Pub Date : 2019-01-01DOI: 10.1080/24709360.2019.1660111
A. Biswas, Rahul Bhattacharya, Soumyadeep Das
ABSTRACT Weighing the cumulative odds ratios suitably, a two treatment response adaptive design for phase III clinical trial is proposed for ordinal categorical responses. Properties of the proposed design are investigated theoretically as well as empirically. Applicability of the design is further verified using a data pertaining to a real clinical trial with trauma patients, where the responses are observed in an ordinal categorical scale.
{"title":"A response adaptive design for ordinal categorical responses weighing the cumulative odds ratios","authors":"A. Biswas, Rahul Bhattacharya, Soumyadeep Das","doi":"10.1080/24709360.2019.1660111","DOIUrl":"https://doi.org/10.1080/24709360.2019.1660111","url":null,"abstract":"ABSTRACT Weighing the cumulative odds ratios suitably, a two treatment response adaptive design for phase III clinical trial is proposed for ordinal categorical responses. Properties of the proposed design are investigated theoretically as well as empirically. Applicability of the design is further verified using a data pertaining to a real clinical trial with trauma patients, where the responses are observed in an ordinal categorical scale.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"3 1","pages":"109 - 125"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2019.1660111","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47455932","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 : 2019-01-01Epub Date: 2018-12-31DOI: 10.1080/24709360.2018.1557797
Madan Gopal Kundu, Jaroslaw Harezlak
Longitudinal changes in a population of interest are often heterogeneous and may be influenced by a combination of baseline factors. In such cases, traditional linear mixed effects models (Laird and Ware, 1982) assuming common parametric form for the mean structure may not be applicable. We show that the regression tree methodology for longitudinal data can identify and characterize longitudinally homogeneous subgroups. Most of the currently available regression tree construction methods are either limited to a repeated measures scenario or combine the heterogeneity among subgroups with the random inter-subject variability. We propose a longitudinal classification and regression tree (LongCART) algorithm under conditional inference framework (Hothorn, Hornik and Zeileis, 2006) that overcomes these limitations utilizing a two-step approach. The LongCART algorithm first selects the partitioning variable via a parameter instability test and then finds the optimal split for the selected partitioning variable. Thus, at each node, the decision of further splitting is type-I error controlled and thus it guards against variable selection bias, over-fitting and spurious splitting. We have obtained the asymptotic results for the proposed instability test and examined its finite sample behavior through simulation studies. Comparative performance of LongCART algorithm were evaluated empirically via simulation studies. Finally, we applied LongCART to study the longitudinal changes in choline levels among HIV-positive patients.
研究人群的纵向变化往往是异质的，可能受到基线因素组合的影响。在这种情况下，传统的线性混合效应模型(Laird and Ware, 1982)假设平均结构的共同参数形式可能不适用。我们证明了纵向数据的回归树方法可以识别和表征纵向均匀的子群。目前大多数可用的回归树构建方法要么局限于重复测量场景，要么将子组之间的异质性与随机的主体间变异性结合起来。我们提出了一种在条件推理框架下的纵向分类和回归树(LongCART)算法(Hothorn, Hornik和Zeileis, 2006)，该算法利用两步法克服了这些限制。LongCART算法首先通过参数不稳定性测试选择分区变量，然后为所选分区变量找到最优分割。因此，在每个节点上，进一步分裂的决策是类型- i错误控制的，从而防止了变量选择偏差，过拟合和虚假分裂。我们得到了所提出的不稳定性试验的渐近结果，并通过模拟研究检验了其有限样本行为。通过仿真研究，对LongCART算法的性能进行了实证评价。最后，我们应用LongCART研究hiv阳性患者胆碱水平的纵向变化。
{"title":"Regression Trees for Longitudinal Data with Baseline Covariates.","authors":"Madan Gopal Kundu, Jaroslaw Harezlak","doi":"10.1080/24709360.2018.1557797","DOIUrl":"https://doi.org/10.1080/24709360.2018.1557797","url":null,"abstract":"<p><p>Longitudinal changes in a population of interest are often heterogeneous and may be influenced by a combination of baseline factors. In such cases, traditional linear mixed effects models (Laird and Ware, 1982) assuming common parametric form for the mean structure may not be applicable. We show that the regression tree methodology for longitudinal data can identify and characterize longitudinally homogeneous subgroups. Most of the currently available regression tree construction methods are either limited to a repeated measures scenario or combine the heterogeneity among subgroups with the random inter-subject variability. We propose a longitudinal classification and regression tree (LongCART) algorithm under conditional inference framework (Hothorn, Hornik and Zeileis, 2006) that overcomes these limitations utilizing a two-step approach. The LongCART algorithm first selects the partitioning variable via a <i>parameter instability test</i> and then finds the optimal split for the selected partitioning variable. Thus, at each node, the decision of further splitting is type-I error controlled and thus it guards against variable selection bias, over-fitting and spurious splitting. We have obtained the asymptotic results for the proposed instability test and examined its finite sample behavior through simulation studies. Comparative performance of LongCART algorithm were evaluated empirically via simulation studies. Finally, we applied LongCART to study the longitudinal changes in <i>choline</i> levels among HIV-positive patients.</p>","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"3 1","pages":"1-22"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2018.1557797","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36896395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.1080/24709360.2019.1670513
D. Rubin
ABSTRACT Causal inference refers to the process of inferring what would happen in the future if we change what we are doing, or inferring what would have happened in the past, if we had done something different in the distant past. Humans adjust our behaviors by anticipating what will happen if we act in different ways, using past experiences to inform these choices. ‘Essential’ here means in the mathematical sense of excluding the unnecessary and including only the necessary, e.g. stating that the Pythagorean theorem works for an isosceles right triangle is bad mathematics because it includes the unnecessary adjective isosceles; of course this is not as bad as omitting the adjective ‘right.’ I find much of what is written about causal inference to be mathematically inapposite in one of these senses because the descriptions either include irrelevant clutter or omit conditions required for the correctness of the assertions. The history of formal causal inference is remarkable because its correct formulation is so recent, a twentieth century phenomenon, and its future is intriguing because it is currently undeveloped when applied to investigate interventions applied to conscious humans, and moreover will utilize tools impossible without modern computing.
{"title":"Essential concepts of causal inference: a remarkable history and an intriguing future","authors":"D. Rubin","doi":"10.1080/24709360.2019.1670513","DOIUrl":"https://doi.org/10.1080/24709360.2019.1670513","url":null,"abstract":"ABSTRACT Causal inference refers to the process of inferring what would happen in the future if we change what we are doing, or inferring what would have happened in the past, if we had done something different in the distant past. Humans adjust our behaviors by anticipating what will happen if we act in different ways, using past experiences to inform these choices. ‘Essential’ here means in the mathematical sense of excluding the unnecessary and including only the necessary, e.g. stating that the Pythagorean theorem works for an isosceles right triangle is bad mathematics because it includes the unnecessary adjective isosceles; of course this is not as bad as omitting the adjective ‘right.’ I find much of what is written about causal inference to be mathematically inapposite in one of these senses because the descriptions either include irrelevant clutter or omit conditions required for the correctness of the assertions. The history of formal causal inference is remarkable because its correct formulation is so recent, a twentieth century phenomenon, and its future is intriguing because it is currently undeveloped when applied to investigate interventions applied to conscious humans, and moreover will utilize tools impossible without modern computing.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"3 1","pages":"140 - 155"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2019.1670513","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43617355","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: 10.1080/24709360.2019.1663665
A. Masud, Zhangsheng Yu, W. Tu
Survival data with long-term survivors are common in clinical investigations. Such data are often analyzed with mixture cure rate models. Existing model selection procedures do not readily discriminate nonlinear effects from linear ones. Here, we propose a procedure for accommodating nonlinear effects and for determining the cure rate model composition. The procedure is based on the Least Absolute Shrinkage and Selection Operators (LASSO). Specifically, by partitioning each variable into linear and nonlinear components, we use LASSO to select linear and nonlinear components. Operationally, we model the nonlinear components by cubic B-splines. The procedure adds to the existing variable selection methods an ability to discover hidden nonlinear effects in a cure rate model setting. To implement, we ascertain the maximum likelihood estimates by using an Expectation Maximization (EM) algorithm. We conduct an extensive simulation study to assess the operating characteristics of the selection procedure. We illustrate the use of the method by analyzing data from a real clinical study.
{"title":"Variable selection and nonlinear effect discovery in partially linear mixture cure rate models","authors":"A. Masud, Zhangsheng Yu, W. Tu","doi":"10.1080/24709360.2019.1663665","DOIUrl":"https://doi.org/10.1080/24709360.2019.1663665","url":null,"abstract":"Survival data with long-term survivors are common in clinical investigations. Such data are often analyzed with mixture cure rate models. Existing model selection procedures do not readily discriminate nonlinear effects from linear ones. Here, we propose a procedure for accommodating nonlinear effects and for determining the cure rate model composition. The procedure is based on the Least Absolute Shrinkage and Selection Operators (LASSO). Specifically, by partitioning each variable into linear and nonlinear components, we use LASSO to select linear and nonlinear components. Operationally, we model the nonlinear components by cubic B-splines. The procedure adds to the existing variable selection methods an ability to discover hidden nonlinear effects in a cure rate model setting. To implement, we ascertain the maximum likelihood estimates by using an Expectation Maximization (EM) algorithm. We conduct an extensive simulation study to assess the operating characteristics of the selection procedure. We illustrate the use of the method by analyzing data from a real clinical study.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"3 1","pages":"156 - 177"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2019.1663665","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47487854","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: 10.1080/24709360.2019.1580463
E. Lorenz, C. Jenkner, W. Sauerbrei, H. Becher
Risk and prognostic factors in epidemiological and clinical research are often semicontinuous such that a proportion of individuals have exposure zero, and a continuous distribution among those exposed. We call this a spike at zero (SAZ). Typical examples are consumption of alcohol and tobacco, or hormone receptor levels. To additionally model non-linear functional relationships for SAZ variables, an extension of the fractional polynomial (FP) approach was proposed. To indicate whether or not a value is zero, a binary variable is added to the model. In a two-stage procedure, called FP-spike, it is assessed whether the binary variable and/or the continuous FP function for the positive part is required for a suitable fit. In this paper, we compared the performance of two approaches – standard FP and FP-spike – in the Cox model in a motivating example on breast cancer prognosis and a simulation study. The comparisons lead to the suggestion to generally using FP-spike rather than standard FP when the SAZ effect is considerably large because the method performed better in real data applications and simulation in terms of deviance and functional form. Abbreviations: CI: confidence interval; FP: fractional polynomial; FP1: first degree fractional polynomial; FP2: second degree fractional polynomial; FSP: function selection procedure; HT: hormone therapy; OR: odds ratio; SAZ: spike at zero
{"title":"Modeling exposures with a spike at zero: simulation study and practical application to survival data","authors":"E. Lorenz, C. Jenkner, W. Sauerbrei, H. Becher","doi":"10.1080/24709360.2019.1580463","DOIUrl":"https://doi.org/10.1080/24709360.2019.1580463","url":null,"abstract":"Risk and prognostic factors in epidemiological and clinical research are often semicontinuous such that a proportion of individuals have exposure zero, and a continuous distribution among those exposed. We call this a spike at zero (SAZ). Typical examples are consumption of alcohol and tobacco, or hormone receptor levels. To additionally model non-linear functional relationships for SAZ variables, an extension of the fractional polynomial (FP) approach was proposed. To indicate whether or not a value is zero, a binary variable is added to the model. In a two-stage procedure, called FP-spike, it is assessed whether the binary variable and/or the continuous FP function for the positive part is required for a suitable fit. In this paper, we compared the performance of two approaches – standard FP and FP-spike – in the Cox model in a motivating example on breast cancer prognosis and a simulation study. The comparisons lead to the suggestion to generally using FP-spike rather than standard FP when the SAZ effect is considerably large because the method performed better in real data applications and simulation in terms of deviance and functional form. Abbreviations: CI: confidence interval; FP: fractional polynomial; FP1: first degree fractional polynomial; FP2: second degree fractional polynomial; FSP: function selection procedure; HT: hormone therapy; OR: odds ratio; SAZ: spike at zero","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"3 1","pages":"23 - 37"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2019.1580463","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48298967","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: 10.1080/24709360.2019.1587264
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: 10.1080/24709360.2019.1660110
M. Soltanifar, A. Dupuis, R. Schachar, M. Escobar
The stop signal reaction time (SSRT), a measure of the latency of the stop signal process, has been theoretically formulated using a horse race model of go and stop signal processes by the American scientist Gordon Logan (1994). The SSRT assumes equal impact of the preceding trial type (go/stop) on its measurement. In the case of a violation of this assumption, we consider estimation of SSRT based on the idea of earlier analysis of cluster type go reaction times (GORT) and linear mixed model (LMM) data analysis results. Two clusters of trials were considered including those trials preceded by a go trial and other trials preceded by a stop trial. Given disparities between cluster type SSRTs, we need to consider some new indexes considering the unused cluster type information in the calculations. We introduce mixture SSRT and weighted SSRT as two new distinct indexes of SSRT that address the violated assumption. Mixture SSRT and weighted SSRT are theoretically asymptotically equivalent under special conditions. An example of stop single task (SST) real data is presented to show equivalency of these two new SSRT indexes and their larger magnitude compared to Logan's single 1994 SSRT. Abbreviations: ADHD: attention deficit hyperactivity disorder; ExG: Ex-Gaussiandistribution; GORT: reaction time in a go trial; GORTA: reaction time in a type A gotrial; GORTB: reaction time in a type B go trial; LMM: linear mixed model; SWAN:strengths and weakness of ADHD symptoms and normal behavior rating scale; SSD: stop signal delay; SR: signal respond; SRRT: reaction time in a failedstop trial; SSRT: stop signal reaction times in a stop trial; SST: stop signaltask.
{"title":"A frequentist mixture modeling of stop signal reaction times","authors":"M. Soltanifar, A. Dupuis, R. Schachar, M. Escobar","doi":"10.1080/24709360.2019.1660110","DOIUrl":"https://doi.org/10.1080/24709360.2019.1660110","url":null,"abstract":"The stop signal reaction time (SSRT), a measure of the latency of the stop signal process, has been theoretically formulated using a horse race model of go and stop signal processes by the American scientist Gordon Logan (1994). The SSRT assumes equal impact of the preceding trial type (go/stop) on its measurement. In the case of a violation of this assumption, we consider estimation of SSRT based on the idea of earlier analysis of cluster type go reaction times (GORT) and linear mixed model (LMM) data analysis results. Two clusters of trials were considered including those trials preceded by a go trial and other trials preceded by a stop trial. Given disparities between cluster type SSRTs, we need to consider some new indexes considering the unused cluster type information in the calculations. We introduce mixture SSRT and weighted SSRT as two new distinct indexes of SSRT that address the violated assumption. Mixture SSRT and weighted SSRT are theoretically asymptotically equivalent under special conditions. An example of stop single task (SST) real data is presented to show equivalency of these two new SSRT indexes and their larger magnitude compared to Logan's single 1994 SSRT. Abbreviations: ADHD: attention deficit hyperactivity disorder; ExG: Ex-Gaussiandistribution; GORT: reaction time in a go trial; GORTA: reaction time in a type A gotrial; GORTB: reaction time in a type B go trial; LMM: linear mixed model; SWAN:strengths and weakness of ADHD symptoms and normal behavior rating scale; SSD: stop signal delay; SR: signal respond; SRRT: reaction time in a failedstop trial; SSRT: stop signal reaction times in a stop trial; SST: stop signaltask.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"3 1","pages":"108 - 90"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2019.1660110","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41851070","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.
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