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Bayesian statistics in medical research: an intuitive alternative to conventional data analysis

Authors

  • Lyle C. Gurrin BSc (Hons), PhD, AStat,

    1. Biostatistician, Women and Infants Research Foundation, King Edward Memorial Hospital, Subiaco, Perth, Australia
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  • Jennifer J. Kurinczuk MD, MSc, MFPHM, FAFPHM,

    1. Perinatal Epidemiologist, TVW Telethon Institute for Child Health Research, West Perth, Australia and Clinical Senior Lecturer, Department of Public Health, The University of Western Australia, Australia;
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  • Paul R. Burton MD, MSc, MFPHM, CStat

    1. Professor of Genetic Epidemiology, Department of Epidemiology and Public Health, University of Leicester, Leicester, UK and Head, Division of Biostatistics and Genetic Epidemiology, TVW Telethon Institute for Child Health Research, Department of Paediatrics, University of Western Australia, West Perth, Australia.
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Correspondence DrLyle C.Gurrin Biostatistician Women and Infants Research Foundation King Edward Memorial Hospital PO Box 134, Subiaco Perth, W.A., 6008 Australia

Summary

Statistical analysis of both experimental and observational data is central to medical research. Unfortunately, the process of conventional statistical analysis is poorly understood by many medical scientists. This is due, in part, to the counter-intuitive nature of the basic tools of traditional (frequency-based) statistical inference. For example, the proper definition of a conventional 95% confidence interval is quite confusing. It is based upon the imaginary results of a series of hypothetical repetitions of the data generation process and subsequent analysis. Not surprisingly, this formal definition is often ignored and a 95% confidence interval is widely taken to represent a range of values that is associated with a 95% probability of containing the true value of the parameter being estimated. Working within the traditional framework of frequency-based statistics, this interpretation is fundamentally incorrect. It is perfectly valid, however, if one works within the framework of Bayesian statistics and assumes a ‘prior distribution’ that is uniform on the scale of the main outcome variable. This reflects a limited equivalence between conventional and Bayesian statistics that can be used to facilitate a simple Bayesian interpretation based on the results of a standard analysis. Such inferences provide direct and understandable answers to many important types of question in medical research. For example, they can be used to assist decision making based upon studies with unavoidably low statistical power, where non-significant results are all too often, and wrongly, interpreted as implying ‘no effect’. They can also be used to overcome the confusion that can result when statistically significant effects are too small to be clinically relevant. This paper describes the theoretical basis of the Bayesian-based approach and illustrates its application with a practical example that investigates the prevalence of major cardiac defects in a cohort of children born using the assisted reproduction technique known as ICSI (intracytoplasmic sperm injection).

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