Bayesian statistics in medical research: an intuitive alternative to conventional data analysis
Authors:
- Lyle C. GurrinLGLyle C. Gurrin
- Jennifer J. KurinczukJKJennifer J. Kurinczuk
- Paul R. BurtonPBPaul R. Burton
- 1 May 2000
- journal article
- research article
- Published by Wiley in Journal of Evaluation in Clinical Practice
- Vol. 6 (2) , 193-204
Abstract
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).Keywords
This publication has 30 references indexed in Scilit:
- Analysis of Clinical Data Using Imprecise Prior ProbabilitiesJournal of the Royal Statistical Society: Series D (The Statistician), 1996
- Robust bayesian methods for monitoring clinical trialsStatistics in Medicine, 1995
- The potential of intracytoplasmic sperm injection (ICSI) to transmit genetic defects causing male infertilityReproduction, Fertility and Development, 1995
- Intracytoplasmic sperm injection — ICSIReproductive Medicine Review, 1994
- Graphical Elicitation of a Prior Distribution for a Clinical TrialJournal of the Royal Statistical Society: Series D (The Statistician), 1993
- Pregnancies after intracytoplasmic injection of single spermatozoon into an oocyteThe Lancet, 1992
- Robust Bayesian analysis: sensitivity to the priorJournal of Statistical Planning and Inference, 1990
- The Assessment of the Subjective Opinion and its Use in Relation to Stopping Rules for Clinical TrialsJournal of the Royal Statistical Society: Series D (The Statistician), 1983
- Interactive Elicitation of Opinion for a Normal Linear ModelJournal of the American Statistical Association, 1980
- Inferences for normal distributions1965