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Quasi-experimental design

Quasi-experimental design Quasi-experiments are similar to randomized controlled trials in many respects, but there are many challenges in designing and conducting a quasi-experiment when internal validity threats are introduced from the absence of randomization. This paper outlines design, measurement and statistical issues that must be considered prior to the conduct of a quasi-experimental evaluation. We discuss challenges for the internal validity of quasi-experimental designs, inclusion/exclusion criteria, treatment and comparator cohort definitions, and the five types of explanatory variables that one must classify prior to analysis. We discuss data collection and confidentiality, statistical power and conclude with analytic issues that one must consider. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

Quasi-experimental design

Maciejewski, Matthew L.
Biostatistics & Epidemiology , Volume 4 (1): 10 – Jan 1, 2020
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Quasi-experimental design

Abstract

Quasi-experiments are similar to randomized controlled trials in many respects, but there are many challenges in designing and conducting a quasi-experiment when internal validity threats are introduced from the absence of randomization. This paper outlines design, measurement and statistical issues that must be considered prior to the conduct of a quasi-experimental evaluation. We discuss challenges for the internal validity of quasi-experimental designs, inclusion/exclusion criteria,...
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Publisher
Taylor & Francis
Copyright
© 2018 International Biometric Society – Chinese Region
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2018.1477468
Publisher site
See Article on Publisher Site

Abstract

Quasi-experiments are similar to randomized controlled trials in many respects, but there are many challenges in designing and conducting a quasi-experiment when internal validity threats are introduced from the absence of randomization. This paper outlines design, measurement and statistical issues that must be considered prior to the conduct of a quasi-experimental evaluation. We discuss challenges for the internal validity of quasi-experimental designs, inclusion/exclusion criteria, treatment and comparator cohort definitions, and the five types of explanatory variables that one must classify prior to analysis. We discuss data collection and confidentiality, statistical power and conclude with analytic issues that one must consider.

Journal

Biostatistics & Epidemiology – Taylor & Francis

Published: Jan 1, 2020

Keywords: Quasi-experimental design; retrospective analysis; observational study design; cohort

References

  • For the STROBE initiative. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration
  • Good research practices for comparative effectiveness research: approaches to mitigate bias and confounding in the design of nonrandomized studies of treatment effects using secondary data sources: the international society for pharmacoeconomics and outcomes research good research practices for retrospective database analysis task force report–part II
  • Good research practices for comparative effectiveness research: analytic methods to improve causal inference from nonrandomized studies of treatment effects using secondary data sources: the ISPOR good research practices for retrospective database analysis task force report–part III
  • Prospective observational studies to assess comparative effectiveness: the ISPOR good research practices task force report
  • Misunderstandings between experimentalists and observationalists about causal inference
  • Statistics and causal inference
  • Prespecified falsification end points: can they validate true observational associations
  • Increasing levels of restriction in pharmacoepidemiologic database studies of elderly and comparison with randomized trial results
  • Bias in observational studies of prevalent users: lessons for comparative effectiveness research from a meta-analysis of statins
  • Cooperative studies in health services research in the department of veterans affairs
  • Harmonized patient-reported data elements in the electronic health record: supporting meaningful use by primary care action on health behaviors and key psychosocial factors
  • Negative controls: a tool for detecting confounding and bias in observational studies
  • Synonyms in health services research methodology
  • The effects of stress on body weight: biological and psychological predictors of change in BMI
  • A Tutorial and case study in propensity score analysis: an application to estimating the effect of in-hospital smoking cessation counseling on mortality
  • Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal
  • Tutorial in biostatistics: data-driven subgroup identification and analysis in clinical trials
  • Econometrics in outcomes research: the use of instrumental variables
  • An introduction to instrumental variables for epidemiologists
  • A comparison of observational studies and randomized, controlled trials
  • Randomized, controlled trials, observational studies, and the hierarchy of research designs
  • Methodological quality and homogeneity influenced agreement between randomized trials and nonrandomized studies of the same intervention for back pain