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Introduction
Additional affiliations
September 2007 - June 2013
January 2005 - present
January 2002 - February 2005
Publications
Publications (264)
Effect modification occurs when a covariate alters the relative effectiveness of treatment compared to control. It is widely understood that, when effect modification is present, treatment recommendations may vary by population and by subgroups within the population. Population-adjustment methods are increasingly used to adjust for differences in e...
Survival extrapolation often plays an important role in health technology assessment (HTA), and there are a range of different approaches available. Approaches that can leverage external evidence (i.e. data or information collected outside the main data source of interest) may be helpful, given the extent of uncertainty often present when determini...
Polyhazard models are a class of flexible parametric models for modelling survival over extended time horizons. Their additive hazard structure allows for flexible, non-proportional hazards whose characteristics can change over time while retaining a parametric form, which allows for survival to be extrapolated beyond the observation period of a st...
Patients who are mechanically ventilated in the Intensive Care Unit participate in exercise as a component of their rehabilitation to ameliorate the long-term impact of critical illness on their physical function. The effective implementation of these programmes is limited, however, as clinicians do not have access to a patient's [Formula: see text...
Background
In 2012, the UK Government announced a series of immigration policy reforms known as the hostile environment policy, culminating in the Windrush scandal. We aimed to investigate the effect of the hostile environment policy on mental health for people from minoritised ethnic backgrounds. We hypothesised that people from Black Caribbean ba...
Background
When studying the association between treatment and a clinical outcome, a parametric multivariable model of the conditional outcome expectation is often used to adjust for covariates. The treatment coefficient of the outcome model targets a conditional treatment effect. Model-based standardization is typically applied to average the mode...
In recent years regression discontinuity designs have been used increasingly for the estimation of treatment effects in observational medical data where a rule-based decision to apply treatment is taken using a continuous assignment variable. Most regression discontinuity design applications have focused on effect estimation where the outcome of in...
Objectives:
Despite a substantial epidemiological literature on the incidence of psychotic disorders in Ireland, no systematic review has previously been undertaken. Such evidence can help inform understanding of need for psychosis care.
Methods:
We conducted a prospectively registered systematic review (PROSPERO: CRD42021245891) following PRISM...
Factors contributing to social inequalities are also associated with negative mental health outcomes leading to disparities in mental well-being. We propose a Bayesian hierarchical model which can evaluate the impact of policies on population well-being, accounting for spatial/temporal dependencies. Building on an interrupted time series framework,...
We examine four important considerations in the development of covariate adjustment methodologies for indirect treatment comparisons. First, we consider potential advantages of weighting versus outcome modeling, placing focus on bias-robustness. Second, we outline why model-based extrapolation may be required and useful, in the specific context of...
We examine four important considerations for the development of covariate adjustment methodologies in the context of indirect treatment comparisons. Firstly, we consider potential advantages of weighting versus outcome modeling, placing focus on bias-robustness. Secondly, we outline why model-based extrapolation may be required and useful, in the s...
When studying the association between treatment and a clinical outcome, a parametric multivariable model of the conditional outcome expectation is often used to adjust for covariates. The treatment coefficient of the outcome model targets a conditional treatment effect. Model-based standardization is typically applied to average the model predictio...
Background
External evidence is commonly used to inform survival modeling for health technology assessment (HTA). While there are a range of methodological approaches that have been proposed, it is unclear which methods could be used and how they compare.
Purpose
This review aims to identify, describe, and categorize established methods to incorpo...
Cannabidiol (CBD) has shown promise in treating psychiatric disorders, including cannabis use disorder – a major public health burden with no approved pharmacotherapies. However, the mechanisms through which CBD acts are poorly understood. One potential mechanism of CBD is increasing levels of anandamide, which has been implicated in psychiatric di...
Objectives: Despite a substantial epidemiological literature on the incidence of psychotic disorders in Ireland, no systematic review has previously been undertaken. Such evidence can help inform understanding of need for psychosis care.Methods: We conducted a prospectively-registered systematic review (PROSPERO: CRD42021245891) following PRISMA gu...
Rationale
Chronic cannabis use is associated with impaired cognitive function. Evidence indicates cannabidiol (CBD) might be beneficial for treating cannabis use disorder. CBD may also have pro-cognitive effects; however, its effect on cognition in people with cannabis use disorder is currently unclear.
Objectives
We aimed to assess whether a 4-we...
In this paper, we address the challenge of performing counterfactual inference with observational data via Bayesian nonparametric regression adjustment, with a focus on high-dimensional settings featuring multiple actions and multiple correlated outcomes. We present a general class of counterfactual multi-task deep kernels models that estimate caus...
In this paper, we address the challenge of performing counterfactual inference with observational data via Bayesian nonparametric regression adjustment, with a focus on high-dimensional settings featuring multiple actions and multiple correlated outcomes. We present a general class of counterfactual multi-task deep kernels models that estimate caus...
The regression discontinuity design is a quasi-experimental design that estimates the causal effect of a treatment when its assignment is defined by a threshold for a continuous variable. The regression discontinuity design assumes that subjects with measurements within a bandwidth around the threshold belong to a common population, so that the thr...
Background:
Survival extrapolation is essential in cost-effectiveness analysis to quantify the lifetime survival benefit associated with a new intervention, due to the restricted duration of randomized controlled trials (RCTs). Current approaches of extrapolation often assume that the treatment effect observed in the trial can continue indefinitel...
Objectives:
In the IMPACT trial (NCT02164513), triple therapy with fluticasone furoate/umeclidinium/vilanterol (FF/UMEC/VI) showed clinical benefit compared with dual therapy with either FF/VI or UMEC/VI in the treatment of chronic obstructive pulmonary disease (COPD). We used data from IMPACT to determine whether this translated into differences...
Patients who are mechanically ventilated in the intensive care unit (ICU) participate in exercise as a component of their rehabilitation to ameliorate the long-term impact of critical illness on their physical function. The effective implementation of these programmes is hindered, however, by the lack of a scientific method for quantifying an indiv...
Patients who are mechanically ventilated in the intensive care unit (ICU) participate in exercise as a component of their rehabilitation to ameliorate the long-term impact of critical illness on their physical function. The effective implementation of these programmes is hindered, however, by the lack of a scientific method for quantifying an indiv...
In this extended abstract paper, we address the problem of interpretability and targeted regularization in causal machine learning models. In particular, we focus on the problem of estimating individual causal/treatment effects under observed confounders, which can be controlled for and moderate the effect of the treatment on the outcome of interes...
In this extended abstract paper, we address the problem of interpretability and targeted regularization in causal machine learning models. In particular, we focus on the problem of estimating individual causal/treatment effects under observed confounders, which can be controlled for and moderate the effect of the treatment on the outcome of interes...
Background Survival extrapolation is essential in the cost-effectiveness analysis to quantify the lifetime survival benefit associated with a new intervention, due to the restricted duration of randomized controlled trials (RCTs). Current approaches of extrapolation often assume that the treatment effect observed in the trial can continue indefinit...
This article develops a sparsity-inducing version of Bayesian Causal Forests, a recently proposed nonparametric causal regression model that employs Bayesian Additive Regression Trees and is specifically designed to estimate heterogeneous treatment effects using observational data. The sparsity-inducing component we introduce is motivated by empiri...
Population adjustment methods such as matching‐adjusted indirect comparison (MAIC) are increasingly used to compare marginal treatment effects when there are cross‐trial differences in effect modifiers and limited patient‐level data. MAIC is based on propensity score weighting, which is sensitive to poor covariate overlap and cannot extrapolate bey...
We describe in detail how to perform health economic cost-effectiveness analyses (CEA) using the R package $\textbf{BCEA}$ (Bayesian Cost-Effectiveness Analysis). CEA consist of analytic approaches for combining costs and health consequences of intervention(s). These help to understand how much an intervention may cost (per unit of health gained) c...
Large observational data are increasingly available in disciplines such as health, economic and social sciences, where researchers are interested in causal questions rather than prediction. In this paper, we examine the problem of estimating heterogeneous treatment effects using non‐parametric regression‐based methods, starting from an empirical st...
Objectives
Cost-effectiveness analysis (CEA) alongside randomized controlled trials often relies on self-reported multi-item questionnaires that are invariably prone to missing item-level data. The purpose of this study is to review how missing multi-item questionnaire data are handled in trial-based CEAs.
Methods
We searched the National Institut...
Large observational data are increasingly available in disciplines such as health, economic and social sciences, where researchers are interested in causal questions rather than prediction. In this paper, we examine the problem of estimating heterogeneous treatment effects using non‐parametric regression‐based methods, starting from an empirical st...
Regression discontinuity designs (RDDs) have been developed for the estimation of treatment effects using observational data, where a treatment is administered using an externally defined decision rule, linked to a continuous assignment variable. Typically, RDDs have been applied to situations where the outcome of interest is continuous and non‐tem...
The impact of the COVID-19 pandemic on excess mortality from all causes in 2020 varied across and within European countries. Using data for 2015–2019, we applied Bayesian spatio-temporal models to quantify the expected weekly deaths at the regional level had the pandemic not occurred in England, Greece, Italy, Spain, and Switzerland. With around 30...
COVID-19 related deaths underestimate the pandemic burden on mortality because they suffer from completeness and accuracy issues. Excess mortality is a popular alternative, as it compares observed with expected deaths based on the assumption that the pandemic did not occur. Expected deaths had the pandemic not occurred depend on population trends,...
COVID-19 related deaths underestimate the pandemic burden on mortality because they suffer from completeness and accuracy issues. Excess mortality is a popular alternative, as it compares observed with expected deaths based on the assumption that the pandemic did not occur. Expected deaths had the pandemic not occurred depend on population trends,...
Background and Aims
Communication of personalised disease risk can motivate smoking cessation. We assessed whether routine implementation of this intervention by general practitioners (GPs) in England is cost-effective or whether we need further research to better establish its effectiveness.
Design
Cost-effectiveness analysis (CEA) with value of...
The impact of the COVID-19 pandemic on excess mortality from all causes in 2020 varied across and within European countries. Using data for 2015-2019, we applied Bayesian spatio-temporal models to quantify the expected weekly deaths at the regional level had the pandemic not occurred in England, Greece, Italy, Spain, and Switzerland. With around 30...
Population adjustment methods such as matching-adjusted indirect comparison (MAIC) are increasingly used to compare marginal treatment effects when there are cross-trial differences in effect modifiers and limited patient-level data. MAIC is based on propensity score weighting, which is sensitive to poor covariate overlap and cannot extrapolate bey...
Value of information (VoI) is a decision-theoretic approach to estimating the expected benefits from collecting further information of different kinds, in scientific problems based on combining one or more sources of data. VoI methods can assess the sensitivity of models to different sources of uncertainty and help to set priorities for further dat...
Population-adjusted indirect comparisons estimate treatment effects when access to individual patient data is limited and there are cross-trial differences in effect modifiers. Popular methods include matching-adjusted indirect comparison (MAIC) and simulated treatment comparison (STC). There is limited formal evaluation of these methods and whethe...
In this commentary, we highlight the importance of: (1) carefully considering and clarifying whether a marginal or conditional treatment effect is of interest in a population‐adjusted indirect treatment comparison; and (2) developing distinct methodologies for estimating the different measures of effect. The appropriateness of each methodology depe...
Objectives
Survival extrapolation of trial outcomes is required for health economic evaluation. Generally, all-cause mortality (ACM) is modeled using standard parametric distributions, often without distinguishing disease-specific/excess mortality and general population background mortality (GPM). Recent National Institute for Health and Care Excel...
Background
Mental health policy makers require evidence-based information to optimise effective care provision based on local need, but tools are unavailable.
Aims
To develop and validate a population-level prediction model for need for early intervention in psychosis (EIP) care for first-episode psychosis (FEP) in England up to 2025, based on epi...
This paper develops a sparsity-inducing version of Bayesian Causal Forests, a recently proposed nonparametric causal regression model that employs Bayesian Additive Regression Trees and is specifically designed to estimate heterogeneous treatment effects using observational data. The sparsity-inducing component we introduce is motivated by empirica...
Objectives
In trial-based economic evaluation, some individuals are typically associated with missing data at some time point, so that their corresponding aggregated outcomes (eg, quality-adjusted life-years) cannot be evaluated. Restricting the analysis to the complete cases is inefficient and can result in biased estimates, while imputation metho...
In this commentary, we raise our concerns about a recent simulation study conducted by Aouni, Gaudel-Dedieu and Sebastien, evaluating the performance of different versions of matching-adjusted indirect comparison (MAIC). The following points are highlighted: (1) making a clear distinction between prognostic and effect-modifying covariates is import...
In this commentary, we highlight the importance of: (1) carefully considering and clarifying whether a marginal or conditional treatment effect is of interest in a population-adjusted indirect treatment comparison; and (2) developing distinct methodologies for estimating the different measures of effect. The appropriateness of each methodology depe...
In this study we present the first comprehensive analysis of the spatio-temporal differences in excess mortality during the COVID-19 pandemic in Italy. We used a population-based design on all-cause mortality data, for the 7,904 Italian municipalities. We estimated sex-specific weekly mortality rates for each municipality, based on the first four m...
Survival analysis features heavily as an important part of health economic evaluation, an increasingly important component of medical research. In this setting, it is important to estimate the mean time to the survival endpoint using limited information (typically from randomized trials) and thus it is useful to consider parametric survival models....
Background:
While placebo-controlled randomised controlled trials remain the standard way to evaluate drugs for efficacy, historical data are used extensively across the development cycle. This ranges from supplementing contemporary data to increase the power of trials to cross-trial comparisons in estimating comparative efficacy. In many cases, t...
Population adjustment methods such as matching-adjusted indirect comparison (MAIC) are increasingly used to compare marginal treatment effects when there are cross-trial differences in effect modifiers and limited patient-level data. MAIC is based on propensity score weighting, which is sensitive to poor covariate overlap because of its inability t...
Population adjustment methods such as matching-adjusted indirect comparison (MAIC) are increasingly used to compare marginal treatment effects when there are cross-trial differences in effect modifiers and limited patient-level data. MAIC is sensitive to poor covariate overlap and cannot extrapolate beyond the observed covariate space. Current outc...
Background
A substantial and unmet clinical need exists for pharmacological treatment of cannabis use disorders. Cannabidiol could offer a novel treatment, but it is unclear which doses might be efficacious or safe. Therefore, we aimed to identify efficacious doses and eliminate inefficacious doses in a phase 2a trial using an adaptive Bayesian des...
Background
Excess mortality from all-cause has been estimated at national level for different countries, to provide a picture of the total burden of the COVID-19 pandemic. Nevertheless, there have been no attempts at modelling it at high spatial resolution, needed to understand geographical differences in the mortality patterns, to evaluate tempora...
Background
Individuals with type 2 diabetes (T2D) have a twofold increased risk for cardiovascular events (CVE), and CVE is responsible for nearly 80% of the mortality. Current treatment guidelines state that individuals should immediately initiate antidiabetic treatment and cardiovascular risk-factor management from T2D diagnosis. However, the evi...
Background
Providing timely, adequate and appropriately-resourced care to people experiencing their first episode of psychosis needs to be informed by evidence-based models of future need in the population. We sought to develop a validated prediction model of need for provision of early intervention in psychosis [EIP] services at the small area lev...
Since the early 2000s, there has been increasing interest within the pharmaceutical industry in the application of Bayesian methods at various stages of the research, development, manufacturing, and health economic evaluation of new health care interventions. In 2010, the first Applied Bayesian Biostatistics conference was held, with the primary ob...
Objectives
To assess the performance of unanchored matching-adjusted indirect comparison (MAIC) by matching on first moments or higher moments in a cross-study comparisons under a variety of conditions. A secondary objective was to gauge the performance of the method relative to propensity score weighting (PSW).
Methods
A simulation study was desi...
Value of information (VOI) analyses can help policy makers make informed decisions about whether to conduct and how to design future studies. Historically a computationally expensive method to compute the expected value of sample information (EVSI) restricted the use of VOI to simple decision models and study designs. Recently, 4 EVSI approximation...
Population-adjusted indirect comparisons are used to estimate treatment effects when there are cross-trial differences in effect modifiers and when access to individual patient data is limited. Increasingly, health technology assessment agencies are accepting evaluations that use these methods across a diverse range of therapeutic areas. Popular me...
Background: Mental health service policymakers require evidence-based information to optimise effective care provision based on local need, but tools are unavailable. We developed and validated a population-level prediction model to forecast need for early intervention in psychosis [EIP] services in England up to 2025.Methods: We fitted six candida...
Background. Investing efficiently in future research to improve policy decisions is an important goal. Expected value of sample information (EVSI) can be used to select the specific design and sample size of a proposed study by assessing the benefit of a range of different studies. Estimating EVSI with the standard nested Monte Carlo algorithm has...
The Regression Discontinuity Design (RDD) is a quasi-experimental design that estimates the causal effect of a treatment when its assignment is defined by a threshold value for a continuous assignment variable. The RDD assumes that subjects with measurements within a bandwidth around the threshold belong to a common population, so that the threshol...
The use of population-adjusted indirect comparisons with restricted access to patient-level data and the acceptability of these methods by national health technology assessment agencies is increasing across diverse therapeutic areas. Popular methods include matching-adjusted indirect comparison (MAIC) and, to a lesser degree, simulated treatment co...
Traditionally, the majority of health economic modelling has been performed in spreadsheet calculators such as Microsoft Excel as it is perceived to be more transparent and easy to use. However, as the modelling requirements become more realistic and therefore complex, spreadsheets become increasingly cumbersome and difficult to manage. We argue th...
Value of information (VOI) analyses can help policy-makers make more informed decisions about whether to conduct and how to design future studies. The Expected Value of Sample Information (EVSI) can be used to prioritize research and design future studies to reduce decision uncertainty for policy-makers. Four recently published methods have overcom...
