Share This Article:

Choosing a Method to Reduce Selection Bias: A Tool for Researchers

Abstract Full-Text HTML XML Download Download as PDF (Size:550KB) PP. 155-162
DOI: 10.4236/ojepi.2015.53020    5,042 Downloads   5,933 Views   Citations

ABSTRACT

Selection bias is well known to affect surveys and epidemiological studies. There have been numerous methods proposed to reduce its effects, so many that researchers may be unclear which method is most suitable for their study; the wide choice may even deter some researchers, for fear of choosing a sub-optimal approach. We propose a straightforward tool to inform researchers of the most promising methods available to reduce selection bias and to assist the search for an appropriate method given their study design and details. We demonstrate the tool using three examples where selection bias may occur; the tool quickly eliminates inappropriate methods and guides the researcher towards those to consider implementing. If more studies consider selection bias and adopt methods to reduce it, valuable time and resources will be saved, and should lead to more focused research towards disease prevention or cure.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Keeble, C. , Law, G. , Barber, S. and Baxter, P. (2015) Choosing a Method to Reduce Selection Bias: A Tool for Researchers. Open Journal of Epidemiology, 5, 155-162. doi: 10.4236/ojepi.2015.53020.

References

[1] Hernan, M., Hernandez-Diaz, S. and Robins, J. (2004) A Structural Approach to Selection Bias. Epidemiology, 15, 615-625.
http://dx.doi.org/10.1097/01.ede.0000135174.63482.43
[2] Hennekens, C.H. and Buring, J.E. (1987) Screening. In: Mayrent, S.L., Ed., Epidemiology in Medicine, Little, Brown and Co., Boston, 327-345.
[3] Keeble, C., Barber, S., Law, G. and Baxter, P. (2013) Participation Bias Assessment in Three High Impact Journals. Sage Open, 3, 1-5.
http://dx.doi.org/10.1177/2158244013511260
[4] Horvitz, D. and Thompson, D. (1952) A Generalization of Sampling without Replacement from a Finite Universe. Journal of the American Statistical Association, 47, 663-685.
http://dx.doi.org/10.1080/01621459.1952.10483446
[5] Geneletti, S., Richardson, S. and Best, N. (2009) Adjusting for Selection Bias in Retrospective, Case-Control Studies. Biostatistics, 10, 17-31.
http://dx.doi.org/10.1093/biostatistics/kxn010
[6] Geneletti, S., Mason, A. and Best, N. (2011) Adjusting for Selection Effects in Epidemiologic Studies: Why Sensitivity Analysis Is the Only “Solution”. Epidemiology, 22, 36-39.
http://dx.doi.org/10.1097/EDE.0b013e3182003276
[7] Keeble, C., Barber, S., Baxter, P., Parslow, R. and Law, G. (2014) Reducing Participation Bias in Case-Control Studies: Type 1 Diabetes in Children and Stroke in Adults. Open Journal of Epidemiology, 4, 129-134.
http://dx.doi.org/10.4236/ojepi.2014.43018
[8] Sterne, J., White, I., Carlin, J., Spratt, M., Royston, P., Kenward, M., et al. (2009) Multiple Imputation for Missing Data in Epidemiological and Clinical Research: Potential and Pitfalls. BMJ, 338, 2393-2397.
http://dx.doi.org/10.1136/bmj.b2393
[9] Breslow, N. and Day, N. (1980) Chapter 3: General Considerations for the Analysis of Case-Control Studies. In: Breslow, N.E. and Day, N.E., Eds., Statistical Methods in Cancer Research, IARC Scientific Publications, 84-119.
[10] Rosenbaum, P. and Rubin, D. (1983) The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika, 70, 41-55.
http://dx.doi.org/10.1093/biomet/70.1.41
[11] Sarndal, C.E. (1992) Methods for Estimating the Precision of Survey Estimates when Imputation Has Been Used. Survey Methodology, 18, 241-252.
[12] Kleinbaum, D., Morgenstern, H. and Kupper, L. (1981) Selection Bias in Epidemiological Studies. American Journal of Epidemiology, 113, 452-463.
[13] Hosmer Jr., D., Lemeshow, S. and Sturdivant, R. (2013) Applied Logistic Regression. John Wiley & Sons, Hoboken.
http://dx.doi.org/10.1002/9781118548387
[14] Schlesselman, J. (1982) Case-Control Studies: Design, Conduct, Analysis. Oxford University Press, New York.
[15] Hatch, E., Kleinerman, R., Linet, M., Tarone, R., Kaune, W., Auvinen, A., et al. (2000) Do Confounding or Selection Factors of Residential Wiring Codes and Magnetic Fields Distort Findings of Electromagnetic Field Studies? Epidemiology, 11, 189-198.
http://dx.doi.org/10.1097/00001648-200003000-00019
[16] Madigan, M., Troisi, R., Potischman, N., Brogan, D., Gammon, M., Malone, K., et al. (2000) Characteristics of Respondents and Non-Respondents from a Case-Control Study of Breast Cancer in Younger Women. International Journal of Epidemiology, 29, 793-798.
http://dx.doi.org/10.1093/ije/29.5.793
[17] Wrensch, M. (2000) Are Prior Head Injuries or Diagnostic X-Rays Associated with Glioma in Adults? The Effects of Control Selection Bias. Neuroepidemiology, 19, 234-244.
http://dx.doi.org/10.1159/000026261
[18] Hara, M., Higaki, Y., Imaizumi, T., Taguchi, N., Nakamura, K., Nanri, H., et al. (2010) Factors Influencing Participation Rate in a Baseline Survey of a Genetic Cohort in Japan. Journal of Epidemiology, 20, 40-45.
http://dx.doi.org/10.2188/jea.JE20090062
[19] Perez, D., Nie, J., Ardern, C., Radhu, N. and Ritvo, P. (2013) Impact of Participant Incentives and Direct and Snowball Sampling on Survey Response Rate in an Ethnically Diverse Community: Results from a Pilot Study of Physical Activity and the Built Environment. Journal of Immigrant and Minority Health, 15, 207-214.
http://dx.doi.org/10.1007/s10903-011-9525-y
[20] Koloski, N., Jones, M., Eslick, G. and Talley, N. (2013) Predictors of Response Rates to a Long Term Follow-Up Mail out Survey. PLoS ONE, 8, e79179.
http://dx.doi.org/10.1371/journal.pone.0079179
[21] McLean, S., Paxton, S., Massey, R., Mond, J., Rodgers, B. and Hay, P. (2014) Prenotification but Not Envelope Teaser Increased Response Rates in a Bulimia Nervosa Mental Health Literacy Survey: A Randomized Controlled Trial. Journal of Clinical Epidemiology, 67, 870-876.
http://dx.doi.org/10.1016/j.jclinepi.2013.10.013
[22] Nota, S., Strooker, J. and Ring, D. (2014) Differences in Response Rates between Mail, E-Mail, and Telephone Follow-Up in Hand Surgery Research. Hand, 9, 504-510.
http://dx.doi.org/10.1007/s11552-014-9618-x
[23] Office for National Statistics (2013) Adult Drinking Habits in Great Britain.
http://www.ons.gov.uk/ons/rel/ghs/opinions-and-lifestyle-survey/adult-drinking-habits-in-great-britain–2013/stb-drinking-2013.html
[24] Galea, S. and Tracy, M. (2007) Participation Rates in Epidemiologic Studies. Annals of Epidemiology, 17, 643-653.
http://dx.doi.org/10.1016/j.annepidem.2007.03.013
[25] Li, Y., Wang, W., Wu, Q., van Velthoven, M., Chen, L., Du, X., et al. (2015) Increasing the Response Rate of Text Messaging Data Collection: A Delayed Randomized Controlled Trial. Journal of the American Medical Informatics Association, 22, 51-64.
[26] Thadhani, R. and Tonelli, M. (2006) Cohort Studies: Marching Forward. Clinical Journal of the American Society of Nephrology, 1, 1117-1123.
http://dx.doi.org/10.2215/CJN.00080106

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.