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1. G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study

Author

Florence Gillaizeau, Clemence Leyrat, Chloé Rousseau, Laetitia Barbin, Florent Le Borgne, Yohann Foucher, Arthur Chatton, David Laplaud, Bruno Giraudeau, Maxime Léger, Jonchère, Laurent, Recherche en Informatique et en Statistique pour l'Analyse de Cohortes - - RISCA2016 - ANR-16-LCV1-0003 - LABCOM - VALID, MethodS in Patients-centered outcomes and HEalth ResEarch (SPHERE), Université de Tours (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Nantes - UFR des Sciences Pharmaceutiques et Biologiques, Université de Nantes (UN)-Université de Nantes (UN), London School of Hygiene and Tropical Medicine (LSHTM), Centre d'Investigation Clinique [Rennes] (CIC), Université de Rennes (UR)-Hôpital Pontchaillou-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre d’Investigation Clinique de Nantes (CIC Nantes), Université de Nantes (UN)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre hospitalier universitaire de Nantes (CHU Nantes), Centre de Recherche en Transplantation et Immunologie (U1064 Inserm - CRTI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Nantes - UFR de Médecine et des Techniques Médicales (UFR MEDECINE), Centre Hospitalier Universitaire d'Angers (CHU Angers), PRES Université Nantes Angers Le Mans (UNAM), ANR-16-LCV1-0003-01, Agence Nationale de la Recherche, ANR-16-LCV1-0003,RISCA,Recherche en Informatique et en Statistique pour l'Analyse de Cohortes(2016), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Hôpital Pontchaillou-Institut National de la Santé et de la Recherche Médicale (INSERM), and Université de Tours-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Nantes - UFR des Sciences Pharmaceutiques et Biologiques

Subjects
Matching (statistics), Computer science, Epidemiology, Science, Context (language use), 01 natural sciences, Article, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Statistics, Covariate, 030212 general & internal medicine, 0101 mathematics, Multidisciplinary, [STAT.ME] Statistics [stat]/Methodology [stat.ME], Confounding, Variance (accounting), Outcome (probability), Risk factors, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Causal inference, Propensity score matching, Medicine, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [STAT.ME]Statistics [stat]/Methodology [stat.ME]
Abstract

Controlling for confounding bias is crucial in causal inference. Distinct methods are currently employed to mitigate the effects of confounding bias. Each requires the introduction of a set of covariates, which remains difficult to choose, especially regarding the different methods. We conduct a simulation study to compare the relative performance results obtained by using four different sets of covariates (those causing the outcome, those causing the treatment allocation, those causing both the outcome and the treatment allocation, and all the covariates) and four methods: g-computation, inverse probability of treatment weighting, full matching and targeted maximum likelihood estimator. Our simulations are in the context of a binary treatment, a binary outcome and baseline confounders. The simulations suggest that considering all the covariates causing the outcome led to the lowest bias and variance, particularly for g-computation. The consideration of all the covariates did not decrease the bias but significantly reduced the power. We apply these methods to two real-world examples that have clinical relevance, thereby illustrating the real-world importance of using these methods. We propose an R package RISCA to encourage the use of g-computation in causal inference.

Published
2020
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2. Intervention effect estimates in cluster randomized versus individually randomized trials: a meta-epidemiological study

Author

Bruno Giraudeau, Agnès Caille, Sandra Eldridge, Sally Kerry, Agnès Dechartres, Clemence Leyrat, London School of Hygiene and Tropical Medicine (LSHTM), MethodS in Patients-centered outcomes and HEalth ResEarch (SPHERE), Université de Tours (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Nantes - UFR des Sciences Pharmaceutiques et Biologiques, Université de Nantes (UN)-Université de Nantes (UN), Université de Tours (UT), Université de Nantes - UFR de Médecine et des Techniques Médicales (UFR MEDECINE), Université de Nantes (UN), Centre Hospitalier Régional Universitaire de Tours (CHRU Tours), Queen Mary University of London (QMUL), Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Département de Biostatistique, Santé Publique et Information Médicale [CHU Pitié-Salpêtrière] (BIOSPIM ), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Sorbonne Université (SU), Université de Tours-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Nantes - UFR des Sciences Pharmaceutiques et Biologiques, Université de Tours, Centre Hospitalier Régional Universitaire de Tours (CHRU TOURS), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), and Gestionnaire, Hal Sorbonne Université

Subjects
0301 basic medicine, medicine.medical_specialty, Epidemiology, Cluster randomized trial, Disease cluster, law.invention, 03 medical and health sciences, 0302 clinical medicine, CRTS, Randomized controlled trial, Meta-Analysis as Topic, systematic review, law, Internal medicine, medicine, Humans, 030212 general & internal medicine, Cluster randomised controlled trial, Randomized Controlled Trials as Topic, intervention effect estimate, business.industry, Data Collection, General Medicine, Odds ratio, Confidence interval, 3. Good health, Miscellaneous, Epidemiologic Studies, 030104 developmental biology, individually randomized trial, Sample size determination, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Sample Size, meta-epidemiological study, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business, Systematic Reviews as Topic
Abstract

Background Cluster randomized trials (CRTs) and individually randomized trials (IRTs) are often pooled together in meta-analyses (MAs) of randomized trials. However, the potential systematic differences in intervention effect estimates between these two trial types has never been investigated. Therefore, we conducted a meta-epidemiological study comparing intervention effect estimates between CRTs and IRTs. Methods All Cochrane MAs including at least one CRT and one IRT, published between 1 January 2010 and 31 December 2014, were included. For each MA, we estimated a ratio of odds ratios (ROR) for binary outcomes or a difference of standardized differences (DSMD) for continuous outcomes, where less than 1 (or 0, respectively) indicated a greater intervention effect estimate with CRTs. Results Among 1301 screened reviews, we selected 121 MAs, of which 76 had a binary outcome and 45 had a continuous outcome. For binary outcomes, intervention effect estimates did not differ between CRTs and IRTs [ROR 1.00, 95% confidence interval (0.93 to 1.08)]. Subgroup and adjusted analyses led to consistent results. For continuous outcomes, the DSMD was 0.13 (0.06 to 0.19). It was lower for MAs with a pharmacological intervention [-0.03, (-0.12 to 0.07)], an objective outcome [0.05, (-0.08 to 0.17)] or after adjusting for trial size [0.06, (-0.01 to 0.15)]. Conclusion For binary outcomes, CRTs and IRTs can safely be pooled in MAs because of an absence of systematic differences between effect estimates. For continuous outcomes, the results were less clear although accounting for trial sample sizes led to a non-significant difference. More research is needed for continuous outcomes and, meanwhile, MAs should be completed with subgroup analyses (CRTs vs IRTs).

Published
2019
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3. A Comparison of Imputation Strategies in Cluster Randomized Trials with Missing Binary Outcomes

Author

Bruno Giraudeau, Agnès Caille, Clemence Leyrat, Centre d’Investigation Clinique [Tours] CIC 1415 (CIC ), Centre Hospitalier Régional Universitaire de Tours (CHRU Tours)-Hôpital Bretonneau-Université de Tours-Institut National de la Santé et de la Recherche Médicale (INSERM), MethodS in Patients-centered outcomes and HEalth ResEarch (SPHERE), Université de Tours-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Nantes - UFR des Sciences Pharmaceutiques et Biologiques, Université de Nantes (UN)-Université de Nantes (UN), Université de Tours, London School of Hygiene and Tropical Medicine (LSHTM), Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Université Paris Diderot - Paris 7 (UPD7)-Université Sorbonne Paris Cité (USPC)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA), Université Francois Rabelais [Tours], PRES Centre-Val de Loire Université, Centre Hospitalier Régional Universitaire de Tours (CHRU TOURS), Bruere, Gérard, Centre Hospitalier Régional Universitaire de Tours (CHRU Tours)-Hôpital Bretonneau-Université de Tours (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Tours (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Nantes - UFR des Sciences Pharmaceutiques et Biologiques, Université de Tours (UT), Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Centre Hospitalier Régional Universitaire de Tours (CHRU Tours)

Subjects
Male, Statistics and Probability, multiple imputation, Epidemiology, Cluster randomized trial, [SDV]Life Sciences [q-bio], Binary number, Logistic regression, 01 natural sciences, law.invention, 010104 statistics & probability, 03 medical and health sciences, Intracluster correlation coefficient, missing data, 0302 clinical medicine, Bias, Health Information Management, Randomized controlled trial, law, Statistics, Econometrics, Animals, Humans, Medicine, 030212 general & internal medicine, Imputation (statistics), Cluster randomised controlled trial, 0101 mathematics, Randomized Controlled Trials as Topic, Ivermectin, Binary outcome, business.industry, Pediculus, Lice Infestations, Missing data, [SDV] Life Sciences [q-bio], Logistic Models, Data Interpretation, Statistical, outcome, Female, business, Hair
Abstract

International audience; In cluster randomized trials, clusters of subjects are randomized rather than subjects themselves, and missing outcomes are a concern as in individual randomized trials. We assessed strategies for handling missing data when analysing cluster randomized trials with a binary outcome; strategies included complete case, adjusted complete case, and simple and multiple imputation approaches. We performed a simulation study to assess bias and coverage rate of the population-averaged intervention-effect estimate. Both multiple imputation with a random-effects logistic regression model or classical logistic regression provided unbiased estimates of the intervention effect. Both strategies also showed good coverage properties, even slightly better for multiple imputation with a random-effects logistic regression approach. Finally, this latter approach led to a slightly negatively biased intracluster correlation coefficient estimate but less than that with a classical logistic regression model strategy. We applied these strategies to a real trial randomizing households and comparing ivermectin and malathion to treat head lice.

Published
2016
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