Literature DB >> 7407314

An analysis of comparative carcinogenesis experiments based on multiple times to tumor.

M H Gail, T J Santner, C C Brown.   

Abstract

Methods are given for the comparison of two treatment groups in experiments giving rise to multiple tumors. Methods based on the gaps in time between successive tumors are emphasized, but, for comparison, one method based directly on times to tumor is also presented. When applied to the results of an experimental animal carcinogenesis study, these analyses show that a diet supplemented with retinyl acetate reduces the hazard of mammary tumors, compared with controls, in every gap, and they allow one to combine evidence from the several gaps to obtain a powerful test for treatment effect.

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Year:  1980        PMID: 7407314

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  14 in total

1.  Dynamic random effects models for times between repeated events.

Authors:  D Y Fong; K F Lam; J F Lawless; Y W Lee
Journal:  Lifetime Data Anal       Date:  2001-12       Impact factor: 1.588

2.  Flexible Cure Rate Modeling Under Latent Activation Schemes.

Authors:  Freda Cooner; Sudipto Banerjee; Bradley P Carlin; Debajyoti Sinha
Journal:  J Am Stat Assoc       Date:  2007-06-01       Impact factor: 5.033

3.  Additive mixed effect model for recurrent gap time data.

Authors:  Jieli Ding; Liuquan Sun
Journal:  Lifetime Data Anal       Date:  2015-08-22       Impact factor: 1.588

4.  Modelling intervention effects after cancer relapses.

Authors:  Juan R González; Edsel A Peña; Elizabeth H Slate
Journal:  Stat Med       Date:  2005-12-30       Impact factor: 2.373

5.  Dynamic Modelling and Statistical Analysis of Event Times.

Authors:  Edsel A Peña
Journal:  Stat Sci       Date:  2006-11       Impact factor: 2.901

6.  Nonparametric modeling of the gap time in recurrent event data.

Authors:  Pang Du
Journal:  Lifetime Data Anal       Date:  2009-01-03       Impact factor: 1.588

7.  Current Methods for Recurrent Events Data with Dependent Termination: A Bayesian Perspective.

Authors:  Debajyoti Sinha; Tapabrata Maiti; Joseph G Ibrahim; Bichun Ouyang
Journal:  J Am Stat Assoc       Date:  2008-06-01       Impact factor: 5.033

8.  Analyzing Recurrent Event Data With Informative Censoring.

Authors:  Mei-Cheng Wang; Jing Qin; Chin-Tsang Chiang
Journal:  J Am Stat Assoc       Date:  2001       Impact factor: 5.033

9.  Semiparametric Inference for a General Class of Models for Recurrent Events.

Authors:  Edsel A Peña; Elizabeth H Slate; Juan R González
Journal:  J Stat Plan Inference       Date:  2007-06-01       Impact factor: 1.111

Review 10.  Ways of measuring rates of recurrent events.

Authors:  R J Glynn; J E Buring
Journal:  BMJ       Date:  1996-02-10
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