1
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Buchanan AL, Hernández-Ramírez RU, Lok JJ, Vermund SH, Friedman SR, Forastiere L, Spiegelman D. Assessing Direct and Spillover Effects of Intervention Packages in Network-randomized Studies. Epidemiology 2024; 35:481-488. [PMID: 38709023 DOI: 10.1097/ede.0000000000001742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
BACKGROUND Intervention packages may result in a greater public health impact than single interventions. Understanding the separate impact of each component on the overall package effectiveness can improve intervention delivery. METHODS We adapted an approach to evaluate the effects of a time-varying intervention package in a network-randomized study. In some network-randomized studies, only a subset of participants in exposed networks receive the intervention themselves. The spillover effect contrasts average potential outcomes if a person was not exposed to themselves under intervention in the network versus no intervention in a control network. We estimated the effects of components of the intervention package in HIV Prevention Trials Network 037, a Phase III network-randomized HIV prevention trial among people who inject drugs and their risk networks using marginal structural models to adjust for time-varying confounding. The index participant in an intervention network received a peer education intervention initially at baseline, then boosters at 6 and 12 months. All participants were followed to ascertain HIV risk behaviors. RESULTS There were 560 participants with at least one follow-up visit, 48% of whom were randomized to the intervention, and 1,598 participant visits were observed. The spillover effect of the boosters in the presence of initial peer education training was a 39% rate reduction (rate ratio = 0.61; 95% confidence interval = 0.43, 0.87). CONCLUSIONS These methods will be useful for evaluating intervention packages in studies with network features.
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Affiliation(s)
- Ashley L Buchanan
- From the Department of Pharmacy Practice and Clinical Research, College of Pharmacy, University of Rhode Island, Kingston, RI
| | - Raúl U Hernández-Ramírez
- Department of Biostatistics, Center for Methods in Implementation and Prevention Science, and Center for Interdisciplinary Research on AIDS, Yale School of Public Health, New Haven, CT
| | - Judith J Lok
- Department of Mathematics and Statistics, Boston University, Boston, MA
| | - Sten H Vermund
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT
| | - Samuel R Friedman
- Department of Population Health, New York University Grossman School of Medicine, New York, NY
| | - Laura Forastiere
- Department of Biostatistics, Yale School of Public Health, New Haven, CT
| | - Donna Spiegelman
- Department of Biostatistics, Center for Methods in Implementation and Prevention Science, and Center for Interdisciplinary Research on AIDS, Yale School of Public Health, New Haven, CT
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2
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Dahabreh IJ, Bibbins-Domingo K. Causal Inference About the Effects of Interventions From Observational Studies in Medical Journals. JAMA 2024; 331:1845-1853. [PMID: 38722735 DOI: 10.1001/jama.2024.7741] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
Importance Many medical journals, including JAMA, restrict the use of causal language to the reporting of randomized clinical trials. Although well-conducted randomized clinical trials remain the preferred approach for answering causal questions, methods for observational studies have advanced such that causal interpretations of the results of well-conducted observational studies may be possible when strong assumptions hold. Furthermore, observational studies may be the only practical source of information for answering some questions about the causal effects of medical or policy interventions, can support the study of interventions in populations and settings that reflect practice, and can help identify interventions for further experimental investigation. Identifying opportunities for the appropriate use of causal language when describing observational studies is important for communication in medical journals. Observations A structured approach to whether and how causal language may be used when describing observational studies would enhance the communication of research goals, support the assessment of assumptions and design and analytic choices, and allow for more clear and accurate interpretation of results. Building on the extensive literature on causal inference across diverse disciplines, we suggest a framework for observational studies that aim to provide evidence about the causal effects of interventions based on 6 core questions: what is the causal question; what quantity would, if known, answer the causal question; what is the study design; what causal assumptions are being made; how can the observed data be used to answer the causal question in principle and in practice; and is a causal interpretation of the analyses tenable? Conclusions and Relevance Adoption of the proposed framework to identify when causal interpretation is appropriate in observational studies promises to facilitate better communication between authors, reviewers, editors, and readers. Practical implementation will require cooperation between editors, authors, and reviewers to operationalize the framework and evaluate its effect on the reporting of empirical research.
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Affiliation(s)
- Issa J Dahabreh
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
- Statistical Editor, JAMA
| | - Kirsten Bibbins-Domingo
- Department of Medicine, University of California, San Francisco
- Department of Epidemiology and Biostatistics, University of California, San Francisco
- Editor in Chief, JAMA and JAMA Network
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3
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Egami N, Tchetgen Tchetgen EJ. Identification and estimation of causal peer effects using double negative controls for unmeasured network confounding. J R Stat Soc Series B Stat Methodol 2024; 86:487-511. [PMID: 38618143 PMCID: PMC11009281 DOI: 10.1093/jrsssb/qkad132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 09/27/2023] [Accepted: 10/06/2023] [Indexed: 04/16/2024]
Abstract
Identification and estimation of causal peer effects are challenging in observational studies for two reasons. The first is the identification challenge due to unmeasured network confounding, for example, homophily bias and contextual confounding. The second is network dependence of observations. We establish a framework that leverages a pair of negative control outcome and exposure variables (double negative controls) to non-parametrically identify causal peer effects in the presence of unmeasured network confounding. We then propose a generalised method of moments estimator and establish its consistency and asymptotic normality under an assumption about ψ-network dependence. Finally, we provide a consistent variance estimator.
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Affiliation(s)
- Naoki Egami
- Department of Political Science, Columbia University, New York, NY, USA
| | - Eric J Tchetgen Tchetgen
- Department of Statistics and Data Science and Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
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4
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Buchanan AL, Hernández-Ramírez RU, Lok JJ, Vermund SH, Friedman SR, Forastiere L, Spiegelman D. Assessing Direct and Spillover Effects of Intervention Packages in Network-Randomized Studies. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2022.03.24.22272909. [PMID: 38352598 PMCID: PMC10863001 DOI: 10.1101/2022.03.24.22272909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/25/2024]
Abstract
Intervention packages may result in a greater public health impact than single interventions. Understanding the separate impact of each component in the overall package effectiveness can improve intervention delivery. We adapted an approach to evaluate the effects of a time-varying intervention package in a network-randomized study. In some network-randomized studies, only a subset of participants in exposed networks receive the intervention themselves. The spillover effect contrasts average potential outcomes if a person was not exposed themselves under intervention in the network versus no intervention in a control network. We estimated effects of components of the intervention package in HIV Prevention Trials Network 037, a Phase III network-randomized HIV prevention trial among people who inject drugs and their risk networks using Marginal Structural Models to adjust for time-varying confounding. The index participant in an intervention network received a peer education intervention initially at baseline, then boosters at 6 and 12 months. All participants were followed to ascertain HIV risk behaviors. There were 560 participants with at least one follow-up visit, 48% of whom were randomized to the intervention, and 1,598 participant-visits were observed. The spillover effect of the boosters in the presence of initial peer education training was a 39% rate reduction (Rate Ratio = 0.61; 95% confidence interval= 0.43, 0.87). These methods will be useful to evaluate intervention packages in studies with network features.
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Affiliation(s)
- Ashley L Buchanan
- Department of Pharmacy Practice, College of Pharmacy, University of Rhode Island, Kingston, RI 02881
| | - Raúl Ulises Hernández-Ramírez
- Department of Biostatistics, Center for Methods in Implementation and Prevention Science, and Center for Interdisciplinary Research on AIDS, Yale School of Public Health, New Haven, CT 06520
| | - Judith J Lok
- Department of Mathematics and Statistics, Boston University, Boston MA 02215
| | - Sten H Vermund
- Departments of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06520
| | - Samuel R Friedman
- Department of Population Health, New York University Grossman School of Medicine, New York, NY 10016
| | - Laura Forastiere
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520
| | - Donna Spiegelman
- Department of Biostatistics, Center for Methods in Implementation and Prevention Science, and Center for Interdisciplinary Research on AIDS, Yale School of Public Health, New Haven, CT 06520
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5
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Amaral YNDVD, Marano D, Filha MMT, Moreira MEL. Pre-gestational overweight and polyunsaturated fatty acids in human milk: theoretical causality model. CIENCIA & SAUDE COLETIVA 2024; 29:e10752022. [PMID: 38324828 DOI: 10.1590/1413-81232024292.10752022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 06/07/2023] [Indexed: 02/09/2024] Open
Abstract
A number of studies have focused on the evaluation of the relationship between pre-pregnancy overweight and polyunsaturated fatty acids content in human milk. However, given the complexity of potentially confounding risk factors, the use of graphical tools is recommended to identify possible biases. This article aims to propose a theoretical model of causality using the directed acyclic graph between pre-pregnancy overweight and polyunsaturated fatty acids content in human milk. Methods: An extensive literature review was performed to identify variables with causal relationships with exposure and/or outcome. The choice of variables for adjustment followed the graphic algorithm that comprises six criteria for selecting a minimum set of potentially confounding variables. Socioeconomic conditions, interpartum interval, maternal age and food consumption pattern were the variables that would have to be adjusted in order to estimate the total effect of pre-pregnancy overweight on polyunsaturated fatty acids content in human milk. The minimum set of variables found in the present study can be used in the analysis of other studies that evaluate this association.
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Affiliation(s)
| | - Daniele Marano
- Instituto Nacional de Saúde da Mulher, da Criança e do Adolescente Fernandes Figueira Fundação Oswaldo Cruz. Av. Rui Barbosa 716, Flamengo. 22250-020 Rio de Janeiro RJ Brasil.
| | | | - Maria Elisabeth Lopes Moreira
- Instituto Nacional de Saúde da Mulher, da Criança e do Adolescente Fernandes Figueira Fundação Oswaldo Cruz. Av. Rui Barbosa 716, Flamengo. 22250-020 Rio de Janeiro RJ Brasil.
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Goyal R, De Gruttola V, Onnela JP. Framework for converting mechanistic network models to probabilistic models. JOURNAL OF COMPLEX NETWORKS 2023; 11:cnad034. [PMID: 37873517 PMCID: PMC10588735 DOI: 10.1093/comnet/cnad034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 08/25/2023] [Indexed: 10/25/2023]
Abstract
There are two prominent paradigms for the modelling of networks: in the first, referred to as the mechanistic approach, one specifies a set of domain-specific mechanistic rules that are used to grow or evolve the network over time; in the second, referred to as the probabilistic approach, one describes a model that specifies the likelihood of observing a given network. Mechanistic models (models developed based on the mechanistic approach) are appealing because they capture scientific processes that are believed to be responsible for network generation; however, they do not easily lend themselves to the use of inferential techniques when compared with probabilistic models. We introduce a general framework for converting a mechanistic network model (MNM) to a probabilistic network model (PNM). The proposed framework makes it possible to identify the essential network properties and their joint probability distribution for some MNMs; doing so makes it possible to address questions such as whether two different mechanistic models generate networks with identical distributions of properties, or whether a network property, such as clustering, is over- or under-represented in the networks generated by the model of interest compared with a reference model. The proposed framework is intended to bridge some of the gap that currently exists between the formulation and representation of mechanistic and PNMs. We also highlight limitations of PNMs that need to be addressed in order to close this gap.
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Affiliation(s)
- Ravi Goyal
- Division of Infectious Diseases and Global Public, Health, University of California San Diego, 9500 Gilman Drive, La Jolla, CA USA
| | - Victor De Gruttola
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA USA
| | - Jukka-Pekka Onnela
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA USA
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7
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Noorani T, Bedi G, Muthukumaraswamy S. Dark loops: contagion effects, consistency and chemosocial matrices in psychedelic-assisted therapy trials. Psychol Med 2023; 53:5892-5901. [PMID: 37466178 PMCID: PMC10520581 DOI: 10.1017/s0033291723001289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 04/05/2023] [Accepted: 04/13/2023] [Indexed: 07/20/2023]
Abstract
What happens when an emerging programme of medical research overlaps with a surging social movement? In this article we draw on the anthropological term 'chemosociality' to describe forms of sociality born of shared chemical exposure. Psychedelic administration in the context of recent clinical trials appears to have been particularly chemosocial in nature. We argue that one consequence is that psychedelic-assisted therapy (PAT) clinical research trials tend to breach key assumptions underlying the logic of causal inference used to establish efficacy. We propose the concept of dark loops to describe forms of sociality variously emerging from, and impacting participant experiences in, PAT trials. These dark loops are not recorded, let alone incorporated into the causal pathways in the interpretation of psychedelic trial data to date. We end with three positions which researchers might adopt in response to these issues: chemosocial minimisation where research is designed to attenuate or eliminate the effects of dark loops in trials; chemosocial description where dark loops (and their impacts) are openly and candidly documented and chemosocial valorisation where dark loops are hypothesised to contribute to trial outcomes and actively drawn upon for positive effect. Our goal is to fold in an appreciation of how the increasingly-discussed hype surrounding psychedelic research and therapeutics continues to shape the phenomena under study in complex ways, even as trials become larger and more rigorous in their design.
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Affiliation(s)
| | - Gillinder Bedi
- Orygen, Parkville, VIC, Australia
- Center for Youth Mental Health, University of Melbourne, Parkville, VIC, Australia
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8
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Shi J, Wu Z, Dempsey W. ASSESSING TIME-VARYING CAUSAL EFFECT MODERATION IN THE PRESENCE OF CLUSTER-LEVEL TREATMENT EFFECT HETEROGENEITY AND INTERFERENCE. Biometrika 2023; 110:645-662. [PMID: 37711671 PMCID: PMC10501736 DOI: 10.1093/biomet/asac065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/16/2023] Open
Abstract
The micro-randomized trial (MRT) is a sequential randomized experimental design to empirically evaluate the effectiveness of mobile health (mHealth) intervention components that may be delivered at hundreds or thousands of decision points. MRTs have motivated a new class of causal estimands, termed "causal excursion effects", for which semiparametric inference can be conducted via a weighted, centered least squares criterion (Boruvka et al., 2018). Existing methods assume between-subject independence and non-interference. Deviations from these assumptions often occur. In this paper, causal excursion effects are revisited under potential cluster-level treatment effect heterogeneity and interference, where the treatment effect of interest may depend on cluster-level moderators. Utility of the proposed methods is shown by analyzing data from a multi-institution cohort of first year medical residents in the United States.
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Affiliation(s)
- Jieru Shi
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA
| | - Zhenke Wu
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA
| | - Walter Dempsey
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA
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9
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Lee T, Buchanan AL, Katenka NV, Forastiere L, Halloran ME, Friedman SR, Nikolopoulos G. Estimating Causal Effects of HIV Prevention Interventions with Interference in Network-based Studies among People Who Inject Drugs. Ann Appl Stat 2023; 17:2165-2191. [PMID: 38250709 PMCID: PMC10798667 DOI: 10.1214/22-aoas1713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
Evaluating causal effects in the presence of interference is challenging in network-based studies of hard-to-reach populations. Like many such populations, people who inject drugs (PWID) are embedded in social networks and often exert influence on others in their network. In our setting, the study design is observational with a non-randomized network-based HIV prevention intervention. Information is available on each participant and their connections that confer possible HIV risk through injection and sexual behaviors. We considered two inverse probability weighted (IPW) estimators to quantify the population-level spillover effects of non-randomized interventions on subsequent health outcomes. We demonstrated that these two IPW estimators are consistent, asymptotically normal, and derived a closed-form estimator for the asymptotic variance, while allowing for overlapping interference sets (groups of individuals in which the interference is assumed possible). A simulation study was conducted to evaluate the finite-sample performance of the estimators. We analyzed data from the Transmission Reduction Intervention Project, which ascertained a network of PWID and their contacts in Athens, Greece, from 2013 to 2015. We evaluated the effects of community alerts on subsequent HIV risk behavior in this observed network, where the connections or links between participants were defined by using substances or having unprotected sex together. In the study, community alerts were distributed to inform people of recent HIV infections among individuals in close proximity in the observed network. The estimates of the risk differences for spillover using either IPW estimator demonstrated a protective effect. The results suggest that HIV risk behavior could be mitigated by exposure to a community alert when an increased risk of HIV is detected in the network.
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Affiliation(s)
- TingFang Lee
- Department of Pharmacy Practice, University of Rhode Island
| | | | - Natallia V Katenka
- Department of Computer Science and Statistics, University of Rhode Island
| | | | - M Elizabeth Halloran
- Biostatistics, Bioinformatics, and Epidemiology Program, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, and Department of Biostatistics, University of Washington
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10
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Muthukumaraswamy SD. Overcoming blinding confounds in psychedelic randomized controlled trials using biomarker driven causal mediation analysis. Expert Rev Clin Pharmacol 2023; 16:1163-1173. [PMID: 37947758 DOI: 10.1080/17512433.2023.2279736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 11/01/2023] [Indexed: 11/12/2023]
Abstract
INTRODUCTION There is great interest in the use of psychedelic-assisted therapies to treat a range of mental health conditions and initial randomized controlled trials (RCTs) have generated positive results. However, the effect sizes reported in psychedelic RCTs are likely inflated due to expectancy effects due to the de-blinding of both participants and study personnel to treatment allocation caused by the distinctive psychoactive effects of psychedelic drugs. AREAS COVERED An introduction to causal inference for RCTs, the underlying assumptions, and potential confounders along with graphical illustrations is provided. It is proposed that causal mediation analysis using objectively measured mediating biomarkers could be used to identify causal pathways between treatment and outcome in psychedelic RCTs, even with de-blinding of participants and give greater confidence as to the mechanistic basis and efficacy of psychedelic therapies. EXPERT OPINION It is argued that psychedelic therapies should not be approved as licensed medicines until causal pathways are clearly established between treatment and outcome. Potential downsides of doing so include, future indication expansion based on low quality clinical trial evidence, the approval of other therapies based on similarly low-quality evidence, and the potential for efficacy to change over time after approvals has been granted.
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11
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Núñez I, Soto-Mota A. Uneven Resources Threaten Causal Consistency in Randomized Trials. Epidemiology 2023; 34:531-534. [PMID: 36976717 DOI: 10.1097/ede.0000000000001616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Affiliation(s)
- Isaac Núñez
- From the Department of Medical Education, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
- Division of Postgraduate Studies, Faculty of Medicine, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Adrian Soto-Mota
- Metabolic Diseases Research Unit, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
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12
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Vonk MC, Malekovic N, Bäck T, Kononova AV. Disentangling causality: assumptions in causal discovery and inference. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10411-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
AbstractCausality has been a burgeoning field of research leading to the point where the literature abounds with different components addressing distinct parts of causality. For researchers, it has been increasingly difficult to discern the assumptions they have to abide by in order to glean sound conclusions from causal concepts or methods. This paper aims to disambiguate the different causal concepts that have emerged in causal inference and causal discovery from observational data by attributing them to different levels of Pearl’s Causal Hierarchy. We will provide the reader with a comprehensive arrangement of assumptions necessary to engage in causal reasoning at the desired level of the hierarchy. Therefore, the assumptions underlying each of these causal concepts will be emphasized and their concomitant graphical components will be examined. We show which assumptions are necessary to bridge the gaps between causal discovery, causal identification and causal inference from a parametric and a non-parametric perspective. Finally, this paper points to further research areas related to the strong assumptions that researchers have glibly adopted to take part in causal discovery, causal identification and causal inference.
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Methods for Assessing Spillover in Network-Based Studies of HIV/AIDS Prevention among People Who Use Drugs. Pathogens 2023; 12:pathogens12020326. [PMID: 36839598 PMCID: PMC9967280 DOI: 10.3390/pathogens12020326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 02/03/2023] [Accepted: 02/08/2023] [Indexed: 02/17/2023] Open
Abstract
Human Immunodeficiency Virus (HIV) interventions among people who use drugs (PWUD) often have spillover, also known as interference or dissemination, which occurs when one participant's exposure affects another participant's outcome. PWUD are often members of networks defined by social, sexual, and drug-use partnerships and their receipt of interventions can affect other members in their network. For example, HIV interventions with possible spillover include educational training about HIV risk reduction, pre-exposure prophylaxis, or treatment as prevention. In turn, intervention effects frequently depend on the network structure, and intervention coverage levels and spillover can occur even if not measured in a study, possibly resulting in an underestimation of intervention effects. Recent methodological approaches were developed to assess spillover in the context of network-based studies. This tutorial provides an overview of different study designs for network-based studies and related methodological approaches for assessing spillover in each design. We also provide an overview of other important methodological issues in network studies, including causal influence in networks and missing data. Finally, we highlight applications of different designs and methods from studies of PWUD and conclude with an illustrative example from the Transmission Reduction Intervention Project (TRIP) in Athens, Greece.
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14
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Bajari P, Burdick B, Imbens GW, Masoero L, McQueen J, Richardson TS, Rosen IM. Experimental Design in Marketplaces. Stat Sci 2023. [DOI: 10.1214/23-sts883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Affiliation(s)
- Patrick Bajari
- Patrick Bajari is Vice President, Amazon, Seattle, WA 98109, USA
| | - Brian Burdick
- Brian Burdick was Director of Research at Core-AI at Amazon while doing this work
| | - Guido W. Imbens
- Guido W. Imbens is Professor of Economics, Graduate School of Business and Department of Economics, Stanford University, SIEPR, NBER, Stanford, California 94305, USA
| | - Lorenzo Masoero
- Lorenzo Masoero is Research Scientist, Amazon, Seattle, WA 98109, USA
| | - James McQueen
- James McQueen is Principal Scientist, Amazon, Seattle, WA 98109, USA
| | - Thomas S. Richardson
- Thomas S. Richardson is Professor of Statistics, University of Washington, Seattle, WA 98195, USA
| | - Ido M. Rosen
- Ido M. Rosen is Sr Principal Scientist, Core AI, Amazon, Seattle, WA 98109, USA
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15
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Ogburn EL, Sofrygin O, Díaz I, van der Laan MJ. Causal Inference for Social Network Data. J Am Stat Assoc 2022; 119:597-611. [PMID: 38800714 PMCID: PMC11114213 DOI: 10.1080/01621459.2022.2131557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 09/26/2022] [Indexed: 10/17/2022]
Abstract
We describe semiparametric estimation and inference for causal effects using observational data from a single social network. Our asymptotic results are the first to allow for dependence of each observation on a growing number of other units as sample size increases. In addition, while previous methods have implicitly permitted only one of two possible sources of dependence among social network observations, we allow for both dependence due to transmission of information across network ties and for dependence due to latent similarities among nodes sharing ties. We propose new causal effects that are specifically of interest in social network settings, such as interventions on network ties and network structure. We use our methods to reanalyze an influential and controversial study that estimated causal peer effects of obesity using social network data from the Framingham Heart Study; after accounting for network structure we find no evidence for causal peer effects.
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Affiliation(s)
- Elizabeth L Ogburn
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Oleg Sofrygin
- Kaiser Permanente Division of Research, 2000 Broadway, Oakland, CA, 94612, USA
| | - Iván Díaz
- Division of Biostatistics and Epidemiology, Weill Cornell Medicine, New York, NY, USA
| | - Mark J van der Laan
- Department of Biostatistics, University of California Berkeley, 2121 Berkeley Way, Berkeley, CA, 94720, USA
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16
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Nabi R, Pfeiffer J, Charles D, Kıcıman E. Causal Inference in the Presence of Interference in Sponsored Search Advertising. Front Big Data 2022; 5:888592. [PMID: 35800414 PMCID: PMC9253562 DOI: 10.3389/fdata.2022.888592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 05/16/2022] [Indexed: 11/18/2022] Open
Abstract
In classical causal inference, inferring cause-effect relations from data relies on the assumption that units are independent and identically distributed. This assumption is violated in settings where units are related through a network of dependencies. An example of such a setting is ad placement in sponsored search advertising, where the likelihood of a user clicking on a particular ad is potentially influenced by where it is placed and where other ads are placed on the search result page. In such scenarios, confounding arises due to not only the individual ad-level covariates but also the placements and covariates of other ads in the system. In this paper, we leverage the language of causal inference in the presence of interference to model interactions among the ads. Quantification of such interactions allows us to better understand the click behavior of users, which in turn impacts the revenue of the host search engine and enhances user satisfaction. We illustrate the utility of our formalization through experiments carried out on the ad placement system of the Bing search engine.
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Affiliation(s)
- Razieh Nabi
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, United States
- Microsoft Research, Redmond, WA, United States
- *Correspondence: Razieh Nabi
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Liu L, Tchetgen Tchetgen E. Regression-based negative control of homophily in dyadic peer effect analysis. Biometrics 2022; 78:668-678. [PMID: 33914905 PMCID: PMC11087064 DOI: 10.1111/biom.13483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 01/27/2021] [Accepted: 02/24/2021] [Indexed: 12/17/2022]
Abstract
A prominent threat to causal inference about peer effects in social science studies is the presence of homophily bias , that is, social influence between friends and families is entangled with common characteristics or underlying similarities that form close connections. Analysis of social study data has suggested that certain health conditions such as obesity and psychological states including happiness and loneliness can spread between friends and relatives. However, such analyses of peer effects or contagion effects have come under criticism because homophily bias may compromise the causal statement. We develop a regression-based approach which leverages a negative control exposure for identification and estimation of contagion effects on additive or multiplicative scales, in the presence of homophily bias. We apply our methods to evaluate the peer effect of obesity in Framingham Offspring Study.
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Affiliation(s)
- Lan Liu
- School of Statistics, University of Minnesota at Twin Cities, Minneapolis, Minnesota, USA
| | - Eric Tchetgen Tchetgen
- Department of Statistics of the Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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18
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Coman EN, Steinbach S, Cao G. Spatial perspectives in family health research. Fam Pract 2022; 39:556-562. [PMID: 34910138 DOI: 10.1093/fampra/cmab165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Emil N Coman
- University of Connecticut School of Medicine, Health Disparities Institute, Hartford, CT, United States
| | - Sandro Steinbach
- University of Connecticut, Department of Agricultural and Resource Economics, Storrs, CT, United States
| | - Guofeng Cao
- University of Colorado Boulder, Department of Geography, Boulder, CO, United States
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19
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Schnitzer ME. Estimands and Estimation of COVID-19 Vaccine Effectiveness Under the Test-Negative Design: Connections to Causal Inference. Epidemiology 2022; 33:325-333. [PMID: 35239518 PMCID: PMC8983614 DOI: 10.1097/ede.0000000000001470] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 01/26/2022] [Indexed: 11/26/2022]
Abstract
The test-negative design is routinely used for the monitoring of seasonal flu vaccine effectiveness. More recently, it has become integral to the estimation of COVID-19 vaccine effectiveness, in particular for more severe disease outcomes. Because the design has many important advantages and is becoming a mainstay for monitoring postlicensure vaccine effectiveness, epidemiologists and biostatisticians may be interested in further understanding the effect measures being estimated in these studies and connections to causal effects. Logistic regression is typically applied to estimate the conditional risk ratio but relies on correct outcome model specification and may be biased in the presence of effect modification by a confounder. We give and justify an inverse probability of treatment weighting (IPTW) estimator for the marginal risk ratio, which is valid under effect modification. We use causal directed acyclic graphs, and counterfactual arguments under assumptions about no interference and partial interference to illustrate the connection between these statistical estimands and causal quantities. We conduct a simulation study to illustrate and confirm our derivations and to evaluate the performance of the estimators. We find that if the effectiveness of the vaccine varies across patient subgroups, the logistic regression can lead to misleading estimates, but the IPTW estimator can produce unbiased estimates. We also find that in the presence of partial interference both estimators can produce misleading estimates.
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Affiliation(s)
- Mireille E. Schnitzer
- From the Faculty of Pharmacy, Université de Montréal, Montréal, QC, Canada
- Department of Social and Preventive Medicine, School of Public Health, Université de Montréal, Montréal, QC, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada
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20
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Rojas-Saunero LP, Labrecque JA, Swanson SA. Invited Commentary: Conducting and Emulating Trials to Study Effects of Social Interventions. Am J Epidemiol 2022; 191:1453-1456. [PMID: 35445692 PMCID: PMC9347019 DOI: 10.1093/aje/kwac066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 01/24/2022] [Accepted: 03/15/2022] [Indexed: 01/28/2023] Open
Abstract
All else being equal, if we had 1 causal effect we wished to estimate, we would conduct a randomized trial with a protocol that mapped onto that causal question, or we would attempt to emulate that target trial with observational data. However, studying the social determinants of health often means there are not just 1 but several causal contrasts of simultaneous interest and importance, and each of these related but distinct causal questions may have varying degrees of feasibility in conducting trials. With this in mind, we discuss challenges and opportunities that arise when conducting and emulating such trials. We describe designing trials with the simultaneous goals of estimating the intention-to-treat effect, the per-protocol effect, effects of alternative protocols or joint interventions, effects within subgroups, and effects under interference, and we describe ways to make the most of all feasible randomized trials and emulated trials using observational data. Our comments are grounded in the study results of Courtin et al. (Am J Epidemiol. 2022;191(8):1444-1452).
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Affiliation(s)
| | | | - Sonja A Swanson
- Correspondence to Dr. Sonja A. Swanson, Department of Epidemiology, School of Public Health, University of Pittsburgh, 130 De Soto Street, Pittsburgh, PA 15261 (e-mail: )
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21
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Li Y, Undurraga EA, Zubizarreta JR. Effectiveness of Localized Lockdowns in the COVID-19 Pandemic. Am J Epidemiol 2022; 191:812-824. [PMID: 35029649 PMCID: PMC8807239 DOI: 10.1093/aje/kwac008] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 11/17/2021] [Accepted: 01/06/2022] [Indexed: 12/13/2022] Open
Abstract
Nonpharmaceutical interventions, such as social distancing and lockdowns, have been essential to control of the coronavirus disease 2019 (COVID-19) pandemic. In particular, localized lockdowns in small geographic areas have become an important policy intervention for preventing viral spread in cases of resurgence. These localized lockdowns can result in lower social and economic costs compared with larger-scale suppression strategies. Using an integrated data set from Chile (March 3-June 15, 2020) and a novel synthetic control approach, we estimated the effect of localized lockdowns, disentangling its direct and indirect causal effects on transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our results showed that the effects of localized lockdowns are strongly modulated by their duration and are influenced by indirect effects from neighboring geographic areas. Our estimates suggest that extending localized lockdowns can slow down SARS-CoV-2 transmission; however, localized lockdowns on their own are insufficient to control pandemic growth in the presence of indirect effects from contiguous neighboring areas that do not have lockdowns. These results provide critical empirical evidence about the effectiveness of localized lockdowns in interconnected geographic areas.
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Affiliation(s)
- Yige Li
- Department of Biostatistics and CAUSALab, Harvard T.H. School of Public Health, Huntington Avenue, Boston, Massachusetts 02115
| | - Eduardo A Undurraga
- Escuela de Gobierno, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Macul, Santiago 7820436, Chile
- Millennium Initiative for Collaborative Research in Bacterial Resistance (MICROB-R), Chile
- CIFAR Azrieli Global Scholars program, CIFAR, Toronto, ON M5G 1M1, Canada
- Research Center for Integrated Disaster Risk Management (CIGIDEN), Santiago, Chile
| | - José R Zubizarreta
- Correspondence Address: Department of Health Care Policy, Harvard Medical School, 180A Longwood Avenue, Office 307-Z, Boston, MA 02115,
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22
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Mallinson DC, Elwert F. Estimating sibling spillover effects with unobserved confounding using gain-scores. Ann Epidemiol 2022; 67:73-80. [PMID: 34990828 PMCID: PMC8960330 DOI: 10.1016/j.annepidem.2021.12.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 12/05/2021] [Accepted: 12/22/2021] [Indexed: 11/01/2022]
Abstract
PURPOSE A growing area of research in epidemiology is the identification of health-related sibling spillover effects, or the effect of one individual's exposure on their sibling's outcome. The health within families may be confounded by unobserved factors, rendering identification of sibling spillovers challenging. METHODS We demonstrate a gain-score (fixed effects) regression method for identifying exposure-to-outcome spillover effects within sibling pairs in linear models. The method identifies the exposure-to-outcome spillover effect if only one sibling's exposure affects the other's outcome, and it identifies the difference between the spillover effects if both siblings' exposures affect the others' outcomes. The method fails with outcome-to-exposure spillover or with outcome-to-outcome spillover. Analytic results, Monte Carlo simulations, and a brief application demonstrate the method and its limitations. RESULTS We estimate the spillover effect of a child's preterm birth on an older sibling's literacy skills, measured by the Phonological Awareness Literacy Screening-Kindergarten test. We analyze 20,010 sibling pairs from a population-wide, Wisconsin-based (United States) birth cohort. Without covariate adjustment, we estimate that preterm birth modestly decreases an older sibling's test score. CONCLUSIONS Gain-scores are a promising strategy for identifying exposure-to-outcome spillover effects in sibling pairs while controlling for sibling-invariant unobserved confounding.
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Affiliation(s)
| | - Felix Elwert
- Department of Sociology, College of Letters and Sciences, University of Wisconsin, Madison, WI; Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin, Madison, WI; Center for Demography and Ecology, University of Wisconsin, Madison, WI
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23
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Digitale JC, Martin JN, Glymour MM. Tutorial on directed acyclic graphs. J Clin Epidemiol 2022; 142:264-267. [PMID: 34371103 PMCID: PMC8821727 DOI: 10.1016/j.jclinepi.2021.08.001] [Citation(s) in RCA: 64] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 07/28/2021] [Accepted: 08/02/2021] [Indexed: 02/03/2023]
Abstract
Directed acyclic graphs (DAGs) are an intuitive yet rigorous tool to communicate about causal questions in clinical and epidemiologic research and inform study design and statistical analysis. DAGs are constructed to depict prior knowledge about biological and behavioral systems related to specific causal research questions. DAG components portray who receives treatment or experiences exposures; mechanisms by which treatments and exposures operate; and other factors that influence the outcome of interest or which persons are included in an analysis. Once assembled, DAGs - via a few simple rules - guide the researcher in identifying whether the causal effect of interest can be identified without bias and, if so, what must be done either in study design or data analysis to achieve this. Specifically, DAGs can identify variables that, if controlled for in the design or analysis phase, are sufficient to eliminate confounding and some forms of selection bias. DAGs also help recognize variables that, if controlled for, bias the analysis (e.g., mediators or factors influenced by both exposure and outcome). Finally, DAGs help researchers recognize insidious sources of bias introduced by selection of individuals into studies or failure to completely observe all individuals until study outcomes are reached. DAGs, however, are not infallible, largely owing to limitations in prior knowledge about the system in question. In such instances, several alternative DAGs are plausible, and researchers should assess whether results differ meaningfully across analyses guided by different DAGs and be forthright about uncertainty. DAGs are powerful tools to guide the conduct of clinical research.
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Affiliation(s)
- Jean C Digitale
- Department of Epidemiology and Biostatistics, University of California, 550 16th St, 2nd Floor, San Francisco, CA 94158
| | - Jeffrey N Martin
- Department of Epidemiology and Biostatistics, University of California, 550 16th St, 2nd Floor, San Francisco, CA 94158
| | - Medellena Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, 550 16th St, 2nd Floor, San Francisco, CA 94158.
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24
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Buchanan AL, Bessey S, Goedel WC, King M, Murray EJ, Friedman SR, Halloran ME, Marshall BDL. Disseminated Effects in Agent-Based Models: A Potential Outcomes Framework and Application to Inform Preexposure Prophylaxis Coverage Levels for HIV Prevention. Am J Epidemiol 2021; 190:939-948. [PMID: 33128066 DOI: 10.1093/aje/kwaa239] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 09/14/2020] [Accepted: 10/20/2020] [Indexed: 12/25/2022] Open
Abstract
Preexposure prophylaxis (PrEP) for prevention of human immunodeficiency virus (HIV) infection may benefit not only the person who uses it but also their uninfected sexual risk contacts. We developed an agent-based model using a novel trial emulation approach to quantify disseminated effects of PrEP use among men who have sex with men in Atlanta, Georgia, from 2015 to 2017. Model components (subsets of agents connected through partnerships in a sexual network but not sharing partnerships with any other agents) were first randomized to an intervention coverage level or the control group; then, within intervention components, eligible agents were randomized to receive or not receive PrEP. We calculated direct and disseminated (indirect) effects using randomization-based estimators and report corresponding 95% simulation intervals across scenarios ranging from 10% coverage in the intervention components to 90% coverage. A population of 11,245 agents was simulated, with an average of 1,551 components identified. When comparing agents randomized to no PrEP in 70% coverage components with control agents, there was a 15% disseminated risk reduction in HIV incidence (risk ratio = 0.85, 95% simulation interval: 0.65, 1.05). Persons not on PrEP may receive a protective benefit by being in a sexual network with higher PrEP coverage. Agent-based models are useful for evaluating possible direct and disseminated effects of HIV prevention modalities in sexual networks.
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25
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Cai X, Loh WW, Crawford FW. Identification of causal intervention effects under contagion. JOURNAL OF CAUSAL INFERENCE 2021; 9:9-38. [PMID: 34676152 PMCID: PMC8528235 DOI: 10.1515/jci-2019-0033] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Defining and identifying causal intervention effects for transmissible infectious disease outcomes is challenging because a treatment - such as a vaccine - given to one individual may affect the infection outcomes of others. Epidemiologists have proposed causal estimands to quantify effects of interventions under contagion using a two-person partnership model. These simple conceptual models have helped researchers develop causal estimands relevant to clinical evaluation of vaccine effects. However, many of these partnership models are formulated under structural assumptions that preclude realistic infectious disease transmission dynamics, limiting their conceptual usefulness in defining and identifying causal treatment effects in empirical intervention trials. In this paper, we propose causal intervention effects in two-person partnerships under arbitrary infectious disease transmission dynamics, and give nonparametric identification results showing how effects can be estimated in empirical trials using time-to-infection or binary outcome data. The key insight is that contagion is a causal phenomenon that induces conditional independencies on infection outcomes that can be exploited for the identification of clinically meaningful causal estimands. These new estimands are compared to existing quantities, and results are illustrated using a realistic simulation of an HIV vaccine trial.
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Affiliation(s)
- Xiaoxuan Cai
- Department of Biostatistics, Yale School of Public Health
| | - Wen Wei Loh
- Department of Data Analysis, University of Ghent
| | - Forrest W Crawford
- Department of Biostatistics, Yale School of Public Health
- Department of Statistics & Data Science, Yale University
- Department of Ecology and Evolutionary Biology, Yale University
- Yale School of Management
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26
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Tchetgen Tchetgen EJ, Fulcher IR, Shpitser I. Auto-G-Computation of Causal Effects on a Network. J Am Stat Assoc 2020; 116:833-844. [PMID: 34366505 PMCID: PMC8345318 DOI: 10.1080/01621459.2020.1811098] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2018] [Revised: 02/05/2020] [Accepted: 07/31/2020] [Indexed: 10/23/2022]
Abstract
Methods for inferring average causal effects have traditionally relied on two key assumptions: (i) the intervention received by one unit cannot causally influence the outcome of another; and (ii) units can be organized into nonoverlapping groups such that outcomes of units in separate groups are independent. In this article, we develop new statistical methods for causal inference based on a single realization of a network of connected units for which neither assumption (i) nor (ii) holds. The proposed approach allows both for arbitrary forms of interference, whereby the outcome of a unit may depend on interventions received by other units with whom a network path through connected units exists; and long range dependence, whereby outcomes for any two units likewise connected by a path in the network may be dependent. Under network versions of consistency and no unobserved confounding, inference is made tractable by an assumption that the networks outcome, treatment and covariate vectors are a single realization of a certain chain graph model. This assumption allows inferences about various network causal effects via the auto-g-computation algorithm, a network generalization of Robins' well-known g-computation algorithm previously described for causal inference under assumptions (i) and (ii). Supplementary materials for this article are available online.
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Affiliation(s)
| | - Isabel R. Fulcher
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA
| | - Ilya Shpitser
- Department of Computer Science, Johns Hopkins Whiting School of Engineering, Baltimore, MD
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27
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Rafiee A, Delgado-Saborit JM, Sly PD, Quémerais B, Hashemi F, Akbari S, Hoseini M. Environmental chronic exposure to metals and effects on attention and executive function in the general population. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 705:135911. [PMID: 31838411 DOI: 10.1016/j.scitotenv.2019.135911] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2019] [Revised: 10/16/2019] [Accepted: 12/01/2019] [Indexed: 05/19/2023]
Abstract
Heavy metals are neurotoxic, associated with brain dysfunction, and have been linked with cognitive decline in adults. This study was aimed to characterize chronic exposure to metals (Cd, Be, Co, Hg, Sn, V, Al, Ba, Cr, Cu, Fe, Li, Mn, Ni, Pb, and Zn) and metalloids (As, B, Sb) and assess its impact on cognitive performance of Tehran's residents, capital of Iran. Scalp hair samples gathered from 200 volunteered participants (110 men and 90 women), aged 14-70 years and quantified by inductively coupled plasma atomic emission spectroscopy (ICP-OES). Attention and executive function, two measures of cognitive performance, were characterized using the trail making test (TMT) part A and B, respectively. Mental flexibility was characterized as the Delta TMT B-A scores and cognitive efficiency or dissimulation as the ration between TMT B and A scores. A comprehensive questionnaire was used to gather information on demographic and socioeconomic as well as lifestyle and health status. The highest and lowest mean concentrations were observed for B (325 μg/g) and As (0.29 μg/g), respectively. Results indicated that chronic metal exposure measured in hair changed significantly based on gender and age (p < 0.05). The levels of Cr, Fe, Ni, Si, Hg, Pb and B were significantly higher in males' hair, whereas those of Ag and Ba were greater in females' hair (p < 0.05). The results of the cognitive TMT test were significantly different between gender and age groups (p < 0.05). Moreover, results revealed that As, Hg, Mn, and Pb levels in hair were significantly associated with poorer participants' performance scores in the TMT test (p < 0.05). Age, gender, cigarette smoking, water-pipe smoking, traffic density in the area of residence, and dental amalgam filling were significant factors affecting the TMT test scores. The results suggest that chronic exposure to metals has detrimental effects on attention, executive function, mental flexibility and cognitive efficiency.
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Affiliation(s)
- Ata Rafiee
- Department of Medicine, University of Alberta, Edmonton, AB, Canada.
| | - Juana Maria Delgado-Saborit
- ISGlobal Barcelona Institute for Global Health, Barcelona Biomedical Research Park, Barcelona, Spain; Population Health and Environmental Sciences, Analytical Environmental and Forensic Sciences, King's College London, United Kingdom; Division of Environmental Health & Risk Management, School of Geography, Earth & Environmental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Peter D Sly
- Children's Health and Environment Program, Child Health Research Centre, The University of Queensland, South Brisbane, Australia
| | | | - Fallah Hashemi
- Department of Environmental Health, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Sadaf Akbari
- Trauma and Injury Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad Hoseini
- Research Center for Health Sciences, Institute of Health, Department of Environmental Health, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
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28
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Hunter RF, de la Haye K, Murray JM, Badham J, Valente TW, Clarke M, Kee F. Social network interventions for health behaviours and outcomes: A systematic review and meta-analysis. PLoS Med 2019; 16:e1002890. [PMID: 31479454 PMCID: PMC6719831 DOI: 10.1371/journal.pmed.1002890] [Citation(s) in RCA: 141] [Impact Index Per Article: 28.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 07/30/2019] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND There has been a growing interest in understanding the effects of social networks on health-related behaviour, with a particular backdrop being the emerging prominence of complexity or systems science in public health. Social network interventions specifically use or alter the characteristics of social networks to generate, accelerate, or maintain health behaviours. We conducted a systematic review and meta-analysis to investigate health behaviour outcomes of social network interventions. METHODS AND FINDINGS We searched eight databases and two trial registries from 1990 to May 28, 2019, for English-language reports of randomised controlled trials (RCTs) and before-and-after studies investigating social network interventions for health behaviours and outcomes. Trials that did not specifically use social networks or that did not include a comparator group were excluded. We screened studies and extracted data from published reports independently. The primary outcome of health behaviours or outcomes at ≤6 months was assessed by random-effects meta-analysis. Secondary outcomes included those measures at >6-12 months and >12 months. This study is registered with the International Prospective Register of Systematic Reviews, PROSPERO: CRD42015023541. We identified 26,503 reports; after exclusion, 37 studies, conducted between 1996 and 2018 from 11 countries, were eligible for analysis, with a total of 53,891 participants (mean age 32.4 years [SD 12.7]; 45.5% females). A range of study designs were included: 27 used RCT/cluster RCT designs, and 10 used other study designs. Eligible studies addressed a variety of health outcomes, in particular sexual health and substance use. Social network interventions showed a significant intervention effect compared with comparator groups for sexual health outcomes. The pooled odds ratio (OR) was 1.46 (95% confidence interval [CI] 1.01-2.11; I2 = 76%) for sexual health outcomes at ≤6 months and OR 1.51 (95% CI 1.27-1.81; I2 = 40%) for sexual health outcomes at >6-12 months. Intervention effects for drug risk outcomes at each time point were not significant. There were also significant intervention effects for some other health outcomes including alcohol misuse, well-being, change in haemoglobin A1c (HbA1c), and smoking cessation. Because of clinical and measurement heterogeneity, it was not appropriate to pool data on these other behaviours in a meta-analysis. For sexual health outcomes, prespecified subgroup analyses were significant for intervention approach (p < 0.001), mean age of participants (p = 0.002), and intervention length (p = 0.05). Overall, 22 of the 37 studies demonstrated a high risk of bias, as measured by the Cochrane Risk of Bias tool. The main study limitations identified were the inclusion of studies of variable quality; difficulty in isolating the effects of specific social network intervention components on health outcomes, as interventions included other active components; and reliance on self-reported outcomes, which have inherent recall and desirability biases. CONCLUSIONS Our findings suggest that social network interventions can be effective in the short term (<6 months) and longer term (>6 months) for sexual health outcomes. Intervention effects for drug risk outcomes at each time point were not significant. There were also significant intervention effects for some other health outcomes including alcohol misuse, well-being, change in HbA1c, and smoking cessation.
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Affiliation(s)
- Ruth F. Hunter
- UKCRC Centre of Excellence for Public Health (NI)/Centre for Public Health, Queen’s University Belfast, Belfast, United Kingdom
| | - Kayla de la Haye
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Jennifer M. Murray
- UKCRC Centre of Excellence for Public Health (NI)/Centre for Public Health, Queen’s University Belfast, Belfast, United Kingdom
| | - Jennifer Badham
- UKCRC Centre of Excellence for Public Health (NI)/Centre for Public Health, Queen’s University Belfast, Belfast, United Kingdom
| | - Thomas W. Valente
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Mike Clarke
- Northern Ireland Methodology Hub, Centre for Public Health, Queen’s University Belfast, Belfast, United Kingdom
| | - Frank Kee
- UKCRC Centre of Excellence for Public Health (NI)/Centre for Public Health, Queen’s University Belfast, Belfast, United Kingdom
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29
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Affiliation(s)
- Xinran Li
- Department of Statistics, Harvard University, Cambridge, MA
| | - Peng Ding
- Department of Statistics, University of California, Berkeley, CA
| | - Qian Lin
- Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing, P. R. China
| | - Dawei Yang
- Bureau of Personnel of Chinese Academy of Sciences & School of Education of Peking University, Beijing, P. R. China
| | - Jun S. Liu
- Department of Statistics, Harvard University, Cambridge, MA
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30
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Arnold KF, Harrison WJ, Heppenstall AJ, Gilthorpe MS. DAG-informed regression modelling, agent-based modelling and microsimulation modelling: a critical comparison of methods for causal inference. Int J Epidemiol 2019; 48:243-253. [PMID: 30520989 PMCID: PMC6380300 DOI: 10.1093/ije/dyy260] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/02/2018] [Indexed: 01/06/2023] Open
Abstract
The current paradigm for causal inference in epidemiology relies primarily on the evaluation of counterfactual contrasts via statistical regression models informed by graphical causal models (often in the form of directed acyclic graphs, or DAGs) and their underlying mathematical theory. However, there have been growing calls for supplementary methods, and one such method that has been proposed is agent-based modelling due to its potential for simulating counterfactuals. However, within the epidemiological literature, there currently exists a general lack of clarity regarding what exactly agent-based modelling is (and is not) and, importantly, how it differs from microsimulation modelling-perhaps its closest methodological comparator. We clarify this distinction by briefly reviewing the history of each method, which provides a context for their similarities and differences, and casts light on the types of research questions that they have evolved (and thus are well suited) to answering; we do the same for DAG-informed regression methods. The distinct historical evolutions of DAG-informed regression modelling, microsimulation modelling and agent-based modelling have given rise to distinct features of the methods themselves, and provide a foundation for critical comparison. Not only are the three methods well suited to addressing different types of causal questions, but, in doing so, they place differing levels of emphasis on fixed and random effects, and also tend to operate on different timescales and in different timeframes.
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Affiliation(s)
- Kellyn F Arnold
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- School of Medicine, University of Leeds, Leeds, UK
| | - Wendy J Harrison
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- School of Medicine, University of Leeds, Leeds, UK
| | - Alison J Heppenstall
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- School of Geography, University of Leeds, Leeds, UK
| | - Mark S Gilthorpe
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- School of Medicine, University of Leeds, Leeds, UK
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31
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Buchanan AL, Vermund SH, Friedman SR, Spiegelman D. Assessing Individual and Disseminated Effects in Network-Randomized Studies. Am J Epidemiol 2018; 187:2449-2459. [PMID: 30052722 DOI: 10.1093/aje/kwy149] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Accepted: 07/17/2018] [Indexed: 11/14/2022] Open
Abstract
Implementation trials often involve clustering via risk networks, where only some participants directly receive the intervention. The individual effect is that among directly treated persons beyond being in an intervention network; the disseminated effect is that among persons engaged with those directly treated. In this article, we employ a causal inference framework and discuss assumptions and estimators for individual and disseminated effects and apply them to the HIV Prevention Trials Network 037 Study. HIV Prevention Trials Network 037 was a phase III, network-level, randomized controlled human immunodeficiency virus (HIV) prevention trial conducted in the United States and Thailand from 2002 to 2006 that recruited injection drug users, who were assigned to either an intervention group or a control group, and their risk network members, who received no direct intervention. Combining individual and disseminated effects, we observed a 35% composite rate reduction in the adjusted model (risk ratio = 0.65, 95% confidence interval: 0.47, 0.90). Methodology is now available for estimating the full set of these effects, enhancing knowledge gained from network-randomized trials. Although the overall effect gains validity from network randomization, we show that it will generally be less than the composite effect. Additionally, if only index participants benefit from the intervention, as the network size increases, the overall effect tends toward the null-an unfortunate and misleading conclusion.
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Affiliation(s)
- Ashley L Buchanan
- Department of Pharmacy Practice, College of Pharmacy, University of Rhode Island, Kingston, Rhode Island
| | - Sten H Vermund
- Vanderbilt Institute for Global Health and Department of Pediatrics, School of Medicine, Vanderbilt University, Nashville, Tennessee
| | - Samuel R Friedman
- Institute for Infectious Disease Research, National Development and Research Institutes, Inc., New York, New York
| | - Donna Spiegelman
- Departments of Epidemiology, Biostatistics, Nutrition, and Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
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Wu J, Crawford FW, Kim DA, Stafford D, Christakis NA. Exposure, hazard, and survival analysis of diffusion on social networks. Stat Med 2018; 37:2561-2585. [PMID: 29707798 PMCID: PMC6933552 DOI: 10.1002/sim.7658] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 12/05/2017] [Accepted: 02/15/2018] [Indexed: 11/09/2022]
Abstract
Sociologists, economists, epidemiologists, and others recognize the importance of social networks in the diffusion of ideas and behaviors through human societies. To measure the flow of information on real-world networks, researchers often conduct comprehensive sociometric mapping of social links between individuals and then follow the spread of an "innovation" from reports of adoption or change in behavior over time. The innovation is introduced to a small number of individuals who may also be encouraged to spread it to their network contacts. In conjunction with the known social network, the pattern of adoptions gives researchers insight into the spread of the innovation in the population and factors associated with successful diffusion. Researchers have used widely varying statistical tools to estimate these quantities, and there is disagreement about how to analyze diffusion on fully observed networks. Here, we describe a framework for measuring features of diffusion processes on social networks using the epidemiological concepts of exposure and competing risks. Given a realization of a diffusion process on a fully observed network, we show that classical survival regression models can be adapted to estimate the rate of diffusion, and actor/edge attributes associated with successful transmission or adoption, while accounting for the topology of the social network. We illustrate these tools by applying them to a randomized network intervention trial conducted in Honduras to estimate the rate of adoption of 2 health-related interventions-multivitamins and chlorine bleach for water purification-and determine factors associated with successful social transmission.
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Affiliation(s)
- Jiacheng Wu
- Department of Biostatistics, University of Washington, Seattle, WA 98105, U.S.A
| | - Forrest W. Crawford
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, U.S.A
- Department of Operations, Yale School of Management, New Haven, CT 06511, U.S.A
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, U.S.A
| | - David A. Kim
- Department of Emergency Medicine, Stanford University, Stanford, CA 94305, U.S.A
| | - Derek Stafford
- Department of Political Science, University of Michigan, Ann Arbor, MI 48109, U.S.A
| | - Nicholas A. Christakis
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, U.S.A
- Department of Sociology, Yale University, New Haven, CT 06511, U.S.A
- Department of Medicine, Yale School of Medicine, New Haven, CT 06510, U.S.A
- Department of Biomedical Engineering, New Haven, CT 06511, U.S.A
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Affiliation(s)
- Weihua An
- Department of Sociology and Institute for Quantitative Theory and Methods, Emory University, Atlanta, GA
| | - Ying Ding
- School of Informatics and Computing, Indiana University, IN
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Mitchell S, Gelman A, Ross R, Chen J, Bari S, Huynh UK, Harris MW, Sachs SE, Stuart EA, Feller A, Makela S, Zaslavsky AM, McClellan L, Ohemeng-Dapaah S, Namakula P, Palm CA, Sachs JD. The Millennium Villages Project: a retrospective, observational, endline evaluation. LANCET GLOBAL HEALTH 2018; 6:e500-e513. [PMID: 29653625 DOI: 10.1016/s2214-109x(18)30065-2] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 02/09/2018] [Indexed: 11/18/2022]
Abstract
BACKGROUND The Millennium Villages Project (MVP) was a 10 year, multisector, rural development project, initiated in 2005, operating across ten sites in ten sub-Saharan African countries to achieve the Millennium Development Goals (MDGs). In this study, we aimed to estimate the project's impact, target attainment, and on-site spending. METHODS In this endline evaluation of the MVP, we retrospectively selected comparison villages that best matched the project villages on possible confounding variables. Cross-sectional survey data on 40 outcomes of interest were collected from both the project and the comparison villages in 2015. Using these data, as well as on-site spending data collected during the project, we estimated project impacts as differences in outcomes between the project and comparison villages; target attainment as differences between project outcomes and prespecified targets; and on-site spending as expenditures reported by communities, donors, governments, and the project. Spending data were not collected in the comparison villages. FINDINGS Averaged across the ten project sites, we found that impact estimates for 30 of 40 outcomes were significant (95% uncertainty intervals [UIs] for these outcomes excluded zero) and favoured the project villages. In particular, substantial effects were seen in agriculture and health, in which some outcomes were roughly one SD better in the project villages than in the comparison villages. The project was estimated to have no significant impact on the consumption-based measures of poverty, but a significant favourable impact on an index of asset ownership. Impacts on nutrition and education outcomes were often inconclusive (95% UIs included zero). Averaging across outcomes within categories, the project had significant favourable impacts on agriculture, nutrition, education, child health, maternal health, HIV and malaria, and water and sanitation. A third of the targets were met in the project sites. Total on-site spending decreased from US$132 per person in the first half of the project (of which $66 was from the MVP) to $109 per person in the second half of the project (of which $25 was from the MVP). INTERPRETATION The MVP had favourable impacts on outcomes in all MDG areas, consistent with an integrated rural development approach. The greatest effects were in agriculture and health, suggesting support for the project's emphasis on agriculture and health systems strengthening. The project conclusively met one third of its targets. FUNDING The Open Society Foundations, the Islamic Development Bank, and the governments of Japan, South Korea, Mali, Senegal, and Uganda.
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Affiliation(s)
| | - Andrew Gelman
- Statistics Department, Columbia University, New York, NY, USA
| | - Rebecca Ross
- Earth Institute, Columbia University, New York, NY, USA
| | - Joyce Chen
- Center for Sustainable Development, Columbia University, New York, NY, USA
| | - Sehrish Bari
- Earth Institute, Columbia University, New York, NY, USA
| | | | | | | | - Elizabeth A Stuart
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Avi Feller
- Goldman School of Public Policy, UC Berkeley, Berkeley, CA, USA
| | - Susanna Makela
- Statistics Department, Columbia University, New York, NY, USA
| | - Alan M Zaslavsky
- Health Care Policy Department, Harvard Medical School, Boston, MA, USA
| | | | | | | | - Cheryl A Palm
- Institute for Sustainable Food Systems, Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, USA
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Aronow PM, Samii C. Estimating average causal effects under general interference, with application to a social network experiment. Ann Appl Stat 2017. [DOI: 10.1214/16-aoas1005] [Citation(s) in RCA: 89] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Ackley SF, Mayeda ER, Worden L, Enanoria WTA, Glymour MM, Porco TC. Compartmental Model Diagrams as Causal Representations in Relation to DAGs. EPIDEMIOLOGIC METHODS 2017; 6:20060007. [PMID: 30555771 PMCID: PMC6294476 DOI: 10.1515/em-2016-0007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Compartmental model diagrams have been used for nearly a century to depict causal relationships in infectious disease epidemiology. Causal directed acyclic graphs (DAGs) have been used more broadly in epidemiology since the 1990s to guide analyses of a variety of public health problems. Using an example from chronic disease epidemiology, the effect of type 2 diabetes on dementia incidence, we illustrate how compartmental model diagrams can represent the same concepts as causal DAGs, including causation, mediation, confounding, and collider bias. We show how to use compartmental model diagrams to explicitly depict interaction and feedback cycles. While DAGs imply a set of conditional independencies, they do not define conditional distributions parametrically. Compartmental model diagrams parametrically (or semiparametrically) describe state changes based on known biological processes or mechanisms. Compartmental model diagrams are part of a long-term tradition of causal thinking in epidemiology and can parametrically express the same concepts as DAGs, as well as explicitly depict feedback cycles and interactions. As causal inference efforts in epidemiology increasingly draw on simulations and quantitative sensitivity analyses, compartmental model diagrams may be of use to a wider audience. Recognizing simple links between these two common approaches to representing causal processes may facilitate communication between researchers from different traditions.
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Affiliation(s)
- S F Ackley
- Francis I. Proctor Foundation, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - E R Mayeda
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - L Worden
- Francis I. Proctor Foundation, University of California, San Francisco, CA, USA
| | - W T A Enanoria
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - M M Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - T C Porco
- Francis I. Proctor Foundation, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
- Department of Ophthalmology, University of California, San Francisco, San Francisco, CA, USA
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Wang R, De Gruttola V. The use of permutation tests for the analysis of parallel and stepped-wedge cluster-randomized trials. Stat Med 2017; 36:2831-2843. [PMID: 28464567 PMCID: PMC5507602 DOI: 10.1002/sim.7329] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2015] [Revised: 04/05/2017] [Accepted: 04/10/2017] [Indexed: 11/07/2022]
Abstract
We investigate the use of permutation tests for the analysis of parallel and stepped-wedge cluster-randomized trials. Permutation tests for parallel designs with exponential family endpoints have been extensively studied. The optimal permutation tests developed for exponential family alternatives require information on intraclass correlation, a quantity not yet defined for time-to-event endpoints. Therefore, it is unclear how efficient permutation tests can be constructed for cluster-randomized trials with such endpoints. We consider a class of test statistics formed by a weighted average of pair-specific treatment effect estimates and offer practical guidance on the choice of weights to improve efficiency. We apply the permutation tests to a cluster-randomized trial evaluating the effect of an intervention to reduce the incidence of hospital-acquired infection. In some settings, outcomes from different clusters may be correlated, and we evaluate the validity and efficiency of permutation test in such settings. Lastly, we propose a permutation test for stepped-wedge designs and compare its performance with mixed-effect modeling and illustrate its superiority when sample sizes are small, the underlying distribution is skewed, or there is correlation across clusters. Copyright © 2017 John Wiley & Sons, Ltd.
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Affiliation(s)
- Rui Wang
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 401 Park Drive, Suite 401 East, Boston, MA 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA
| | - Victor De Gruttola
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA
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Abstract
Peer effects, in which the behavior of an individual is affected by the behavior of their peers, are central to social science. Because peer effects are often confounded with homophily and common external causes, recent work has used randomized experiments to estimate effects of specific peer behaviors. These experiments have often relied on the experimenter being able to randomly modulate mechanisms by which peer behavior is transmitted to a focal individual. We describe experimental designs that instead randomly assign individuals' peers to encouragements to behaviors that directly affect those individuals. We illustrate this method with a large peer encouragement design on Facebook for estimating the effects of receiving feedback from peers on posts shared by focal individuals. We find evidence for substantial effects of receiving marginal feedback on multiple behaviors, including giving feedback to others and continued posting. These findings provide experimental evidence for the role of behaviors directed at specific individuals in the adoption and continued use of communication technologies. In comparison, observational estimates differ substantially, both underestimating and overestimating effects, suggesting that researchers and policy makers should be cautious in relying on them.
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Turner EL, Prague M, Gallis JA, Li F, Murray DM. Review of Recent Methodological Developments in Group-Randomized Trials: Part 2-Analysis. Am J Public Health 2017; 107:1078-1086. [PMID: 28520480 PMCID: PMC5463203 DOI: 10.2105/ajph.2017.303707] [Citation(s) in RCA: 81] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/05/2017] [Indexed: 12/13/2022]
Abstract
In 2004, Murray et al. reviewed methodological developments in the design and analysis of group-randomized trials (GRTs). We have updated that review with developments in analysis of the past 13 years, with a companion article to focus on developments in design. We discuss developments in the topics of the earlier review (e.g., methods for parallel-arm GRTs, individually randomized group-treatment trials, and missing data) and in new topics, including methods to account for multiple-level clustering and alternative estimation methods (e.g., augmented generalized estimating equations, targeted maximum likelihood, and quadratic inference functions). In addition, we describe developments in analysis of alternative group designs (including stepped-wedge GRTs, network-randomized trials, and pseudocluster randomized trials), which require clustering to be accounted for in their design and analysis.
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Affiliation(s)
- Elizabeth L Turner
- Elizabeth L. Turner and John A. Gallis are with the Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, and the Duke Global Health Institute, Duke University. Melanie Prague is with the Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, and Inria, project team SISTM, Bordeaux, France. Fan Li is with the Department of Biostatistics and Bioinformatics, Duke University. David M. Murray is with the Office of Disease Prevention, Division of Program Coordination and Strategic Planning, and the Office of the Director, National Institutes of Health, Rockville, MD
| | - Melanie Prague
- Elizabeth L. Turner and John A. Gallis are with the Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, and the Duke Global Health Institute, Duke University. Melanie Prague is with the Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, and Inria, project team SISTM, Bordeaux, France. Fan Li is with the Department of Biostatistics and Bioinformatics, Duke University. David M. Murray is with the Office of Disease Prevention, Division of Program Coordination and Strategic Planning, and the Office of the Director, National Institutes of Health, Rockville, MD
| | - John A Gallis
- Elizabeth L. Turner and John A. Gallis are with the Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, and the Duke Global Health Institute, Duke University. Melanie Prague is with the Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, and Inria, project team SISTM, Bordeaux, France. Fan Li is with the Department of Biostatistics and Bioinformatics, Duke University. David M. Murray is with the Office of Disease Prevention, Division of Program Coordination and Strategic Planning, and the Office of the Director, National Institutes of Health, Rockville, MD
| | - Fan Li
- Elizabeth L. Turner and John A. Gallis are with the Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, and the Duke Global Health Institute, Duke University. Melanie Prague is with the Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, and Inria, project team SISTM, Bordeaux, France. Fan Li is with the Department of Biostatistics and Bioinformatics, Duke University. David M. Murray is with the Office of Disease Prevention, Division of Program Coordination and Strategic Planning, and the Office of the Director, National Institutes of Health, Rockville, MD
| | - David M Murray
- Elizabeth L. Turner and John A. Gallis are with the Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, and the Duke Global Health Institute, Duke University. Melanie Prague is with the Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, and Inria, project team SISTM, Bordeaux, France. Fan Li is with the Department of Biostatistics and Bioinformatics, Duke University. David M. Murray is with the Office of Disease Prevention, Division of Program Coordination and Strategic Planning, and the Office of the Director, National Institutes of Health, Rockville, MD
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Cortes TR, Faerstein E, Struchiner CJ. [Use of causal diagrams in Epidemiology: application to a situation with confounding]. CAD SAUDE PUBLICA 2017; 32:e00103115. [PMID: 27509550 DOI: 10.1590/0102-311x00103115] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Accepted: 03/11/2016] [Indexed: 11/21/2022] Open
Abstract
Epidemiological research still rarely uses causal diagrams, despite growing recognition of their explanatory potential. One possible reason is that many research programs involve themes in which there is a certain degree of uncertainty as to mechanisms in the processes that generate the data. In this study, the relationship between occupational stress and obesity is used as an example of the application of causal diagrams to questions related to confounding. The article presents the selection stages for variables in statistical adjustment and the derivation of a causal diagram's statistical implications. The main advantage of causal diagrams is that they explicitly reveal the respective model's underlying hypotheses, allowing critical analysis of the implications and thereby facilitating identification of sources of bias and uncertainty in the epidemiological study's results.
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Affiliation(s)
- Taísa Rodrigues Cortes
- Instituto de Medicina Social, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brasil
| | - Eduardo Faerstein
- Instituto de Medicina Social, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brasil
| | - Claudio José Struchiner
- Instituto de Medicina Social, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brasil.,Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, Brasil
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Prague M, Wang R, Stephens A, Tchetgen Tchetgen E, DeGruttola V. Accounting for interactions and complex inter-subject dependency in estimating treatment effect in cluster-randomized trials with missing outcomes. Biometrics 2016; 72:1066-1077. [PMID: 27060877 PMCID: PMC5055852 DOI: 10.1111/biom.12519] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Revised: 02/01/2016] [Accepted: 02/01/2016] [Indexed: 11/27/2022]
Abstract
Semi-parametric methods are often used for the estimation of intervention effects on correlated outcomes in cluster-randomized trials (CRTs). When outcomes are missing at random (MAR), Inverse Probability Weighted (IPW) methods incorporating baseline covariates can be used to deal with informative missingness. Also, augmented generalized estimating equations (AUG) correct for imbalance in baseline covariates but need to be extended for MAR outcomes. However, in the presence of interactions between treatment and baseline covariates, neither method alone produces consistent estimates for the marginal treatment effect if the model for interaction is not correctly specified. We propose an AUG-IPW estimator that weights by the inverse of the probability of being a complete case and allows different outcome models in each intervention arm. This estimator is doubly robust (DR); it gives correct estimates whether the missing data process or the outcome model is correctly specified. We consider the problem of covariate interference which arises when the outcome of an individual may depend on covariates of other individuals. When interfering covariates are not modeled, the DR property prevents bias as long as covariate interference is not present simultaneously for the outcome and the missingness. An R package is developed implementing the proposed method. An extensive simulation study and an application to a CRT of HIV risk reduction-intervention in South Africa illustrate the method.
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Affiliation(s)
- Melanie Prague
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A
| | - Rui Wang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital, Boston, MA, U.S.A
| | - Alisa Stephens
- Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, U.S.A
| | - Eric Tchetgen Tchetgen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A
| | - Victor DeGruttola
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A
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Abstract
One hundred years ago Sir Ronald Ross published his treatise on a general Theory of Happenings. Dependent happenings are those in which the frequency depends on the number already affected. When there is dependency of events, interventions can have different types of effects. Interventions such as vaccination can have direct protective effects for the person receiving the treatment, as well as indirect/spillover effects for others in the population. Causal inference is a framework for carefully defining the causal effect of a treatment, exposure, or policy, and then determining conditions under which such effects can be estimated from the observed data. We consider here scenarios in which the potential outcomes of an individual can depend on the treatment of other individuals in the population, known as causal inference with interference. Much of the research so far has assumed the population is divided into groups or clusters, and individuals can interfere with others within their clusters but not across clusters. Recent developments have assumed more general forms of interference. We review some of the different types of effects that have been defined for dependent happenings, particularly using the methods of causal inference with interference. Many of the methods are applicable across disciplines, such as infectious diseases, social sciences, and economics.
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Affiliation(s)
- M Elizabeth Halloran
- Center for Inference and Dynamics of Infectious Diseases, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center; Department of Biostatistics, School of Public Health, University of Washington
| | - Michael G Hudgens
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill
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Ogburn EL, Zeger SL. Statistical Reasoning and Methods in Epidemiology to Promote Individualized Health: In Celebration of the 100th Anniversary of the Johns Hopkins Bloomberg School of Public Health. Am J Epidemiol 2016; 183:427-34. [PMID: 26867776 DOI: 10.1093/aje/kwv453] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2015] [Accepted: 12/23/2015] [Indexed: 11/12/2022] Open
Abstract
Epidemiology is concerned with determining the distribution and causes of disease. Throughout its history, epidemiology has drawn upon statistical ideas and methods to achieve its aims. Because of the exponential growth in our capacity to measure and analyze data on the underlying processes that define each person's state of health, there is an emerging opportunity for population-based epidemiologic studies to influence health decisions made by individuals in ways that take into account the individuals' characteristics, circumstances, and preferences. We refer to this endeavor as "individualized health." The present article comprises 2 sections. In the first, we describe how graphical, longitudinal, and hierarchical models can inform the project of individualized health. We propose a simple graphical model for informing individual health decisions using population-based data. In the second, we review selected topics in causal inference that we believe to be particularly useful for individualized health. Epidemiology and biostatistics were 2 of the 4 founding departments in the world's first graduate school of public health at Johns Hopkins University, the centennial of which we honor. This survey of a small part of the literature is intended to demonstrate that the 2 fields remain just as inextricably linked today as they were 100 years ago.
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Semiparametric Theory and Empirical Processes in Causal Inference. STATISTICAL CAUSAL INFERENCES AND THEIR APPLICATIONS IN PUBLIC HEALTH RESEARCH 2016. [DOI: 10.1007/978-3-319-41259-7_8] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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