Abstract

In medical, health, and sports sciences, researchers desire a device with high reliability and validity. This article focuses on reliability and validity studies with n subjects and m ≥2 repeated measurements per subject. High statistical power can be achieved by increasing n or m, and increasing m is often easier than increasing n in practice unless m is too high to result in systematic bias. The sequential probability ratio test (SPRT) is a useful statistical method which can conclude a null hypothesis H0 or an alternative hypothesis H1 with 50% of the required sample size of a non-sequential test on average. The traditional SPRT requires the likelihood function for each observed random variable, and it can be a practical burden for evaluating the likelihood ratio after each observation of a subject. Instead, m observed random variables per subject can be transformed into a test statistic which has a known sampling distribution under H0 and under H1. This allows us to formulate a SPRT based on a sequence of test statistics. In this article, three types of study are considered: reliabilityof a device, reliability of a device relative to a criterion device, and validity of a device relative to a  criterion device. Using SPRT for testing the reliability of a device, for small m, results in an average sample size of about 50% of the fixed sample size for a non-sequential test. For comparing a device to criterion, the average sample size approaches to 60% approximately as m increases. The SPRT tolerates violation of normality assumption for validity study, but it does not for reliability study.

Highlights

  • In medical, health, and sports sciences, researchers and practitioners want to use a highly valid and reliable device to conduct research or to make an important decision

  • When it is appropriate under practical considerations, it would be cost effective to increase m, the number of repetitions, and to implement sequential probability ratio test (SPRT) based on the exact sampling distribution of a test statistic calculated after each subject

  • The SPRT for σ does not require any assumption about β, and the average sample size is about 57–58% for small m under H0 and about 49–50% for small m under H1

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Summary

Introduction

Health, and sports sciences, researchers and practitioners want to use a highly valid and reliable device to conduct research or to make an important decision. We can increase precision of parameter estimation and statistical power of hypothesis testing by increasing the sample size (i.e., the number of subjects) and/or the repetitions (i.e., the number of repeated measurements per subject). For reliability and validity studies which typically involve m repeated measurements per subject, the novelty of this article is that we apply the SPRT based on the sampling distribution of g(Yi1, . Yim), still preserves significance level and statistical power while significantly reducing the average sample size To this end, researchers who study reliability and validity of measurement devices can terminate their studies early with a substantially fewer number of subjects by taking m repeated measurements per subject.

Normal Error Model
Estimable Parameters
Practical Research Questions for Studying a Single Device
Review of SPRT
Formulation of Hypothesis Testing
Exact Sampling Distribution
Power Analysis in Non-Sequential Test for σ
Power Analysis in Non-Sequential Test for τ
SPRT for τ
Power Analysis in Non-Sequential Test for θ
SPRT for θ
Impact of Violating Normality Assumption
Findings
Discussion
Full Text
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