Title Participants Abstract
"Causal inference concepts applied to three observational studies in the context of vaccine development : from theory to practice" "Emilia Gvozdenovic, Lucio Malvisi, Elisa Cinconze, Stijn Vansteelandt, Phoebe Nakanwagi, Emmanuel Aris, Dominique Rosillon" "Background Randomized controlled trials are considered the gold standard to evaluate causal associations, whereas assessing causality in observational studies is challenging. Methods We applied Hill's Criteria, counterfactual reasoning, and causal diagrams to evaluate a potentially causal relationship between an exposure and outcome in three published observational studies: a) one burden of disease cohort study to determine the association between type 2 diabetes and herpes zoster, b) one post-authorization safety cohort study to assess the effect of AS04-HPV-16/18 vaccine on the risk of autoimmune diseases, and c) one matched case-control study to evaluate the effectiveness of a rotavirus vaccine in preventing hospitalization for rotavirus gastroenteritis. Results Among the 9 Hill's criteria, 8 (Strength, Consistency, Specificity, Temporality, Plausibility, Coherence, Analogy, Experiment) were considered as met for study c, 3 (Temporality, Plausibility, Coherence) for study a, and 2 (Temporary, Plausibility) for study b. For counterfactual reasoning criteria, exchangeability, the most critical assumption, could not be tested. Using these tools, we concluded that causality was very unlikely in study b, unlikely in study a, and very likely in study c. Directed acyclic graphs provided complementary visual structures that identified confounding bias and helped determine the most accurate design and analysis to assess causality. Conclusions Based on our assessment we found causal Hill's criteria and counterfactual thinking valuable in determining some level of certainty about causality in observational studies. Application of causal inference frameworks should be considered in designing and interpreting observational studies."
"Attributable mortality of ventilator-associated pneumonia : replicating findings, revisiting methods" "Johan Steen, Stijn Vansteelandt, Liesbet De Bus, Pieter Depuydt, Bram Gadeyne, Dominique Benoit, Johan Decruyenaere" "Rationale: Estimating the impact of ventilator-associated pneumonia (VAP) from routinely collected ICU data is methodologically challenging.Objectives: We aim to replicate earlier findings of limited VAP-attributable ICU mortality in an independent cohort. By refining statistical analyses, we gradually tackle different sources of bias.Methods: Records of 2,720 adult patients admitted to Ghent University Hospital ICUs (2013U+20142017) and receiving mechanical ventilation within 48 hours following admission were extracted from linked ICIS and COSARA databases. The VAP-attributable fraction of ICU mortality was estimated using a competing risk analysis that is restricted to VAP-free patients (approach 1), accounts for VAP onset by treating it as either a competing (approach 2) or censoring event (approach 3), or additionally adjusts for time-dependent confounding via inverse probability weighting (approach 4).Results: Two hundred ten patients (7.7%) acquired VAP. Based on benchmark approach 4, we estimated that (compared to current preventive measures) hypothetical eradication of VAP would lead to a relative ICU mortality reduction of 1.7% (95% confidence interval: -1.3U+20144.6) by day 10 and of 3.6% (95% confidence interval: 0.7U+20146.5) by day 60. Approaches 1U+20143 produced estimates ranging from -0.7 to 2.5% by day 10, and from 5.2 to 5.5% at day 60.Conclusions: In line with previous studies using appropriate methodology, we found limited VAP-attributable ICU mortality given current state-of-the-art VAP prevention measures. Our study illustrates that inappropriate accounting of the time-dependency of exposure and confounding of its effects may misleadingly suggest protective effects of early-onset VAP and systematically overestimate attributable mortality."
"Confounder selection strategies targeting stable treatment effect estimators" "Wen Wei Loh, Stijn Vansteelandt" "Inferring the causal effect of a treatment on an outcome in an observational study requires adjusting for observed baseline confounders to avoid bias. However, adjusting for all observed baseline covariates, when only a subset are confounders of the effect of interest, is known to yield potentially inefficient and unstable estimators of the treatment effect. Furthermore, it raises the risk of finite-sample bias and bias due to model misspecification. For these stated reasons, confounder (or covariate) selection is commonly used to determine a subset of the available covariates that is sufficient for confounding adjustment. In this article, we propose a confounder selection strategy that focuses on stable estimation of the treatment effect. In particular, when the propensity score (PS) model already includes covariates that are sufficient to adjust for confounding, then the addition of covariates that are associated with either treatment or outcome alone, but not both, should not systematically change the effect estimator. The proposal, therefore, entails first prioritizing covariates for inclusion in the PS model, then using a change-in-estimate approach to select the smallest adjustment set that yields a stable effect estimate. The ability of the proposal to correctly select confounders, and to ensure valid inference of the treatment effect following data-driven covariate selection, is assessed empirically and compared with existing methods using simulation studies. We demonstrate the procedure using three different publicly available datasets commonly used for causal inference."
"How much can we remedy very low learning levels in rural parts of low-income countries? Impact and generalizability of a multi-pronged para-teacher intervention from a cluster-randomized trial in the Gambia" "Alex Eble, Chris Frost, Alpha Camara, Baboucarr Bouy, Momodou Bah, Maitri Sivaraman, Pei-Tseng Jenny Hsieh, Chitra Jayanty, Tony Brady, Piotr Gawron, Stijn Vansteelandt, Peter Boone, Diana Elbourne" "Despite large schooling and learning gains in many developing countries, children in highly deprived areas are often unlikely to achieve even basic literacy and numeracy. We study how much of this problem can be resolved using a multi-pronged intervention combining three interventions known to be separately effective. We conducted a cluster-randomized trial in The Gambia evaluating a literacy and numeracy intervention designed for primary-aged children in remote parts of poor countries. The intervention combines para teachers delivering after-school supplementary classes, scripted lesson plans, and frequent monitoring focusing on improving teacher practice (coaching). A similar intervention previously demonstrated large learning gains in rural India. After three academic years, Gambian children allocated to the intervention scored 46 percentage points (3.2 SD) better on a combined literacy and numeracy test than control children. Our results demonstrate that, in this type of area, aggressive interventions can yield far greater learning gains than previously shown."
"Using generalized linear models to implement gU+2010estimation for survival data with timeU+2010varying confounding" "Shaun R. Seaman, Ruth H. Keogh, Oliver Dukes, Stijn Vansteelandt" "Using data from observational studies to estimate the causal effect of a time-varying exposure, repeatedly measured over time, on an outcome of interest requires careful adjustment for confounding. Standard regression adjustment for observed time-varying confounders is unsuitable, as it can eliminate part of the causal effect and induce bias. Inverse probability weighting, g-computation, and g-estimation have been proposed as being more suitable methods. G-estimation has some advantages over the other two methods, but until recently there has been a lack of flexible g-estimation methods for a survival time outcome. The recently proposed Structural Nested Cumulative Survival Time Model (SNCSTM) is such a method. Efficient estimation of the parameters of this model required bespoke software. In this article we show how the SNCSTM can be fitted efficiently via g-estimation using standard software for fitting generalised linear models. The ability to implement g-estimation for a survival outcome using standard statistical software greatly increases the potential uptake of this method. We illustrate the use of this method of fitting the SNCSTM by reanalyzing data from the UK Cystic Fibrosis Registry, and provide example R code to facilitate the use of this approach by other researchers."
"Caution against examining the role of reverse causality in Mendelian Randomization" "Sharon M. Lutz, Ann Chen Wu, John E. Hokanson, Stijn Vansteelandt, Christoph Lange" "Recently, Mendelian Randomization (MR) has gained in popularity as a concept to assess the causal relationship between phenotypes in genetic association studies. An extension of standard MR methodology, the MR Steiger approach, has recently been developed to infer the causal direction between two phenotypes in prospective studies. Through simulation studies, we examined and quantified the ability of the MR Steiger approach to determine the causal direction between two phenotypes (i.e., effect direction). Through simulation studies, our results show that the MR Steiger approach may fail to correctly identify the direction of causality. This is true, especially in the presence of pleiotropy. We also applied the MR Steiger method to the COPDGene study, a case-control study of chronic obstructive pulmonary disease (COPD) in current and former smokers, to examine the role of smoking on lung function. We have created an R package on Github called reverseDirection which runs simulations for user-specified scenarios to examine when the MR Steiger approach can correctly determine the causal direction between two phenotypes in any user specified scenario. In summary, our results emphasize the importance of caution when the MR Steiger approach is used in to infer the direction of causality."
"Prediction of hospital bed capacity during the COVID-19 pandemic" "Mieke Deschepper, Kristof Eeckloo, Simon Malfait, Dominique Benoit, Steven Callens, Stijn Vansteelandt" "Background: Prediction of the necessary capacity of beds by ward type (e.g. ICU) is essential for planning purposes during epidemics, such as the COVIDU+2212U+200919 pandemic. The COVIDU+2212U+200919 taskforce within the Ghent University hospital made use of ten-day forecasts on the required number of beds for COVIDU+2212U+200919 patients across different wards.Methods: The planning tool combined a Poisson model for the number of newly admitted patients on each day with a multistate model for the transitions of admitted patients to the different wards, discharge or death. These models were used to simulate the required capacity of beds by ward type over the next 10U+2009days, along with worst-case and best-case bounds.Results: Overall, the models resulted in good predictions of the required number of beds across different hospital wards. Short-term predictions were especially accurate as these are less sensitive to sudden changes in number of beds on a given ward (e.g. due to referrals). Code snippets and details on the set-up are provided to guide the reader to apply the planning tool on oneU+2019s own hospital data.Conclusions: We were able to achieve a fast setup of a planning tool useful within the COVIDU+2212U+200919 pandemic, with a fair prediction on the needed capacity by ward type. This methodology can also be applied for other epidemics."
"Estimating the effect of healthcare-associated infections on excess length of hospital stay using inverse probability-weighted survival curves" "Koen B. Pouwels, Stijn Vansteelandt, Rahul Batra, Jonathan Edgeworth, Sarah Wordsworth, Julie V. Robotham" "Background: Studies estimating excess length of stay (LOS) attributable to nosocomial infections have failed to address time-varying confounding, likely leading to overestimation of their impact. We present a methodology based on inverse probabilityU+2013weighted survival curves to address this limitation.Methods: A case study focusing on intensive care unitU+2013acquired bacteremia using data from 2 general intensive care units (ICUs) from 2 London teaching hospitals were used to illustrate the methodology. The area under the curve of a conventional Kaplan-Meier curve applied to the observed data was compared with that of an inverse probabilityU+2013weighted Kaplan-Meier curve applied after treating bacteremia as censoring events. Weights were based on the daily probability of acquiring bacteremia. The difference between the observed average LOS and the average LOS that would be observed if all bacteremia cases could be prevented was multiplied by the number of admitted patients to obtain the total excess LOS.Results: The estimated total number of extra ICU days caused by 666 bacteremia cases was estimated at 2453 (95% confidence interval [CI], 1803U+20133103) days. The excess number of days was overestimated when ignoring time-varying confounding (2845 [95% CI, 2276U+20133415]) or when completely ignoring confounding (2838 [95% CI, 2101U+20133575]).Conclusions: ICU-acquired bacteremia was associated with a substantial excess LOS. Wider adoption of inverse probabilityU+2013weighted survival curves or alternative techniques that address time-varying confounding could lead to better informed decision making around nosocomial infections and other time-dependent exposures."
"Causal graphs for the analysis of genetic cohort data" "Oliver Hines, Karla Diaz-Ordaz, Stijn Vansteelandt, Yalda Jamshidi" "The increasing availability of genetic cohort data has led to many genome-wide association studies (GWAS) successfully identifying genetic associations with an ever-expanding list of phenotypic traits. Association, however, does not imply causation, and therefore methods have been developed to study the issue of causality. Under additional assumptions, Mendelian randomization (MR) studies have proved popular in identifying causal effects between two phenotypes, often using GWAS summary statistics. Given the widespread use of these methods, it is more important than ever to understand, and communicate, the causal assumptions upon which they are based, so that methods are transparent, and findings are clinically relevant. Causal graphs can be used to represent causal assumptions graphically and provide insights into the limitations associated with different analysis methods. Here we review GWAS and MR from a causal perspective, to build up intuition for causal diagrams in genetic problems. We also examine issues of confounding by ancestry and comment on approaches for dealing with such confounding, as well as discussing approaches for dealing with selection biases arising from study design."
"Subtleties in the interpretation of hazard contrasts" "Torben Martinussen, Stijn Vansteelandt, Per Kragh Andersen" "The hazard ratio is one of the most commonly reported measures of treatment effect in randomised trials, yet the source of much misinterpretation. This point was made clear by Hernan (Epidemiology (Cambridge, Mass) 21(1):13-15, 2010) in a commentary, which emphasised that the hazard ratio contrasts populations of treated and untreated individuals who survived a given period of time, populations that will typically fail to be comparable-even in a randomised trial-as a result of different pressures or intensities acting on different populations. The commentary has been very influential, but also a source of surprise and confusion. In this note, we aim to provide more insight into the subtle interpretation of hazard ratios and differences, by investigating in particular what can be learned about a treatment effect from the hazard ratio becoming 1 (or the hazard difference 0) after a certain period of time. We further define a hazard ratio that has a causal interpretation and study its relationship to the Cox hazard ratio, and we also define a causal hazard difference. These quantities are of theoretical interest only, however, since they rely on assumptions that cannot be empirically evaluated. Throughout, we will focus on the analysis of randomised experiments."