Addressing unmeasured confounding in comparative observational research

Zhang, Xiang; Faries, Douglas E.; Li, Hu; Stamey, James D.; Imbens, Guido W.

doi:10.1002/pds.4394

Cited by 43 publications

(32 citation statements)

References 78 publications

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“…Marginal structural models can be used to address confounding by time‐dependent variables and has recently been applied to EHR in Sperrin et al Techniques for reducing and eliminating confounding often assume that the potential confounders are measured. When key confounders are not measured, sensitivity analyses and related statistical methods can be used to explore the impact of and to correct for potential unmeasured confounding …”

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%

“…When key confounders are not measured, sensitivity analyses and related statistical methods can be used to explore the impact of and to correct for potential unmeasured confounding. [45][46][47][48]…”

Section: Confounding Biasmentioning

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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“…13 However, this approach has been criticized 11 because concurrent "competing" interventions to curb opioid prescribing or reduce drug abuse are threats to validity. Recent methodological advances, such as the trend-in-trend design, have been developed for addressing unmeasured time-varying confounding, [24][25][26][27] and we posit that competing interventions are a specialized case.…”

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confidence: 99%

Causal inference for evaluating prescription opioid abuse using trend‐in‐trend design

Dasgupta

Schwarz

Hennessy

et al. 2019

Pharmacoepidemiology and Drug

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Purpose One response to the opioid crisis in the United States has been the development of opioid analgesics with properties intended to reduce non‐oral use. Previous evaluations of abuse in the community have relied on population averaged interrupted time series Poisson models with utilization offsets. However, competing interventions and secular trends complicate interpretation of time‐series analyses. An alternative research design, trend‐in‐trend, accounts for heterogeneity in per capita opioid dispensing and unmeasured time‐varying confounding, which provides a causal evaluation, provided that underlying assumptions are met. Methods Trend‐in‐trend can be modeled using a logistic regression framework. In logistic regression, exposure was any product‐specific outpatient dispensing by three‐digit ZIP code and calendar quarter, for 22 opioids. The outcome was any product‐specific abuse case ascertained from poison centers and drug treatment programs, covering 94% of the US population, between July 2009 and December 2016. Product‐specific odds ratios compared places without dispensing with places with any dispensing; the causal contrast represents the odds of product‐specific abuse in the community given exposure. Results Dispensing of new and low‐volume opioids varied considerably across the country, with no region showing high of all products. Of 22 opioids analyzed, the three with approved labeling as intended to deter abuse ranked near the lowest in both absolute (population‐adjusted rates: 1.7, 0.9, and 8.2 per million people per quarter, respectively) and relative measures (trend‐in‐trend ORs: 1.96, 1.79, 1.69, respectively). Conclusions Postmarketing studies of prescription opioid abuse may benefit by evolving from unadjusted surveillance rates to a causal inference approach.

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“…In such settings, sensitivity analyses and related statistical methods can be used to explore the impact of and to correct for potential unmeasured confounding. [158][159][160] Biobank data provides several design-based strategies for dealing with confounding as well. In a case-control sampling framework, controls can be matched to cases based on potential confounders such as age and gender, which can make the case and control populations more similar in terms of their age and gender distributions (e.g.…”

Section: Dealing With Confoundingmentioning

confidence: 99%

The Emerging Landscape of Epidemiological Research Based on Biobanks Linked to Electronic Health Records: Existing Resources, Analytic Challenges and Potential Opportunities

Beesley¹,

Salvatore²,

Fritsche³

et al. 2018

Preprint

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Biobanks linked to electronic health records provide a rich data resource for health-related research. With the establishment of large-scale infrastructure, the availability and utility of data from biobanks has dramatically increased over time. As more researchers become interested in using biobank data to explore a diverse spectrum of scientific questions, resources guiding the data access, design, and analysis of biobank-based studies will be crucial.  The first aim of this review is to characterize the types of biobanks that are discussed in the recent literature and provide detailed descriptions of specific biobanks including their location, size, data access, data linkages and more. The development and accessibility of large-scale biorepositories provide the opportunity to accelerate agnostic searches, new discoveries, and hypothesis-generating studies of disease-treatment, disease-exposure and disease-gene associations. Rather than spending time and money designing and implementing a single study with pre-defined objectives, researchers can use biobanks’ existing data-rich resources to answer scientific questions as quickly as they can analyze them. While the data are becoming increasingly available, additional thought is needed to address issues related to the design of such studies and analysis of these data. In the second aim of this review, we discuss statistical issues related to biobank research in general including study design, sampling strategy, phenotype identification, and missing data. These issues are illustrated using data from the Michigan Genomics Initiative, UK Biobank, and Genes for Good. We summarize the current body of statistical literature aimed at addressing some of these challenges and discuss some of the standing open problems in this area. This work serves to complement and extend recent reviews about biobank-based research and aims to provide a resource catalog with statistical and practical guidance to researchers pursuing biobank-based research.

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Addressing unmeasured confounding in comparative observational research

Cited by 43 publications

References 78 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

Causal inference for evaluating prescription opioid abuse using trend‐in‐trend design

The Emerging Landscape of Epidemiological Research Based on Biobanks Linked to Electronic Health Records: Existing Resources, Analytic Challenges and Potential Opportunities

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