Improved confidence interval for average annual percent change in trend analysis
This paper considers an improved confidence interval for the average annual percent change in trend analysis, which is based on a weighted average of the regression slopes in the segmented line regression model with unknown change points. The performance of the improved confidence interval proposed...
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| Veröffentlicht in: | Statistics in medicine 2017-08, Vol.36 (19), p.3059-3074 |
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| container_issue | 19 |
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| container_title | Statistics in medicine |
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| creator | Kim, Hyune‐Ju Luo, Jun Chen, Huann‐Sheng Green, Don Buckman, Dennis Byrne, Jeffrey Feuer, Eric J. |
| description | This paper considers an improved confidence interval for the average annual percent change in trend analysis, which is based on a weighted average of the regression slopes in the segmented line regression model with unknown change points. The performance of the improved confidence interval proposed by Muggeo is examined for various distribution settings, and two new methods are proposed for further improvement. The first method is practically equivalent to the one proposed by Muggeo, but its construction is simpler, and it is modified to use the t‐distribution instead of the standard normal distribution. The second method is based on the empirical distribution of the residuals and the resampling using a uniform random sample, and its satisfactory performance is indicated by a simulation study. Copyright © 2017 John Wiley & Sons, Ltd. |
| doi_str_mv | 10.1002/sim.7344 |
| format | Article |
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The performance of the improved confidence interval proposed by Muggeo is examined for various distribution settings, and two new methods are proposed for further improvement. The first method is practically equivalent to the one proposed by Muggeo, but its construction is simpler, and it is modified to use the t‐distribution instead of the standard normal distribution. The second method is based on the empirical distribution of the residuals and the resampling using a uniform random sample, and its satisfactory performance is indicated by a simulation study. 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The performance of the improved confidence interval proposed by Muggeo is examined for various distribution settings, and two new methods are proposed for further improvement. The first method is practically equivalent to the one proposed by Muggeo, but its construction is simpler, and it is modified to use the t‐distribution instead of the standard normal distribution. The second method is based on the empirical distribution of the residuals and the resampling using a uniform random sample, and its satisfactory performance is indicated by a simulation study. Copyright © 2017 John Wiley & Sons, Ltd.</description><subject>Biometry - methods</subject><subject>Computer Simulation</subject><subject>confidence interval</subject><subject>Confidence Intervals</subject><subject>empirical distribution</subject><subject>Epidemiologic Methods</subject><subject>Humans</subject><subject>joinpoint</subject><subject>Medical statistics</subject><subject>Mortality - trends</subject><subject>Neoplasms - epidemiology</subject><subject>Regression Analysis</subject><subject>resampling</subject><subject>segmented line regression</subject><issn>0277-6715</issn><issn>1097-0258</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kU1r3DAQhkVpaTZpIb8gGHrpxemMLFn2JRBCmyyktND2LLTyeKNgS1tpvWX_feTmqy30NDDz8PAOL2PHCKcIwD8kN56qSogXbIHQqhK4bF6yBXClylqhPGCHKd0CIEquXrMD3shGciEX7Oty3MSwo66wwfeuI2-pcH5LcWeGog-xMDuKZk2F8X7Kqw1FS35b2Bvj1zNabCP5Lp_NsE8uvWGvejMkevswj9iPTx-_X1yV118ulxfn16UVVSNKLnsgRaByxooqMggKVyDqRiLJvBeV5V3bCmkN2spg00vssCXojVkhVkfs7N67mVYjdXOmaAa9iW40ca-Dcfrvi3c3eh12WtaSg2qz4P2DIIafE6WtHl2yNAzGU5iSxhZqUUvFIaPv_kFvwxTzwzOFNQjZNtWz0MaQUqT-KQyCnmvSuSY915TRkz_DP4GPvWSgvAd-uYH2_xXpb8vPv4V3_tmcHA</recordid><startdate>20170830</startdate><enddate>20170830</enddate><creator>Kim, Hyune‐Ju</creator><creator>Luo, Jun</creator><creator>Chen, Huann‐Sheng</creator><creator>Green, Don</creator><creator>Buckman, Dennis</creator><creator>Byrne, Jeffrey</creator><creator>Feuer, Eric J.</creator><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>K9.</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-5905-8050</orcidid><orcidid>https://orcid.org/0000-0003-1750-9201</orcidid></search><sort><creationdate>20170830</creationdate><title>Improved confidence interval for average annual percent change in trend analysis</title><author>Kim, Hyune‐Ju ; Luo, Jun ; Chen, Huann‐Sheng ; Green, Don ; Buckman, Dennis ; Byrne, Jeffrey ; Feuer, Eric J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4384-25f0e7e070273e3ea1071b046851e5e0743c2d9945ca1c3a18f51d19e0faab113</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Biometry - methods</topic><topic>Computer Simulation</topic><topic>confidence interval</topic><topic>Confidence Intervals</topic><topic>empirical distribution</topic><topic>Epidemiologic Methods</topic><topic>Humans</topic><topic>joinpoint</topic><topic>Medical statistics</topic><topic>Mortality - trends</topic><topic>Neoplasms - epidemiology</topic><topic>Regression Analysis</topic><topic>resampling</topic><topic>segmented line regression</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kim, Hyune‐Ju</creatorcontrib><creatorcontrib>Luo, Jun</creatorcontrib><creatorcontrib>Chen, Huann‐Sheng</creatorcontrib><creatorcontrib>Green, Don</creatorcontrib><creatorcontrib>Buckman, Dennis</creatorcontrib><creatorcontrib>Byrne, Jeffrey</creatorcontrib><creatorcontrib>Feuer, Eric J.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Statistics in medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, Hyune‐Ju</au><au>Luo, Jun</au><au>Chen, Huann‐Sheng</au><au>Green, Don</au><au>Buckman, Dennis</au><au>Byrne, Jeffrey</au><au>Feuer, Eric J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improved confidence interval for average annual percent change in trend analysis</atitle><jtitle>Statistics in medicine</jtitle><addtitle>Stat Med</addtitle><date>2017-08-30</date><risdate>2017</risdate><volume>36</volume><issue>19</issue><spage>3059</spage><epage>3074</epage><pages>3059-3074</pages><issn>0277-6715</issn><eissn>1097-0258</eissn><abstract>This paper considers an improved confidence interval for the average annual percent change in trend analysis, which is based on a weighted average of the regression slopes in the segmented line regression model with unknown change points. 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| ispartof | Statistics in medicine, 2017-08, Vol.36 (19), p.3059-3074 |
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| language | eng |
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| source | MEDLINE; Wiley Online Library All Journals |
| subjects | Biometry - methods Computer Simulation confidence interval Confidence Intervals empirical distribution Epidemiologic Methods Humans joinpoint Medical statistics Mortality - trends Neoplasms - epidemiology Regression Analysis resampling segmented line regression |
| title | Improved confidence interval for average annual percent change in trend analysis |
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