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Defining representativeness of study samples in medical and population health research.
Rudolph, Jacqueline E; Zhong, Yongqi; Duggal, Priya; Mehta, Shruti H; Lau, Bryan.
  • Rudolph JE; Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
  • Zhong Y; Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
  • Duggal P; Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
  • Mehta SH; Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
  • Lau B; Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
BMJ Med ; 2(1): e000399, 2023.
Article in English | MEDLINE | ID: covidwho-2325941
ABSTRACT
Medical and population health science researchers frequently make ambiguous statements about whether they believe their study sample or results are representative of some (implicit or explicit) target population. This article provides a comprehensive definition of representativeness, with the goal of capturing the different ways in which a study can be representative of a target population. It is proposed that a study is representative if the estimate obtained in the study sample is generalisable to the target population (owing to representative sampling, estimation of stratum specific effects, or quantitative methods to generalise or transport estimates) or the interpretation of the results is generalisable to the target population (based on fundamental scientific premises and substantive background knowledge). This definition is explored in the context of four covid-19 studies, ranging from laboratory science to descriptive epidemiology. All statements regarding representativeness should make clear the way in which the study results generalise, the target population the results are being generalised to, and the assumptions that must hold for that generalisation to be scientifically or statistically justifiable.
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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: BMJ Med Year: 2023 Document Type: Article Affiliation country: Bmjmed-2022-000399

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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: BMJ Med Year: 2023 Document Type: Article Affiliation country: Bmjmed-2022-000399