Sample size determination for disease prevalence studies with partially validated data.
Disease prevalence is an important topic in medical research, and its study is based on data that are obtained by classifying subjects according to whether a disease has been contracted. Classification can be conducted with high-cost gold standard tests or low-cost screening tests, but the latter are subject to the misclassification of subjects. As a compromise between the two, many research studies use partially validated datasets in which all data points are classified by fallible tests, and some of the data points are validated in the sense that they are also classified by the completely accurate gold-standard test. In this article, we investigate the determination of sample sizes for disease prevalence studies with partially validated data. We use two approaches. The first is to find sample sizes that can achieve a pre-specified power of a statistical test at a chosen significance level, and the second is to find sample sizes that can control the width of a confidence interval with a pre-specified confidence level. Empirical studies have been conducted to demonstrate the performance of various testing procedures with the proposed sample sizes. The applicability of the proposed methods are illustrated by a real-data example.
['Qiu|Shi-Fang|SF|', 'Poon|Wai-Yin|WY|', 'Tang|Man-Lai|ML|']
D000741:Anemia, Aplastic D056808:Biostatistics D016026:Bone Marrow Transplantation D003198:Computer Simulation D016001:Confidence Intervals D016208:Databases, Factual D006086:Graft vs Host Disease D006801:Humans D016013:Likelihood Functions D015233:Models, Statistical D015995:Prevalence D018401:Sample Size D054928:Validation Studies as Topic
sample size disease prevalence partially validated data Asymptotic inference double-sampling