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Perils and potentials of self-selected entry to epidemiological studies and surveys

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  • Niels Keiding
  • Thomas A. Louis

Abstract

type="main" xml:id="rssa12136-abs-0001"> Low front-end cost and rapid accrual make Web-based surveys and enrolment in studies attractive, but participants are often self-selected with little reference to a well-defined study base. Of course, high quality studies must be internally valid (validity of inferences for the sample at hand), but Web-based enrolment reactivates discussion of external validity (generalization of within-study inferences to a target population or context) in epidemiology and clinical trials. Survey research relies on a representative sample produced by a sampling frame, prespecified sampling process and weighting that maps results to an intended population. In contrast, recent analytical epidemiology has shifted the focus away from survey-type representativity to internal validity in the sample. Against this background, it is a good time for statisticians to take stock of our role and position regarding surveys, observational research in epidemiology and clinical studies. The central issue is whether conditional effects in the sample (the study population) may be transported to desired target populations. Success depends on compatibility of causal structures in study and target populations, and will require subject matter considerations in each concrete case. Statisticians, epidemiologists and survey researchers should work together to increase understanding of these challenges and to develop improved tools to handle them.

Suggested Citation

  • Niels Keiding & Thomas A. Louis, 2016. "Perils and potentials of self-selected entry to epidemiological studies and surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 319-376, February.
    • Handle: RePEc:bla:jorssa:v:179:y:2016:i:2:p:319-376
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    File URL: http://hdl.handle.net/10.1111/rssa.2016.179.issue-2
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    Cited by:

    1. J. N. K. Rao, 2021. "On Making Valid Inferences by Integrating Data from Surveys and Other Sources," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 242-272, May.
    2. Cauane Blumenberg & Aluísio J. D. Barros, 2018. "Response rate differences between web and alternative data collection methods for public health research: a systematic review of the literature," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 63(6), pages 765-773, July.
    3. Saegusa Takumi, 2020. "Confidence bands for a distribution function with merged data from multiple sources," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 144-158, August.
    4. David J. Hand, 2018. "Statistical challenges of administrative and transaction data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 555-605, June.
    5. David McConnell & Conor Hickey & Norma Bargary & Lea Trela-Larsen & Cathal Walsh & Michael Barry & Roisin Adams, 2021. "Understanding the Challenges and Uncertainties of Seroprevalence Studies for SARS-CoV-2," IJERPH, MDPI, vol. 18(9), pages 1-19, April.
    6. Jae‐Kwang Kim & Siu‐Ming Tam, 2021. "Data Integration by Combining Big Data and Survey Sample Data for Finite Population Inference," International Statistical Review, International Statistical Institute, vol. 89(2), pages 382-401, August.
    7. Rendtel, Ulrich & Alho, Juha M., 2022. "On the fade-away of an initial bias in longitudinal surveys," Discussion Papers 2022/4, Free University Berlin, School of Business & Economics.
    8. Ashley L. Buchanan & Michael G. Hudgens & Stephen R. Cole & Katie R. Mollan & Paul E. Sax & Eric S. Daar & Adaora A. Adimora & Joseph J. Eron & Michael J. Mugavero, 2018. "Generalizing evidence from randomized trials using inverse probability of sampling weights," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1193-1209, October.
    9. Paul Allin & David J. Hand, 2017. "New statistics for old?—measuring the wellbeing of the UK," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 3-43, January.
    10. Takumi Saegusa, 2020. "Confidence bands for a distribution function with merged data from multiple sources," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 144-158, August.
    11. Lingxiao Wang & Barry I. Graubard & Hormuzd A. Katki & and Yan Li, 2020. "Improving external validity of epidemiologic cohort analyses: a kernel weighting approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1293-1311, June.
    12. Yingli Pan & Wen Cai & Zhan Liu, 2022. "Inference for non-probability samples under high-dimensional covariate-adjusted superpopulation model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 955-979, October.
    13. Jiayin Zheng & Yingye Zheng & Li Hsu, 2022. "Re‐calibrating pure risk integrating individual data from two‐phase studies with external summary statistics," Biometrics, The International Biometric Society, vol. 78(4), pages 1515-1529, December.
    14. Xiaojun Mao & Zhonglei Wang & Shu Yang, 2023. "Matrix completion under complex survey sampling," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(3), pages 463-492, June.

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