Gestational Diabetes validation data study: an example of testing differences in sensitivity and specificity of two diagnostic methods using non-random sampling in a paired study design Open Access

Tian, Ganzhong (2017)

Permanent URL: https://etd.library.emory.edu/concern/etds/nk322f195?locale=en
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Abstract

Abstract

Gestational Diabetes validation data study: an example of testing differences in sensitivity and specificity of two diagnostic methods using non-random sampling in a paired study design

By Ganzhong Tian

Background: Comparing the accuracy of two diagnostic methods is a common problem in public health. This is especially the case when using a third ‘gold standard' to determine patients' true disease status is either too expensive or time-consuming. For this kind of problem, it is highly desirable to have an efficient way of sampling and comparing the diagnostic properties of the two diagnostic methods.

Methods: In this study, we used a CDC study of Gestational Diabetes data as an example and considered an efficient design for validation sampling, which gives us more useful information regarding the diagnostic properties of two diagnostic methods for Gestational Diabetes. Also, to match with this sampling design, we proposed a new Wald test based on a 12-level multinomial distribution to compare the difference of the two diagnostic methods, in terms of some commonly evaluated diagnostic properties (e.g., sensitivity and specificity).

Computer simulation based on SAS/STAT and SAS/IML was used to implement the sampling process and assess the results of the hypothesis test, under different sampling designs and disease prevalence. We compared the results of the multinomial-based test against a more conventional McNemar's test, assumptions for which might be partially violated under our study setting and proposed sampling design.

Results: The results show that validation sampling only from the discordant pairs (those with disagreeing diagnostic results from the two diagnostic methods) will greatly boost the statistical power of testing the difference in sensitivity and specificity of the two diagnostic methods. Also, the Wald test we proposed performs well under different parameter settings and different sampling designs. In addition, our new test is superior to conventional McNemar's test in terms of statistical power and type-I error under the null hypothesis.

Table of Contents

Contents
Introduction .................................................................................................................................... 1
Methods ........................................................................................................................................... 4
1. Data generation method .................................................................................................... 4
2. Reparameterization and sampling schemes .................................................................. 10
3. Hypothesis testing for difference in sensitivity and specificity .................................... 15
Results ........................................................................................................................................... 27
Discussion ..................................................................................................................................... 40
References ..................................................................................................................................... 43

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