Characterizing bias due to differential exposure ascertainment in electronic health record data

Hubbard, Rebecca A.; Lett, Elle; Ho, Gloria Y.F.; Chubak, Jessica

doi:10.1007/s10742-020-00235-3

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…Future work should investigate extensions to differential missingness of exposure variables, which may also be found in studies with EHR and genomic data [ 27 , 28 ], or joint missingness of exposure and outcome variables. The performance of machine-learning approaches, such as random forests and k -nearest neighbor algorithms, can also be investigated for this setting.…”

Section: Discussionmentioning

confidence: 99%

Survival analysis under imperfect record linkage using historic census data

Marks-Anglin,

Barg,

Ross

et al. 2024

BMC Med Res Methodol

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Background Advancements in linking publicly available census records with vital and administrative records have enabled novel investigations in epidemiology and social history. However, in the absence of unique identifiers, the linkage of the records may be uncertain or only be successful for a subset of the census cohort, resulting in missing data. For survival analysis, differential ascertainment of event times can impact inference on risk associations and median survival. Methods We modify some existing approaches that are commonly used to handle missing survival times to accommodate this imperfect linkage situation including complete case analysis, censoring, weighting, and several multiple imputation methods. We then conduct simulation studies to compare the performance of the proposed approaches in estimating the associations of a risk factor or exposure in terms of hazard ratio (HR) and median survival times in the presence of missing survival times. The effects of different missing data mechanisms and exposure-survival associations on their performance are also explored. The approaches are applied to a historic cohort of residents in Ambler, PA, established using the 1930 US census, from which only 2,440 out of 4,514 individuals (54%) had death records retrievable from publicly available data sources and death certificates. Using this cohort, we examine the effects of occupational and paraoccupational asbestos exposure on survival and disparities in mortality by race and gender. Results We show that imputation based on conditional survival results in less bias and greater efficiency relative to a complete case analysis when estimating log-hazard ratios and median survival times. When the approaches are applied to the Ambler cohort, we find a significant association between occupational exposure and mortality, particularly among black individuals and males, but not between paraoccupational exposure and mortality. Discussion This investigation illustrates the strengths and weaknesses of different imputation methods for missing survival times due to imperfect linkage of the administrative or registry data. The performance of the methods may depend on the missingness process as well as the parameter being estimated and models of interest, and such factors should be considered when choosing the methods to address the missing event times.

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Section: Discussionmentioning

confidence: 99%

Survival analysis under imperfect record linkage using historic census data

Marks-Anglin,

Barg,

Ross

et al. 2024

BMC Med Res Methodol

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“…50 This is because certain exposures may only be investigated and recorded at the time of an event's occurrence. 51 Therefore, it is necessary to assess the timing of the recording exposures and events, not just the timing of their occurrence. 52 The causal contrast of interest section should describe intention-to-treat and/or per-protocol effects.…”

Section: Components Of the Target Trial Protocol And Their Emulation ...mentioning

confidence: 99%

Making causal inferences from transactional data: A narrative review of opportunities and challenges when implementing the target trial framework

Esteban,

Szmulewicz

2024

J Int Med Res

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The target trial framework has emerged as a powerful tool for addressing causal questions in clinical practice and in public health. In the healthcare sector, where decision-making is increasingly data-driven, transactional databases, such as electronic health records (EHR) and insurance claims, present an untapped potential for answering complex causal questions. This narrative review explores the potential of the integration of the target trial framework with real-world data to enhance healthcare decision-making processes. We outline essential elements of the target trial framework, and identify pertinent challenges in data quality, privacy concerns, and methodological limitations, proposing solutions to overcome these obstacles and optimize the framework’s application.

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“…35 One may also evaluate multiple approaches and select the best analysis method. 36 A selective approach may potentially produce higher quality data, but can be associated with the highest selection bias. In contrast, a common data approach (most inclusive) may produce lower quality data, but be associated with information/misclassification bias.…”

Section: Statistical Analysesmentioning

confidence: 99%

Challenges in and Opportunities for Electronic Health Record-Based Data Analysis and Interpretation

Kim,

Rouphael,

McMichael

et al. 2023

Gut and Liver

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Electronic health records (EHRs) have been increasingly adopted in clinical practices across the United States, providing a primary source of data for clinical research, particularly observational cohort studies. EHRs are a high-yield, low-maintenance source of longitudinal real-world data for large patient populations and provide a wealth of information and clinical contexts that are useful for clinical research and translation into practice. Despite these strengths, it is important to recognize the multiple limitations and challenges related to the use of EHR data in clinical research. Missing data are a major source of error and biases and can affect the representativeness of the cohort of interest, as well as the accuracy of the outcomes and exposures. Here, we aim to provide a critical understanding of the types of data available in EHRs and describe the impact of data heterogeneity, quality, and generalizability, which should be evaluated prior to and during the analysis of EHR data. We also identify challenges pertaining to data quality, including errors and biases, and examine potential sources of such biases and errors. Finally, we discuss approaches to mitigate and remediate these limitations. A proactive approach to addressing these issues can help ensure the integrity and quality of EHR data and the appropriateness of their use in clinical studies.

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Characterizing bias due to differential exposure ascertainment in electronic health record data

Cited by 3 publications

References 14 publications

Survival analysis under imperfect record linkage using historic census data

Survival analysis under imperfect record linkage using historic census data

Making causal inferences from transactional data: A narrative review of opportunities and challenges when implementing the target trial framework

Challenges in and Opportunities for Electronic Health Record-Based Data Analysis and Interpretation

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