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Proportional hazards regression of survival-sacrifice data with cause-of-death information in animal carcinogenicity studies.
Stat Med 2019; 38(19):3628-3641SM

Abstract

Rodent survival-sacrifice experiments are routinely conducted to assess the tumor-inducing potential of a certain exposure or drug. Because most tumors under study are impalpable, animals are examined at death for evidence of tumor formation. In some studies, the cause of death is ascertained by a pathologist to account for possible correlation between tumor development and death. Existing methods for survival-sacrifice data with cause-of-death information have been restricted to multi-group testing or one-sample estimation of tumor onset distribution and thus do not provide a natural way to quantify treatment effect or dose-response relationship. In this paper, we propose semiparametric regression methods under the popular proportional hazards model for both tumor onset and tumor-caused death. For inference, we develop a maximum pseudo-likelihood estimation procedure using a modified iterative convex minorant algorithm, which is guaranteed to converge to the unique maximizer of the objective function. Simulation studies under different tumor rates show that the new methods provide valid inference on the covariate-outcome relationship and outperform alternative approaches. A real study investigating the effects of benzidine dihydrochloride on liver tumor in mice is analyzed as an illustration.

Authors+Show Affiliations

Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31074119

Citation

Mao, Lu. "Proportional Hazards Regression of Survival-sacrifice Data With Cause-of-death Information in Animal Carcinogenicity Studies." Statistics in Medicine, vol. 38, no. 19, 2019, pp. 3628-3641.
Mao L. Proportional hazards regression of survival-sacrifice data with cause-of-death information in animal carcinogenicity studies. Stat Med. 2019;38(19):3628-3641.
Mao, L. (2019). Proportional hazards regression of survival-sacrifice data with cause-of-death information in animal carcinogenicity studies. Statistics in Medicine, 38(19), pp. 3628-3641. doi:10.1002/sim.8201.
Mao L. Proportional Hazards Regression of Survival-sacrifice Data With Cause-of-death Information in Animal Carcinogenicity Studies. Stat Med. 2019 Aug 30;38(19):3628-3641. PubMed PMID: 31074119.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Proportional hazards regression of survival-sacrifice data with cause-of-death information in animal carcinogenicity studies. A1 - Mao,Lu, Y1 - 2019/05/09/ PY - 2018/07/27/received PY - 2019/04/13/revised PY - 2019/04/18/accepted PY - 2019/5/11/pubmed PY - 2019/5/11/medline PY - 2019/5/11/entrez KW - current status data KW - dependent censoring KW - maximum pseudo-likelihood estimation KW - modified iterative convex minorant KW - semiparametric inference SP - 3628 EP - 3641 JF - Statistics in medicine JO - Stat Med VL - 38 IS - 19 N2 - Rodent survival-sacrifice experiments are routinely conducted to assess the tumor-inducing potential of a certain exposure or drug. Because most tumors under study are impalpable, animals are examined at death for evidence of tumor formation. In some studies, the cause of death is ascertained by a pathologist to account for possible correlation between tumor development and death. Existing methods for survival-sacrifice data with cause-of-death information have been restricted to multi-group testing or one-sample estimation of tumor onset distribution and thus do not provide a natural way to quantify treatment effect or dose-response relationship. In this paper, we propose semiparametric regression methods under the popular proportional hazards model for both tumor onset and tumor-caused death. For inference, we develop a maximum pseudo-likelihood estimation procedure using a modified iterative convex minorant algorithm, which is guaranteed to converge to the unique maximizer of the objective function. Simulation studies under different tumor rates show that the new methods provide valid inference on the covariate-outcome relationship and outperform alternative approaches. A real study investigating the effects of benzidine dihydrochloride on liver tumor in mice is analyzed as an illustration. SN - 1097-0258 UR - https://www.unboundmedicine.com/medline/citation/31074119/Proportional_hazards_regression_of_survival-sacrifice_data_with_cause-of-death_information_in_animal_carcinogenicity_studies DB - PRIME DP - Unbound Medicine ER -
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