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Díaz I, Lee H, Kıcıman E, Schenck EJ, Akacha M, Follman D, Ghosh D. Sensitivity analysis for causality in observational studies for regulatory science. J Clin Transl Sci 2023; 7:e267. [PMID: 38380390 PMCID: PMC10877517 DOI: 10.1017/cts.2023.688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 10/30/2023] [Accepted: 11/16/2023] [Indexed: 02/22/2024] Open
Abstract
Objective The United States Congress passed the 21st Century Cures Act mandating the development of Food and Drug Administration guidance on regulatory use of real-world evidence. The Forum on the Integration of Observational and Randomized Data conducted a meeting with various stakeholder groups to build consensus around best practices for the use of real-world data (RWD) to support regulatory science. Our companion paper describes in detail the context and discussion of the meeting, which includes a recommendation to use a causal roadmap for study designs using RWD. This article discusses one step of the roadmap: the specification of a sensitivity analysis for testing robustness to violations of causal model assumptions. Methods We present an example of a sensitivity analysis from a RWD study on the effectiveness of Nifurtimox in treating Chagas disease, and an overview of various methods, emphasizing practical considerations on their use for regulatory purposes. Results Sensitivity analyses must be accompanied by careful design of other aspects of the causal roadmap. Their prespecification is crucial to avoid wrong conclusions due to researcher degrees of freedom. Sensitivity analysis methods require auxiliary information to produce meaningful conclusions; it is important that they have at least two properties: the validity of the conclusions does not rely on unverifiable assumptions, and the auxiliary information required by the method is learnable from the corpus of current scientific knowledge. Conclusions Prespecified and assumption-lean sensitivity analyses are a crucial tool that can strengthen the validity and trustworthiness of effectiveness conclusions for regulatory science.
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Affiliation(s)
- Iván Díaz
- Division of Biostatistics, Department of Population Health,
New York University Grossman School of Medicine, New
York, NY, USA
| | - Hana Lee
- Office of Biostatistics, Office of Translational Sciences, Center for Drug
Evaluation and Research, U.S. Food and Drug Administration, Silver
Spring, MD, USA
| | | | | | | | - Dean Follman
- Biostatistics Research Branch, National Institute of Allergy and Infectious
Disease, Silver Spring, MD,
USA
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, Colorado School
of Public Health, University of Colorado Anschutz Medical Campus,
Colorado, USA
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2
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Dang LE, Gruber S, Lee H, Dahabreh IJ, Stuart EA, Williamson BD, Wyss R, Díaz I, Ghosh D, Kıcıman E, Alemayehu D, Hoffman KL, Vossen CY, Huml RA, Ravn H, Kvist K, Pratley R, Shih MC, Pennello G, Martin D, Waddy SP, Barr CE, Akacha M, Buse JB, van der Laan M, Petersen M. A causal roadmap for generating high-quality real-world evidence. J Clin Transl Sci 2023; 7:e212. [PMID: 37900353 PMCID: PMC10603361 DOI: 10.1017/cts.2023.635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 09/01/2023] [Accepted: 09/17/2023] [Indexed: 10/31/2023] Open
Abstract
Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized trials with outcomes assessed using RWD to fully observational studies. Yet, many proposals for generating RWE lack sufficient detail, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to prespecify study design and analysis plans; it addresses a wide range of guidance within a single framework. By supporting the transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on prespecified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers; three companion papers demonstrate applications of the Causal Roadmap for specific use cases.
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Affiliation(s)
- Lauren E. Dang
- Department of Biostatistics, University of California, Berkeley, CA, USA
| | | | - Hana Lee
- Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Issa J. Dahabreh
- CAUSALab, Department of Epidemiology and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Elizabeth A. Stuart
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Brian D. Williamson
- Biostatistics Division, Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Richard Wyss
- Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Iván Díaz
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | | | - Katherine L. Hoffman
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Carla Y. Vossen
- Syneos Health Clinical Solutions, Amsterdam, The Netherlands
| | | | | | | | - Richard Pratley
- AdventHealth Translational Research Institute, Orlando, FL, USA
| | - Mei-Chiung Shih
- Cooperative Studies Program Coordinating Center, VA Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Gene Pennello
- Division of Imaging Diagnostics and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - David Martin
- Global Real World Evidence Group, Moderna, Cambridge, MA, USA
| | - Salina P. Waddy
- National Center for Advancing Translational Sciences, Bethesda, MD, USA
| | - Charles E. Barr
- Graticule Inc., Newton, MA, USA
- Adaptic Health Inc., Palo Alto, CA, USA
| | | | - John B. Buse
- Division of Endocrinology, Department of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Mark van der Laan
- Department of Biostatistics, University of California, Berkeley, CA, USA
| | - Maya Petersen
- Department of Biostatistics, University of California, Berkeley, CA, USA
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Gligorić K, Chiolero A, Kıcıman E, White RW, West R. Population-scale dietary interests during the COVID-19 pandemic. Nat Commun 2022; 13:1073. [PMID: 35228539 PMCID: PMC8885865 DOI: 10.1038/s41467-022-28498-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 01/26/2022] [Indexed: 11/23/2022] Open
Abstract
The SARS-CoV-2 virus has altered people’s lives around the world. Here we document population-wide shifts in dietary interests in 18 countries in 2020, as revealed through time series of Google search volumes. We find that during the first wave of the COVID-19 pandemic there was an overall surge in food interest, larger and longer-lasting than the surge during typical end-of-year holidays in Western countries. The shock of decreased mobility manifested as a drastic increase in interest in consuming food at home and a corresponding decrease in consuming food outside of home. The largest (up to threefold) increases occurred for calorie-dense carbohydrate-based foods such as pastries, bakery products, bread, and pies. The observed shifts in dietary interests have the potential to globally affect food consumption and health outcomes. These findings can inform governmental and organizational decisions regarding measures to mitigate the effects of the COVID-19 pandemic on diet and nutrition. The SARS-CoV-2 virus has altered people’s lives around the world, not only through the disease it causes, but also through unprecedented restrictions. Here the authors document population-wide shifts in dietary interests in 18 countries in 2020, as revealed through time series of Google search volumes.
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Affiliation(s)
- Jiuyong Li
- University of South Australia, Australia
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Abstract
Social data in digital form-including user-generated content, expressed or implicit relations between people, and behavioral traces-are at the core of popular applications and platforms, driving the research agenda of many researchers. The promises of social data are many, including understanding "what the world thinks" about a social issue, brand, celebrity, or other entity, as well as enabling better decision-making in a variety of fields including public policy, healthcare, and economics. Many academics and practitioners have warned against the naïve usage of social data. There are biases and inaccuracies occurring at the source of the data, but also introduced during processing. There are methodological limitations and pitfalls, as well as ethical boundaries and unexpected consequences that are often overlooked. This paper recognizes the rigor with which these issues are addressed by different researchers varies across a wide range. We identify a variety of menaces in the practices around social data use, and organize them in a framework that helps to identify them. "For your own sanity, you have to remember that not all problems can be solved. Not all problems can be solved, but all problems can be illuminated." -Ursula Franklin.
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Affiliation(s)
- Alexandra Olteanu
- Microsoft Research, New York, NY, United States
- Microsoft Research, Montreal, QC, Canada
| | - Carlos Castillo
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
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7
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Saha K, Sugar B, Torous J, Abrahao B, Kıcıman E, De Choudhury M. A Social Media Study on the Effects of Psychiatric Medication Use. Proc Int AAAI Conf Weblogs Soc Media 2019; 13:440-451. [PMID: 32280562 PMCID: PMC7152507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Understanding the effects of psychiatric medications during mental health treatment constitutes an active area of inquiry. While clinical trials help evaluate the effects of these medications, many trials suffer from a lack of generalizability to broader populations. We leverage social media data to examine psychopathological effects subject to self-reported usage of psychiatric medication. Using a list of common approved and regulated psychiatric drugs and a Twitter dataset of 300M posts from 30K individuals, we develop machine learning models to first assess effects relating to mood, cognition, depression, anxiety, psychosis, and suicidal ideation. Then, based on a stratified propensity score based causal analysis, we observe that use of specific drugs are associated with characteristic changes in an individual's psychopathology. We situate these observations in the psychiatry literature, with a deeper analysis of pre-treatment cues that predict treatment outcomes. Our work bears potential to inspire novel clinical investigations and to build tools for digital therapeutics.
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Leypunskiy E, Kıcıman E, Shah M, Walch OJ, Rzhetsky A, Dinner AR, Rust MJ. Geographically Resolved Rhythms in Twitter Use Reveal Social Pressures on Daily Activity Patterns. Curr Biol 2018; 28:3763-3775.e5. [PMID: 30449672 PMCID: PMC6590897 DOI: 10.1016/j.cub.2018.10.016] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 08/22/2018] [Accepted: 10/04/2018] [Indexed: 12/31/2022]
Abstract
Daily rhythms in human physiology and behavior are driven by the interplay of circadian rhythms, environmental cycles, and social schedules. Much research has focused on the mechanism and function of circadian rhythms in constant conditions or in idealized light-dark environments. There have been comparatively few studies into how social pressures, such as work and school schedules, affect human activity rhythms day to day and season to season. To address this issue, we analyzed activity on Twitter in >1,500 US counties throughout the 2012-2013 calendar years in 15-min intervals using geographically tagged tweets representing ≈0.1% of the total population each day. We find that sustained periods of low Twitter activity are correlated with sufficient sleep as measured by conventional surveys. We show that this nighttime lull in Twitter activity is shifted to later times on weekends relative to weekdays, a phenomenon we term "Twitter social jet lag." The magnitude of this social jet lag varies seasonally and geographically-with the West Coast experiencing less Twitter social jet lag compared to the Central and Eastern US-and is correlated with average commuting schedules and disease risk factors such as obesity. Most counties experience the largest amount of Twitter social jet lag in February and the lowest in June or July. We present evidence that these shifts in weekday activity coincide with relaxed social pressures due to local K-12 school holidays and that the direct seasonal effect of altered day length is comparatively weaker.
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Affiliation(s)
- Eugene Leypunskiy
- Graduate Program in Biophysical Sciences, The University of Chicago, Chicago, IL 60637, USA
| | - Emre Kıcıman
- Information and Data Science Group, Microsoft Research, Redmond, WA, 98052, USA
| | - Mili Shah
- The University of Chicago Laboratory Schools, Chicago, IL 60637, USA
| | - Olivia J Walch
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Andrey Rzhetsky
- Departments of Medicine and Human Genetics, The University of Chicago, Chicago, IL 60637, USA
| | - Aaron R Dinner
- Department of Chemistry and the James Franck Institute, The University of Chicago, Chicago, IL 60637, USA
| | - Michael J Rust
- Department of Molecular Genetics and Cell Biology and Department of Physics, The University of Chicago, Chicago, IL 60637, USA.
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9
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Farajtabar M, Kıcıman E, Nathan G, White RW. Modeling behaviors and lifestyle with online and social data for predicting and analyzing sleep and exercise quality. Int J Data Sci Anal 2018. [DOI: 10.1007/s41060-018-0136-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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10
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De Choudhury M, Kıcıman E. The Language of Social Support in Social Media and its Effect on Suicidal Ideation Risk. Proc Int AAAI Conf Weblogs Soc Media 2017; 2017:32-41. [PMID: 28840079 PMCID: PMC5565730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Online social support is known to play a significant role in mental well-being. However, current research is limited in its ability to quantify this link. Challenges exist due to the paucity of longitudinal, pre- and post mental illness risk data, and reliable methods that can examine causality between past availability of support and future risk. In this paper, we propose a method to measure how the language of comments in Reddit mental health communities influences risk to suicidal ideation in the future. Incorporating human assessments in a stratified propensity score analysis based framework, we identify comparable subpopulations of individuals and measure the effect of online social support language. We interpret these linguistic cues with an established theoretical model of social support, and find that esteem and network support play a more prominent role in reducing forthcoming risk. We discuss the implications of our work for designing tools that can improve support provisions in online communities.
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