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Littell JH. The Logic of Generalization From Systematic Reviews and Meta-Analyses of Impact Evaluations. EVALUATION REVIEW 2024; 48:427-460. [PMID: 38261473 DOI: 10.1177/0193841x241227481] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
Systematic reviews and meta-analyses are viewed as potent tools for generalized causal inference. These reviews are routinely used to inform decision makers about expected effects of interventions. However, the logic of generalization from research reviews to diverse policy and practice contexts is not well developed. Building on sampling theory, concerns about epistemic uncertainty, and principles of generalized causal inference, this article presents a pragmatic approach to generalizability assessment for use with systematic reviews and meta-analyses. This approach is applied to two systematic reviews and meta-analyses of effects of "evidence-based" psychosocial interventions for youth and families. Evaluations included in systematic reviews are not necessarily representative of populations and treatments of interest. Generalizability of results is limited by high risks of bias, uncertain estimates, and insufficient descriptive data from impact evaluations. Systematic reviews and meta-analyses can be used to test generalizability claims, explore heterogeneity, and identify potential moderators of effects. These reviews can also produce pooled estimates that are not representative of any larger sets of studies, programs, or people. Further work is needed to improve the conduct and reporting of impact evaluations and systematic reviews, and to develop practical approaches to generalizability assessment and guide applications of interventions in diverse policy and practice contexts.
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Affiliation(s)
- Julia H Littell
- Graduate School of Social Work and Social Research, Bryn Mawr College, Bryn Mawr, PA, USA
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2
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Hu M, Shi X, Song PXK. Collaborative inference for treatment effect with distributed data-sharing management in multicenter studies. Stat Med 2024; 43:2263-2279. [PMID: 38551130 DOI: 10.1002/sim.10068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 02/01/2024] [Accepted: 03/14/2024] [Indexed: 05/18/2024]
Abstract
Data sharing barriers present paramount challenges arising from multicenter clinical studies where multiple data sources are stored and managed in a distributed fashion at different local study sites. Merging such data sources into a common data storage for a centralized statistical analysis requires a data use agreement, which is often time-consuming. Data merging may become more burdensome when propensity score modeling is involved in the analysis because combining many confounding variables, and systematic incorporation of this additional modeling in a meta-analysis has not been thoroughly investigated in the literature. Motivated from a multicenter clinical trial of basal insulin treatment for reducing the risk of post-transplantation diabetes mellitus, we propose a new inference framework that avoids the merging of subject-level raw data from multiple sites at a centralized facility but needs only the sharing of summary statistics. Unlike the architecture of federated learning, the proposed collaborative inference does not need a center site to combine local results and thus enjoys maximal protection of data privacy and minimal sensitivity to unbalanced data distributions across data sources. We show theoretically and numerically that the new distributed inference approach has little loss of statistical power compared to the centralized method that requires merging the entire data. We present large-sample properties and algorithms for the proposed method. We illustrate its performance by simulation experiments and the motivating example on the differential average treatment effect of basal insulin to lower risk of diabetes among kidney-transplant patients compared to the standard-of-care.
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Affiliation(s)
- Mengtong Hu
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Xu Shi
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Peter X-K Song
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
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3
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Dahabreh IJ. Invited Commentary: Combining Information to Answer Epidemiologic Questions About a Target Population. Am J Epidemiol 2024; 193:741-750. [PMID: 38456780 DOI: 10.1093/aje/kwad014] [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: 06/09/2022] [Revised: 11/08/2022] [Accepted: 01/12/2023] [Indexed: 03/09/2024] Open
Abstract
Epidemiologists are attempting to address research questions of increasing complexity by developing novel methods for combining information from diverse sources. Cole et al. (Am J Epidemiol. 2023;192(3)467-474) provide 2 examples of the process of combining information to draw inferences about a population proportion. In this commentary, we consider combining information to learn about a target population as an epidemiologic activity and distinguish it from more conventional meta-analyses. We examine possible rationales for combining information and discuss broad methodological considerations, with an emphasis on study design, assumptions, and sources of uncertainty.
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4
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Steingrimsson JA, Barker DH, Bie R, Dahabreh IJ. Systematically missing data in causally interpretable meta-analysis. Biostatistics 2024; 25:289-305. [PMID: 36977366 PMCID: PMC11017122 DOI: 10.1093/biostatistics/kxad006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 02/15/2023] [Accepted: 03/13/2023] [Indexed: 03/30/2023] Open
Abstract
Causally interpretable meta-analysis combines information from a collection of randomized controlled trials to estimate treatment effects in a target population in which experimentation may not be possible but from which covariate information can be obtained. In such analyses, a key practical challenge is the presence of systematically missing data when some trials have collected data on one or more baseline covariates, but other trials have not, such that the covariate information is missing for all participants in the latter. In this article, we provide identification results for potential (counterfactual) outcome means and average treatment effects in the target population when covariate data are systematically missing from some of the trials in the meta-analysis. We propose three estimators for the average treatment effect in the target population, examine their asymptotic properties, and show that they have good finite-sample performance in simulation studies. We use the estimators to analyze data from two large lung cancer screening trials and target population data from the National Health and Nutrition Examination Survey (NHANES). To accommodate the complex survey design of the NHANES, we modify the methods to incorporate survey sampling weights and allow for clustering.
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Affiliation(s)
- Jon A Steingrimsson
- Department of Biostatistics, Brown University, 121 South Main Street, Providence, RI 02903, USA
| | - David H Barker
- Department of Psychiatry, Rhode Island Hospital, Providence, RI 02904, USA
| | - Ruofan Bie
- Department of Biostatistics, Brown University, 121 South Main Street, Providence, RI 02903, USA
| | - Issa J Dahabreh
- Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA and CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
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5
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Lee D, Gao C, Ghosh S, Yang S. Transporting survival of an HIV clinical trial to the external target populations. J Biopharm Stat 2024:1-22. [PMID: 38520697 DOI: 10.1080/10543406.2024.2330216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 02/20/2024] [Indexed: 03/25/2024]
Abstract
Due to the heterogeneity of the randomized controlled trial (RCT) and external target populations, the estimated treatment effect from the RCT is not directly applicable to the target population. For example, the patient characteristics of the ACTG 175 HIV trial are significantly different from that of the three external target populations of interest: US early-stage HIV patients, Thailand HIV patients, and southern Ethiopia HIV patients. This paper considers several methods to transport the treatment effect from the ACTG 175 HIV trial to the target populations beyond the trial population. Most transport methods focus on continuous and binary outcomes; on the contrary, we derive and discuss several transport methods for survival outcomes: an outcome regression method based on a Cox proportional hazard (PH) model, an inverse probability weighting method based on the models for treatment assignment, sampling score, and censoring, and a doubly robust method that combines both methods, called the augmented calibration weighting (ACW) method. However, as the PH assumption was found to be incorrect for the ACTG 175 trial, the methods that depend on the PH assumption may lead to the biased quantification of the treatment effect. To account for the violation of the PH assumption, we extend the ACW method with the linear spline-based hazard regression model that does not require the PH assumption. Applying the aforementioned methods for transportability, we explore the effect of PH assumption, or the violation thereof, on transporting the survival results from the ACTG 175 trial to various external populations.
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Affiliation(s)
- Dasom Lee
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
| | - Chenyin Gao
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
| | - Sujit Ghosh
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
| | - Shu Yang
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
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6
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Shook-Sa BE, Zivich PN, Rosin SP, Edwards JK, Adimora AA, Hudgens MG, Cole SR. Fusing trial data for treatment comparisons: Single vs multi-span bridging. Stat Med 2024; 43:793-815. [PMID: 38110289 PMCID: PMC10843571 DOI: 10.1002/sim.9989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 10/23/2023] [Accepted: 11/30/2023] [Indexed: 12/20/2023]
Abstract
While randomized controlled trials (RCTs) are critical for establishing the efficacy of new therapies, there are limitations regarding what comparisons can be made directly from trial data. RCTs are limited to a small number of comparator arms and often compare a new therapeutic to a standard of care which has already proven efficacious. It is sometimes of interest to estimate the efficacy of the new therapy relative to a treatment that was not evaluated in the same trial, such as a placebo or an alternative therapy that was evaluated in a different trial. Such dual-study comparisons are challenging because of potential differences between trial populations that can affect the outcome. In this article, two bridging estimators are considered that allow for comparisons of treatments evaluated in different trials, accounting for measured differences in trial populations. A "multi-span" estimator leverages a shared arm between two trials, while a "single-span" estimator does not require a shared arm. A diagnostic statistic that compares the outcome in the standardized shared arms is provided. The two estimators are compared in simulations, where both estimators demonstrate minimal empirical bias and nominal confidence interval coverage when the identification assumptions are met. The estimators are applied to data from the AIDS Clinical Trials Group 320 and 388 to compare the efficacy of two-drug vs four-drug antiretroviral therapy on CD4 cell counts among persons with advanced HIV. The single-span approach requires weaker identification assumptions and was more efficient in simulations and the application.
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Affiliation(s)
- Bonnie E. Shook-Sa
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Paul N. Zivich
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Institute of Global Health and Infectious Diseases, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Samuel P. Rosin
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jessie K. Edwards
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Adaora A. Adimora
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Michael G. Hudgens
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Stephen R. Cole
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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7
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Konnyu KJ, Grimshaw JM, Trikalinos TA, Ivers NM, Moher D, Dahabreh IJ. Evidence Synthesis for Complex Interventions Using Meta-Regression Models. Am J Epidemiol 2024; 193:323-338. [PMID: 37689835 PMCID: PMC10840082 DOI: 10.1093/aje/kwad184] [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: 01/06/2023] [Revised: 08/22/2023] [Accepted: 08/22/2023] [Indexed: 09/11/2023] Open
Abstract
A goal of evidence synthesis for trials of complex interventions is to inform the design or implementation of novel versions of complex interventions by predicting expected outcomes with each intervention version. Conventional aggregate data meta-analyses of studies comparing complex interventions have limited ability to provide such information. We argue that evidence synthesis for trials of complex interventions should forgo aspirations of estimating causal effects and instead model the response surface of study results to 1) summarize the available evidence and 2) predict the average outcomes of future studies or in new settings. We illustrate this modeling approach using data from a systematic review of diabetes quality improvement (QI) interventions involving at least 1 of 12 QI strategy components. We specify a series of meta-regression models to assess the association of specific components with the posttreatment outcome mean and compare the results to conventional meta-analysis approaches. Compared with conventional approaches, modeling the response surface of study results can better reflect the associations between intervention components and study characteristics with the posttreatment outcome mean. Modeling study results using a response surface approach offers a useful and feasible goal for evidence synthesis of complex interventions that rely on aggregate data.
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Affiliation(s)
- Kristin J Konnyu
- Correspondence to Dr. Kristin J. Konnyu, Health Services Research Unit, University of Aberdeen, 3rd Floor, Health Sciences Building, Foresterhill, Aberdeen AB25 2ZD, United Kingdom (e-mail: )
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8
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Woolf B, Mason A, Zagkos L, Sallis H, Munafò MR, Gill D. MRSamePopTest: introducing a simple falsification test for the two-sample mendelian randomisation 'same population' assumption. BMC Res Notes 2024; 17:27. [PMID: 38233927 PMCID: PMC10795421 DOI: 10.1186/s13104-024-06684-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 01/03/2024] [Indexed: 01/19/2024] Open
Abstract
Two-sample MR is an increasingly popular method for strengthening causal inference in epidemiological studies. For the effect estimates to be meaningful, variant-exposure and variant-outcome associations must come from comparable populations. A recent systematic review of two-sample MR studies found that, if assessed at all, MR studies evaluated this assumption by checking that the genetic association studies had similar demographics. However, it is unclear if this is sufficient because less easily accessible factors may also be important. Here we propose an easy-to-implement falsification test. Since recent theoretical developments in causal inference suggest that a causal effect estimate can generalise from one study to another if there is exchangeability of effect modifiers, we suggest testing the homogeneity of variant-phenotype associations for a phenotype which has been measured in both genetic association studies as a method of exploring the 'same-population' test. This test could be used to facilitate designing MR studies with diverse populations. We developed a simple R package to facilitate the implementation of our proposed test. We hope that this research note will result in increased attention to the same-population assumption, and the development of better sensitivity analyses.
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Affiliation(s)
- Benjamin Woolf
- School of Psychological Science, University of Bristol, Bristol, UK.
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
| | - Amy Mason
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Loukas Zagkos
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Hannah Sallis
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Marcus R Munafò
- School of Psychological Science, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Dipender Gill
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
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9
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Robertson SE, Joyce NR, Steingrimsson JA, Stuart EA, Aberle DR, Gatsonis CA, Dahabreh IJ. Comparing Lung Cancer Screening Strategies in a Nationally Representative US Population Using Transportability Methods for the National Lung Cancer Screening Trial. JAMA Netw Open 2024; 7:e2346295. [PMID: 38289605 PMCID: PMC10828917 DOI: 10.1001/jamanetworkopen.2023.46295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 10/19/2023] [Indexed: 02/01/2024] Open
Abstract
Importance The National Lung Screening Trial (NLST) found that screening for lung cancer with low-dose computed tomography (CT) reduced lung cancer-specific and all-cause mortality compared with chest radiography. It is uncertain whether these results apply to a nationally representative target population. Objective To extend inferences about the effects of lung cancer screening strategies from the NLST to a nationally representative target population of NLST-eligible US adults. Design, Setting, and Participants This comparative effectiveness study included NLST data from US adults at 33 participating centers enrolled between August 2002 and April 2004 with follow-up through 2009 along with National Health Interview Survey (NHIS) cross-sectional household interview survey data from 2010. Eligible participants were adults aged 55 to 74 years, and were current or former smokers with at least 30 pack-years of smoking (former smokers were required to have quit within the last 15 years). Transportability analyses combined baseline covariate, treatment, and outcome data from the NLST with covariate data from the NHIS and reweighted the trial data to the target population. Data were analyzed from March 2020 to May 2023. Interventions Low-dose CT or chest radiography screening with a screening assessment at baseline, then yearly for 2 more years. Main Outcomes and Measures For the outcomes of lung-cancer specific and all-cause death, mortality rates, rate differences, and ratios were calculated at a median (25th percentile and 75th percentile) follow-up of 5.5 (5.2-5.9) years for lung cancer-specific mortality and 6.5 (6.1-6.9) years for all-cause mortality. Results The transportability analysis included 51 274 NLST participants and 685 NHIS participants representing the target population (of approximately 5 700 000 individuals after survey-weighting). Compared with the target population, NLST participants were younger (median [25th percentile and 75th percentile] age, 60 [57 to 65] years vs 63 [58 to 67] years), had fewer comorbidities (eg, heart disease, 6551 of 51 274 [12.8%] vs 1 025 951 of 5 739 532 [17.9%]), and were more educated (bachelor's degree or higher, 16 349 of 51 274 [31.9%] vs 859 812 of 5 739 532 [15.0%]). In the target population, for lung cancer-specific mortality, the estimated relative rate reduction was 18% (95% CI, 1% to 33%) and the estimated absolute rate reduction with low-dose CT vs chest radiography was 71 deaths per 100 000 person-years (95% CI, 4 to 138 deaths per 100 000 person-years); for all-cause mortality the estimated relative rate reduction was 6% (95% CI, -2% to 12%). In the NLST, for lung cancer-specific mortality, the estimated relative rate reduction was 21% (95% CI, 9% to 32%) and the estimated absolute rate reduction was 67 deaths per 100 000 person-years (95% CI, 27 to 106 deaths per 100 000 person-years); for all-cause mortality, the estimated relative rate reduction was 7% (95% CI, 0% to 12%). Conclusions and Relevance Estimates of the comparative effectiveness of low-dose CT screening compared with chest radiography in a nationally representative target population were similar to those from unweighted NLST analyses, particularly on the relative scale. Increased uncertainty around effect estimates for the target population reflects large differences in the observed characteristics of trial participants and the target population.
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Affiliation(s)
- Sarah E. Robertson
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Nina R. Joyce
- Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island
| | - Jon A. Steingrimsson
- Department of Biostatistics, Brown University School of Public Health, Providence, Rhode Island
| | - Elizabeth A. Stuart
- Departments of Mental Health, Biostatistics, and Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Denise R. Aberle
- Medical & Imaging Informatics Group, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles
| | - Constantine A. Gatsonis
- Department of Biostatistics, Brown University School of Public Health, Providence, Rhode Island
| | - 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
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10
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Zivich PN, Edwards JK, Lofgren ET, Cole SR, Shook-Sa BE, Lessler J. Transportability Without Positivity: A Synthesis of Statistical and Simulation Modeling. Epidemiology 2024; 35:23-31. [PMID: 37757864 PMCID: PMC10841168 DOI: 10.1097/ede.0000000000001677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Studies designed to estimate the effect of an action in a randomized or observational setting often do not represent a random sample of the desired target population. Instead, estimates from that study can be transported to the target population. However, transportability methods generally rely on a positivity assumption, such that all relevant covariate patterns in the target population are also observed in the study sample. Strict eligibility criteria, particularly in the context of randomized trials, may lead to violations of this assumption. Two common approaches to address positivity violations are restricting the target population and restricting the relevant covariate set. As neither of these restrictions is ideal, we instead propose a synthesis of statistical and simulation models to address positivity violations. We propose corresponding g-computation and inverse probability weighting estimators. The restriction and synthesis approaches to addressing positivity violations are contrasted with a simulation experiment and an illustrative example in the context of sexually transmitted infection testing uptake. In both cases, the proposed synthesis approach accurately addressed the original research question when paired with a thoughtfully selected simulation model. Neither of the restriction approaches was able to accurately address the motivating question. As public health decisions must often be made with imperfect target population information, model synthesis is a viable approach given a combination of empirical data and external information based on the best available knowledge.
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Affiliation(s)
- Paul N Zivich
- From the Institute of Global Health and Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Jessie K Edwards
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Eric T Lofgren
- Paul G. Allen School for Global Health, Washington State University, Pullman, WA
| | - Stephen R Cole
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Bonnie E Shook-Sa
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Justin Lessler
- From the Institute of Global Health and Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
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11
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Rott KW, Bronfort G, Chu H, Huling JD, Leininger B, Murad MH, Wang Z, Hodges JS. Causally interpretable meta-analysis: Clearly defined causal effects and two case studies. Res Synth Methods 2024; 15:61-72. [PMID: 37696604 DOI: 10.1002/jrsm.1671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 08/30/2023] [Accepted: 08/31/2023] [Indexed: 09/13/2023]
Abstract
Meta-analysis is commonly used to combine results from multiple clinical trials, but traditional meta-analysis methods do not refer explicitly to a population of individuals to whom the results apply and it is not clear how to use their results to assess a treatment's effect for a population of interest. We describe recently-introduced causally interpretable meta-analysis methods and apply their treatment effect estimators to two individual-participant data sets. These estimators transport estimated treatment effects from studies in the meta-analysis to a specified target population using the individuals' potentially effect-modifying covariates. We consider different regression and weighting methods within this approach and compare the results to traditional aggregated-data meta-analysis methods. In our applications, certain versions of the causally interpretable methods performed somewhat better than the traditional methods, but the latter generally did well. The causally interpretable methods offer the most promise when covariates modify treatment effects and our results suggest that traditional methods work well when there is little effect heterogeneity. The causally interpretable approach gives meta-analysis an appealing theoretical framework by relating an estimator directly to a specific population and lays a solid foundation for future developments.
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Affiliation(s)
- Kollin W Rott
- Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, Minnesota, USA
| | - Gert Bronfort
- Earl E. Bakken Center for Spirituality & Healing, University of Minnesota, Minneapolis, Minnesota, USA
| | - Haitao Chu
- Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, Minnesota, USA
| | - Jared D Huling
- Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, Minnesota, USA
| | - Brent Leininger
- Earl E. Bakken Center for Spirituality & Healing, University of Minnesota, Minneapolis, Minnesota, USA
| | | | - Zhen Wang
- Evidence-based Practice Center, Mayo Clinic, Rochester, Minnesota, USA
| | - James S Hodges
- Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, Minnesota, USA
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12
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Wu X, Weinberger KR, Wellenius GA, Dominici F, Braun D. Assessing the causal effects of a stochastic intervention in time series data: are heat alerts effective in preventing deaths and hospitalizations? Biostatistics 2023; 25:57-79. [PMID: 36815555 PMCID: PMC11032723 DOI: 10.1093/biostatistics/kxad002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 01/07/2023] [Accepted: 02/03/2023] [Indexed: 02/24/2023] Open
Abstract
The methodological development of this article is motivated by the need to address the following scientific question: does the issuance of heat alerts prevent adverse health effects? Our goal is to address this question within a causal inference framework in the context of time series data. A key challenge is that causal inference methods require the overlap assumption to hold: each unit (i.e., a day) must have a positive probability of receiving the treatment (i.e., issuing a heat alert on that day). In our motivating example, the overlap assumption is often violated: the probability of issuing a heat alert on a cooler day is near zero. To overcome this challenge, we propose a stochastic intervention for time series data which is implemented via an incremental time-varying propensity score (ItvPS). The ItvPS intervention is executed by multiplying the probability of issuing a heat alert on day $t$-conditional on past information up to day $t$-by an odds ratio $\delta_t$. First, we introduce a new class of causal estimands, which relies on the ItvPS intervention. We provide theoretical results to show that these causal estimands can be identified and estimated under a weaker version of the overlap assumption. Second, we propose nonparametric estimators based on the ItvPS and derive an upper bound for the variances of these estimators. Third, we extend this framework to multisite time series using a spatial meta-analysis approach. Fourth, we show that the proposed estimators perform well in terms of bias and root mean squared error via simulations. Finally, we apply our proposed approach to estimate the causal effects of increasing the probability of issuing heat alerts on each warm-season day in reducing deaths and hospitalizations among Medicare enrollees in 2837 US counties.
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Affiliation(s)
- Xiao Wu
- Department of Biostatistics, Mailman School of Public Health, Columbia University, 722 W 168th St, New York, NY 10032, USA and Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA
| | - Kate R Weinberger
- School of Population and Public Health, University of British Columbia, 2206 E Mall, Vancouver, BC V6T 1Z3, Canada
| | - Gregory A Wellenius
- Department of Environmental Health, Boston University School of Public Health, 715 Albany St, Boston, MA 02118, USA
| | - Francesca Dominici
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA
| | - Danielle Braun
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA and Department of Data Science, Dana-Farber Cancer Institute, 450 Brookline Ave, Boston, MA 02215, USA
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13
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Brantner CL, Chang TH, Nguyen TQ, Hong H, Stefano LD, Stuart EA. Methods for Integrating Trials and Non-experimental Data to Examine Treatment Effect Heterogeneity. Stat Sci 2023; 38:640-654. [PMID: 38638306 PMCID: PMC11025720 DOI: 10.1214/23-sts890] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
Estimating treatment effects conditional on observed covariates can improve the ability to tailor treatments to particular individuals. Doing so effectively requires dealing with potential confounding, and also enough data to adequately estimate effect moderation. A recent influx of work has looked into estimating treatment effect heterogeneity using data from multiple randomized controlled trials and/or observational datasets. With many new methods available for assessing treatment effect heterogeneity using multiple studies, it is important to understand which methods are best used in which setting, how the methods compare to one another, and what needs to be done to continue progress in this field. This paper reviews these methods broken down by data setting: aggregate-level data, federated learning, and individual participant-level data. We define the conditional average treatment effect and discuss differences between parametric and nonparametric estimators, and we list key assumptions, both those that are required within a single study and those that are necessary for data combination. After describing existing approaches, we compare and contrast them and reveal open areas for future research. This review demonstrates that there are many possible approaches for estimating treatment effect heterogeneity through the combination of datasets, but that there is substantial work to be done to compare these methods through case studies and simulations, extend them to different settings, and refine them to account for various challenges present in real data.
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Affiliation(s)
- Carly Lupton Brantner
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA
| | - Ting-Hsuan Chang
- Department of Biostatistics, Columbia Mailman School of Public Health, New York, New York 10032, USA
| | - Trang Quynh Nguyen
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA
| | - Hwanhee Hong
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina 27710, USA
| | - Leon Di Stefano
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA
| | - Elizabeth A Stuart
- Departments of Biostatistics, Mental Health, and Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA
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14
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Kaizar E, Lin CY, Faries D, Johnston J. Reweighting estimators to extend the external validity of clinical trials: methodological considerations. J Biopharm Stat 2023; 33:515-543. [PMID: 36688658 DOI: 10.1080/10543406.2022.2162067] [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: 01/10/2022] [Accepted: 12/10/2022] [Indexed: 01/24/2023]
Abstract
Methods to extend the strong internal validity of randomized controlled trials to reliably estimate treatment effects in target populations are gaining attention. This paper enumerates steps recommended for undertaking such extended inference, discusses currently viable choices for each one, and provides recommendations. We demonstrate a complete extended inference from a clinical trial studying a pharmaceutical treatment for Alzheimer's disease (AD) to a realistic target population of European residents diagnosed with AD. This case study highlights approaches to overcoming practical difficulties and demonstrates limitations of reliably extending inference from a trial to a real-world population.
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Affiliation(s)
- Eloise Kaizar
- Department of Statistics, Ohio State University, Columbus, Ohio, USA
| | - Chen-Yen Lin
- FSP Biometrics, Syneos Health, Toronto, Ontario, Canada
| | - Douglas Faries
- Real World Analytics, Eli Lilly and Company, Indianapolis, Indiana, USA
| | - Joseph Johnston
- Value, Evidence, and Outcomes, Eli Lilly and Company, Indianapolis, Indiana, USA
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15
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Li B, Gatsonis C, Dahabreh IJ, Steingrimsson JA. Estimating the area under the ROC curve when transporting a prediction model to a target population. Biometrics 2023; 79:2382-2393. [PMID: 36385607 PMCID: PMC10188769 DOI: 10.1111/biom.13796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 10/10/2022] [Indexed: 11/19/2022]
Abstract
We propose methods for estimating the area under the receiver operating characteristic (ROC) curve (AUC) of a prediction model in a target population that differs from the source population that provided the data used for original model development. If covariates that are associated with model performance, as measured by the AUC, have a different distribution in the source and target populations, then AUC estimators that only use data from the source population will not reflect model performance in the target population. Here, we provide identification results for the AUC in the target population when outcome and covariate data are available from the sample of the source population, but only covariate data are available from the sample of the target population. In this setting, we propose three estimators for the AUC in the target population and show that they are consistent and asymptotically normal. We evaluate the finite-sample performance of the estimators using simulations and use them to estimate the AUC in a nationally representative target population from the National Health and Nutrition Examination Survey for a lung cancer risk prediction model developed using source population data from the National Lung Screening Trial.
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Affiliation(s)
- Bing Li
- Department of Biostatistics, Brown University, Providence, Rhode Island, USA
| | | | - Issa J. Dahabreh
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
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16
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Dahabreh IJ, Robertson SE, Petito LC, Hernán MA, Steingrimsson JA. Efficient and robust methods for causally interpretable meta-analysis: Transporting inferences from multiple randomized trials to a target population. Biometrics 2023; 79:1057-1072. [PMID: 35789478 PMCID: PMC10948002 DOI: 10.1111/biom.13716] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 05/10/2022] [Indexed: 11/27/2022]
Abstract
We present methods for causally interpretable meta-analyses that combine information from multiple randomized trials to draw causal inferences for a target population of substantive interest. We consider identifiability conditions, derive implications of the conditions for the law of the observed data, and obtain identification results for transporting causal inferences from a collection of independent randomized trials to a new target population in which experimental data may not be available. We propose an estimator for the potential outcome mean in the target population under each treatment studied in the trials. The estimator uses covariate, treatment, and outcome data from the collection of trials, but only covariate data from the target population sample. We show that it is doubly robust in the sense that it is consistent and asymptotically normal when at least one of the models it relies on is correctly specified. We study the finite sample properties of the estimator in simulation studies and demonstrate its implementation using data from a multicenter randomized trial.
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Affiliation(s)
- Issa J. Dahabreh
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Sarah E. Robertson
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Lucia C. Petito
- Department of Preventative Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Miguel A. Hernán
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
- Harvard-MIT Division of Health Sciences and Technology, Boston, MA
| | - Jon A. Steingrimsson
- Department of Biostatistics, School of Public Health, Brown University, Providence, RI
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17
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Li X, Miao W, Lu F, Zhou XH. Improving efficiency of inference in clinical trials with external control data. Biometrics 2023; 79:394-403. [PMID: 34694626 DOI: 10.1111/biom.13583] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 07/29/2021] [Accepted: 09/30/2021] [Indexed: 01/13/2023]
Abstract
Suppose we are interested in the effect of a treatment in a clinical trial. The efficiency of inference may be limited due to small sample size. However, external control data are often available from historical studies. Motivated by an application to Helicobacter pylori infection, we show how to borrow strength from such data to improve efficiency of inference in the clinical trial. Under an exchangeability assumption about the potential outcome mean, we show that the semiparametric efficiency bound for estimating the average treatment effect can be reduced by incorporating both the clinical trial data and external controls. We then derive a doubly robust and locally efficient estimator. The improvement in efficiency is prominent especially when the external control data set has a large sample size and small variability. Our method allows for a relaxed overlap assumption, and we illustrate with the case where the clinical trial only contains a treated group. We also develop doubly robust and locally efficient approaches that extrapolate the causal effect in the clinical trial to the external population and the overall population. Our results also offer a meaningful implication for trial design and data collection. We evaluate the finite-sample performance of the proposed estimators via simulation. In the Helicobacter pylori infection application, our approach shows that the combination treatment has potential efficacy advantages over the triple therapy.
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Affiliation(s)
- Xinyu Li
- School of Mathematical Sciences & Center for Statistical Science, Peking University, Beijing, China
| | - Wang Miao
- School of Mathematical Sciences & Center for Statistical Science, Peking University, Beijing, China
| | - Fang Lu
- Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xiao-Hua Zhou
- Department of Biostatistics & Beijing International Center for Mathematical Research, Peking University, Beijing, China
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18
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Cole SR, Edwards JK, Breskin A, Rosin S, Zivich PN, Shook-Sa BE, Hudgens MG. Illustration of 2 Fusion Designs and Estimators. Am J Epidemiol 2023; 192:467-474. [PMID: 35388406 PMCID: PMC10372880 DOI: 10.1093/aje/kwac067] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 03/25/2022] [Accepted: 03/31/2022] [Indexed: 11/12/2022] Open
Abstract
"Fusion" study designs combine data from different sources to answer questions that could not be answered (as well) by subsets of the data. Studies that augment main study data with validation data, as in measurement-error correction studies or generalizability studies, are examples of fusion designs. Fusion estimators, here solutions to stacked estimating functions, produce consistent answers to identified research questions using data from fusion designs. In this paper, we describe a pair of examples of fusion designs and estimators, one where we generalize a proportion to a target population and one where we correct measurement error in a proportion. For each case, we present an example motivated by human immunodeficiency virus research and summarize results from simulation studies. Simulations demonstrate that the fusion estimators provide approximately unbiased results with appropriate 95% confidence interval coverage. Fusion estimators can be used to appropriately combine data in answering important questions that benefit from multiple sources of information.
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Affiliation(s)
- Stephen R Cole
- Correspondence to Dr. Stephen Cole, Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Campus Box 7435, Chapel Hill, NC 27599-7435 (e-mail: )
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19
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Gleadhill C, Lee H, Kamper SJ, Cashin A, Hansford H, Traeger AC, Viana Da Silva P, Nolan E, Davidson SRE, Wilczynska M, Robson E, Williams CM. Mixed messages: most spinal pain and osteoarthritis observational research is unclear or misaligned. J Clin Epidemiol 2023; 155:39-47. [PMID: 36736708 DOI: 10.1016/j.jclinepi.2023.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 12/17/2022] [Accepted: 01/19/2023] [Indexed: 02/05/2023]
Abstract
OBJECTIVES We assessed authors' language and methods to determine alignment between reported aims, methods, intent, and interpretations in observational studies in spinal pain or osteoarthritis. STUDY DESIGN AND SETTING We searched five databases for observational studies that included people with spinal pain or osteoarthritis published in the last 5 years. We randomized 100 eligible studies, and classified study intent (aims and methods) and interpretations as causal, non-causal, unclear, or misaligned. RESULTS Overall, 38% of studies were aligned regarding their intent and interpretation (either causally (22%) or non-causally (16%)). 29% of studies' aims and 29% of study methods were unclear. Intent was misaligned in 16% of studies (where aim differed to method) and 23% of studies had misaligned interpretations (where there were multiple conflicting claims). The most common kind of aim was non-causal (38%), and the most common type of method (39%), intent (38%), and interpretations (35%) was causal. CONCLUSIONS Misalignment and mixed messages are common in observational research of spinal pain and osteoarthritis. More than 6 in 10 observational studies may be uninterpretable, because study intent and interpretations do not align. While causal methods and intent are most common in observational research, authors commonly shroud causal intent in non-causal terminology.
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Affiliation(s)
- Connor Gleadhill
- School of Medicine and Public Health, University of Newcastle, Callaghan, NSW, 2308, Australia; Hunter New England Population Health, Hunter New England Local Health District, Wallsend, NSW, 2287, Australia.
| | - Hopin Lee
- Centre for Statistics in Medicine, Rehabilitation Research in Oxford, Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Steven J Kamper
- School of Health Sciences, University of Sydney, PO Box M179, Camperdown, NSW, 2050, Australia; Allied Health Department, Nepean Blue Mountains Local Health District, Nepean Hospital, Penrith, NSW, 2750, Australia
| | - Aidan Cashin
- Centre for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia; School of Health Sciences, Faculty of Medicine & Health, University of New South Wales, Sydney, Australia
| | - Harrison Hansford
- Centre for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia; School of Health Sciences, Faculty of Medicine & Health, University of New South Wales, Sydney, Australia
| | - Adrian C Traeger
- Faculty of Medicine and Health, Institute for Musculoskeletal Health, Sydney School of Public Health, The University of Sydney, King George V Building, Camperdown, NSW, 2050, Australia
| | - Priscilla Viana Da Silva
- School of Medicine and Public Health, University of Newcastle, Callaghan, NSW, 2308, Australia; Hunter New England Population Health, Hunter New England Local Health District, Wallsend, NSW, 2287, Australia
| | - Erin Nolan
- School of Medicine and Public Health, University of Newcastle, Callaghan, NSW, 2308, Australia; Hunter Medical Research Institute, New Lambton Heights, NSW, 2305, Australia
| | - Simon R E Davidson
- School of Medicine and Public Health, University of Newcastle, Callaghan, NSW, 2308, Australia; Hunter New England Population Health, Hunter New England Local Health District, Wallsend, NSW, 2287, Australia
| | - Magdalena Wilczynska
- School of Medicine and Public Health, University of Newcastle, Callaghan, NSW, 2308, Australia; Hunter New England Population Health, Hunter New England Local Health District, Wallsend, NSW, 2287, Australia
| | - Emma Robson
- School of Medicine and Public Health, University of Newcastle, Callaghan, NSW, 2308, Australia; Hunter New England Population Health, Hunter New England Local Health District, Wallsend, NSW, 2287, Australia
| | - Christopher M Williams
- Hunter New England Population Health, Hunter New England Local Health District, Wallsend, NSW, 2287, Australia; School of Health Sciences, University of Sydney, PO Box M179, Camperdown, NSW, 2050, Australia; Research and Knowledge Translation Directorate, Mid North Coast Local Health District, Port Macquarie, Australia
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20
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Shi X, Pan Z, Miao W. Data Integration in Causal Inference. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL STATISTICS 2023; 15:e1581. [PMID: 36713955 PMCID: PMC9880960 DOI: 10.1002/wics.1581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 02/24/2022] [Accepted: 03/01/2022] [Indexed: 04/12/2023]
Abstract
Integrating data from multiple heterogeneous sources has become increasingly popular to achieve a large sample size and diverse study population. This paper reviews development in causal inference methods that combines multiple datasets collected by potentially different designs from potentially heterogeneous populations. We summarize recent advances on combining randomized clinical trial with external information from observational studies or historical controls, combining samples when no single sample has all relevant variables with application to two-sample Mendelian randomization, distributed data setting under privacy concerns for comparative effectiveness and safety research using real-world data, Bayesian causal inference, and causal discovery methods.
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Affiliation(s)
- Xu Shi
- Department of BiostatisticsUniversity of MichiganAnn ArborMichiganUSA
| | - Ziyang Pan
- Department of BiostatisticsUniversity of MichiganAnn ArborMichiganUSA
| | - Wang Miao
- Department of Probability and StatisticsPeking UniversityBeijingChina
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21
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Diemer EW, Zuccolo L, Swanson SA. Partial Identification of the Average Causal Effect in Multiple Study Populations: The Challenge of Combining Mendelian Randomization Studies. Epidemiology 2023; 34:20-28. [PMID: 35944150 PMCID: PMC9719801 DOI: 10.1097/ede.0000000000001526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 07/18/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Researchers often use random-effects or fixed-effects meta-analysis to combine findings from multiple study populations. However, the causal interpretation of these models is not always clear, and they do not easily translate to settings where bounds, rather than point estimates, are computed. METHODS If bounds on an average causal effect of interest in a well-defined population are computed in multiple study populations under specified identifiability assumptions, then under those assumptions the average causal effect would lie within all study-specific bounds and thus the intersection of the study-specific bounds. We demonstrate this by pooling bounds on the average causal effect of prenatal alcohol exposure on attention deficit-hyperactivity disorder symptoms, computed in two European cohorts and under multiple sets of assumptions in Mendelian randomization (MR) analyses. RESULTS For all assumption sets considered, pooled bounds were wide and did not identify the direction of effect. The narrowest pooled bound computed implied the risk difference was between -4 and 34 percentage points. CONCLUSIONS All pooled bounds computed in our application covered the null, illustrating how strongly point estimates from prior MR studies of this effect rely on within-study homogeneity assumptions. We discuss how the interpretation of both pooled bounds and point estimation in MR is complicated by possible heterogeneity of effects across populations.
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Affiliation(s)
- Elizabeth W. Diemer
- From the Department of Child and Adolescent Psychiatry, Erasmus MC, Rotterdam, the Netherlands
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Luisa Zuccolo
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Center for Health Data Science, Human Technopole Foundation, Milan, Italy
| | - Sonja A. Swanson
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA
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22
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Remiro‐Azócar A. Some considerations on target estimands for health technology assessment. Stat Med 2022; 41:5592-5596. [PMID: 36385477 PMCID: PMC9828791 DOI: 10.1002/sim.9566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 08/18/2022] [Accepted: 08/18/2022] [Indexed: 11/18/2022]
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23
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Zuo S, Josey KP, Raghavan S, Yang F, Juaréz-Colunga E, Ghosh D. Transportability Methods for Time-to-Event Outcomes: Application in Adjuvant Colon Cancer Trials. JCO Clin Cancer Inform 2022; 6:e2200088. [PMID: 36516368 PMCID: PMC10166520 DOI: 10.1200/cci.22.00088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
PURPOSE Differences in the benefits of treatment on 5-year overall survival have been observed in 12 randomized phase III colon cancer adjuvant clinical trials from the ACCENT group. We investigated the reasons for these differences by incorporating the distribution of the observed covariates from each trial. MATERIALS AND METHODS We applied state-of-the-art transportability methods on the basis of causal inference, and compared them with a conventional meta-analysis approach to predict the treatment effect for the target population. Prediction errors were defined to evaluate whether the identifiability conditions necessary for causal inference were satisfied among the 12 trials, and to measure the performance of each method. RESULTS In the one-trial-at-a-time transportability analysis, the ranks of prediction errors for the target population were mostly consistent with the discrepancy in treatment effects among the 12 trials across the three models. The overall prediction errors between the leave-one-trial-out transportability method and the conventional individual participant data meta-analysis approach were very similar, and more than 40% lower than the overall prediction errors from the one-trial-at-a-time transportability method. CONCLUSION The discrepancy in treatment effects among the 12 trials is unlikely to arise from the choice of model specification or distribution of observed covariates but from the distribution of unobserved covariates or study-level features. The ability to quantify heterogeneity among the 12 trials was greatly reduced in both the leave-one-trial-out transportability method and the conventional meta-analysis approach compared with the one-trial-at-a-time transportability method.
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Affiliation(s)
- Shuozhi Zuo
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO
| | - Kevin P Josey
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Sridharan Raghavan
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, CO
| | - Fan Yang
- Yau Mathematical Sciences Center, Tsinghua University, Beijing, China
| | | | - Debashis Ghosh
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO
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24
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Diao G, Ma H, Zeng D, Ke C, Ibrahim JG. Synthesizing studies for comparing different treatment sequences in clinical trials. Stat Med 2022; 41:5134-5149. [PMID: 36005293 DOI: 10.1002/sim.9559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 07/29/2022] [Accepted: 08/02/2022] [Indexed: 11/09/2022]
Abstract
With advances in cancer treatments and improved patient survival, more patients may go through multiple lines of treatment. It is of clinical importance to choose a sequence of effective treatments (eg, lines of treatment) for individual patients with the goal of optimizing their long-term clinical outcome (eg, survival). Several important issues arise in cancer studies. First, cancer clinical trials are usually conducted by each line of treatment. For a treatment sequence, we may have first line and second line treatment data from two different studies. Second, there is typically a treatment initiation period varying from patient to patient between progression of disease and the start of the second line treatment due to administrative reasons. Additionally, the choice of the second line treatment for patients with progression of disease may depend on their characteristics. We address all these issues and develop semiparametric methods under the potential outcome framework for the estimation of the overall survival probability for a treatment sequence and for comparing different treatment sequences. We establish the large sample properties of the proposed inferential procedures. Simulation studies and an application to a colorectal clinical trial are provided.
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Affiliation(s)
- Guoqing Diao
- Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington, District of Columbia, USA
| | - Haijun Ma
- Exelixis, Inc., Alameda, California, USA
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Chunlei Ke
- Apellis Pharmaceuticals, Waltham, Massachusetts, USA
| | - Joseph G Ibrahim
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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25
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Systematic review and meta-analysis of randomized trials of hydroxychloroquine for the prevention of COVID-19. Eur J Epidemiol 2022; 37:789-796. [PMID: 35943669 PMCID: PMC9360718 DOI: 10.1007/s10654-022-00891-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 06/16/2022] [Indexed: 11/06/2022]
Abstract
Background Recruitment into randomized trials of hydroxychloroquine (HCQ) for prevention of COVID-19 has been adversely affected by a widespread conviction that HCQ is not effective for prevention. In the absence of an updated systematic review, we conducted a meta-analysis of randomized trials that study the effectiveness of HCQ to prevent COVID-19. Methods A search of PubMed, medRxiv, and clinicaltrials.gov combined with expert consultation found 11 completed randomized trials: 7 pre-exposure prophylaxis trials and 4 post-exposure prophylaxis trials. We obtained or calculated the risk ratio of COVID-19 diagnosis for assignment to HCQ versus no HCQ (either placebo or usual care) for each trial, and then pooled the risk ratio estimates. Results The pooled risk ratio estimate of the pre-exposure prophylaxis trials was 0.72 (95% CI: 0.58–0.90) when using either a fixed effect or a standard random effects approach, and 0.72 (95% CI: 0.55–0.95) when using a conservative modification of the Hartung-Knapp random effects approach. The corresponding estimates for the post-exposure prophylaxis trials were 0.91 (95% CI: 0.72–1.16) and 0.91 (95% CI: 0.62–1.35). All trials found a similar rate of serious adverse effects in the HCQ and no HCQ groups. Discussion A benefit of HCQ as prophylaxis for COVID-19 cannot be ruled out based on the available evidence from randomized trials. However, the “not statistically significant” findings from early prophylaxis trials were widely interpreted as definite evidence of lack of effectiveness of HCQ. This interpretation disrupted the timely completion of the remaining trials and thus the generation of precise estimates for pandemic management before the development of vaccines.
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26
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Vo TT, Vansteelandt S. Challenges in Systematic Reviews and Meta-Analyses of Mediation Analyses. Am J Epidemiol 2022; 191:1098-1106. [PMID: 35136939 DOI: 10.1093/aje/kwac028] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 01/25/2022] [Accepted: 02/04/2022] [Indexed: 11/14/2022] Open
Abstract
Systematic reviews and meta-analyses of mediation studies are increasingly being implemented in practice. Nonetheless, the methodology for conducting such review and analysis is still in a development phase, with much room for improvement. In this paper, we highlight and discuss challenges that investigators face in systematic reviews and meta-analyses of mediation studies and propose ways of accommodating these in practice.
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27
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Keil AP. Real and Illusory Precision Gains in Meta-Analysis to Speed Action on Carcinogens. Cancer Epidemiol Biomarkers Prev 2022; 31:695-697. [PMID: 35373265 PMCID: PMC10116503 DOI: 10.1158/1055-9965.epi-21-1286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 02/03/2022] [Accepted: 02/08/2022] [Indexed: 11/16/2022] Open
Abstract
One should avoid benzene exposure, all other things being equal. Risk assessment can help inform human health outcomes when all other things are not equal, as when competing legal or economic interests arise. In sparse literatures where exposures may be highly deleterious and yet understudied, there is a dire need for evidence synthesis, such as meta-analysis, to maximally inform risk assessment. Here, using the analysis and approach of Scholten and colleagues from the current issue as a touch point, I describe how meta-analysis could ideally meet this aim and how it often fails to do so. Some of the current literature on transportability of causal effects is illustrative, and I describe how some of the lessons from this literature could be applied within the innovative framework of Scholten and colleagues to leverage meta-analysis within the broader decision-making framework of risk-assessment. See related article by Scholten et al., p. 751.
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Lin LA, Zhang Y, Straus W, Wang W. Integrative Analysis of Randomized Clinical Trial and Observational Study Data to Inform Post-marketing Safety Decision-Making. Ther Innov Regul Sci 2022; 56:423-432. [PMID: 35138577 DOI: 10.1007/s43441-021-00349-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Accepted: 10/27/2021] [Indexed: 11/30/2022]
Abstract
Safety evaluation is a continual and iterative process throughout the drug development life cycle and requires long time horizons and large amounts of data to fully understand the safety profile of a medical product. Although randomized clinical trials (RCT) provide high-quality data for an initial assessment of safety signals, the safety signals may not all have been known at the time of approval because safety data collected from RCT only involve a relatively small number of subjects during a relatively short follow-up period. The increased accumulation of post-marketing real-world data (RWD) presents an opportunity to utilize them for safety decision-making; these include identifying new safety signals, further characterization of safety concerns that are raised in pre-marketing RCT, and further generalization of RCT findings to the broader patient populations not previously studied in RCT. In this paper, we use cardiovascular safety outcome trial for antidiabetic therapies as an illustrative example and discuss how integrative analysis of RCT and observational study data can answer regulatory concerns about cardiovascular risk in a post-marketing setting. A novel statistical analysis strategy is proposed to combine both sources of safety data in a data fusion approach. The proposed approach includes three stages: (1) feasibility analysis that uses an RCT to validate an observational study, applying estimand framework and emulating RCT with RWD; (2) integrative analysis that combines evidence from the RCT and observational study data cooperatively; and (3) sensitivity analysis that examines the consistency of the previous analyses. Two potential utilities of the proposed integrative analysis for the cardiovascular safety outcome trial are discussed.
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Affiliation(s)
- Li-An Lin
- Clinical Safety Statistics, Merck & Co., Inc, Kenilworth, NJ, USA.
| | - Yafei Zhang
- Clinical Safety Statistics, Merck & Co., Inc, Kenilworth, NJ, USA
| | | | - William Wang
- Clinical Safety Statistics, Merck & Co., Inc, Kenilworth, NJ, USA
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Scholten B, Portengen L, Pronk A, Stierum R, Downward GS, Vlaanderen J, Vermeulen R. Estimation of the exposure response relation between benzene and acute myeloid leukemia by combining epidemiological, human biomarker, and animal data. Cancer Epidemiol Biomarkers Prev 2021; 31:751-757. [PMID: 34906966 DOI: 10.1158/1055-9965.epi-21-0287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 06/21/2021] [Accepted: 11/22/2021] [Indexed: 11/16/2022] Open
Abstract
Background Chemical risk assessment can benefit from integrating data across multiple evidence bases, especially in exposure-response cure (ERC) modelling when data across the exposure range is sparse. Methods We estimated the ERC for benzene and acute myeloid leukemia (AML), by fitting linear and spline-based Bayesian meta-regression models that included summary risk estimates from non-AML and non-human studies as prior information. Our complete dataset included six human AML studies, three human leukemia studies, ten human biomarker studies, and four experimental animal studies. Results A linear meta-regression model with intercept best predicted AML risks after cross-validation, both for the full dataset and AML studies only. Risk estimates in the low exposure range (<40 ppm yrs) from this model were comparable, but more precise, when the ERC was derived using all available data than when using AML data only. Allowing for between-study heterogeneity, RRs and 95% prediction intervals [95%PI] at 5 ppm years were 1.58 [1.01, 3.22]) and 1.44 [0.85, 3.42], respectively. Conclusions Integrating the available epidemiological, biomarker, and animal data resulted in more precise risk estimates for benzene exposure and AML, although the large between-study heterogeneity hampers interpretation of these results. The harmonization steps required to fit the Bayesian meta-regression model involve a range of assumptions that need to be critically evaluated, as they seem crucial for successful implementation. Impact By describing a framework for data-integration and explicitly describing the necessary data harmonization steps, we hope to enable risk assessors to better understand the advantages and assumptions underlying a data integration approach.
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Affiliation(s)
| | | | - Anjoeka Pronk
- RAPID, Netherlands Organisation for Applied Scientific Research
| | - Rob Stierum
- RAPID, Netherlands Organisation for Applied Scientific Research
| | | | | | - Roel Vermeulen
- Institute for Risk Assessment Sciences, Utrecht University
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Mathur MB, VanderWeele TJ. Methods to Address Confounding and Other Biases in Meta-Analyses: Review and Recommendations. Annu Rev Public Health 2021; 43:19-35. [PMID: 34535060 DOI: 10.1146/annurev-publhealth-051920-114020] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Meta-analyses contribute critically to cumulative science, but they can produce misleading conclusions if their constituent primary studies are biased, for example by unmeasured confounding in nonrandomized studies. We provide practical guidance on how meta-analysts can address confounding and other biases that affect studies' internal validity, focusing primarily on sensitivity analyses that help quantify how biased the meta-analysis estimates might be. We review a number of sensitivity analysis methods to do so, especially recent developments that are straightforward to implement and interpret and that use somewhat less stringent statistical assumptions than do earlier methods. We give recommendations for how these newer methods could be applied in practice and illustrate using a previously published meta-analysis. Sensitivity analyses can provide informative quantitative summaries of evidence strength, and we suggest reporting them routinely in meta-analyses of potentially biased studies. This recommendation in no way diminishes the importance of defining study eligibility criteria that reduce bias and of characterizing studies' risks of bias qualitatively. Expected final online publication date for the Annual Review of Public Health, Volume 43 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Maya B Mathur
- Quantitative Sciences Unit and Department of Pediatrics, Stanford University, Stanford, California, USA;
| | - Tyler J VanderWeele
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
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Causally Interpretable Meta-analysis: Application in Adolescent HIV Prevention. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2021; 23:403-414. [PMID: 34241752 DOI: 10.1007/s11121-021-01270-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/15/2021] [Indexed: 12/30/2022]
Abstract
Endowing meta-analytic results with a causal interpretation is challenging when there are differences in the distribution of effect modifiers among the populations underlying the included trials and the target population where the results of the meta-analysis will be applied. Recent work on transportability methods has described identifiability conditions under which the collection of randomized trials in a meta-analysis can be used to draw causal inferences about the target population. When the conditions hold, the methods enable estimation of causal quantities such as the average treatment effect and conditional average treatment effect in target populations that differ from the populations underlying the trial samples. The methods also facilitate comparison of treatments not directly compared in a head-to-head trial and assessment of comparative effectiveness within subgroups of the target population. We briefly describe these methods and present a worked example using individual participant data from three HIV prevention trials among adolescents in mental health care. We describe practical challenges in defining the target population, obtaining individual participant data from included trials and a sample of the target population, and addressing systematic missing data across datasets. When fully realized, methods for causally interpretable meta-analysis can provide decision-makers valid estimates of how treatments will work in target populations of substantive interest as well as in subgroups of these populations.
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Zhang Y, Lin LA, Starkopf L, Chen J, Wang WWB. Estimation of causal effect in integrating randomized clinical trial and observational data - An example application to cardiovascular outcome trial. Contemp Clin Trials 2021; 107:106492. [PMID: 34175491 DOI: 10.1016/j.cct.2021.106492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 05/21/2021] [Accepted: 06/18/2021] [Indexed: 10/21/2022]
Abstract
Safety evaluation of drug development is a comprehensive process across the product lifecycle. While a randomized clinical trial (RCT) can provide high-quality data to assess the efficacy and safety of a new intervention, the pre-marketing trials are limited in statistical power to detect causal elevation of rare but potentially serious adverse events. On the other hand, real-world data (RWD) sources play a critical role in further understanding the safety profile of the new intervention. Bringing together the breadth and strength of RWD and RCT data, we can maximize the utility of RWD and answer broader questions. In this manuscript, we propose a three-step statistical framework to corroborate findings from both RCT and RWD for evaluating important safety concerns identified in the pre-marketing setting. By the proposed approach, we first match the observational study to RCT, then the causal estimation is validated via the matched observational study with the target RCT by targeted maximum likelihood estimation (TMLE) method, and lastly the evidence from RCT and RWD can be combined in an integrative analysis. A potential application to cardiovascular outcome trials for type 2 diabetes mellitus is illustrated. Finally, simulation results suggest that the heterogeneity of patient population from RCT and RWD can lead to varying degrees of treatment effect estimation and the proposed approach may be able to mitigate such difference in the integrative analysis.
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Affiliation(s)
- Yafei Zhang
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, NJ, USA
| | - Li-An Lin
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, NJ, USA.
| | - Liis Starkopf
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, NJ, USA
| | - Jie Chen
- Overland Pharmaceuticals, Dover, DE, USA
| | - William W B Wang
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, NJ, USA
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Markozannes G, Vourli G, Ntzani E. A survey of methodologies on causal inference methods in meta-analyses of randomized controlled trials. Syst Rev 2021; 10:170. [PMID: 34108033 PMCID: PMC8188671 DOI: 10.1186/s13643-021-01726-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 06/01/2021] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Meta-analyses of randomized controlled trials (RCTs) have been considered as the highest level of evidence in the pyramid of the evidence-based medicine. However, the causal interpretation of such results is seldom studied. METHODS We systematically searched for methodologies pertaining to the implementation of a causally explicit framework for meta-analysis of randomized controlled trials and discussed the interpretation and scientific relevance of such causal estimands. We performed a systematic search in four databases to identify relevant methodologies, supplemented with hand-search. We included methodologies that described causality under counterfactuals and potential outcomes framework. RESULTS We only identified three efforts explicitly describing a causal framework on meta-analysis of RCTs. Two approaches required individual participant data, while for the last one, only summary data were required. All three approaches presented a sufficient framework under which a meta-analytical estimate is identifiable and estimable. However, several conceptual limitations remain, mainly in regard to the data generation process under which the selected RCTs rise. CONCLUSIONS We undertook a review of methodologies on causal inference methods in meta-analyses. Although all identified methodologies provide valid causal estimates, there are limitations in the assumptions regarding the data generation process and sampling of the potential RCTs to be included in the meta-analysis which pose challenges to the interpretation and scientific relevance of the identified causal effects. Despite both causal inference and meta-analysis being extensively studied in the literature, limited effort exists of combining those two frameworks.
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Affiliation(s)
- Georgios Markozannes
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
| | - Georgia Vourli
- Deptartment of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Evangelia Ntzani
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
- Center for Evidence Synthesis in Health, Department of Health Services, Policy and Practice, School of Public Health, Brown University, Providence, RI, USA
- Institute of Biosciences, University Research Center of Ioannina, University of Ioannina, Ioannina, Greece
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Mo W, Qi Z, Liu Y. Rejoinder: Learning Optimal Distributionally Robust Individualized Treatment Rules. J Am Stat Assoc 2021; 116:699-707. [PMID: 34177008 PMCID: PMC8221610 DOI: 10.1080/01621459.2020.1866581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 12/12/2020] [Indexed: 10/21/2022]
Abstract
We thank the opportunity offered by editors for this discussion and the discussants for their insightful comments and thoughtful contributions. We also want to congratulate Kallus (2020) for his inspiring work in improving the effciency of policy learning by retargeting. Motivated from the discussion in Dukes and Vansteelandt (2020), we first point out interesting connections and distinctions between our work and Kallus (2020) in Section 1. In particular, the assumptions and sources of variation for consideration in these two papers lead to different research problems with different scopes and focuses. In Section 2, following the discussions in Li et al. (2020); Liang and Zhao (2020), we also consider the efficient policy evaluation problem when we have some data from the testing distribution available at the training stage. We show that under the assumption that the sample sizes from training and testing are growing in the same order, efficient value function estimates can deliver competitive performance. We further show some connections of these estimates with existing literature. However, when the growth of testing sample size available for training is in a slower order, efficient value function estimates may not perform well anymore. In contrast, the requirement of the testing sample size for DRITR is not as strong as that of efficient policy evaluation using the combined data. Finally, we highlight the general applicability and usefulness of DRITR in Section 3.
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Affiliation(s)
- Weibin Mo
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zhengling Qi
- Department of Decision Sciences, George Washington University, Washington, D.C. 20052, USA
| | - Yufeng Liu
- Department of Statistics and Operations Research, Department of Genetics, Department of Biostatistics, Carolina Center for Genome Science, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, NC 27599, USA
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Generalizing experimental results by leveraging knowledge of mechanisms. Eur J Epidemiol 2020; 36:149-164. [PMID: 33070298 DOI: 10.1007/s10654-020-00687-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 09/22/2020] [Indexed: 10/23/2022]
Abstract
We show how experimental results can be generalized across diverse populations by leveraging knowledge of local mechanisms that produce the outcome of interest, only some of which may differ in the target domain. We use structural causal models and a refined version of selection diagrams to represent such knowledge, and to decide whether it entails the invariance of probabilities of causation across populations, which then enables generalization. We further provide: (i) bounds for the target effect when some of these conditions are violated; (ii) new identification results for probabilities of causation and the transported causal effect when trials from multiple source domains are available; as well as (iii) a Bayesian approach for estimating the transported causal effect from finite samples. We illustrate these methods both with simulated data and with a real example that transports the effects of Vitamin A supplementation on childhood mortality across different regions.
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36
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Kemp J, Barker D, Benito K, Herren J, Freeman J. Moderators of Psychosocial Treatment for Pediatric Obsessive-Compulsive Disorder: Summary and Recommendations for Future Directions. JOURNAL OF CLINICAL CHILD AND ADOLESCENT PSYCHOLOGY 2020; 50:478-485. [DOI: 10.1080/15374416.2020.1790378] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Joshua Kemp
- Pediatric Anxiety Research Center, Alpert Medical School of Brown University
| | - David Barker
- Pediatric Anxiety Research Center, Alpert Medical School of Brown University
| | - Kristen Benito
- Pediatric Anxiety Research Center, Alpert Medical School of Brown University
| | - Jennifer Herren
- Pediatric Anxiety Research Center, Alpert Medical School of Brown University
| | - Jennifer Freeman
- Pediatric Anxiety Research Center, Alpert Medical School of Brown University
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37
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Steele RJ, Schnitzer ME, Shrier I. Importance of Homogeneous Effect Modification for Causal Interpretation of Meta-analyses. Epidemiology 2020; 31:353-355. [DOI: 10.1097/ede.0000000000001181] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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