time- or physiology-based deferred cord clamping, and cord milking. Individual participant data (IPD) enables exploration of different strategies within subgroups. Network meta-analysis (NMA) enables comparison and ranking of all available interventions using a combination of direct and indirect comparisons. Objectives: 1) To evaluate the effectiveness of cord management strategies for preterm infants on neonatal mortality and morbidity overall and for different participant characteristics using IPD meta-analysis; and 2) to evaluate and rank the effect of different cord management strategies for preterm births on mortality and other key outcomes using NMA....

Research paper thumbnail of Guidance from key organisations on exploring, confirming and interpreting subgroup effects of medical treatments: a scoping review

BMJ Open

ObjectivesWith the increasing interest in personalised medicine, the use of subgroup analyses is ... more ObjectivesWith the increasing interest in personalised medicine, the use of subgroup analyses is likely to increase. Subgroup analyses are challenging and often misused, possibly leading to false interpretations of the effect. It remains unclear to what extent key organisations warn for such pitfalls and translate current methodological research to detect these effects into research guidelines. The aim of this scoping review is to determine and evaluate the current guidance used by organisations for exploring, confirming and interpreting subgroup effects.DesignScoping review.Eligibility criteriaWe identified four types of key stakeholder organisations: industry, health technology assessment organisations (HTA), academic/non-profit research organisations and regulatory bodies. After literature search and expert consultation, we identified international and national organisations of each type. For each organisation that was identified, we searched for official research guidance docume...

Research paper thumbnail of Assessment of heterogeneity in an individual participant data meta‐analysis of prediction models: An overview and illustration

Statistics in Medicine

Clinical prediction models aim to provide estimates of absolute risk for a diagnostic or prognost... more Clinical prediction models aim to provide estimates of absolute risk for a diagnostic or prognostic endpoint. Such models may be derived from data from various studies in the context of a meta-analysis. We describe and propose approaches for assessing heterogeneity in predictor effects and predictions arising from models based on data from different sources. These methods are illustrated in a case study with patients suffering from traumatic brain injury, where we aim to predict 6-month mortality based on individual patient data using meta-analytic techniques (15 studies, n = 11 022 patients). The insights into various aspects of heterogeneity are important to develop better models and understand problems with the transportability of absolute risk predictions.

Research paper thumbnail of Evidence synthesis in prognosis research

Diagnostic and Prognostic Research

Over the past few years, evidence synthesis has become essential to investigate and improve the g... more Over the past few years, evidence synthesis has become essential to investigate and improve the generalizability of medical research findings. This strategy often involves a meta-analysis to formally summarize quantities of interest, such as relative treatment effect estimates. The use of meta-analysis methods is, however, less straightforward in prognosis research because substantial variation exists in research objectives, analysis methods and the level of reported evidence. We present a gentle overview of statistical methods that can be used to summarize data of prognostic factor and prognostic model studies. We discuss how aggregate data, individual participant data, or a combination thereof can be combined through meta-analysis methods. Recent examples are provided throughout to illustrate the various methods.

Research paper thumbnail of A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes

Statistical Methods in Medical Research

It is widely recommended that any developed—diagnostic or prognostic—prediction model is external... more It is widely recommended that any developed—diagnostic or prognostic—prediction model is externally validated in terms of its predictive performance measured by calibration and discrimination. When multiple validations have been performed, a systematic review followed by a formal meta-analysis helps to summarize overall performance across multiple settings, and reveals under which circumstances the model performs suboptimal (alternative poorer) and may need adjustment. We discuss how to undertake meta-analysis of the performance of prediction models with either a binary or a time-to-event outcome. We address how to deal with incomplete availability of study-specific results (performance estimates and their precision), and how to produce summary estimates of the c-statistic, the observed:expected ratio and the calibration slope. Furthermore, we discuss the implementation of frequentist and Bayesian meta-analysis methods, and propose novel empirically-based prior distributions to impr...

Research paper thumbnail of Validation of an imaging based cardiovascular risk score in a Scottish population

European Journal of Radiology

Research paper thumbnail of The use of prognostic scores for causal inference with general treatment regimes

Statistics in Medicine

In nonrandomised studies, inferring causal effects requires appropriate methods for addressing co... more In nonrandomised studies, inferring causal effects requires appropriate methods for addressing confounding bias. Although it is common to adopt propensity score analysis to this purpose, prognostic score analysis has recently been proposed as an alternative strategy. While both approaches were originally introduced to estimate causal effects for binary interventions, the theory of propensity score has since been extended to the case of general treatment regimes. Indeed, many treatments are not assigned in a binary fashion and require a certain extent of dosing. Hence, researchers may often be interested in estimating treatment effects across multiple exposures. To the best of our knowledge, the prognostic score analysis has not been yet generalised to this case. In this article, we describe the theory of prognostic scores for causal inference with general treatment regimes. Our methods can be applied to compare multiple treatments using nonrandomised data, a topic of great relevance in contemporary evaluations of clinical interventions. We propose estimators for the average treatment effects in different populations of interest, the validity of which is assessed through a series of simulations. Finally, we present an illustrative case in which we estimate the effect of the delay to Aspirin administration on a composite outcome of death or dependence at 6 months in stroke patients.

Research paper thumbnail of Multiple Imputation for Multilevel Data with Continuous and Binary Variables

Statistical Science

We present and compare multiple imputation methods for multilevel continuous and binary data wher... more We present and compare multiple imputation methods for multilevel continuous and binary data where variables are systematically and sporadically missing. The methods are compared from a theoretical point of view and through an extensive simulation study motivated by a real dataset comprising multiple studies. The comparisons show that these multiple imputation methods are the most appropriate to handle missing values in a multilevel setting and why their relative performances can vary according to the missing data pattern, the multilevel structure and the type of missing variables. This study shows that valid inferences can only be obtained if the dataset includes a large number of clusters. In addition, it highlights that heteroscedastic multiple imputation methods provide more accurate inferences than homoscedastic methods, which should be reserved for data with few individuals per cluster. Finally, guidelines are given to choose the most suitable multiple imputation method according to the structure of the data.

Research paper thumbnail of Real-time imputation of missing predictor values in clinical practice

European Heart Journal - Digital Health

Aims Use of prediction models is widely recommended by clinical guidelines, but usually requires ... more Aims Use of prediction models is widely recommended by clinical guidelines, but usually requires complete information on all predictors, which is not always available in daily practice. We aim to describe two methods for real-time handling of missing predictor values when using prediction models in practice. Methods and results We compare the widely used method of mean imputation (M-imp) to a method that personalizes the imputations by taking advantage of the observed patient characteristics. These characteristics may include both prediction model variables and other characteristics (auxiliary variables). The method was implemented using imputation from a joint multivariate normal model of the patient characteristics (joint modelling imputation; JMI). Data from two different cardiovascular cohorts with cardiovascular predictors and outcome were used to evaluate the real-time imputation methods. We quantified the prediction model’s overall performance [mean squared error (MSE) of lin...

Research paper thumbnail of Internal-external cross-validation helped to evaluate the generalizability of prediction models in large clustered datasets

Journal of Clinical Epidemiology

Research paper thumbnail of Real-time imputation of missing predictor values improved the application of prediction models in daily practice

Journal of Clinical Epidemiology

Research paper thumbnail of Prognostic models for chronic kidney disease: a systematic review and external validation

Nephrology Dialysis Transplantation

Background Accurate risk prediction is needed in order to provide personalized healthcare for chr... more Background Accurate risk prediction is needed in order to provide personalized healthcare for chronic kidney disease (CKD) patients. An overload of prognosis studies is being published, ranging from individual biomarker studies to full prediction studies. We aim to systematically appraise published prognosis studies investigating multiple biomarkers and their role in risk predictions. Our primary objective was to investigate if the prognostic models that are reported in the literature were of sufficient quality and to externally validate them. Methods We undertook a systematic review and appraised the quality of studies reporting multivariable prognosis models for end-stage renal disease (ESRD), cardiovascular (CV) events and mortality in CKD patients. We subsequently externally validated these models in a randomized trial that included patients from a broad CKD population. Results We identified 91 papers describing 36 multivariable models for prognosis of ESRD, 50 for CV events, 46...

Research paper thumbnail of Handling missing predictor values when validating and applying a prediction model to new patients

Research paper thumbnail of Individual participant data meta‐analysis to examine interactions between treatment effect and participant‐level covariates: Statistical recommendations for conduct and planning

Research paper thumbnail of Can personalized treatment prediction improve the outcomes, compared with the group average approach, in a randomized trial? Developing and validating a multivariable prediction model in a pragmatic megatrial of acute treatment for major depression

Journal of Affective Disorders

Research paper thumbnail of On the aggregation of published prognostic scores for causal inference in observational studies

Statistics in Medicine

As real world evidence on drug efficacy involves nonrandomized studies, statistical methods adjus... more As real world evidence on drug efficacy involves nonrandomized studies, statistical methods adjusting for confounding are needed. In this context, prognostic score (PGS) analysis has recently been proposed as a method for causal inference. It aims to restore balance across the different treatment groups by identifying subjects with a similar prognosis for a given reference exposure ("control"). This requires the development of a multivariable prognostic model in the control arm of the study sample, which is then extrapolated to the different treatment arms. Unfortunately, large cohorts for developing prognostic models are not always available. Prognostic models are therefore subject to a dilemma between overfitting and parsimony; the latter being prone to a violation of the assumption of no unmeasured confounders when important covariates are ignored. Although it is possible to limit overfitting by using penalization strategies, an alternative approach is to adopt evidence synthesis. Aggregating previously published prognostic models may improve the generalizability of PGS, while taking account of a large set of covariates-even when limited individual participant data are available. In this article, we extend a method for prediction model aggregation to PGS analysis in nonrandomized studies. We conduct extensive simulations to assess the validity of model aggregation, compared with other methods of PGS analysis for estimating marginal treatment effects. We show that aggregating existing PGS into a "meta-score" is robust to misspecification, even when elementary scores wrongfully omit confounders or focus on different outcomes. We illustrate our methods in a setting of treatments for asthma.

Research paper thumbnail of Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection

Objective To review and critically appraise published and preprint reports of models that aim to ... more Objective To review and critically appraise published and preprint reports of models that aim to predict either (i) presence of existing COVID-19 infection, (ii) future complications in individuals already diagnosed with COVID-19, or (iii) models to identify individuals at high risk for COVID-19 in the general population. Design Rapid systematic review and critical appraisal of prediction models for diagnosis or prognosis of COVID-19 infection. Data sources PubMed, EMBASE via Ovid, Arxiv, medRxiv and bioRxiv until 24th March 2020. Study selection Studies that developed or validated a multivariable COVID-19 related prediction model. Two authors independently screened titles, abstracts and full text. Data extraction Data from included studies were extracted independently by at least two authors based on the CHARMS checklist, and risk of bias was assessed using PROBAST. Data were extracted on various domains including the participants, predictors, outcomes, data analysis, and predictio...

Research paper thumbnail of Individual participant data meta‐analysis of intervention studies with time‐to‐event outcomes: A review of the methodology and an applied example

Research Synthesis Methods

Research paper thumbnail of Predicting disability progression in multiple sclerosis: Insights from advanced statistical modeling

Multiple Sclerosis Journal

Background: There is an unmet need for precise methods estimating disease prognosis in multiple s... more Background: There is an unmet need for precise methods estimating disease prognosis in multiple sclerosis (MS). Objective: Using advanced statistical modeling, we assessed the prognostic value of various clinical measures for disability progression. Methods: Advanced models to assess baseline prognostic factors for disability progression over 2 years were applied to a pooled sample of patients from placebo arms in four different phase III clinical trials. least absolute shrinkage and selection operator (LASSO) and ridge regression, elastic nets, support vector machines, and unconditional and conditional random forests were applied to model time to clinical disability progression confirmed at 24 weeks. Sensitivity analyses for different definitions of a combined endpoint were carried out, and bootstrap was used to assess prediction model performance. Results: A total of 1582 patients were included, of which 434 (27.4%) had disability progression in a combined endpoint over 2 years. O...

Research paper thumbnail of Empirical evidence of the impact of study characteristics on the performance of prediction models: a meta-epidemiological study

BMJ Open

ObjectivesTo empirically assess the relation between study characteristics and prognostic model p... more ObjectivesTo empirically assess the relation between study characteristics and prognostic model performance in external validation studies of multivariable prognostic models.DesignMeta-epidemiological study.Data sources and study selectionOn 16 October 2018, we searched electronic databases for systematic reviews of prognostic models. Reviews from non-overlapping clinical fields were selected if they reported common performance measures (either the concordance (c)-statistic or the ratio of observed over expected number of events (OE ratio)) from 10 or more validations of the same prognostic model.Data extraction and analysesStudy design features, population characteristics, methods of predictor and outcome assessment, and the aforementioned performance measures were extracted from the included external validation studies. Random effects meta-regression was used to quantify the association between the study characteristics and model performance.ResultsWe included 10 systematic review...

Research paper thumbnail of Systematic review and network meta-analysis with individual participant data on Cord Management at Preterm Birth (iCOMP): study protocol

Introduction: Timing of cord clamping and other cord management strategies may improve outcomes a... more Introduction: Timing of cord clamping and other cord management strategies may improve outcomes at preterm birth. However, it is unclear whether benefits apply to all preterm subgroups such as those who usually receive immediate neonatal care. Previous and current trials compare various policies, including immediate cord clamping, time- or physiology-based deferred cord clamping, and cord milking. Individual participant data (IPD) enables exploration of different strategies within subgroups. Network meta-analysis (NMA) enables comparison and ranking of all available interventions using a combination of direct and indirect comparisons. Objectives: 1) To evaluate the effectiveness of cord management strategies for preterm infants on neonatal mortality and morbidity overall and for different participant characteristics using IPD meta-analysis; and 2) to evaluate and rank the effect of different cord management strategies for preterm births on mortality and other key outcomes using NMA....

Research paper thumbnail of Guidance from key organisations on exploring, confirming and interpreting subgroup effects of medical treatments: a scoping review

BMJ Open

ObjectivesWith the increasing interest in personalised medicine, the use of subgroup analyses is ... more ObjectivesWith the increasing interest in personalised medicine, the use of subgroup analyses is likely to increase. Subgroup analyses are challenging and often misused, possibly leading to false interpretations of the effect. It remains unclear to what extent key organisations warn for such pitfalls and translate current methodological research to detect these effects into research guidelines. The aim of this scoping review is to determine and evaluate the current guidance used by organisations for exploring, confirming and interpreting subgroup effects.DesignScoping review.Eligibility criteriaWe identified four types of key stakeholder organisations: industry, health technology assessment organisations (HTA), academic/non-profit research organisations and regulatory bodies. After literature search and expert consultation, we identified international and national organisations of each type. For each organisation that was identified, we searched for official research guidance docume...

Research paper thumbnail of Assessment of heterogeneity in an individual participant data meta‐analysis of prediction models: An overview and illustration

Statistics in Medicine

Clinical prediction models aim to provide estimates of absolute risk for a diagnostic or prognost... more Clinical prediction models aim to provide estimates of absolute risk for a diagnostic or prognostic endpoint. Such models may be derived from data from various studies in the context of a meta-analysis. We describe and propose approaches for assessing heterogeneity in predictor effects and predictions arising from models based on data from different sources. These methods are illustrated in a case study with patients suffering from traumatic brain injury, where we aim to predict 6-month mortality based on individual patient data using meta-analytic techniques (15 studies, n = 11 022 patients). The insights into various aspects of heterogeneity are important to develop better models and understand problems with the transportability of absolute risk predictions.

Research paper thumbnail of Evidence synthesis in prognosis research

Diagnostic and Prognostic Research

Over the past few years, evidence synthesis has become essential to investigate and improve the g... more Over the past few years, evidence synthesis has become essential to investigate and improve the generalizability of medical research findings. This strategy often involves a meta-analysis to formally summarize quantities of interest, such as relative treatment effect estimates. The use of meta-analysis methods is, however, less straightforward in prognosis research because substantial variation exists in research objectives, analysis methods and the level of reported evidence. We present a gentle overview of statistical methods that can be used to summarize data of prognostic factor and prognostic model studies. We discuss how aggregate data, individual participant data, or a combination thereof can be combined through meta-analysis methods. Recent examples are provided throughout to illustrate the various methods.

Research paper thumbnail of A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes

Statistical Methods in Medical Research

It is widely recommended that any developed—diagnostic or prognostic—prediction model is external... more It is widely recommended that any developed—diagnostic or prognostic—prediction model is externally validated in terms of its predictive performance measured by calibration and discrimination. When multiple validations have been performed, a systematic review followed by a formal meta-analysis helps to summarize overall performance across multiple settings, and reveals under which circumstances the model performs suboptimal (alternative poorer) and may need adjustment. We discuss how to undertake meta-analysis of the performance of prediction models with either a binary or a time-to-event outcome. We address how to deal with incomplete availability of study-specific results (performance estimates and their precision), and how to produce summary estimates of the c-statistic, the observed:expected ratio and the calibration slope. Furthermore, we discuss the implementation of frequentist and Bayesian meta-analysis methods, and propose novel empirically-based prior distributions to impr...

Research paper thumbnail of Validation of an imaging based cardiovascular risk score in a Scottish population

European Journal of Radiology

Research paper thumbnail of The use of prognostic scores for causal inference with general treatment regimes

Statistics in Medicine

In nonrandomised studies, inferring causal effects requires appropriate methods for addressing co... more In nonrandomised studies, inferring causal effects requires appropriate methods for addressing confounding bias. Although it is common to adopt propensity score analysis to this purpose, prognostic score analysis has recently been proposed as an alternative strategy. While both approaches were originally introduced to estimate causal effects for binary interventions, the theory of propensity score has since been extended to the case of general treatment regimes. Indeed, many treatments are not assigned in a binary fashion and require a certain extent of dosing. Hence, researchers may often be interested in estimating treatment effects across multiple exposures. To the best of our knowledge, the prognostic score analysis has not been yet generalised to this case. In this article, we describe the theory of prognostic scores for causal inference with general treatment regimes. Our methods can be applied to compare multiple treatments using nonrandomised data, a topic of great relevance in contemporary evaluations of clinical interventions. We propose estimators for the average treatment effects in different populations of interest, the validity of which is assessed through a series of simulations. Finally, we present an illustrative case in which we estimate the effect of the delay to Aspirin administration on a composite outcome of death or dependence at 6 months in stroke patients.

Research paper thumbnail of Multiple Imputation for Multilevel Data with Continuous and Binary Variables

Statistical Science

We present and compare multiple imputation methods for multilevel continuous and binary data wher... more We present and compare multiple imputation methods for multilevel continuous and binary data where variables are systematically and sporadically missing. The methods are compared from a theoretical point of view and through an extensive simulation study motivated by a real dataset comprising multiple studies. The comparisons show that these multiple imputation methods are the most appropriate to handle missing values in a multilevel setting and why their relative performances can vary according to the missing data pattern, the multilevel structure and the type of missing variables. This study shows that valid inferences can only be obtained if the dataset includes a large number of clusters. In addition, it highlights that heteroscedastic multiple imputation methods provide more accurate inferences than homoscedastic methods, which should be reserved for data with few individuals per cluster. Finally, guidelines are given to choose the most suitable multiple imputation method according to the structure of the data.

Research paper thumbnail of Meta-analysis of clinical prediction models

This PhD thesis explores statistical methods for adopting evidence synthesis in the development a... more This PhD thesis explores statistical methods for adopting evidence synthesis in the development and validation of risk prediction models. The ultimate aim is to improve the future performance and to enhance understanding of their potential generalizability across different settings and populations.