Antonio Martín Andrés, Francisco Gayá Moreno, María Álvarez Hernández, Inmaculada Herranz Tejedor
{"title":"Miettinen and Nurminen score statistics revisited.","authors":"Antonio Martín Andrés, Francisco Gayá Moreno, María Álvarez Hernández, Inmaculada Herranz Tejedor","doi":"10.1080/10543406.2024.2311242","DOIUrl":null,"url":null,"abstract":"<p><p>It is commonly necessary to perform inferences on the difference, ratio, and odds ratio of two proportions <i>p</i><sub><i>1</i></sub> and <i>p</i><sub><i>2</i></sub> based on two independent samples. For this purpose, the most common asymptotic statistics are based on the score statistics (<i>S</i>-type statistics). As these do not correct the bias of the estimator of the product <i>p</i><sub><i>i</i></sub> (1-<i>p</i><sub><i>i</i></sub>), Miettinen and Nurminen proposed the <i>MN</i>-type statistics, which consist of multiplying the statistics <i>S</i> by (<i>N</i>-1)/<i>N</i>, where <i>N</i> is the sum of the two sample sizes. This paper demonstrates that the factor (<i>N</i>-1)/<i>N</i> is only correct in the case of the test of equality of two proportions, providing the estimation of the correct factor (<i>AU</i>-type statistics) and the minimum value of the same (<i>AUM-</i>type statistics). Moreover, this paper assesses the performance of the four-type statistics mentioned (<i>S</i>, <i>MN</i>, <i>AU</i> and <i>AUM</i>) in one and two-tailed tests, and for each of the three parameters cited (<i>d</i>, <i>R</i> and <i>OR</i>). We found that the <i>AUM-</i>type statistics are the best, followed by the <i>MN</i> type (whose performance was most similar to that of <i>AU-</i>type). Finally, this paper also provides the correct factors when the data are from a multinomial distribution, with the novelty that the <i>MN</i> and AU statistics are similar in the case of the test for the odds ratio.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"283-296"},"PeriodicalIF":1.2000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Biopharmaceutical Statistics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/10543406.2024.2311242","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/2/9 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
Abstract
It is commonly necessary to perform inferences on the difference, ratio, and odds ratio of two proportions p1 and p2 based on two independent samples. For this purpose, the most common asymptotic statistics are based on the score statistics (S-type statistics). As these do not correct the bias of the estimator of the product pi (1-pi), Miettinen and Nurminen proposed the MN-type statistics, which consist of multiplying the statistics S by (N-1)/N, where N is the sum of the two sample sizes. This paper demonstrates that the factor (N-1)/N is only correct in the case of the test of equality of two proportions, providing the estimation of the correct factor (AU-type statistics) and the minimum value of the same (AUM-type statistics). Moreover, this paper assesses the performance of the four-type statistics mentioned (S, MN, AU and AUM) in one and two-tailed tests, and for each of the three parameters cited (d, R and OR). We found that the AUM-type statistics are the best, followed by the MN type (whose performance was most similar to that of AU-type). Finally, this paper also provides the correct factors when the data are from a multinomial distribution, with the novelty that the MN and AU statistics are similar in the case of the test for the odds ratio.
期刊介绍:
The Journal of Biopharmaceutical Statistics, a rapid publication journal, discusses quality applications of statistics in biopharmaceutical research and development. Now publishing six times per year, it includes expositions of statistical methodology with immediate applicability to biopharmaceutical research in the form of full-length and short manuscripts, review articles, selected/invited conference papers, short articles, and letters to the editor. Addressing timely and provocative topics important to the biostatistical profession, the journal covers:
Drug, device, and biological research and development;
Drug screening and drug design;
Assessment of pharmacological activity;
Pharmaceutical formulation and scale-up;
Preclinical safety assessment;
Bioavailability, bioequivalence, and pharmacokinetics;
Phase, I, II, and III clinical development including complex innovative designs;
Premarket approval assessment of clinical safety;
Postmarketing surveillance;
Big data and artificial intelligence and applications.