Clinical Generalizability Measure

A Clinical Generalizability Measure is a clinical measure that describes the extent to which a clinical trial findings can be reliably extrapolated from clinical trial subject to a broader patient population.

AKA: External Validity Measure, Clinical Generalization Measure.
Example(s):
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Counter-Example(s):
See: Clinical Trial, Good Clinical Practice, Clinical Dataset, Clinical Data Analysis, Clinical Trial Protocol.

References

2022

(Wikipedia) ⇒ https://en.wikipedia.org/wiki/Glossary_of_clinical_research#G Retrieved:2022-01-02.
- QUOTE: Generalisability, Generalisation
  - The extent to which the findings of a clinical trial can be reliably extrapolated from the subjects who participated in the trial to a broader patient population and a broader range of clinical settings. (ICH E9)

2020

(Futoma, 2020) ⇒ Joseph Futoma, Morgan Simons, Trishan Panch, Finale Doshi-Velez, and Leo Anthony Celi (2020). "The myth of generalisability in clinical research and machine learning in health care". In: The Lancet Digital Health -Viewpoint, 2(9). DOI:10.1016/S2589-7500(20)30186-2.

2018

(Shantikumar, 2018) ⇒ Saran Shantikumar (2018). "Validity, reliability and generalisability". In: HealthKnowledge.
- QUOTE: Generalisability is the extent to which the findings of a study can be applicable to other settings. It is also known as external validity. Generalisability requires internal validity as well as a judgement on whether the findings of a study are applicable to a particular group. In making such a judgement, you can consider factors such as the characteristics of the participants (including the demographic and clinical characteristics, as affected by the source population, response rate, inclusion criteria, etc.), the setting of the study, and the interventions or exposures studied. Threats to external validity, that may result in an incorrect generalisation, include restrictions within the original study (eligibility criteria), and pre-test/post-test effects (where cause-effect relationships within a study are only found when pre-tests or post-tests are also carried out).

2012

(Kukull & Ganguli, 2012) ⇒ Walter A. Kukull, and Mary Ganguli (2012) "Generalizability: The trees, the forest, and the low-hanging fruit". In: American Academy of Neurology - Neurology, 78(23). DOI:10.1212/WNL.0b013e318258f812.
- QUOTE: Confusion around generalizability has arisen from the conflation of 2 fundamental questions. First, are the results of the study true, or are they an artifact of the way the study was designed or conducted; i.e., is the study is internally valid? Second, are the study results likely to apply, generally or specifically, in other study settings or samples; i.e., are the study results externally valid?
  Thoughtful study design, careful data collection, and appropriate statistical analysis are at the core of any study's internal validity. Whether or not those internally valid results will then broadly “generalize", to other study settings, samples, or populations, is as much a matter of judgment as of statistical inference. The generalizability of a study's results depends on the researcher's ability to separate the “relevant” from the “irrelevant” facts of the study, and then carry forward a judgment about the relevant facts, 2 which would be easy if we always knew what might eventually turn out to be relevant. After all, we generalize results from animal studies to humans, if the common biologic process or disease mechanism is “relevant” and species is relatively “irrelevant.” We also draw broad inferences from randomized controlled trials, even though these studies often have specific inclusion and exclusion criteria, rather than being population probability samples. In other words, generalization is the “big picture” interpretation of a study's results once they are determined to be internally valid.