Pub Date : 2019-01-01DOI: 10.1080/24709360.2019.1587264
Sumith Gunasekera, Lakmali Weerasena, Aruna Saram, O. Ajumobi
It has become increasingly common in epidemiological studies to pool specimens across subjects as a useful cot-cutting technique to achieve accurate quantification of biomarkers and certain environmental chemicals. The data collected from these pooled samples can then be utilized to estimate the Youden Index, which measures biomarker's effectiveness and aids in the selection of an optimal threshold value, as a summary measure of the Receiver Operating Characteristic curve. The aim of this paper is to make use of generalized approach to estimate and testing of the Youden index. This goal is accomplished by the comparison of classical and generalized procedures for the Youden Index with the aid of pooled samples from the shifted-exponentially distributed biomarkers for the low-risk and high-risk patients. These are juxtaposed using confidence intervals, p-values, power of the test, size of the test, and coverage probability with a wide-ranging simulation study featuring a selection of various scenarios. In order to demonstrate the advantages of the proposed generalized procedures over its classical counterpart, an illustrative example is discussed using the Duchenne Muscular Dystrophy data available at http://biostat.mc.vanderbilt.edu/wiki/Main/DataSets or http://lib.stat.cmu.edu/datasets/.
{"title":"Exact inference for the Youden index to discriminate individuals using two-parameter exponentially distributed pooled samples","authors":"Sumith Gunasekera, Lakmali Weerasena, Aruna Saram, O. Ajumobi","doi":"10.1080/24709360.2019.1587264","DOIUrl":"https://doi.org/10.1080/24709360.2019.1587264","url":null,"abstract":"It has become increasingly common in epidemiological studies to pool specimens across subjects as a useful cot-cutting technique to achieve accurate quantification of biomarkers and certain environmental chemicals. The data collected from these pooled samples can then be utilized to estimate the Youden Index, which measures biomarker's effectiveness and aids in the selection of an optimal threshold value, as a summary measure of the Receiver Operating Characteristic curve. The aim of this paper is to make use of generalized approach to estimate and testing of the Youden index. This goal is accomplished by the comparison of classical and generalized procedures for the Youden Index with the aid of pooled samples from the shifted-exponentially distributed biomarkers for the low-risk and high-risk patients. These are juxtaposed using confidence intervals, p-values, power of the test, size of the test, and coverage probability with a wide-ranging simulation study featuring a selection of various scenarios. In order to demonstrate the advantages of the proposed generalized procedures over its classical counterpart, an illustrative example is discussed using the Duchenne Muscular Dystrophy data available at http://biostat.mc.vanderbilt.edu/wiki/Main/DataSets or http://lib.stat.cmu.edu/datasets/.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"3 1","pages":"38 - 61"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2019.1587264","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48586931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.1080/24709360.2019.1671096
Nidhiya Menon, Binukumar Bhaskarapillai, A. Richardson
ABSTRACT Many studies have addressed the factors associated with HIV in the Indian population. Some of these studies have used sampling weights for the risk estimation of factors associated with HIV, but few studies have adjusted for the multilevel structure of survey data. The National Family Health Survey 3 collected data across India between 2005 and 2006. 38,715 females and 66,212 males with complete information were analyzed. To account for the correlations within clusters, a three-level model was employed. Bivariate and multivariable mixed effect logistic regression analysis were performed to identify factors associated with HIV. Intracluster correlation coefficients were used to assess the relatedness of each pair of variables within clusters. Variables pertaining to no knowledge of contraceptive methods, age at first marriage, wealth index and noncoverage of PSUs by Anganwadis were significant risk factors for HIV when the multileveled model was used for analysis. This study has identified the risk profile for HIV infection using an appropriate modeling strategy and has highlighted the consequences of ignoring the structure of the data. It offers a methodological guide towards an applied approach to the identification of future risk and the need to customize intervention to address HIV infection in the Indian population.
{"title":"Effect of modeling a multilevel structure on the Indian population to identify the factors influencing HIV infection","authors":"Nidhiya Menon, Binukumar Bhaskarapillai, A. Richardson","doi":"10.1080/24709360.2019.1671096","DOIUrl":"https://doi.org/10.1080/24709360.2019.1671096","url":null,"abstract":"ABSTRACT Many studies have addressed the factors associated with HIV in the Indian population. Some of these studies have used sampling weights for the risk estimation of factors associated with HIV, but few studies have adjusted for the multilevel structure of survey data. The National Family Health Survey 3 collected data across India between 2005 and 2006. 38,715 females and 66,212 males with complete information were analyzed. To account for the correlations within clusters, a three-level model was employed. Bivariate and multivariable mixed effect logistic regression analysis were performed to identify factors associated with HIV. Intracluster correlation coefficients were used to assess the relatedness of each pair of variables within clusters. Variables pertaining to no knowledge of contraceptive methods, age at first marriage, wealth index and noncoverage of PSUs by Anganwadis were significant risk factors for HIV when the multileveled model was used for analysis. This study has identified the risk profile for HIV infection using an appropriate modeling strategy and has highlighted the consequences of ignoring the structure of the data. It offers a methodological guide towards an applied approach to the identification of future risk and the need to customize intervention to address HIV infection in the Indian population.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"3 1","pages":"126 - 139"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2019.1671096","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46923546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-16DOI: 10.1080/24709360.2018.1553362
T. Kashner, Christopher Clarke, D. Aron, John M. Byrne, G. Cannon, D. Deemer, S. Gilman, C. Kaminetzky, L. Loo, Sophia Li, Annie B. Wicker, S. Keitz
ABSTRACT For its clinical, epidemiologic, educational, and health services research, evaluation, administrative, regulatory, and accreditation purposes, the perceptions survey is a data collection tool that asks observers to describe perceptions of their experiences with a defined phenomenon of interest. In practice, these surveys are often subject to criticism for not having been thoroughly evaluated before its first application using a consistent and comprehensive set of criteria for validity and reliability. This paper introduces a 9-criteria framework to assess perceptions surveys that integrates criteria from multiple evaluation sources. The 9-criteria framework was applied to data from the Department of Veterans Affairs’ Learners’ Perceptions Survey (LPS) that had been administered to national and local samples, and from findings obtained through a literature review involving LPS survey data. We show that the LPS is a robust tool that may serve as a model for design and validation of other perceptions surveys. Findings underscore the importance of using all nine criteria to validate perceptions survey data.
{"title":"The 9-criteria evaluation framework for perceptions survey: the case of VA’s Learners’ Perceptions Survey","authors":"T. Kashner, Christopher Clarke, D. Aron, John M. Byrne, G. Cannon, D. Deemer, S. Gilman, C. Kaminetzky, L. Loo, Sophia Li, Annie B. Wicker, S. Keitz","doi":"10.1080/24709360.2018.1553362","DOIUrl":"https://doi.org/10.1080/24709360.2018.1553362","url":null,"abstract":"ABSTRACT For its clinical, epidemiologic, educational, and health services research, evaluation, administrative, regulatory, and accreditation purposes, the perceptions survey is a data collection tool that asks observers to describe perceptions of their experiences with a defined phenomenon of interest. In practice, these surveys are often subject to criticism for not having been thoroughly evaluated before its first application using a consistent and comprehensive set of criteria for validity and reliability. This paper introduces a 9-criteria framework to assess perceptions surveys that integrates criteria from multiple evaluation sources. The 9-criteria framework was applied to data from the Department of Veterans Affairs’ Learners’ Perceptions Survey (LPS) that had been administered to national and local samples, and from findings obtained through a literature review involving LPS survey data. We show that the LPS is a robust tool that may serve as a model for design and validation of other perceptions surveys. Findings underscore the importance of using all nine criteria to validate perceptions survey data.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"4 1","pages":"140 - 171"},"PeriodicalIF":0.0,"publicationDate":"2018-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2018.1553362","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48159892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-17DOI: 10.1080/24709360.2018.1519990
W. B. Vogel, Guoqing Chen
Department of Veterans Affairs (VA) health services researchers often adjust for the differing risk profiles of selected patient populations for a variety of purposes. This paper explains the major reasons to conduct risk adjustment and provides a high level overview of what risk adjustment actually does and how the results of risk adjustment can be used in different ways for different purposes. The paper also discusses choosing a diagnostic classification system and describes some of the systems commonly used in risk adjustment along with comorbidity/severity indices and individual disease taxonomies. The factors influencing the choice of diagnostic classification systems and other commonly used risk adjustors are also presented along with a discussion of data requirements. Statistical approaches to risk adjustment are also briefly discussed. The paper concludes with some recommendations concerning risk adjustment that should be considering when developing research proposals.
{"title":"An introduction to the why and how of risk adjustment","authors":"W. B. Vogel, Guoqing Chen","doi":"10.1080/24709360.2018.1519990","DOIUrl":"https://doi.org/10.1080/24709360.2018.1519990","url":null,"abstract":"Department of Veterans Affairs (VA) health services researchers often adjust for the differing risk profiles of selected patient populations for a variety of purposes. This paper explains the major reasons to conduct risk adjustment and provides a high level overview of what risk adjustment actually does and how the results of risk adjustment can be used in different ways for different purposes. The paper also discusses choosing a diagnostic classification system and describes some of the systems commonly used in risk adjustment along with comorbidity/severity indices and individual disease taxonomies. The factors influencing the choice of diagnostic classification systems and other commonly used risk adjustors are also presented along with a discussion of data requirements. Statistical approaches to risk adjustment are also briefly discussed. The paper concludes with some recommendations concerning risk adjustment that should be considering when developing research proposals.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"4 1","pages":"84 - 97"},"PeriodicalIF":0.0,"publicationDate":"2018-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2018.1519990","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48627650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-07DOI: 10.1080/24709360.2018.1477467
W. Henderson
ABSTRACT This article attempts to outline the most important aspects to consider when planning a randomized controlled clinical trial (RCT) and writing a proposal for the RCT. RCTs are generally formulated by a planning committee that should be comprised of members with expertise in the different important features of the trial. Important considerations include background, objectives/hypotheses, experimental design, patient population and recruitment/retention plan, stratification/randomization, experimental treatment, control or comparison treatment, blinding, primary and secondary outcomes, patient follow-up, data to be collected, capture of data and confidentiality, handling of adverse events, sample size/statistical power and feasibility, statistical analysis, ethical issues, and governance of the trial. Real world examples, mostly drawn from the US Department of Veterans Affairs Cooperative Studies Program, are used to illustrate the various important considerations.
{"title":"Clinical trials design and conduct","authors":"W. Henderson","doi":"10.1080/24709360.2018.1477467","DOIUrl":"https://doi.org/10.1080/24709360.2018.1477467","url":null,"abstract":"ABSTRACT This article attempts to outline the most important aspects to consider when planning a randomized controlled clinical trial (RCT) and writing a proposal for the RCT. RCTs are generally formulated by a planning committee that should be comprised of members with expertise in the different important features of the trial. Important considerations include background, objectives/hypotheses, experimental design, patient population and recruitment/retention plan, stratification/randomization, experimental treatment, control or comparison treatment, blinding, primary and secondary outcomes, patient follow-up, data to be collected, capture of data and confidentiality, handling of adverse events, sample size/statistical power and feasibility, statistical analysis, ethical issues, and governance of the trial. Real world examples, mostly drawn from the US Department of Veterans Affairs Cooperative Studies Program, are used to illustrate the various important considerations.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"4 1","pages":"24 - 37"},"PeriodicalIF":0.0,"publicationDate":"2018-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2018.1477467","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42968453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-07DOI: 10.1080/24709360.2018.1477468
M. Maciejewski
ABSTRACT Quasi-experiments are similar to randomized controlled trials in many respects, but there are many challenges in designing and conducting a quasi-experiment when internal validity threats are introduced from the absence of randomization. This paper outlines design, measurement and statistical issues that must be considered prior to the conduct of a quasi-experimental evaluation. We discuss challenges for the internal validity of quasi-experimental designs, inclusion/exclusion criteria, treatment and comparator cohort definitions, and the five types of explanatory variables that one must classify prior to analysis. We discuss data collection and confidentiality, statistical power and conclude with analytic issues that one must consider.
{"title":"Quasi-experimental design","authors":"M. Maciejewski","doi":"10.1080/24709360.2018.1477468","DOIUrl":"https://doi.org/10.1080/24709360.2018.1477468","url":null,"abstract":"ABSTRACT Quasi-experiments are similar to randomized controlled trials in many respects, but there are many challenges in designing and conducting a quasi-experiment when internal validity threats are introduced from the absence of randomization. This paper outlines design, measurement and statistical issues that must be considered prior to the conduct of a quasi-experimental evaluation. We discuss challenges for the internal validity of quasi-experimental designs, inclusion/exclusion criteria, treatment and comparator cohort definitions, and the five types of explanatory variables that one must classify prior to analysis. We discuss data collection and confidentiality, statistical power and conclude with analytic issues that one must consider.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"4 1","pages":"38 - 47"},"PeriodicalIF":0.0,"publicationDate":"2018-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2018.1477468","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46679140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01DOI: 10.1080/24709360.2018.1469809
Zhicheng Du, Wangjian Zhang, Dingmei Zhang, Shicheng Yu, Y. Hao
ABSTRACT Hand, foot, and mouth disease (HFMD) has become a major public health issue in China, especially in Guangdong. The burden of severe cases deserves further attention. We hereby explored the epidemiological features of severe HFMD in Guangdong. Patients who were from rural areas (OR = 2.03, 95% CI: 1.86–2.21), males (OR = 1.17, 1.07–1.28), aged ≤3 years old (2.48, 1.68–3.68, and 1.63, 1.10–2.41, for ≤1 and 2–3 years, respectively), and/or infected with EV71 (6.69, 5.98–7.49) tended to progress to severe status. Cases from rural areas tended to have a longer interval from onset to diagnosis (p < .001; i.e. the proportions of each interval (≤1, ∼2, ∼3, ∼4, and >4 days) for rural and urban areas in 2009 were 14%, 13%, 14%, 8%, 51%, and 21%, 21%, 15%, 11%, 31%, respectively). The spatial pattern of the epidemics clarified by the flexible scan statistic showed that the clusters of severe cases were observed to be expanding from the Pearl River Delta Region to the Eastern Region and the Mountainous Region. Overall, the relative risk of the most likely clusters ranged from 5.548 to 15.558 (all p < .001). Our results were particularly practical and important for developing severe HFMD-targeted control programs in the context of disease surveillance. Abbreviations: CA16: Coxsackievirus A16; EV71: enterovirus 71; GDP: gross domestic product; HFMD: hand; foot and mouth disease.
{"title":"Epidemiological characteristics of severe cases of hand, foot, and mouth disease in Guangdong, China","authors":"Zhicheng Du, Wangjian Zhang, Dingmei Zhang, Shicheng Yu, Y. Hao","doi":"10.1080/24709360.2018.1469809","DOIUrl":"https://doi.org/10.1080/24709360.2018.1469809","url":null,"abstract":"ABSTRACT Hand, foot, and mouth disease (HFMD) has become a major public health issue in China, especially in Guangdong. The burden of severe cases deserves further attention. We hereby explored the epidemiological features of severe HFMD in Guangdong. Patients who were from rural areas (OR = 2.03, 95% CI: 1.86–2.21), males (OR = 1.17, 1.07–1.28), aged ≤3 years old (2.48, 1.68–3.68, and 1.63, 1.10–2.41, for ≤1 and 2–3 years, respectively), and/or infected with EV71 (6.69, 5.98–7.49) tended to progress to severe status. Cases from rural areas tended to have a longer interval from onset to diagnosis (p < .001; i.e. the proportions of each interval (≤1, ∼2, ∼3, ∼4, and >4 days) for rural and urban areas in 2009 were 14%, 13%, 14%, 8%, 51%, and 21%, 21%, 15%, 11%, 31%, respectively). The spatial pattern of the epidemics clarified by the flexible scan statistic showed that the clusters of severe cases were observed to be expanding from the Pearl River Delta Region to the Eastern Region and the Mountainous Region. Overall, the relative risk of the most likely clusters ranged from 5.548 to 15.558 (all p < .001). Our results were particularly practical and important for developing severe HFMD-targeted control programs in the context of disease surveillance. Abbreviations: CA16: Coxsackievirus A16; EV71: enterovirus 71; GDP: gross domestic product; HFMD: hand; foot and mouth disease.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"2 1","pages":"114 - 99"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2018.1469809","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43540542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01DOI: 10.1080/24709360.2017.1406567
Binyan Jiang, Jialiang Li, J. Fine
ABSTRACT Instrumental variable (IV) methods are popular in non-experimental settings to estimate the causal effects of scientific interventions. These approaches allow for the consistent estimation of treatment effects even if major confounders are unavailable. There have been some extensions of IV methods to survival analysis recently. We specifically consider the two-step residual inclusion (2SRI) estimator proposed recently in the literature for the additive hazards regression model in this paper. Assuming linear structural equation models for the hazard function, we may attain a closed-form, two-stage estimator for the causal effect in the additive hazards model. The main contribution of this paper is to provide theoretical works for the 2SRI approach. The asymptotic properties of the estimators are rigorously established and the resulting inferences are shown to perform well in numerical studies.
{"title":"On two-step residual inclusion estimator for instrument variable additive hazards model","authors":"Binyan Jiang, Jialiang Li, J. Fine","doi":"10.1080/24709360.2017.1406567","DOIUrl":"https://doi.org/10.1080/24709360.2017.1406567","url":null,"abstract":"ABSTRACT Instrumental variable (IV) methods are popular in non-experimental settings to estimate the causal effects of scientific interventions. These approaches allow for the consistent estimation of treatment effects even if major confounders are unavailable. There have been some extensions of IV methods to survival analysis recently. We specifically consider the two-step residual inclusion (2SRI) estimator proposed recently in the literature for the additive hazards regression model in this paper. Assuming linear structural equation models for the hazard function, we may attain a closed-form, two-stage estimator for the causal effect in the additive hazards model. The main contribution of this paper is to provide theoretical works for the 2SRI approach. The asymptotic properties of the estimators are rigorously established and the resulting inferences are shown to perform well in numerical studies.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"2 1","pages":"47 - 60"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2017.1406567","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48126753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01DOI: 10.1080/24709360.2018.1529347
R. Sakurai, S. Hattori
ABSTRACT Interval-censored data are common in medical research. Fully parametric models provide simple and efficient inference for the estimation of survival functions using interval-censored observations. Inference based on a parametric regression model requires the complete specification of the probability density function, and therefore, correctly specifying the model is crucial, while the regression diagnostic is a very important step. However, regression diagnostic methods for use with the interval-censored data have not been completely developed. Here, we developed a model-checking procedure based on the cumulative martingale residuals for the interval-censored observations. We employed the conditional expectation of residuals for the diagnostics, because the data showing the exact failure time cannot be obtained for the interval-censoring analyses, and developed the formal resampling-based supremum-type test and graphical model-checking techniques. A simulation study demonstrated an excellent performance of the proposed method during the detection of a misspecified functional form of covariates in the finite sample. Furthermore, we used this method for the analysis of the medical checkup data obtained in Japan.
{"title":"Goodness-of-fit test for the parametric proportional hazard regression model with interval-censored data","authors":"R. Sakurai, S. Hattori","doi":"10.1080/24709360.2018.1529347","DOIUrl":"https://doi.org/10.1080/24709360.2018.1529347","url":null,"abstract":"ABSTRACT Interval-censored data are common in medical research. Fully parametric models provide simple and efficient inference for the estimation of survival functions using interval-censored observations. Inference based on a parametric regression model requires the complete specification of the probability density function, and therefore, correctly specifying the model is crucial, while the regression diagnostic is a very important step. However, regression diagnostic methods for use with the interval-censored data have not been completely developed. Here, we developed a model-checking procedure based on the cumulative martingale residuals for the interval-censored observations. We employed the conditional expectation of residuals for the diagnostics, because the data showing the exact failure time cannot be obtained for the interval-censoring analyses, and developed the formal resampling-based supremum-type test and graphical model-checking techniques. A simulation study demonstrated an excellent performance of the proposed method during the detection of a misspecified functional form of covariates in the finite sample. Furthermore, we used this method for the analysis of the medical checkup data obtained in Japan.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"2 1","pages":"115 - 131"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2018.1529347","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48434276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01DOI: 10.1080/24709360.2018.1529346
Duo Jiang, Miaoyan Wang
ABSTRACT The advent of large-scale genetic studies has helped bring a new era of biomedical research on dissecting the genetic architecture of complex human disease. Genome-wide association studies (GWASs) and next-generation sequencing studies are two popular tools for identifying genetic variants that are associated with complex traits. This article overviews some of the most important statistical tools for analyzing data from these two types of studies, with an emphasis on single-SNP tests for common variants and region-based tests for rare variants. We compare various statistical methods for common and rare variants in humans, and describe some critical considerations to guide the choice of an analysis method. Also discussed are the related topics of sample ascertainment, missing heritability, and multiple testing correction, as well as some remaining analytical challenges presented by complex trait association mapping using genomic data obtained via high-throughput technologies.
{"title":"Recent developments in statistical methods for GWAS and high-throughput sequencing association studies of complex traits","authors":"Duo Jiang, Miaoyan Wang","doi":"10.1080/24709360.2018.1529346","DOIUrl":"https://doi.org/10.1080/24709360.2018.1529346","url":null,"abstract":"ABSTRACT The advent of large-scale genetic studies has helped bring a new era of biomedical research on dissecting the genetic architecture of complex human disease. Genome-wide association studies (GWASs) and next-generation sequencing studies are two popular tools for identifying genetic variants that are associated with complex traits. This article overviews some of the most important statistical tools for analyzing data from these two types of studies, with an emphasis on single-SNP tests for common variants and region-based tests for rare variants. We compare various statistical methods for common and rare variants in humans, and describe some critical considerations to guide the choice of an analysis method. Also discussed are the related topics of sample ascertainment, missing heritability, and multiple testing correction, as well as some remaining analytical challenges presented by complex trait association mapping using genomic data obtained via high-throughput technologies.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"2 1","pages":"132 - 159"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2018.1529346","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45235221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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