From Sample Average Treatment Effect to Population Average Treatment Effect on the Treated: Combining Experimental with Observational Studies to Estimate Population Treatment Effects

Hartman, Erin; Grieve, Richard; Ramsahai, Roland R.; Sekhon, Jasjeet S.

doi:10.1111/rssa.12094

Cited by 159 publications

(208 citation statements)

References 65 publications

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“…The proposed methods may have applications in other areas, such as bench-marking small area estimates to match larger area estimates (Mugglin and Carlin, 1998; Bell, et al, 2013; Zhang, et al, 2014), analyzing randomized clinical trial data so that they are generalizable to larger populations (Greenhouse, et al, 2008; Frangakis, 2009; Stuart, et al, 2011; Pearl and Bareinboim, 2014; Hartman, et al, 2015) and standardization and control of confounding for observational studies (Keiding and Clayton, 2014). In general, we foresee that model synthesis using disparate types of data sources will be increasingly important for biomedical research in the future.…”

Section: Discussionmentioning

confidence: 99%

Constrained Maximum Likelihood Estimation for Model Calibration Using Summary-Level Information From External Big Data Sources

Chatterjee¹,

Chen²,

Maas³

et al. 2016

Journal of the American Statistical Association

120

170

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Information from various public and private data sources of extremely large sample sizes are now increasingly available for research purposes. Statistical methods are needed for utilizing information from such big data sources while analyzing data from individual studies that may collect more detailed information required for addressing specific hypotheses of interest. In this article, we consider the problem of building regression models based on individual-level data from an “internal” study while utilizing summary-level information, such as information on parameters for reduced models, from an “external” big data source. We identify a set of very general constraints that link internal and external models. These constraints are used to develop a framework for semiparametric maximum likelihood inference that allows the distribution of covariates to be estimated using either the internal sample or an external reference sample. We develop extensions for handling complex stratified sampling designs, such as case-control sampling, for the internal study. Asymptotic theory and variance estimators are developed for each case. We use simulation studies and a real data application to assess the performance of the proposed methods in contrast to the generalized regression (GR) calibration methodology that is popular in the sample survey literature.

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Section: Discussionmentioning

confidence: 99%

Constrained Maximum Likelihood Estimation for Model Calibration Using Summary-Level Information From External Big Data Sources

Chatterjee¹,

Chen²,

Maas³

et al. 2016

Journal of the American Statistical Association

120

170

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“…If confounding can be addressed, then observational trials can increase sample size, add diversity, and handle more complex interventions such as sequences of treatments. In addition to the characterization illustrated in this study to assess the risk of generalization failure, observational databases may be able to improve generalization (35,36). By mimicking the randomized clinical trial in the study population as well as other target populations, the observational version may reveal trends in effect sizes that are applicable to the randomized trial.…”

Section: For Each Disease Diabetes (A-c) Hypertension (D-f) and mentioning

confidence: 99%

Characterizing treatment pathways at scale using the OHDSI network

Hripcsak

Ryan

Duke

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

280

222

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Observational research promises to complement experimental research by providing large, diverse populations that would be infeasible for an experiment. Observational research can test its own clinical hypotheses, and observational studies also can contribute to the design of experiments and inform the generalizability of experimental research. Understanding the diversity of populations and the variance in care is one component. In this study, the Observational Health Data Sciences and Informatics (OHDSI) collaboration created an international data network with 11 data sources from four countries, including electronic health records and administrative claims data on 250 million patients. All data were mapped to common data standards, patient privacy was maintained by using a distributed model, and results were aggregated centrally. Treatment pathways were elucidated for type 2 diabetes mellitus, hypertension, and depression. The pathways revealed that the world is moving toward more consistent therapy over time across diseases and across locations, but significant heterogeneity remains among sources, pointing to challenges in generalizing clinical trial results. Diabetes favored a single first-line medication, metformin, to a much greater extent than hypertension or depression. About 10% of diabetes and depression patients and almost 25% of hypertension patients followed a treatment pathway that was unique within the cohort. Aside from factors such as sample size and underlying population (academic medical center versus general population), electronic health records data and administrative claims data revealed similar results. Large-scale international observational research is feasible.observational research | data network | treatment pathways A learning health system (1) must systematically evaluate the effects of medical interventions to enable evidence-based medical decision-making. Randomized clinical trials serve as the cornerstone for causal evidence about medical products (2, 3), but evidence from these trials may be limited by an insufficient number of persons exposed, insufficient length of exposure, and inadequate coverage of the target population, factors that limit external generalizability. Observational studies can contribute to the larger goal of causal inference at three stages: (i) the design of experiments, such as determining what are the current therapies that should be compared with a new therapy; (ii) the direct testing of clinical hypotheses on observational data (4-8) using methods to correct for nonrandom treatment assignment as part of the effect estimation process; and (iii) better understanding of population characteristics to improve the extrapolation of both observational and experimental results to new groups.Without sufficiently broad databases available in the first stage, randomized trials are designed without explicit knowledge of actual disease status and treatment practice. Literature reviews are restricted to the population choices of previous investigations, and pilot studi...

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“…In graphical terms, these assumptions may require several d-separation tests on several sub-graphs. It is utterly unimaginable therefore that such assumptions could be managed by unaided human judgment, as is normally assumed in the potential outcomes literature (Hartman et al, 2015;Stuart et al, 2015).…”

Section: Transportability and Selection Biasmentioning

confidence: 99%

Generalizing Experimental Findings

Pearl¹

2015

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This note examines one of the most crucial questions in causal inference: "How generalizable are randomized clinical trials?" The question has received a formal treatment recently, using a non-parametric setting which has led to a simple and general solution. I will describe this solution and several of its ramifications, and compare it to the way researchers have attempted to tackle the problem using the language of ignorability. We will see that ignorability-type assumptions need to be enriched with structural assumptions in order to capture the full spectrum of conditions that permit generalizations, and in order to judge their plausibility in specific applications.

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From Sample Average Treatment Effect to Population Average Treatment Effect on the Treated: Combining Experimental with Observational Studies to Estimate Population Treatment Effects

Cited by 159 publications

References 65 publications

Constrained Maximum Likelihood Estimation for Model Calibration Using Summary-Level Information From External Big Data Sources

Constrained Maximum Likelihood Estimation for Model Calibration Using Summary-Level Information From External Big Data Sources

Characterizing treatment pathways at scale using the OHDSI network

Generalizing Experimental Findings

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