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J Gen Intern Med. 2017 Feb;32(2):204-209. doi: 10.1007/s11606-016-3841-9. Epub 2016 Oct 18.

False Dichotomies and Health Policy Research Designs: Randomized Trials Are Not Always the Answer.

Author information

  • 1Harvard Medical School Department of Population Medicine, Harvard Pilgrim Healthcare Institute, Boston, MA, USA. ssoumerai@hms.harvard.edu.
  • 2Harvard Medical School Department of Population Medicine, Harvard Pilgrim Healthcare Institute, Boston, MA, USA.
  • 3Sociology Department & LDI Wharton & School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

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.

KEYWORDS:

health interventions; quasi-experimental design; randomization; research design

PMID:
27757714
PMCID:
PMC5264670
DOI:
10.1007/s11606-016-3841-9
[PubMed - in process]
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