Causal inference on electronic health records to assess blood pressure treatment targets: an application of the parametric g formula

Johnson, Kipp W.; Glicksberg, Benjamin S.; Hodos, Rachel; Shameer, Khader; Dudley, Joel T.

doi:10.1142/9789813235533_0017

Cited by 10 publications

(12 citation statements)

References 16 publications

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“…In terms of methods designed for large‐scale EHR‐based studies, Schuemie et al and Schuemie et al propose a P ‐value calibration method that may be able to account for both random and systematic (eg, confounding, sampling biases) sources of error using distributions of effect estimates believed to be null effects. Modern causal inference methods using the potential outcome/counterfactual framework are also being integrated in biobank analysis …”

Section: Statistical Issues Related To Biobank Researchmentioning

confidence: 99%

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The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

Beesley

Salvatore

Fritsche

et al. 2019

Statistics in Medicine

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Biobanks linked to electronic health records provide rich resources for health‐related research. With improvements in administrative and informatics infrastructure, the availability and utility of data from biobanks have dramatically increased. In this paper, we first aim to characterize the current landscape of available biobanks and to describe specific biobanks, including their place of origin, size, and data types. The development and accessibility of large‐scale biorepositories provide the opportunity to accelerate agnostic searches, expedite discoveries, and conduct hypothesis‐generating studies of disease‐treatment, disease‐exposure, and disease‐gene associations. Rather than designing and implementing a single study focused on a few targeted hypotheses, researchers can potentially use biobanks' existing resources to answer an expanded selection of exploratory questions as quickly as they can analyze them. However, there are many obvious and subtle challenges with the design and analysis of biobank‐based studies. Our second aim is to discuss statistical issues related to biobank research such as study design, sampling strategy, phenotype identification, and missing data. We focus our discussion on biobanks that are linked to electronic health records. Some of the analytic issues are illustrated using data from the Michigan Genomics Initiative and UK Biobank, two biobanks with two different recruitment mechanisms. We summarize the current body of literature for addressing these challenges and discuss some standing open problems. This work complements and extends recent reviews about biobank‐based research and serves as a resource catalog with analytical and practical guidance for statisticians, epidemiologists, and other medical researchers pursuing research using biobanks.

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Section: Statistical Issues Related To Biobank Researchmentioning

confidence: 99%

“…Modern causal inference methods using the potential outcome/counterfactual framework are also being integrated in biobank analysis. [118][119][120]…”

Section: Considerations Related To Study Designmentioning

confidence: 99%

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

Beesley

Salvatore

Fritsche

et al. 2019

Statistics in Medicine

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“…Eighteen (29%) studies had a clear primary focus of informing clinical practice. The remaining 45 (71%) of studies used observational data only to illustrate the application of statistical methodology [ 40 , 79 ]. The median sample size of clinical studies was 9793 participants (IQR: 3084, 39,887), considerably higher than that of methodological studies (median: 2604, IQR: 710, 13,039).…”

Section: Resultsmentioning

confidence: 99%

A scoping review of studies using observational data to optimise dynamic treatment regimens

Mahar

McGuinness

Chakraborty

et al. 2021

BMC Med Res Methodol

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Background Dynamic treatment regimens (DTRs) formalise the multi-stage and dynamic decision problems that clinicians often face when treating chronic or progressive medical conditions. Compared to randomised controlled trials, using observational data to optimise DTRs may allow a wider range of treatments to be evaluated at a lower cost. This review aimed to provide an overview of how DTRs are optimised with observational data in practice. Methods Using the PubMed database, a scoping review of studies in which DTRs were optimised using observational data was performed in October 2020. Data extracted from eligible articles included target medical condition, source and type of data, statistical methods, and translational relevance of the included studies. Results From 209 PubMed abstracts, 37 full-text articles were identified, and a further 26 were screened from the reference lists, totalling 63 articles for inclusion in a narrative data synthesis. Observational DTR models are a recent development and their application has been concentrated in a few medical areas, primarily HIV/AIDS (27, 43%), followed by cancer (8, 13%), and diabetes (6, 10%). There was substantial variation in the scope, intent, complexity, and quality between the included studies. Statistical methods that were used included inverse-probability weighting (26, 41%), the parametric G-formula (16, 25%), Q-learning (10, 16%), G-estimation (4, 6%), targeted maximum likelihood/minimum loss-based estimation (4, 6%), regret regression (3, 5%), and other less common approaches (10, 16%). Notably, studies that were primarily intended to address real-world clinical questions (18, 29%) tended to use inverse-probability weighting and the parametric G-formula, relatively well-established methods, along with a large amount of data. Studies focused on methodological developments (45, 71%) tended to be more complicated and included a demonstrative real-world application only. Conclusions As chronic and progressive conditions become more common, the need will grow for personalised treatments and methods to estimate the effects of DTRs. Observational DTR studies will be necessary, but so far their use to inform clinical practice has been limited. Focusing on simple DTRs, collecting large and rich clinical datasets, and fostering tight partnerships between content experts and data analysts may result in more clinically relevant observational DTR studies.

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“…Applications using administrative healthcare data are still scarce. 31 , 44 The few available demonstrated the potential of healthcare databases for comparison of (dynamic) treatment strategies at least in settings with frequent outcomes (blood pressure targets, death) and showed the g-formula to be a promising tool to emulate ideal trials that are not practically feasible for cost, time or ethical reasons. However, the validity of the results strongly depends on the assumption that the considered covariates are sufficient to control for (time-dependent) confounding.…”

Section: Discussionmentioning

confidence: 99%

Avoiding Time-Related Biases: A Feasibility Study on Antidiabetic Drugs and Pancreatic Cancer Applying the Parametric g-Formula to a Large German Healthcare Database

Börnhorst

Reinders

Rathmann

et al. 2021

CLEP

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Causal inference on electronic health records to assess blood pressure treatment targets: an application of the parametric g formula

Cited by 10 publications

References 16 publications

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

A scoping review of studies using observational data to optimise dynamic treatment regimens

Avoiding Time-Related Biases: A Feasibility Study on Antidiabetic Drugs and Pancreatic Cancer Applying the Parametric g-Formula to a Large German Healthcare Database

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