Estimating the grid of time-points for the piecewise exponential model
One of the greatest challenges related to the use of piecewise exponential models (PEMs) is to find an adequate grid of time-points needed in its construction. In general, the number of intervals in such a grid and the position of their endpoints are ad-hoc choices. We extend previous works by intro...
Gespeichert in:
| Veröffentlicht in: | Lifetime data analysis 2008-09, Vol.14 (3), p.333-356 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 356 |
|---|---|
| container_issue | 3 |
| container_start_page | 333 |
| container_title | Lifetime data analysis |
| container_volume | 14 |
| creator | Demarqui, Fabio N. Loschi, Rosangela H. Colosimo, Enrico A. |
| description | One of the greatest challenges related to the use of piecewise exponential models (PEMs) is to find an adequate grid of time-points needed in its construction. In general, the number of intervals in such a grid and the position of their endpoints are ad-hoc choices. We extend previous works by introducing a full Bayesian approach for the piecewise exponential model in which the grid of time-points (and, consequently, the endpoints and the number of intervals) is random. We estimate the failure rates using the proposed procedure and compare the results with the non-parametric piecewise exponential estimates. Estimates for the survival function using the most probable partition are compared with the Kaplan–Meier estimators (KMEs). A sensitivity analysis for the proposed model is provided considering different prior specifications for the failure rates and for the grid. We also evaluate the effect of different percentage of censoring observations in the estimates. An application to a real data set is also provided. We notice that the posteriors are strongly influenced by prior specifications, mainly for the failure rates parameters. Thus, the priors must be fairly built, say, really disclosing the expert prior opinion. |
| doi_str_mv | 10.1007/s10985-008-9086-0 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_69255375</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>69255375</sourcerecordid><originalsourceid>FETCH-LOGICAL-c369t-df7c0b63e59c98a4e18f894b29a016765f25fbe8720cda55ea78d840267d2b003</originalsourceid><addsrcrecordid>eNp1kE1LAzEURYMotlZ_gBsZXLiLvmQmX0uRVoWCG12H-XhTp0wnYzKD-u9NbaEguErIPe_mcQi5ZHDLANRdYGC0oACaGtCSwhGZMqE4NTwzx_GeaqBKK5iQsxDWEGeMNKdkwnQmY8amZDEPQ7PJh6ZbJcM7JivfVImrk_iItHdNN4Skdv436xss8bMJmOBX7zrshiZvk42rsD0nJ3XeBrzYnzPytpi_PjzR5cvj88P9kpapNAOtalVCIVMUpjQ6z5DpWpus4CYHJpUUNRd1gVpxKKtcCMyVrnQGXKqKFwDpjNzsenvvPkYMg900ocS2zTt0Y7DScCFSJSJ4_Qdcu9F3cTfLIRMqVTKLENtBpXcheKxt76MM_20Z2K1huzNso2G7NWy3G1zti8dig9VhYq80AnwHhBh1K_SHn_9v_QEmvoVz</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>204573764</pqid></control><display><type>article</type><title>Estimating the grid of time-points for the piecewise exponential model</title><source>MEDLINE</source><source>SpringerNature Journals</source><source>EBSCOhost Business Source Complete</source><creator>Demarqui, Fabio N. ; Loschi, Rosangela H. ; Colosimo, Enrico A.</creator><creatorcontrib>Demarqui, Fabio N. ; Loschi, Rosangela H. ; Colosimo, Enrico A.</creatorcontrib><description>One of the greatest challenges related to the use of piecewise exponential models (PEMs) is to find an adequate grid of time-points needed in its construction. In general, the number of intervals in such a grid and the position of their endpoints are ad-hoc choices. We extend previous works by introducing a full Bayesian approach for the piecewise exponential model in which the grid of time-points (and, consequently, the endpoints and the number of intervals) is random. We estimate the failure rates using the proposed procedure and compare the results with the non-parametric piecewise exponential estimates. Estimates for the survival function using the most probable partition are compared with the Kaplan–Meier estimators (KMEs). A sensitivity analysis for the proposed model is provided considering different prior specifications for the failure rates and for the grid. We also evaluate the effect of different percentage of censoring observations in the estimates. An application to a real data set is also provided. We notice that the posteriors are strongly influenced by prior specifications, mainly for the failure rates parameters. Thus, the priors must be fairly built, say, really disclosing the expert prior opinion.</description><identifier>ISSN: 1380-7870</identifier><identifier>EISSN: 1572-9249</identifier><identifier>DOI: 10.1007/s10985-008-9086-0</identifier><identifier>PMID: 18463801</identifier><language>eng</language><publisher>Boston: Springer US</publisher><subject>Bayes Theorem ; Bayesian analysis ; Computer Simulation ; Datasets ; Economics ; Estimates ; Finance ; Health Sciences ; Insurance ; Kaplan-Meier Estimate ; Management ; Markov analysis ; Markov Chains ; Mathematical models ; Mathematics and Statistics ; Medicine ; Models, Statistical ; Monte Carlo Method ; Monte Carlo simulation ; Operations Research/Decision Theory ; Quality Control ; Random variables ; Reliability ; Safety and Risk ; Sensitivity analysis ; Statistical methods ; Statistics ; Statistics for Business ; Statistics for Life Sciences ; Studies ; Survival analysis ; Telecommunications</subject><ispartof>Lifetime data analysis, 2008-09, Vol.14 (3), p.333-356</ispartof><rights>Springer Science+Business Media, LLC 2008</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c369t-df7c0b63e59c98a4e18f894b29a016765f25fbe8720cda55ea78d840267d2b003</citedby><cites>FETCH-LOGICAL-c369t-df7c0b63e59c98a4e18f894b29a016765f25fbe8720cda55ea78d840267d2b003</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10985-008-9086-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10985-008-9086-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/18463801$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Demarqui, Fabio N.</creatorcontrib><creatorcontrib>Loschi, Rosangela H.</creatorcontrib><creatorcontrib>Colosimo, Enrico A.</creatorcontrib><title>Estimating the grid of time-points for the piecewise exponential model</title><title>Lifetime data analysis</title><addtitle>Lifetime Data Anal</addtitle><addtitle>Lifetime Data Anal</addtitle><description>One of the greatest challenges related to the use of piecewise exponential models (PEMs) is to find an adequate grid of time-points needed in its construction. In general, the number of intervals in such a grid and the position of their endpoints are ad-hoc choices. We extend previous works by introducing a full Bayesian approach for the piecewise exponential model in which the grid of time-points (and, consequently, the endpoints and the number of intervals) is random. We estimate the failure rates using the proposed procedure and compare the results with the non-parametric piecewise exponential estimates. Estimates for the survival function using the most probable partition are compared with the Kaplan–Meier estimators (KMEs). A sensitivity analysis for the proposed model is provided considering different prior specifications for the failure rates and for the grid. We also evaluate the effect of different percentage of censoring observations in the estimates. An application to a real data set is also provided. We notice that the posteriors are strongly influenced by prior specifications, mainly for the failure rates parameters. Thus, the priors must be fairly built, say, really disclosing the expert prior opinion.</description><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Computer Simulation</subject><subject>Datasets</subject><subject>Economics</subject><subject>Estimates</subject><subject>Finance</subject><subject>Health Sciences</subject><subject>Insurance</subject><subject>Kaplan-Meier Estimate</subject><subject>Management</subject><subject>Markov analysis</subject><subject>Markov Chains</subject><subject>Mathematical models</subject><subject>Mathematics and Statistics</subject><subject>Medicine</subject><subject>Models, Statistical</subject><subject>Monte Carlo Method</subject><subject>Monte Carlo simulation</subject><subject>Operations Research/Decision Theory</subject><subject>Quality Control</subject><subject>Random variables</subject><subject>Reliability</subject><subject>Safety and Risk</subject><subject>Sensitivity analysis</subject><subject>Statistical methods</subject><subject>Statistics</subject><subject>Statistics for Business</subject><subject>Statistics for Life Sciences</subject><subject>Studies</subject><subject>Survival analysis</subject><subject>Telecommunications</subject><issn>1380-7870</issn><issn>1572-9249</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kE1LAzEURYMotlZ_gBsZXLiLvmQmX0uRVoWCG12H-XhTp0wnYzKD-u9NbaEguErIPe_mcQi5ZHDLANRdYGC0oACaGtCSwhGZMqE4NTwzx_GeaqBKK5iQsxDWEGeMNKdkwnQmY8amZDEPQ7PJh6ZbJcM7JivfVImrk_iItHdNN4Skdv436xss8bMJmOBX7zrshiZvk42rsD0nJ3XeBrzYnzPytpi_PjzR5cvj88P9kpapNAOtalVCIVMUpjQ6z5DpWpus4CYHJpUUNRd1gVpxKKtcCMyVrnQGXKqKFwDpjNzsenvvPkYMg900ocS2zTt0Y7DScCFSJSJ4_Qdcu9F3cTfLIRMqVTKLENtBpXcheKxt76MM_20Z2K1huzNso2G7NWy3G1zti8dig9VhYq80AnwHhBh1K_SHn_9v_QEmvoVz</recordid><startdate>20080901</startdate><enddate>20080901</enddate><creator>Demarqui, Fabio N.</creator><creator>Loschi, Rosangela H.</creator><creator>Colosimo, Enrico A.</creator><general>Springer US</general><general>Springer Nature B.V</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>0U~</scope><scope>1-H</scope><scope>3V.</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X7</scope><scope>7XB</scope><scope>87Z</scope><scope>88C</scope><scope>88E</scope><scope>88I</scope><scope>8AO</scope><scope>8C1</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>K9.</scope><scope>L.-</scope><scope>L.0</scope><scope>L6V</scope><scope>M0C</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>M2P</scope><scope>M7S</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>Q9U</scope><scope>7X8</scope></search><sort><creationdate>20080901</creationdate><title>Estimating the grid of time-points for the piecewise exponential model</title><author>Demarqui, Fabio N. ; Loschi, Rosangela H. ; Colosimo, Enrico A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c369t-df7c0b63e59c98a4e18f894b29a016765f25fbe8720cda55ea78d840267d2b003</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>Computer Simulation</topic><topic>Datasets</topic><topic>Economics</topic><topic>Estimates</topic><topic>Finance</topic><topic>Health Sciences</topic><topic>Insurance</topic><topic>Kaplan-Meier Estimate</topic><topic>Management</topic><topic>Markov analysis</topic><topic>Markov Chains</topic><topic>Mathematical models</topic><topic>Mathematics and Statistics</topic><topic>Medicine</topic><topic>Models, Statistical</topic><topic>Monte Carlo Method</topic><topic>Monte Carlo simulation</topic><topic>Operations Research/Decision Theory</topic><topic>Quality Control</topic><topic>Random variables</topic><topic>Reliability</topic><topic>Safety and Risk</topic><topic>Sensitivity analysis</topic><topic>Statistical methods</topic><topic>Statistics</topic><topic>Statistics for Business</topic><topic>Statistics for Life Sciences</topic><topic>Studies</topic><topic>Survival analysis</topic><topic>Telecommunications</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Demarqui, Fabio N.</creatorcontrib><creatorcontrib>Loschi, Rosangela H.</creatorcontrib><creatorcontrib>Colosimo, Enrico A.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Global News & ABI/Inform Professional</collection><collection>Trade PRO</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 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Healthcare Administration Database (Alumni)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</collection><collection>ProQuest Engineering Collection</collection><collection>ABI/INFORM Global</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Healthcare Administration Database</collection><collection>Medical Database</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Lifetime data analysis</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Demarqui, Fabio N.</au><au>Loschi, Rosangela H.</au><au>Colosimo, Enrico A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimating the grid of time-points for the piecewise exponential model</atitle><jtitle>Lifetime data analysis</jtitle><stitle>Lifetime Data Anal</stitle><addtitle>Lifetime Data Anal</addtitle><date>2008-09-01</date><risdate>2008</risdate><volume>14</volume><issue>3</issue><spage>333</spage><epage>356</epage><pages>333-356</pages><issn>1380-7870</issn><eissn>1572-9249</eissn><abstract>One of the greatest challenges related to the use of piecewise exponential models (PEMs) is to find an adequate grid of time-points needed in its construction. In general, the number of intervals in such a grid and the position of their endpoints are ad-hoc choices. We extend previous works by introducing a full Bayesian approach for the piecewise exponential model in which the grid of time-points (and, consequently, the endpoints and the number of intervals) is random. We estimate the failure rates using the proposed procedure and compare the results with the non-parametric piecewise exponential estimates. Estimates for the survival function using the most probable partition are compared with the Kaplan–Meier estimators (KMEs). A sensitivity analysis for the proposed model is provided considering different prior specifications for the failure rates and for the grid. We also evaluate the effect of different percentage of censoring observations in the estimates. An application to a real data set is also provided. We notice that the posteriors are strongly influenced by prior specifications, mainly for the failure rates parameters. Thus, the priors must be fairly built, say, really disclosing the expert prior opinion.</abstract><cop>Boston</cop><pub>Springer US</pub><pmid>18463801</pmid><doi>10.1007/s10985-008-9086-0</doi><tpages>24</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1380-7870 |
| ispartof | Lifetime data analysis, 2008-09, Vol.14 (3), p.333-356 |
| issn | 1380-7870 1572-9249 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_69255375 |
| source | MEDLINE; SpringerNature Journals; EBSCOhost Business Source Complete |
| subjects | Bayes Theorem Bayesian analysis Computer Simulation Datasets Economics Estimates Finance Health Sciences Insurance Kaplan-Meier Estimate Management Markov analysis Markov Chains Mathematical models Mathematics and Statistics Medicine Models, Statistical Monte Carlo Method Monte Carlo simulation Operations Research/Decision Theory Quality Control Random variables Reliability Safety and Risk Sensitivity analysis Statistical methods Statistics Statistics for Business Statistics for Life Sciences Studies Survival analysis Telecommunications |
| title | Estimating the grid of time-points for the piecewise exponential model |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-22T11%3A38%3A55IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Estimating%20the%20grid%20of%20time-points%20for%20the%20piecewise%20exponential%20model&rft.jtitle=Lifetime%20data%20analysis&rft.au=Demarqui,%20Fabio%20N.&rft.date=2008-09-01&rft.volume=14&rft.issue=3&rft.spage=333&rft.epage=356&rft.pages=333-356&rft.issn=1380-7870&rft.eissn=1572-9249&rft_id=info:doi/10.1007/s10985-008-9086-0&rft_dat=%3Cproquest_cross%3E69255375%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=204573764&rft_id=info:pmid/18463801&rfr_iscdi=true |