Abhaya Indrayan, Aman Mishra, Binukumar Bhaskarapillai
{"title":"检测重要医学效应的简化样本量公式。","authors":"Abhaya Indrayan, Aman Mishra, Binukumar Bhaskarapillai","doi":"10.4103/ijcm.ijcm_787_23","DOIUrl":null,"url":null,"abstract":"<p><p>The sample size is just about the most common question in the minds of many medical researchers. This size determines the reliability of the results and helps to detect a medically important effect when present. Some studies miss an important effect due to inappropriate sample size. Many postgraduate students and established researchers often contact a statistician to help them determine an appropriate sample size for their study. More than 80 formulas are available to calculate sample size for different settings and the choice requires some expertise. Their use is even more difficult because most exact formulas are quite complex. An added difficulty is that different books, software, and websites use different formulas for the same problem. Such discrepancy in the published formulas confounds a biostatistician also. The objective of this communication is to present uniformly looking formulas for many situations together at one place in their simple but correct form, along with the setting where they are applicable. This will help in choosing an appropriate formula for the kind of research one is proposing to do and use it with confidence. This communication is restricted to the sample size required to detect a medically important effect when present - known to the statisticians as the test of hypothesis situation. Such a collection is not available anywhere, not even in any book. The sample size formulas for estimation are different and not discussed here.</p>","PeriodicalId":45040,"journal":{"name":"Indian Journal of Community Medicine","volume":"49 3","pages":"464-471"},"PeriodicalIF":0.9000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11198523/pdf/","citationCount":"0","resultStr":"{\"title\":\"Simplified Sample Size Formulas for Detecting a Medically Important Effect.\",\"authors\":\"Abhaya Indrayan, Aman Mishra, Binukumar Bhaskarapillai\",\"doi\":\"10.4103/ijcm.ijcm_787_23\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The sample size is just about the most common question in the minds of many medical researchers. This size determines the reliability of the results and helps to detect a medically important effect when present. Some studies miss an important effect due to inappropriate sample size. Many postgraduate students and established researchers often contact a statistician to help them determine an appropriate sample size for their study. More than 80 formulas are available to calculate sample size for different settings and the choice requires some expertise. Their use is even more difficult because most exact formulas are quite complex. An added difficulty is that different books, software, and websites use different formulas for the same problem. Such discrepancy in the published formulas confounds a biostatistician also. The objective of this communication is to present uniformly looking formulas for many situations together at one place in their simple but correct form, along with the setting where they are applicable. This will help in choosing an appropriate formula for the kind of research one is proposing to do and use it with confidence. This communication is restricted to the sample size required to detect a medically important effect when present - known to the statisticians as the test of hypothesis situation. Such a collection is not available anywhere, not even in any book. The sample size formulas for estimation are different and not discussed here.</p>\",\"PeriodicalId\":45040,\"journal\":{\"name\":\"Indian Journal of Community Medicine\",\"volume\":\"49 3\",\"pages\":\"464-471\"},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2024-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11198523/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Indian Journal of Community Medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4103/ijcm.ijcm_787_23\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/5/24 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q4\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Indian Journal of Community Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4103/ijcm.ijcm_787_23","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/24 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
Simplified Sample Size Formulas for Detecting a Medically Important Effect.
The sample size is just about the most common question in the minds of many medical researchers. This size determines the reliability of the results and helps to detect a medically important effect when present. Some studies miss an important effect due to inappropriate sample size. Many postgraduate students and established researchers often contact a statistician to help them determine an appropriate sample size for their study. More than 80 formulas are available to calculate sample size for different settings and the choice requires some expertise. Their use is even more difficult because most exact formulas are quite complex. An added difficulty is that different books, software, and websites use different formulas for the same problem. Such discrepancy in the published formulas confounds a biostatistician also. The objective of this communication is to present uniformly looking formulas for many situations together at one place in their simple but correct form, along with the setting where they are applicable. This will help in choosing an appropriate formula for the kind of research one is proposing to do and use it with confidence. This communication is restricted to the sample size required to detect a medically important effect when present - known to the statisticians as the test of hypothesis situation. Such a collection is not available anywhere, not even in any book. The sample size formulas for estimation are different and not discussed here.
期刊介绍:
The Indian Journal of Community Medicine (IJCM, ISSN 0970-0218), is the official organ & the only official journal of the Indian Association of Preventive and Social Medicine (IAPSM). It is a peer-reviewed journal which is published Quarterly. The journal publishes original research articles, focusing on family health care, epidemiology, biostatistics, public health administration, health care delivery, national health problems, medical anthropology and social medicine, invited annotations and comments, invited papers on recent advances, clinical and epidemiological diagnosis and management; editorial correspondence and book reviews.