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Books on the topic 'Proportion hazards model'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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1

Oberc, Margaret. Tree structured methods for the proportional hazards model. Toronto: University of Toronto, 1993.

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2

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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3

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

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4

Hastie, Trevor. Exploring the nature of covariate effects in the proportional hazards model. Toronto: University of Toronto, Dept. of statistics, 1988.

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5

Hein, Putter, ed. Dynamic prediction in clinical survival analysis. Boca Raton: CRC Press, 2012.

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6

Frijters, Paul. Socio-economic status, health shocks, life satisfaction and mortality: Evidence from an increasing mixed proportional hazard model. Bonn, Germany: IZA, 2005.

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7

Fallick, Bruce. The recall and new job search of laid-off workers: A bivariate proportional hazard model with unobserved heterogeneity. Washington, D.C: Federal Reserve Board, 2003.

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8

Vance, Colin. Cities and satellites: Spatial effects and unobserved heterogeneity in the modeling of urban growth. Essen: RWI, 2008.

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9

Bun, Cheung Yin, Parmar Mahesh K. B, and Parmar Mahesh K. B, eds. Survival analysis: A practical approach. 2nd ed. Chichester, West Sussex, England: Wiley, 2006.

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10

Watson, Peter. Survival analysis. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780198527565.003.0018.

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This chapter explores survival analysis. It includes data censoring, functions of duration time (the survival function, and hazard function), Cox’s proportional hazards model, log-linearity, time varying predictors, and odds ratios.
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11

Oberc, Margaret. Tree structured methods for proportional hazards model. 1993.

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12

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

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13

Survival Analysis Using the Proportional Hazards Model Course Notes. Sas Inst, 2003.

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14

Tanaka, Yoko. A proportional hazards model for informatively censored survival times. 1998.

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15

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

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16

Mahmood, Sharif. Multivariate Proportional Hazards Model: An Application To The Birth Interval In Bangladesh. LAP LAMBERT Academic Publishing, 2012.

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17

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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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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18

Birke, Hanna. Model-Based Recursive Partitioning with Adjustment for Measurement Error: Applied to the Cox’s Proportional Hazards and Weibull Model. Springer Spektrum, 2015.

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