Semiparametric Regression Analysis of Longitudinal Data With Informative Observation Times
Statistical analysis of longitudinal data has been discussed by many authors, and a number of methods have been proposed. Most of the research have focused on situations where observation times are independent of or carry no information about the response variable and therefore rely on conditional i...
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| Veröffentlicht in: | Journal of the American Statistical Association 2005-09, Vol.100 (471), p.882-889 |
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| container_issue | 471 |
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| container_title | Journal of the American Statistical Association |
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| creator | Sun, Jianguo Park, Do-Hwan Sun, Liuquan Zhao, Xingqiu |
| description | Statistical analysis of longitudinal data has been discussed by many authors, and a number of methods have been proposed. Most of the research have focused on situations where observation times are independent of or carry no information about the response variable and therefore rely on conditional inference procedures given the observation times. This article considers a different situation, where the independence assumption may not hold; that is, the observation times may carry information about the response variable. For inference, estimating equation approaches are proposed, and both large-sample and final-sample properties of the proposed methods are established. The methodology is applied to a bladder cancer study that motivated this investigation. |
| doi_str_mv | 10.1198/016214505000000060 |
| format | Article |
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Most of the research have focused on situations where observation times are independent of or carry no information about the response variable and therefore rely on conditional inference procedures given the observation times. This article considers a different situation, where the independence assumption may not hold; that is, the observation times may carry information about the response variable. For inference, estimating equation approaches are proposed, and both large-sample and final-sample properties of the proposed methods are established. The methodology is applied to a bladder cancer study that motivated this investigation.</description><identifier>ISSN: 0162-1459</identifier><identifier>EISSN: 1537-274X</identifier><identifier>DOI: 10.1198/016214505000000060</identifier><identifier>CODEN: JSTNAL</identifier><language>eng</language><publisher>Alexandria, VA: Taylor & Francis</publisher><subject>Consistent estimators ; Estimating equation ; Estimation bias ; Estimation methods ; Exact sciences and technology ; Inference ; Informative observation times ; Linear inference, regression ; Longitudinal data ; Mathematics ; Modeling ; Nonhomogeneous poisson process ; Nonparametric inference ; Observational research ; Poisson distribution ; Probability and statistics ; Probability theory and stochastic processes ; Regression analysis ; School dropouts ; Sciences and techniques of general use ; Statistical analysis ; Statistics ; Stochastic processes ; Theory and Methods ; Tumors ; Urinary bladder</subject><ispartof>Journal of the American Statistical Association, 2005-09, Vol.100 (471), p.882-889</ispartof><rights>American Statistical Association 2005</rights><rights>Copyright 2005 American Statistical Association</rights><rights>2005 INIST-CNRS</rights><rights>Copyright American Statistical Association Sep 2005</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c442t-714e524a9cf9f42b7dd91e797312956ac249f26696bd7b6754d9c9d0a86754f13</citedby><cites>FETCH-LOGICAL-c442t-714e524a9cf9f42b7dd91e797312956ac249f26696bd7b6754d9c9d0a86754f13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/27590620$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/27590620$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,780,784,803,832,27924,27925,58017,58021,58250,58254,59647,60436</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17032919$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Sun, Jianguo</creatorcontrib><creatorcontrib>Park, Do-Hwan</creatorcontrib><creatorcontrib>Sun, Liuquan</creatorcontrib><creatorcontrib>Zhao, Xingqiu</creatorcontrib><title>Semiparametric Regression Analysis of Longitudinal Data With Informative Observation Times</title><title>Journal of the American Statistical Association</title><description>Statistical analysis of longitudinal data has been discussed by many authors, and a number of methods have been proposed. Most of the research have focused on situations where observation times are independent of or carry no information about the response variable and therefore rely on conditional inference procedures given the observation times. This article considers a different situation, where the independence assumption may not hold; that is, the observation times may carry information about the response variable. For inference, estimating equation approaches are proposed, and both large-sample and final-sample properties of the proposed methods are established. The methodology is applied to a bladder cancer study that motivated this investigation.</description><subject>Consistent estimators</subject><subject>Estimating equation</subject><subject>Estimation bias</subject><subject>Estimation methods</subject><subject>Exact sciences and technology</subject><subject>Inference</subject><subject>Informative observation times</subject><subject>Linear inference, regression</subject><subject>Longitudinal data</subject><subject>Mathematics</subject><subject>Modeling</subject><subject>Nonhomogeneous poisson process</subject><subject>Nonparametric inference</subject><subject>Observational research</subject><subject>Poisson distribution</subject><subject>Probability and statistics</subject><subject>Probability theory and stochastic processes</subject><subject>Regression analysis</subject><subject>School dropouts</subject><subject>Sciences and techniques of general use</subject><subject>Statistical analysis</subject><subject>Statistics</subject><subject>Stochastic processes</subject><subject>Theory and Methods</subject><subject>Tumors</subject><subject>Urinary 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Regression Analysis of Longitudinal Data With Informative Observation Times</title><author>Sun, Jianguo ; Park, Do-Hwan ; Sun, Liuquan ; Zhao, Xingqiu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c442t-714e524a9cf9f42b7dd91e797312956ac249f26696bd7b6754d9c9d0a86754f13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Consistent estimators</topic><topic>Estimating equation</topic><topic>Estimation bias</topic><topic>Estimation methods</topic><topic>Exact sciences and technology</topic><topic>Inference</topic><topic>Informative observation times</topic><topic>Linear inference, regression</topic><topic>Longitudinal data</topic><topic>Mathematics</topic><topic>Modeling</topic><topic>Nonhomogeneous poisson process</topic><topic>Nonparametric inference</topic><topic>Observational research</topic><topic>Poisson distribution</topic><topic>Probability and statistics</topic><topic>Probability theory and stochastic processes</topic><topic>Regression analysis</topic><topic>School dropouts</topic><topic>Sciences and techniques of general use</topic><topic>Statistical analysis</topic><topic>Statistics</topic><topic>Stochastic processes</topic><topic>Theory and Methods</topic><topic>Tumors</topic><topic>Urinary bladder</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sun, Jianguo</creatorcontrib><creatorcontrib>Park, Do-Hwan</creatorcontrib><creatorcontrib>Sun, Liuquan</creatorcontrib><creatorcontrib>Zhao, Xingqiu</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Access via ABI/INFORM (ProQuest)</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 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Most of the research have focused on situations where observation times are independent of or carry no information about the response variable and therefore rely on conditional inference procedures given the observation times. This article considers a different situation, where the independence assumption may not hold; that is, the observation times may carry information about the response variable. For inference, estimating equation approaches are proposed, and both large-sample and final-sample properties of the proposed methods are established. The methodology is applied to a bladder cancer study that motivated this investigation.</abstract><cop>Alexandria, VA</cop><pub>Taylor & Francis</pub><doi>10.1198/016214505000000060</doi><tpages>8</tpages></addata></record> |
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| subjects | Consistent estimators Estimating equation Estimation bias Estimation methods Exact sciences and technology Inference Informative observation times Linear inference, regression Longitudinal data Mathematics Modeling Nonhomogeneous poisson process Nonparametric inference Observational research Poisson distribution Probability and statistics Probability theory and stochastic processes Regression analysis School dropouts Sciences and techniques of general use Statistical analysis Statistics Stochastic processes Theory and Methods Tumors Urinary bladder |
| title | Semiparametric Regression Analysis of Longitudinal Data With Informative Observation Times |
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