Additive hazards model with time-varying coefficients and imaging predictors
Conventional hazard regression analyses frequently assume constant regression coefficients and scalar covariates. However, some covariate effects may vary with time. Moreover, medical imaging has become an increasingly important tool in screening, diagnosis, and prognosis of various diseases, given...
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| Veröffentlicht in: | Statistical methods in medical research 2023-02, Vol.32 (2), p.353-372 |
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| container_title | Statistical methods in medical research |
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| creator | Yang, Qi Wang, Chuchu He, Haijin Zhou, Xiaoxiao Song, Xinyuan |
| description | Conventional hazard regression analyses frequently assume constant regression coefficients and scalar covariates. However, some covariate effects may vary with time. Moreover, medical imaging has become an increasingly important tool in screening, diagnosis, and prognosis of various diseases, given its information visualization and quantitative assessment. This study considers an additive hazards model with time-varying coefficients and imaging predictors to examine the dynamic effects of potential scalar and imaging risk factors for the failure of interest. We develop a two-stage approach that comprises the high-dimensional functional principal component analysis technique in the first stage and the counting process-based estimating equation approach in the second stage. In addition, we construct the pointwise confidence intervals for the proposed estimators and provide a significance test for the effects of scalar and imaging covariates. Simulation studies demonstrate the satisfactory performance of the proposed method. An application to the Alzheimer’s disease neuroimaging initiative study further illustrates the utility of the methodology. |
| doi_str_mv | 10.1177/09622802221137746 |
| format | Article |
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However, some covariate effects may vary with time. Moreover, medical imaging has become an increasingly important tool in screening, diagnosis, and prognosis of various diseases, given its information visualization and quantitative assessment. This study considers an additive hazards model with time-varying coefficients and imaging predictors to examine the dynamic effects of potential scalar and imaging risk factors for the failure of interest. We develop a two-stage approach that comprises the high-dimensional functional principal component analysis technique in the first stage and the counting process-based estimating equation approach in the second stage. In addition, we construct the pointwise confidence intervals for the proposed estimators and provide a significance test for the effects of scalar and imaging covariates. Simulation studies demonstrate the satisfactory performance of the proposed method. An application to the Alzheimer’s disease neuroimaging initiative study further illustrates the utility of the methodology.</description><subject>Additives</subject><subject>Alzheimer's disease</subject><subject>Computer Simulation</subject><subject>Confidence intervals</subject><subject>Counting</subject><subject>Dimensional analysis</subject><subject>Hazard assessment</subject><subject>Mathematical models</subject><subject>Medical diagnosis</subject><subject>Medical imaging</subject><subject>Medical prognosis</subject><subject>Medical screening</subject><subject>Models, Statistical</subject><subject>Neuroimaging</subject><subject>Principal components analysis</subject><subject>Proportional Hazards Models</subject><subject>Regression Analysis</subject><subject>Regression coefficients</subject><subject>Risk analysis</subject><subject>Risk factors</subject><subject>Scientific visualization</subject><subject>Simulation</subject><subject>Statistical analysis</subject><subject>Visualization</subject><issn>0962-2802</issn><issn>1477-0334</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>7QJ</sourceid><recordid>eNp1kEtLw0AUhQdRbK3-ADcScOMmdd6TWZbiCwpudB0m82in5FFnkkr99Sa0Kiiu7uJ859x7DwCXCE4REuIWSo5xBjHGCBEhKD8CY0SFSCEh9BiMBz0dgBE4i3ENIRSQylMwIpwyxDEag8XMGN_6rU1W6kMFE5OqMbZM3n27Slpf2XSrws7Xy0Q31jmvva3bmKjaJL5Sy0HYBGu8bpsQz8GJU2W0F4c5Aa_3dy_zx3Tx_PA0ny1STTBsU2q4oo5kihVcF1RnWmLEBROEMSUYcVZYJ6FEQjoFMURGYIMYQ1gSwgpNJuBmn7sJzVtnY5tXPmpblqq2TRdzLCjlXEoue_T6F7puulD31_WUQARjmWU9hfaUDk2Mwbp8E_r3wi5HMB-qzv9U3XuuDsldUVnz7fjqtgemeyCqpf1Z-3_iJ0sNhIc</recordid><startdate>202302</startdate><enddate>202302</enddate><creator>Yang, Qi</creator><creator>Wang, Chuchu</creator><creator>He, Haijin</creator><creator>Zhou, Xiaoxiao</creator><creator>Song, Xinyuan</creator><general>SAGE Publications</general><general>Sage Publications Ltd</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>7QJ</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>K9.</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-4877-3200</orcidid></search><sort><creationdate>202302</creationdate><title>Additive hazards model with time-varying coefficients and imaging predictors</title><author>Yang, Qi ; Wang, Chuchu ; He, Haijin ; Zhou, Xiaoxiao ; Song, Xinyuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c320t-4d6a4f38a5b6cb4c8c9216757355a753fe7ef909179fa0201d72d155129335bc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Additives</topic><topic>Alzheimer's disease</topic><topic>Computer Simulation</topic><topic>Confidence intervals</topic><topic>Counting</topic><topic>Dimensional analysis</topic><topic>Hazard assessment</topic><topic>Mathematical models</topic><topic>Medical diagnosis</topic><topic>Medical imaging</topic><topic>Medical prognosis</topic><topic>Medical screening</topic><topic>Models, Statistical</topic><topic>Neuroimaging</topic><topic>Principal components analysis</topic><topic>Proportional Hazards Models</topic><topic>Regression Analysis</topic><topic>Regression coefficients</topic><topic>Risk analysis</topic><topic>Risk factors</topic><topic>Scientific visualization</topic><topic>Simulation</topic><topic>Statistical analysis</topic><topic>Visualization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Qi</creatorcontrib><creatorcontrib>Wang, Chuchu</creatorcontrib><creatorcontrib>He, Haijin</creatorcontrib><creatorcontrib>Zhou, Xiaoxiao</creatorcontrib><creatorcontrib>Song, Xinyuan</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Applied Social Sciences Index & Abstracts (ASSIA)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>MEDLINE - Academic</collection><jtitle>Statistical methods in medical research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Qi</au><au>Wang, Chuchu</au><au>He, Haijin</au><au>Zhou, Xiaoxiao</au><au>Song, Xinyuan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Additive hazards model with time-varying coefficients and imaging predictors</atitle><jtitle>Statistical methods in medical research</jtitle><addtitle>Stat Methods Med Res</addtitle><date>2023-02</date><risdate>2023</risdate><volume>32</volume><issue>2</issue><spage>353</spage><epage>372</epage><pages>353-372</pages><issn>0962-2802</issn><eissn>1477-0334</eissn><abstract>Conventional hazard regression analyses frequently assume constant regression coefficients and scalar covariates. However, some covariate effects may vary with time. Moreover, medical imaging has become an increasingly important tool in screening, diagnosis, and prognosis of various diseases, given its information visualization and quantitative assessment. This study considers an additive hazards model with time-varying coefficients and imaging predictors to examine the dynamic effects of potential scalar and imaging risk factors for the failure of interest. We develop a two-stage approach that comprises the high-dimensional functional principal component analysis technique in the first stage and the counting process-based estimating equation approach in the second stage. In addition, we construct the pointwise confidence intervals for the proposed estimators and provide a significance test for the effects of scalar and imaging covariates. Simulation studies demonstrate the satisfactory performance of the proposed method. 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| subjects | Additives Alzheimer's disease Computer Simulation Confidence intervals Counting Dimensional analysis Hazard assessment Mathematical models Medical diagnosis Medical imaging Medical prognosis Medical screening Models, Statistical Neuroimaging Principal components analysis Proportional Hazards Models Regression Analysis Regression coefficients Risk analysis Risk factors Scientific visualization Simulation Statistical analysis Visualization |
| title | Additive hazards model with time-varying coefficients and imaging predictors |
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