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Léger M, Chatton A, Le Borgne F, Pirracchio R, Lasocki S, Foucher Y. Causal inference in case of near-violation of positivity: comparison of methods. Biom J 2022; 64:1389-1403. [PMID: 34993990 DOI: 10.1002/bimj.202000323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 09/07/2021] [Accepted: 10/24/2021] [Indexed: 12/14/2022]
Abstract
In causal studies, the near-violation of the positivity may occur by chance, because of sample-to-sample fluctuation despite the theoretical veracity of the positivity assumption in the population. It may mostly happen when the exposure prevalence is low or when the sample size is small. We aimed to compare the robustness of g-computation (GC), inverse probability weighting (IPW), truncated IPW, targeted maximum likelihood estimation (TMLE), and truncated TMLE in this situation, using simulations and one real application. We also tested different extrapolation situations for the sub-group with a positivity violation. The results illustrated that the near-violation of the positivity impacted all methods. We demonstrated the robustness of GC and TMLE-based methods. Truncation helped in limiting the bias in near-violation situations, but at the cost of bias in normal conditions. The application illustrated the variability of the results between the methods and the importance of choosing the most appropriate one. In conclusion, compared to propensity score-based methods, methods based on outcome regression should be preferred when suspecting near-violation of the positivity assumption.
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Affiliation(s)
- Maxime Léger
- INSERM UMR 1246 - SPHERE, Université de Nantes, Université de Tours, Nantes, France.,Département d'Anesthésie-Réanimation, Centre Hospitalier Universitaire d'Angers, Angers, France
| | - Arthur Chatton
- INSERM UMR 1246 - SPHERE, Université de Nantes, Université de Tours, Nantes, France.,IDBC-A2COM, Nantes, France
| | - Florent Le Borgne
- INSERM UMR 1246 - SPHERE, Université de Nantes, Université de Tours, Nantes, France.,IDBC-A2COM, Nantes, France
| | - Romain Pirracchio
- Department of Anesthesia and Perioperative Care, University of California, San Francisco, CA, USA
| | - Sigismond Lasocki
- Département d'Anesthésie-Réanimation, Centre Hospitalier Universitaire d'Angers, Angers, France
| | - Yohann Foucher
- INSERM UMR 1246 - SPHERE, Université de Nantes, Université de Tours, Nantes, France.,Centre Hospitalier Universitaire de Nantes, Nantes, France
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2
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Haber NA, Wieten SE, Rohrer JM, Arah OA, Tennant PWG, Stuart EA, Murray EJ, Pilleron S, Lam ST, Riederer E, Howcutt SJ, Simmons AE, Leyrat C, Schoenegger P, Booman A, Dufour MSK, O’Donoghue AL, Baglini R, Do S, Takashima MDLR, Evans TR, Rodriguez-Molina D, Alsalti TM, Dunleavy DJ, Meyerowitz-Katz G, Antonietti A, Calvache JA, Kelson MJ, Salvia MG, Parra CO, Khalatbari-Soltani S, McLinden T, Chatton A, Seiler J, Steriu A, Alshihayb TS, Twardowski SE, Dabravolskaj J, Au E, Hoopsick RA, Suresh S, Judd N, Peña S, Axfors C, Khan P, Rivera Aguirre AE, Odo NU, Schmid I, Fox MP. Causal and Associational Language in Observational Health Research: A Systematic Evaluation. Am J Epidemiol 2022; 191:2084-2097. [PMID: 35925053 PMCID: PMC11043784 DOI: 10.1093/aje/kwac137] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 04/19/2022] [Accepted: 07/26/2022] [Indexed: 02/01/2023] Open
Abstract
We estimated the degree to which language used in the high-profile medical/public health/epidemiology literature implied causality using language linking exposures to outcomes and action recommendations; examined disconnects between language and recommendations; identified the most common linking phrases; and estimated how strongly linking phrases imply causality. We searched for and screened 1,170 articles from 18 high-profile journals (65 per journal) published from 2010-2019. Based on written framing and systematic guidance, 3 reviewers rated the degree of causality implied in abstracts and full text for exposure/outcome linking language and action recommendations. Reviewers rated the causal implication of exposure/outcome linking language as none (no causal implication) in 13.8%, weak in 34.2%, moderate in 33.2%, and strong in 18.7% of abstracts. The implied causality of action recommendations was higher than the implied causality of linking sentences for 44.5% or commensurate for 40.3% of articles. The most common linking word in abstracts was "associate" (45.7%). Reviewers' ratings of linking word roots were highly heterogeneous; over half of reviewers rated "association" as having at least some causal implication. This research undercuts the assumption that avoiding "causal" words leads to clarity of interpretation in medical research.
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Affiliation(s)
- Noah A Haber
- Correspondence to Dr. Noah A. Haber, 1265 Welch Road, Palo Alto, CA 94305 (e-mail: )
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3
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Chatton A, Borgne FL, Leyrat C, Foucher Y. G-computation and doubly robust standardisation for continuous-time data: A comparison with inverse probability weighting. Stat Methods Med Res 2021; 31:706-718. [PMID: 34861799 DOI: 10.1177/09622802211047345] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In time-to-event settings, g-computation and doubly robust estimators are based on discrete-time data. However, many biological processes are evolving continuously over time. In this paper, we extend the g-computation and the doubly robust standardisation procedures to a continuous-time context. We compare their performance to the well-known inverse-probability-weighting estimator for the estimation of the hazard ratio and restricted mean survival times difference, using a simulation study. Under a correct model specification, all methods are unbiased, but g-computation and the doubly robust standardisation are more efficient than inverse-probability-weighting. We also analyse two real-world datasets to illustrate the practical implementation of these approaches. We have updated the R package RISCA to facilitate the use of these methods and their dissemination.
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Affiliation(s)
- Arthur Chatton
- INSERM UMR 1246 - SPHERE, 27045Nantes University, Tours University, France.,IDBC-A2COM, Pacé, France
| | - Florent Le Borgne
- INSERM UMR 1246 - SPHERE, 27045Nantes University, Tours University, France.,IDBC-A2COM, Pacé, France
| | - Clémence Leyrat
- Department of Medical Statistics, 4906London School of Hygiene and Tropical Medicine, UK.,Inequalities in Cancer Outcomes Network (ICON), 4906London School of Hygiene and Tropical Medicine, UK
| | - Yohann Foucher
- INSERM UMR 1246 - SPHERE, 27045Nantes University, Tours University, France.,26922Centre Hospitalier Universitaire de Nantes, France
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4
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Féray C, Taupin JL, Sebagh M, Allain V, Demir Z, Allard MA, Desterke C, Coilly A, Saliba F, Vibert E, Azoulay D, Guettier C, Chatton A, Debray D, Caillat-Zucman S, Samuel D. Donor HLA Class 1 Evolutionary Divergence Is a Major Predictor of Liver Allograft Rejection : A Retrospective Cohort Study. Ann Intern Med 2021; 174:1385-1394. [PMID: 34424731 DOI: 10.7326/m20-7957] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND The HLA evolutionary divergence (HED), a continuous metric quantifying the peptidic differences between 2 homologous HLA alleles, reflects the breadth of the immunopeptidome presented to T lymphocytes. OBJECTIVE To assess the potential effect of donor or recipient HED on liver transplant rejection. DESIGN Retrospective cohort study. SETTING Liver transplant units. PATIENTS 1154 adults and 113 children who had a liver transplant between 2004 and 2018. MEASUREMENTS Liver biopsies were done 1, 2, 5, and 10 years after the transplant and in case of liver dysfunction. Donor-specific anti-HLA antibodies (DSAs) were measured in children at the time of biopsy. The HED was calculated using the physicochemical Grantham distance for class I (HLA-A or HLA-B) and class II (HLA-DRB1 or HLA-DQB1) alleles. The influence of HED on the incidence of liver lesions was analyzed through the inverse probability weighting approach based on covariate balancing, generalized propensity scores. RESULTS In adults, class I HED of the donor was associated with acute rejection (hazard ratio [HR], 1.09 [95% CI, 1.03 to 1.16]), chronic rejection (HR, 1.20 [CI, 1.10 to 1.31]), and ductopenia of 50% or more (HR, 1.33 [CI, 1.09 to 1.62]) but not with other histologic lesions. In children, class I HED of the donor was also associated with acute rejection (HR, 1.16 [CI, 1.03 to 1.30]) independent of the presence of DSAs. There was no effect of either donor class II HED or recipient class I or class II HED on the incidence of liver lesions in adults and children. LIMITATION The DSAs were measured only in children. CONCLUSION Class I HED of the donor predicts acute or chronic rejection of liver transplant. This novel and accessible prognostic marker could orientate donor selection and guide immunosuppression. PRIMARY FUNDING SOURCE Institut National de la Santé et de la Recherche Médicale.
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Affiliation(s)
- Cyrille Féray
- Centre Hépato-Biliaire, Hôpital Paul-Brousse, Assistance Publique-Hôpitaux de Paris, Université Paris-Saclay, unité Institut National de la Santé et de la Recherche Médicale 1193, Villejuif, France (C.F., M.A., C.D., A.C., F.S., E.V., D.A., D.S.)
| | - Jean-Luc Taupin
- Laboratoire d'Immunologie et Histocompatibilité, Hôpital Saint-Louis, Assistance Publique-Hôpitaux de Paris, Université de Paris, and unité Institut National de la Santé et de la Recherche Médicale 976, Université de Paris, Paris, France (J.T., S.C.)
| | - Mylène Sebagh
- Laboratoire d'Anatomopathologie, Assistance Publique-Hôpitaux de Paris, Hôpital Paul-Brousse, Université Paris-Saclay, unité Institut National de la Santé et de la Recherche Médicale, Physiopathogénèse et traitement des maladies du Foie, and FHU Hepatinov, Villejuif, France (M.S., C.G.)
| | - Vincent Allain
- Hôpital Saint-Louis, Assistance Publique-Hôpitaux de Paris, Université de Paris, Laboratoire d'Immunologie et Histocompatibilité, and Institut National de la Santé et de la Recherche Médicale, Paris, France (V.A.)
| | - Zeynep Demir
- Hôpital Necker Enfants Malades, Assistance Publique-Hôpitaux de Paris, Université de Paris, and Unité d'Hépatologie pédiatrique, Paris, France (Z.D., D.D.)
| | - Marc-Antoine Allard
- Centre Hépato-Biliaire, Hôpital Paul-Brousse, Assistance Publique-Hôpitaux de Paris, Université Paris-Saclay, unité Institut National de la Santé et de la Recherche Médicale 1193, Villejuif, France (C.F., M.A., C.D., A.C., F.S., E.V., D.A., D.S.)
| | - Christophe Desterke
- Centre Hépato-Biliaire, Hôpital Paul-Brousse, Assistance Publique-Hôpitaux de Paris, Université Paris-Saclay, unité Institut National de la Santé et de la Recherche Médicale 1193, Villejuif, France (C.F., M.A., C.D., A.C., F.S., E.V., D.A., D.S.)
| | - Audrey Coilly
- Centre Hépato-Biliaire, Hôpital Paul-Brousse, Assistance Publique-Hôpitaux de Paris, Université Paris-Saclay, unité Institut National de la Santé et de la Recherche Médicale 1193, Villejuif, France (C.F., M.A., C.D., A.C., F.S., E.V., D.A., D.S.)
| | - Faouzi Saliba
- Centre Hépato-Biliaire, Hôpital Paul-Brousse, Assistance Publique-Hôpitaux de Paris, Université Paris-Saclay, unité Institut National de la Santé et de la Recherche Médicale 1193, Villejuif, France (C.F., M.A., C.D., A.C., F.S., E.V., D.A., D.S.)
| | - Eric Vibert
- Centre Hépato-Biliaire, Hôpital Paul-Brousse, Assistance Publique-Hôpitaux de Paris, Université Paris-Saclay, unité Institut National de la Santé et de la Recherche Médicale 1193, Villejuif, France (C.F., M.A., C.D., A.C., F.S., E.V., D.A., D.S.)
| | - Daniel Azoulay
- Centre Hépato-Biliaire, Hôpital Paul-Brousse, Assistance Publique-Hôpitaux de Paris, Université Paris-Saclay, unité Institut National de la Santé et de la Recherche Médicale 1193, Villejuif, France (C.F., M.A., C.D., A.C., F.S., E.V., D.A., D.S.)
| | - Catherine Guettier
- Laboratoire d'Anatomopathologie, Assistance Publique-Hôpitaux de Paris, Hôpital Paul-Brousse, Université Paris-Saclay, unité Institut National de la Santé et de la Recherche Médicale, Physiopathogénèse et traitement des maladies du Foie, and FHU Hepatinov, Villejuif, France (M.S., C.G.)
| | - Arthur Chatton
- Institut National de la Santé et de la Recherche Médicale UMR 1246-SPHERE, Nantes University, Tours University, Nantes, and IDBC, Pacé, France (A.C.)
| | - Dominique Debray
- Hôpital Necker Enfants Malades, Assistance Publique-Hôpitaux de Paris, Université de Paris, and Unité d'Hépatologie pédiatrique, Paris, France (Z.D., D.D.)
| | - Sophie Caillat-Zucman
- Laboratoire d'Immunologie et Histocompatibilité, Hôpital Saint-Louis, Assistance Publique-Hôpitaux de Paris, Université de Paris, and unité Institut National de la Santé et de la Recherche Médicale 976, Université de Paris, Paris, France (J.T., S.C.)
| | - Didier Samuel
- Centre Hépato-Biliaire, Hôpital Paul-Brousse, Assistance Publique-Hôpitaux de Paris, Université Paris-Saclay, unité Institut National de la Santé et de la Recherche Médicale 1193, Villejuif, France (C.F., M.A., C.D., A.C., F.S., E.V., D.A., D.S.)
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5
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Le Borgne F, Chatton A, Léger M, Lenain R, Foucher Y. G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes. Sci Rep 2021; 11:1435. [PMID: 33446866 PMCID: PMC7809122 DOI: 10.1038/s41598-021-81110-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 12/24/2020] [Indexed: 11/09/2022] Open
Abstract
In clinical research, there is a growing interest in the use of propensity score-based methods to estimate causal effects. G-computation is an alternative because of its high statistical power. Machine learning is also increasingly used because of its possible robustness to model misspecification. In this paper, we aimed to propose an approach that combines machine learning and G-computation when both the outcome and the exposure status are binary and is able to deal with small samples. We evaluated the performances of several methods, including penalized logistic regressions, a neural network, a support vector machine, boosted classification and regression trees, and a super learner through simulations. We proposed six different scenarios characterised by various sample sizes, numbers of covariates and relationships between covariates, exposure statuses, and outcomes. We have also illustrated the application of these methods, in which they were used to estimate the efficacy of barbiturates prescribed during the first 24 h of an episode of intracranial hypertension. In the context of GC, for estimating the individual outcome probabilities in two counterfactual worlds, we reported that the super learner tended to outperform the other approaches in terms of both bias and variance, especially for small sample sizes. The support vector machine performed well, but its mean bias was slightly higher than that of the super learner. In the investigated scenarios, G-computation associated with the super learner was a performant method for drawing causal inferences, even from small sample sizes.
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Affiliation(s)
- Florent Le Borgne
- INSERM UMR 1246 - SPHERE, Nantes University, Tours University, 22 Boulevard Bénoni Goullin, 44200, Nantes, France.,IDBC-A2COM, Pacé, France
| | - Arthur Chatton
- INSERM UMR 1246 - SPHERE, Nantes University, Tours University, 22 Boulevard Bénoni Goullin, 44200, Nantes, France.,IDBC-A2COM, Pacé, France
| | - Maxime Léger
- INSERM UMR 1246 - SPHERE, Nantes University, Tours University, 22 Boulevard Bénoni Goullin, 44200, Nantes, France.,Département D'Anesthésie Réanimation, Centre Hospitalier Universitaire D'Angers, Angers, France
| | - Rémi Lenain
- INSERM UMR 1246 - SPHERE, Nantes University, Tours University, 22 Boulevard Bénoni Goullin, 44200, Nantes, France.,Lille University Hospital, Lille, France
| | - Yohann Foucher
- INSERM UMR 1246 - SPHERE, Nantes University, Tours University, 22 Boulevard Bénoni Goullin, 44200, Nantes, France. .,Nantes University Hospital, Nantes, France.
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6
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Lejeune F, Chatton A, Laplaud DA, Le Page E, Wiertlewski S, Edan G, Kerbrat A, Veillard D, Hamonic S, Jousset N, Le Frère F, Ouallet JC, Brochet B, Ruet A, Foucher Y, Michel L. SMILE: a predictive model for Scoring the severity of relapses in MultIple scLErosis. J Neurol 2020; 268:669-679. [PMID: 32902734 DOI: 10.1007/s00415-020-10154-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 07/21/2020] [Accepted: 08/10/2020] [Indexed: 11/25/2022]
Abstract
BACKGROUND In relapsing-remitting multiple sclerosis (RRMS), relapse severity and residual disability are difficult to predict. Nevertheless, this information is crucial both for guiding relapse treatment strategies and for informing patients. OBJECTIVE We, therefore, developed and validated a clinical-based model for predicting the risk of residual disability at 6 months post-relapse in MS. METHODS We used the data of 186 patients with RRMS collected during the COPOUSEP multicentre trial. The outcome was an increase of ≥ 1 EDSS point 6 months post-relapse treatment. We used logistic regression with LASSO penalization to construct the model, and bootstrap cross-validation to internally validate it. The model was externally validated with an independent retrospective French single-centre cohort of 175 patients. RESULTS The predictive factors contained in the model were age > 40 years, shorter disease duration, EDSS increase ≥ 1.5 points at time of relapse, EDSS = 0 before relapse, proprioceptive ataxia, and absence of subjective sensory disorders. Discriminative accuracy was acceptable in both the internal (AUC 0.82, 95% CI [0.73, 0.91]) and external (AUC 0.71, 95% CI [0.62, 0.80]) validations. CONCLUSION The predictive model we developed should prove useful for adapting therapeutic strategy of relapse and follow-up to individual patients.
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Affiliation(s)
- F Lejeune
- Neurology Department and CIC 0004, Nantes University Hospital, Nantes, France.,Centre de Recherche en Transplantation et Immunologie, INSERM U1064, Nantes, France
| | - A Chatton
- MethodS in Patient-Centred Outcomes and HEalth ResEarch (SPHERE) Unit, INSERM, Universities of Nantes and Tours, Nantes, France
| | - D-A Laplaud
- Neurology Department and CIC 0004, Nantes University Hospital, Nantes, France.,Centre de Recherche en Transplantation et Immunologie, INSERM U1064, Nantes, France
| | - E Le Page
- Clinical Neuroscience Centre, CIC_P1414 INSERM, Rennes University Hospital, Rennes University, Rennes, France
| | - S Wiertlewski
- Neurology Department and CIC 0004, Nantes University Hospital, Nantes, France.,Centre de Recherche en Transplantation et Immunologie, INSERM U1064, Nantes, France
| | - G Edan
- Clinical Neuroscience Centre, CIC_P1414 INSERM, Rennes University Hospital, Rennes University, Rennes, France
| | - A Kerbrat
- Clinical Neuroscience Centre, CIC_P1414 INSERM, Rennes University Hospital, Rennes University, Rennes, France
| | - D Veillard
- Epidemiology and Public Health Department, Rennes University Hospital, Rennes, France
| | - S Hamonic
- Epidemiology and Public Health Department, Rennes University Hospital, Rennes, France
| | - N Jousset
- Nantes Clinical Investigation Centre, Nantes University Hospital, Nantes, France
| | - F Le Frère
- Nantes Clinical Investigation Centre, Nantes University Hospital, Nantes, France
| | - J-C Ouallet
- Neurology Department, Magendie Neurocentre, Bordeaux University Hospital, INSERM U1215, Bordeaux, France
| | - B Brochet
- Neurology Department, Magendie Neurocentre, Bordeaux University Hospital, INSERM U1215, Bordeaux, France
| | - A Ruet
- Neurology Department, Magendie Neurocentre, Bordeaux University Hospital, INSERM U1215, Bordeaux, France
| | - Y Foucher
- MethodS in Patient-Centred Outcomes and HEalth ResEarch (SPHERE) Unit, INSERM, Universities of Nantes and Tours, Nantes, France.,Nantes University Hospital, Nantes, France
| | - Laure Michel
- Clinical Neuroscience Centre, CIC_P1414 INSERM, Rennes University Hospital, Rennes University, Rennes, France. .,Microenvironment, Cell Differentiation, Immunology and Cancer Unit, INSERM, Rennes I University, French Blood Agency, Rennes, France. .,Neurology Department, Rennes University Hospital, Rennes, France.
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7
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Chatton A, Le Borgne F, Leyrat C, Foucher Y. G-computation et pondération sur le score de propension en analyse de survie. Rev Epidemiol Sante Publique 2020. [DOI: 10.1016/j.respe.2020.03.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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8
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Chatton A, Le Borgne F, Leyrat C, Gillaizeau F, Rousseau C, Barbin L, Laplaud D, Léger M, Giraudeau B, Foucher Y. G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study. Sci Rep 2020; 10:9219. [PMID: 32514028 PMCID: PMC7280276 DOI: 10.1038/s41598-020-65917-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 04/26/2020] [Indexed: 12/25/2022] Open
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.
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Affiliation(s)
- Arthur Chatton
- INSERM UMR 1246 - SPHERE, Université de Nantes, Université de Tours, Nantes, France
- A2COM-IDBC, Pacé, France
| | - Florent Le Borgne
- INSERM UMR 1246 - SPHERE, Université de Nantes, Université de Tours, Nantes, France
- A2COM-IDBC, Pacé, France
| | - Clémence Leyrat
- INSERM UMR 1246 - SPHERE, Université de Nantes, Université de Tours, Nantes, France
- Department of Medical Statistics & Cancer Survival Group, London School of Hygiene and Tropical Medicine, London, UK
| | - Florence Gillaizeau
- INSERM UMR 1246 - SPHERE, Université de Nantes, Université de Tours, Nantes, France
- Centre Hospitalier Universitaire de Nantes, Nantes, France
| | - Chloé Rousseau
- INSERM UMR 1246 - SPHERE, Université de Nantes, Université de Tours, Nantes, France
- Centre Hospitalier Universitaire de Nantes, Nantes, France
- INSERM CIC1414, CHU Rennes, Rennes, France
| | | | - David Laplaud
- Centre Hospitalier Universitaire de Nantes, Nantes, France
- Centre de Recherche en Transplantation et Immunologie INSERM UMR1064, Université de Nantes, Nantes, France
| | - Maxime Léger
- INSERM UMR 1246 - SPHERE, Université de Nantes, Université de Tours, Nantes, France
- Département d'Anesthésie-Réanimation, Centre Hospitalier Universitaire d'Angers, Angers, France
| | - Bruno Giraudeau
- INSERM UMR 1246 - SPHERE, Université de Nantes, Université de Tours, Nantes, France
- INSERM CIC1415, CHRU de Tours, Tours, France
| | - Yohann Foucher
- INSERM UMR 1246 - SPHERE, Université de Nantes, Université de Tours, Nantes, France.
- Centre Hospitalier Universitaire de Nantes, Nantes, France.
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9
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Khazaal Y, Chatton A, Coquard O, Zullino D. [Brief-DISCERN, a possible way to improve patient's search on the health-related web]. Rev Med Suisse 2009; 5:1816-1819. [PMID: 19839369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Internet is increasingly used as a source of information on health issues and is probably a major source of patients' empowerment. This process is however limited by the frequently poor quality of web-based health information designed for consumers. A better diffusion of information about criteria defining the quality of the content of websites, and about useful methods designed for searching such needed information, could be particularly useful to patients and their relatives. A brief, six-items DISCERN version, characterized by a high specificity for detecting websites with good or very good content quality was recently developed. This tool could facilitate the identification of high-quality information on the web by patients and may improve the empowerment process initiated by the development of the health-related web.
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Affiliation(s)
- Y Khazaal
- Service d'addictologie, Département de psychiatrie, HUG, Genève.
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Khazaal Y, Chatton A, Zullino D, Preisig M. Advance directives based cognitive therapy in bipolar disorder. Eur Psychiatry 2008. [DOI: 10.1016/j.eurpsy.2008.01.659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
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Khazaal Y, Fresard E, Chatton A, Zullino D. Cognitive behavioural therapy for obesity and binge eating associated to antipsychotic drugs. Eur Psychiatry 2008. [DOI: 10.1016/j.eurpsy.2008.01.658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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