Covariate Analysis of Survival Data: A Small-Sample Study of Cox's Model
Cox's proportional-hazards model is frequently used to adjust for covariate effects in survival-data analysis. The small-sample performances of the maximum partial likelihood estimators of the regression parameters in a two-covariate hazard function model are evaluated with respect to bias, var...
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| Veröffentlicht in: | Biometrics 1982-09, Vol.38 (3), p.685-698 |
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| container_end_page | 698 |
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| container_title | Biometrics |
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| creator | Johnson, Mark E. Tolley, H. Dennis Bryson, Maurice C. Goldman, Aaron S. |
| description | Cox's proportional-hazards model is frequently used to adjust for covariate effects in survival-data analysis. The small-sample performances of the maximum partial likelihood estimators of the regression parameters in a two-covariate hazard function model are evaluated with respect to bias, variance, and power in hypothesis tests. Previous Monte Carlo work on the two-sample problem is reviewed. |
| doi_str_mv | 10.2307/2530049 |
| format | Article |
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Dennis</creatorcontrib><creatorcontrib>Bryson, Maurice C.</creatorcontrib><creatorcontrib>Goldman, Aaron S.</creatorcontrib><title>Covariate Analysis of Survival Data: A Small-Sample Study of Cox's Model</title><title>Biometrics</title><addtitle>Biometrics</addtitle><description>Cox's proportional-hazards model is frequently used to adjust for covariate effects in survival-data analysis. The small-sample performances of the maximum partial likelihood estimators of the regression parameters in a two-covariate hazard function model are evaluated with respect to bias, variance, and power in hypothesis tests. Previous Monte Carlo work on the two-sample problem is reviewed.</description><subject>560000 - Biomedical Sciences, Applied Studies</subject><subject>570000 - Health & Safety</subject><subject>Analysis of Variance</subject><subject>Asymptotic value</subject><subject>Biometrics</subject><subject>Censorship</subject><subject>Covariance</subject><subject>Estimation bias</subject><subject>GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE</subject><subject>Humans</subject><subject>MATHEMATICAL MODELS</subject><subject>Mathematical monotonicity</subject><subject>MATHEMATICS</subject><subject>Modeling</subject><subject>Models, Biological</subject><subject>MONTE CARLO METHOD</subject><subject>Mortality</subject><subject>Papers on the Analysis of Covariance</subject><subject>PARAMETRIC ANALYSIS</subject><subject>RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTANT EFFECTS ON LIVING ORGS. AND BIOL. MAT</subject><subject>REGRESSION ANALYSIS</subject><subject>Research Design</subject><subject>Sampling bias</subject><subject>Statistical discrepancies</subject><subject>Statistical variance</subject><subject>STATISTICS</subject><subject>SURVIVAL CURVES</subject><subject>SURVIVAL TIME</subject><subject>TIME DEPENDENCE</subject><issn>0006-341X</issn><issn>1541-0420</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1982</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp10EtLw0AUBeBBlFqr-AuEIGJX0TuvJOOuxEeFiosouAuTyQQjk07NTIr996YkuHN1uZyPszgInWO4IRTiW8IpABMHaIo5wyEwAodoCgBRSBn-OEYnzn31r-BAJmgS4xhHIpqiZWq3sq2l18FiLc3O1S6wVZB17bbeShPcSy_vgkWQNdKYMJPNxugg812527PU_sxd8GJLbU7RUSWN02fjnaH3x4e3dBmuXp-e08UqVJQSHypcJHESVSVOCk1imlAuoaCciiRmFcNSKSFYoUquNSdxySoqACpWCMJAM0Vn6HLotc7XuVO11-pT2fVaK5_zGDPMkx5dD2jT2u9OO583tVPaGLnWtnN5AgSSiIgezgeoWutcq6t809aNbHc5hnw_bD4O28uLsbIrGl3-uXHJPr8a8i_nbftvzS8wnnqr</recordid><startdate>198209</startdate><enddate>198209</enddate><creator>Johnson, Mark E.</creator><creator>Tolley, H. 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POLLUTANT EFFECTS ON LIVING ORGS. AND BIOL. MAT</topic><topic>REGRESSION ANALYSIS</topic><topic>Research Design</topic><topic>Sampling bias</topic><topic>Statistical discrepancies</topic><topic>Statistical variance</topic><topic>STATISTICS</topic><topic>SURVIVAL CURVES</topic><topic>SURVIVAL TIME</topic><topic>TIME DEPENDENCE</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Johnson, Mark E.</creatorcontrib><creatorcontrib>Tolley, H. Dennis</creatorcontrib><creatorcontrib>Bryson, Maurice C.</creatorcontrib><creatorcontrib>Goldman, Aaron S.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>OSTI.GOV</collection><jtitle>Biometrics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Johnson, Mark E.</au><au>Tolley, H. Dennis</au><au>Bryson, Maurice C.</au><au>Goldman, Aaron S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Covariate Analysis of Survival Data: A Small-Sample Study of Cox's Model</atitle><jtitle>Biometrics</jtitle><addtitle>Biometrics</addtitle><date>1982-09</date><risdate>1982</risdate><volume>38</volume><issue>3</issue><spage>685</spage><epage>698</epage><pages>685-698</pages><issn>0006-341X</issn><eissn>1541-0420</eissn><abstract>Cox's proportional-hazards model is frequently used to adjust for covariate effects in survival-data analysis. The small-sample performances of the maximum partial likelihood estimators of the regression parameters in a two-covariate hazard function model are evaluated with respect to bias, variance, and power in hypothesis tests. Previous Monte Carlo work on the two-sample problem is reviewed.</abstract><cop>United States</cop><pub>Biometric Society</pub><pmid>7171696</pmid><doi>10.2307/2530049</doi><tpages>14</tpages></addata></record> |
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| ispartof | Biometrics, 1982-09, Vol.38 (3), p.685-698 |
| issn | 0006-341X 1541-0420 |
| language | eng |
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| source | MEDLINE; JSTOR Mathematics & Statistics; Jstor Complete Legacy |
| subjects | 560000 - Biomedical Sciences, Applied Studies 570000 - Health & Safety Analysis of Variance Asymptotic value Biometrics Censorship Covariance Estimation bias GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE Humans MATHEMATICAL MODELS Mathematical monotonicity MATHEMATICS Modeling Models, Biological MONTE CARLO METHOD Mortality Papers on the Analysis of Covariance PARAMETRIC ANALYSIS RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTANT EFFECTS ON LIVING ORGS. AND BIOL. MAT REGRESSION ANALYSIS Research Design Sampling bias Statistical discrepancies Statistical variance STATISTICS SURVIVAL CURVES SURVIVAL TIME TIME DEPENDENCE |
| title | Covariate Analysis of Survival Data: A Small-Sample Study of Cox's Model |
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