Abstract
BackgroundOne of the emerging themes in epidemiology is the use of interval estimates. Currently, three interval estimates for confidence (CI), prediction (PI), and tolerance (TI) are at a researcher's disposal and are accessible within the open access framework in R. These three types of statistical intervals serve different purposes. Confidence intervals are designed to describe a parameter with some uncertainty due to sampling errors. Prediction intervals aim to predict future observation(s), including some uncertainty present in the actual and future samples. Tolerance intervals are constructed to capture a specified proportion of a population with a defined confidence. It is well known that interval estimates support a greater knowledge gain than point estimates. Thus, a good understanding and the use of CI, PI, and TI underlie good statistical practice. While CIs are taught in introductory statistical classes, PIs and TIs are less familiar.ResultsIn this paper, we provide a concise tutorial on two-sided CI, PI and TI for binary variables. This hands-on tutorial is based on our teaching materials. It contains an overview of the meaning and applicability from both a classical and a Bayesian perspective. Based on a worked-out example from veterinary medicine, we provide guidance and code that can be directly applied in R.ConclusionsThis tutorial can be used by others for teaching, either in a class or for self-instruction of students and senior researchers.
Highlights
Data set The data set from Sprick et al [7] assesses the damage inflicted by four different horseshoe materials on the long bones of horses
Because there are many tutorials and methodological papers for quantitative variables [4,5,6], we focus on binary variables
Random sample and point estimates Below, we focus on one random sample with independent observations generated by a binary primary outcome at the patient, specimen or object level attaining only two values (0 = “no”, 1 = “yes”)
Summary
We provide a concise tutorial on two-sided CI, PI and TI for binary variables. This hands-on tutorial is based on our teaching materials. It contains an overview of the meaning and applicability from both a classical and a Bayesian perspective. Based on a worked-out example from veterinary medicine, we provide guidance and code that can be directly applied in R
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