ArticleJournal of general internal medicine2017
False Dichotomies and Health Policy Research Designs: Randomized Trials Are Not Always the Answer.
Article in Journal of general internal medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Impact of hospital mergers: a systematic review focusing on healthcare quality measures.European journal of public health · 2022Pooled it
- Use of interrupted time series methods in the evaluation of health system quality improvement interventions: a methodological systematic review.BMJ global health · 2020Pooled it
- Response to "A critical perspective on patient activation through integrative healthcare counselling in oncology", based on the "CCC-Integrativ" study.Frontiers of medicine · 2025Article
- Response to "Black Box Warning Did Not Cause Increased Suicides".Psychiatric research and clinical practice · 2021Article
- A Novel Intervention for High-Need, High-Cost Medicaid Patients: a Study of ECHO Care.Journal of general internal medicine · 2020Article
- Instrumental variables: The power of wishful thinking vs the confounded reality of comparative effectiveness research.Health services research · 2019Article
- Preventing Falls in Hospitalized Patients: State of the Science.Clinics in geriatric medicine · 2019Review
- Sensory Integration and Functional Reaching in Children With Rett Syndrome/Rett-Related Disorders.Clinical medicine insights. Pediatrics · 2019Article
- Advancing Health Policy and Program Research in Diabetes: Findings from the Natural Experiments for Translation in Diabetes (NEXT-D) Network.Current diabetes reports · 2018Review
- Blood Sugar Regulation for Cardiovascular Health Promotion and Disease Prevention: JACC Health Promotion Series.Journal of the American College of Cardiology · 2018Review
- Opioid Prescribing in the United States Before and After the Centers for Disease Control and Prevention's 2016 Opioid Guideline.Annals of internal medicine · 2018Article
- Comment on 'Measuring the impact of medicines regulatory interventions - systematic review and methodological considerations' by Goedecke et al.British journal of clinical pharmacology · 2018Article
- Counter-Point: Staying Honest When Policy Changes Backfire.Medical care · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Some medical scientists argue that only data from randomized controlled trials (RCTs) are trustworthy. They claim data from natural experiments and administrative data sets are always spurious and cannot be used to evaluate health policies and other population-wide phenomena in the real world. While many acknowledge biases caused by poor study designs, in this article we argue that several valid designs using administrative data can produce strong findings, particularly the interrupted time series (ITS) design. Many policy studies neither permit nor require an RCT for cause-and-effect inference. Framing our arguments using Campbell and Stanley's classic research design monograph, we show that several "quasi-experimental" designs, especially interrupted time series (ITS), can estimate valid effects (or non-effects) of health interventions and policies as diverse as public insurance coverage, speed limits, hospital safety programs, drug abuse regulation and withdrawal of drugs from the market. We further note the recent rapid uptake of ITS and argue for expanded training in quasi-experimental designs in medical and graduate schools and in post-doctoral curricula.
Indexed as
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.