ArticleEGEMS (Washington, DC)2013
Estimating causal effects in observational studies using Electronic Health Data: Challenges and (some) solutions.
Article in EGEMS (Washington, DC), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- A novel average treatment effect estimation approach for integrated incomplete datasets, with application in childhood anemia.BMC medical research methodology · 2026Article
- Cost-effectiveness of proton beam therapy vs. conventional radiotherapy for patients with brain tumors in Sweden: results from a non-randomized prospective multicenter study.Cost effectiveness and resource allocation : C/E · 2024Article
- Veridical Causal Inference using Propensity Score Methods for Comparative Effectiveness Research with Medical Claims.Health services & outcomes research methodology · 2021Article
- Vector-based kernel weighting: A simple estimator for improving precision and bias of average treatment effects in multiple treatment settings.Statistics in medicine · 2021Article
- Propensity score stratification methods for continuous treatments.Statistics in medicine · 2021Article
- General Control Functions for Causal Effect Estimation from Instrumental Variables.Advances in neural information processing systems · 2020Article
- A novel approach for propensity score matching and stratification for multiple treatments: Application to an electronic health record-derived study.Statistics in medicine · 2020Article
- Research and Reporting Considerations for Observational Studies Using Electronic Health Record Data.Annals of internal medicine · 2020Review
- Population-Based Registry Linkages to Improve Validity of Electronic Health Record-Based Cancer Research.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020Article
- The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities.Statistics in medicine · 2020Article
- CRcoder: An Interactive Web Application and SAS Macro to Support Personalized Clinical Decisions.The Permanente journal · 2020Article
- Novel Machine Learning Approach to Identify Preoperative Risk Factors Associated With Super-Utilization of Medicare Expenditure Following Surgery.JAMA surgery · 2019Observational
- Estimating the causal effects of chronic disease combinations on 30-day hospital readmissions based on observational Medicaid data.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Performing an Informatics Consult: Methods and Challenges.Journal of the American College of Radiology : JACR · 2018Article
- Analytical Methods for a Learning Health System: 2. Design of Observational Studies.EGEMS (Washington, DC) · 2017Article
- Analytical Methods for a Learning Health System: 3. Analysis of Observational Studies.EGEMS (Washington, DC) · 2017Article
- Estimating the effects of second-line therapy for type 2 diabetes mellitus: retrospective cohort study.BMJ open diabetes research & care · 2017Article
- Hepatitis C treatment failure is associated with increased risk of hepatocellular carcinoma.Journal of viral hepatitis · 2016Observational
- Ethnicity and cardiovascular health inequalities in people with severe mental illnesses: protocol for the E-CHASM study.Social psychiatry and psychiatric epidemiology · 2016Article
- Making sense of the shadows: priorities for creating a learning healthcare system based on routinely collected data.BMJ quality & safety · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Electronic health data sets, including electronic health records (EHR) and other administrative databases, are rich data sources that have the potential to help answer important questions about the effects of clinical interventions as well as policy changes. However, analyses using such data are almost always non-experimental, leading to concerns that those who receive a particular intervention are likely different from those who do not, in ways that may confound the effects of interest. This paper outlines the challenges in estimating causal effects using electronic health data, and offers some solutions, with particular attention paid to propensity score methods that help ensure comparisons between similar groups. The methods are illustrated with a case study describing the design of a study using Medicare and Medicaid administrative data to estimate the effect of the Medicare Part D prescription drug program among individuals with serious mental illness.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.