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Books on the topic 'Cox proportional hazards regression'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 January 2023

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1

O'Quigley, John. Proportional Hazards Regression. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-68639-4.

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2

Tapia-Aguilar, Alberto. Accurate confidence intervals for regression parameters in proportional hazards model. Toronto: [s.n.], 1994.

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3

LeBlanc, Michael R. Step-function covariate effects in the proportional hazards model. Toronto, Ont: University of Toronto, Department of Statistics, 1993.

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4

Lunde, Asger. Th e hazards of mutual fund performance: A cox regression analysis. London: London School of Economics, Financial Markets Group, 1998.

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5

Persson, Inger. Essays on the Assumption of Proportional Hazards in Cox Regression. Uppsala Universitet, 2002.

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6

O'Quigley, John. Proportional Hazards Regression. Springer New York, 2010.

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7

O'Quigley, John. Survival Analysis: Proportional and Non-Proportional Hazards Regression. Springer International Publishing AG, 2022.

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8

O'Quigley, John. Survival Analysis: Proportional and Non-Proportional Hazards Regression. Springer, 2021.

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9

Brasher, Penelope Margaret Ann. Partial residuals for the Cox proportional hazards model. 1989.

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10

Proportional Hazards Regression (Statistics for Biology and Health). Springer, 2008.

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11

O'Quigley, John. Proportional Hazards Regression (Statistics for Biology and Health). Springer, 2008.

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12

Derryberry, DeWayne R. Extensions of the proportional hazards loglikelihood for censored survival data. 1998.

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13

Furlong, Laurence W. On assessing goodness of fit in Cox's proportional hazards regression model. 1988.

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14

National Aeronautics and Space Administration (NASA) Staff. Cox Proportional Hazards Models for Modeling the Time to Onset of Decompression Sickness in Hypobaric Environments. Independently Published, 2018.

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15

Golub, Jonathan. Survival Analysis. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0023.

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Abstract:

This article provides a discussion of survival analysis that presents another way to incorporate temporal information into analysis in ways that give advantages similar to those from using time series. It describes the main choices researchers face when conducting survival analysis and offers a set of methodological steps that should become standard practice. After introducing the basic terminology, it shows that there is little to lose and much to gain by employing Cox models instead of parametric models. Cox models are superior to parametric models in three main respects: they provide more reliable treatment of the baseline hazard and superior handling of the proportional hazards assumption, and they are the best for handling tied data. Moreover, the illusory benefits of parametric models are presented. The greater use of Cox models enables researchers to elicit more useful information from their data, and allows for more reliable substantive inferences about important political processes.

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