1. Introduction This paper is the second in a 3-part series focusing on missing data. In a clinical study missing data can occur for various reasons, with or without any actual loss of study participants because
1.Introduction Power and sample size estimation constitutes an important component of designing and planning modern scientific studies.It provides information for assessing the feasibility of a study to detect treatme...1.Introduction Power and sample size estimation constitutes an important component of designing and planning modern scientific studies.It provides information for assessing the feasibility of a study to detect treatment effects and for estimating the resources needed to conduct the project.This tutorial discusses the basic concepts of power analysis and the major differences between hypothesis testing and power analyses.We also展开更多
One of the most common challenges in biomedical and psychosocial research is missing data, which occurs when respondents refuse to provide answers to sensitive questions and when study subjects are lost to follow-u...One of the most common challenges in biomedical and psychosocial research is missing data, which occurs when respondents refuse to provide answers to sensitive questions and when study subjects are lost to follow-up during the repeated assessments of longitudinal trials. This paper is the first in a 3-part series focusing on this important topic; it describes different types of missing data and their differential effects on model estimates, focusing on study design strategies that can be used to prevent or minimize missing data and, thus, maintain the scientific integrity of the research. The second paper in the series will discuss implementation strategies展开更多
With recent advances in genotyping and sequencing technologies,many disease susceptibility loci have been identified.However,much of the genetic heritability remains unexplained and the replication rate between indepe...With recent advances in genotyping and sequencing technologies,many disease susceptibility loci have been identified.However,much of the genetic heritability remains unexplained and the replication rate between independent studies is still low.Meanwhile,there have been increasing efforts on functional annotations of the entire human genome,such as the Encyclopedia of DNA Elements(ENCODE)project and other similar projects.It has been shown that incorporating these functional annotations to prioritize genome wide association signals may help identify true association signals.However,to our knowledge,the extent of the improvement when functional annotation data are considered has not been studied in the literature.In this article,we propose a statistical framework to estimate the improvement in replication rate with annotation data,and apply it to Crohn’s disease and DNase I hypersensitive sites.The results show that with cell line specific functional annotations,the expected replication rate is improved,but only at modest level.展开更多
基金supported by the the US Department of Veterans Affairs,Veterans Health Administration,Office of Research and Development,Clinical Science Research and Development Service,Cooperative Studies Program,Study#574
文摘1. Introduction This paper is the second in a 3-part series focusing on missing data. In a clinical study missing data can occur for various reasons, with or without any actual loss of study participants because
基金supported in part by National Institute on Drug Abuse(NIDA)grants K01 DA029643 and R01DA016750National Institute on Alcohol Abuse and Alcoholism(NIAAA)grants R21 AA021380 and R21 AA020319+9 种基金the National Alliance for Research on Schizophrenia and Depression(NARSAD)Award 17616(L.Z.)ABMRF/The Foundation for Alcohol Research(L.Z.)Funding and other supports for phenotype and genotype data were provided through the National Institutes of Health(NIH)Genes,Environment and Health Initiative(GEI)(U01HG004422,U01HG004436 and U01HG004438)the GENEVA Coordinating Center(U01HG004446)the NIAAA(U10AA008401,R01AA013320,P60AA011998)the NIDA(R01DA013423)the National Cancer Institute(P01 CA089392)the NIH contract‘High throughput genotyping for studying the genetic contributions to human disease’(HHSN268200782096C)the Center for Inherited Disease Research(CIDR)the National Center for Biotechnology Information.Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research
基金supported in part by the Clinical and Translational Science Collaborative of Cleveland,UL1TR000439the University of Rochester,5-27607,from the National Institutes of Health
文摘1.Introduction Power and sample size estimation constitutes an important component of designing and planning modern scientific studies.It provides information for assessing the feasibility of a study to detect treatment effects and for estimating the resources needed to conduct the project.This tutorial discusses the basic concepts of power analysis and the major differences between hypothesis testing and power analyses.We also
文摘One of the most common challenges in biomedical and psychosocial research is missing data, which occurs when respondents refuse to provide answers to sensitive questions and when study subjects are lost to follow-up during the repeated assessments of longitudinal trials. This paper is the first in a 3-part series focusing on this important topic; it describes different types of missing data and their differential effects on model estimates, focusing on study design strategies that can be used to prevent or minimize missing data and, thus, maintain the scientific integrity of the research. The second paper in the series will discuss implementation strategies
基金supported in part by the National Institutes of Health(R01 GM59507 and U01 HG005718)the VA Cooperative Studies Program of the Department of Veterans Affairs,Office of Research and Development
文摘With recent advances in genotyping and sequencing technologies,many disease susceptibility loci have been identified.However,much of the genetic heritability remains unexplained and the replication rate between independent studies is still low.Meanwhile,there have been increasing efforts on functional annotations of the entire human genome,such as the Encyclopedia of DNA Elements(ENCODE)project and other similar projects.It has been shown that incorporating these functional annotations to prioritize genome wide association signals may help identify true association signals.However,to our knowledge,the extent of the improvement when functional annotation data are considered has not been studied in the literature.In this article,we propose a statistical framework to estimate the improvement in replication rate with annotation data,and apply it to Crohn’s disease and DNase I hypersensitive sites.The results show that with cell line specific functional annotations,the expected replication rate is improved,but only at modest level.
