Gianluca Baio

Gianluca Baio
University College London | UCL · Department of Statistical Science

PhD

About

236
Publications
42,603
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
4,549
Citations
Additional affiliations
September 2007 - June 2013
Università degli Studi di Milano-Bicocca
January 2005 - present
University College London
January 2002 - February 2005

Publications

Publications (236)
Article
Full-text available
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...
Preprint
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...
Conference Paper
Full-text available
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...
Preprint
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...
Preprint
Full-text available
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...
Article
Full-text available
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...
Article
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...
Preprint
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...
Preprint
Full-text available
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...
Article
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...
Article
Full-text available
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...
Article
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...
Article
Full-text available
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...
Preprint
Full-text available
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,...
Article
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,...
Article
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...
Preprint
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...
Preprint
Full-text available
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...
Article
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...
Article
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...
Article
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...
Article
Full-text available
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...
Article
Full-text available
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...
Preprint
Full-text available
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...
Article
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...
Preprint
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...
Preprint
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...
Article
Full-text available
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...
Article
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....
Article
Full-text available
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...
Preprint
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...
Article
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...
Article
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...
Preprint
Full-text available
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...
Article
Full-text available
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 e...
Article
Full-text available
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...
Book
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...
Article
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...
Article
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...
Preprint
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...
Preprint
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...
Article
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...
Preprint
Full-text available
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...
Conference Paper
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...
Poster
Full-text available
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...
Preprint
Full-text available
Objectives: 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, four EV...
Article
Trial‐based economic evaluations are typically performed on cross‐sectional variables, derived from the responses for only the completers in the study, using methods that ignore the complexities of utility and cost data (e.g. skewness and spikes). We present an alternative and more efficient Bayesian parametric approach to handle missing longitudin...
Article
Background: The expected value of sample information (EVSI) determines the economic value of any future study with a specific design aimed at reducing uncertainty about the parameters underlying a health economic model. This has potential as a tool for trial design; the cost and value of different designs could be compared to find the trial with t...
Chapter
The evidence produced by healthcare economic evaluation studies is a key component of any Health Technology Assessment (HTA) process designed to inform resource allocation decisions in a budget-limited context. To improve the quality (and harmonize the generation process) of such evidence, many HTA agencies have established methodological guideline...
Preprint
Full-text available
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 a notoriousl...
Article
Full-text available
This analysis presents the results of a systematic review for health state utilities in multiple myeloma, as well as analysis of over 9,000 observations taken from registry and trial data. The 27 values identified from 13 papers are then synthesised in a frequentist nonparametric bootstrap model and a Bayesian meta‐regression. Results were similar...
Article
Network meta‐analysis (NMA) technique extends the standard meta‐analysis methods, allowing pairwise comparison of all treatments in a network in the absence of head‐to‐head comparisons. Traditional NMA models consider a single endpoint for each trial. However, in many cases, trials in the network have different durations and/or report data at multi...
Article
Economic models are used in health technology assessments (HTAs) to evaluate the cost-effectiveness of competing medical technologies and inform the efficient use of healthcare resources. Historically, these models have been developed with specialized commercial software (such as TreeAge) or more commonly with spreadsheet software (almost always Mi...
Article
Full-text available
Objective: The aim of this phase 2 trial was to ascertain the feasibility and effect of community-based aerobic exercise training for people with 2 of the more common neuromuscular diseases: Charcot-Marie-Tooth disease type 1A (CMT) and inclusion body myositis (IBM). Methods: A randomized single-blinded crossover trial design was used to compare...
Article
Full-text available
The Publisher regrets an error in the presentation of Table 5.
Article
Full-text available
We compare various extensions of the Bradley–Terry model and a hierarchical Poisson log-linear model in terms of their performance in predicting the outcome of soccer matches (win, draw, or loss). The parameters of the Bradley–Terry extensions are estimated by maximizing the log-likelihood, or an appropriately penalized version of it, while the pos...
Article
Full-text available
Background: The initial roll-out of the English Bowel (Colorectal) Cancer Screening programme, during 2006 and 2009, found uptake to be low (54%) and socially graded. The current analysis used data from 2010 to 2015 to test whether uptake is increasing and becoming less socially graded over time. Methods: Postcode-derived area-level uptake of 4....
Article
Full-text available
Health economic evaluations of interventions against infectious diseases are commonly based on the predictions of compartmental models such as ordinary differential equation (ODE) systems and Markov models. In contrast to standard Markov models which are static, ODE systems are dynamic by definition and therefore able to account for the effects of...