Download e-book for iPad: Advances in Survival Analysis by N. Balakrishnan, C.R. Rao

By N. Balakrishnan, C.R. Rao

ISBN-10: 0444500790

ISBN-13: 9780444500793

Handbook of data: Advances in Survival research covers all vital themes within the zone of Survival research. each one subject has been coated by means of a number of chapters written via the world over popular specialists. every one bankruptcy presents a accomplished and updated evaluation of the subject. a number of new illustrative examples were used to illustrate the methodologies built. The ebook additionally contains an exhaustive checklist of vital references within the zone of Survival Analysis.

  • Includes up to date experiences on many vital topics
  • Chapters written via many the world over popular experts
  • Some Chapters offer thoroughly new methodologies and analyses
  • Includes a few new facts and strategies of reading them

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Extra info for Advances in Survival Analysis

Sample text

Using a proportional hazard model the effect of a binary covariate on outcome is interpreted in terms of the relative risk of a patient with the characteristic as compared to a patient without the characteristic. When the covariate is continuous the interpretation of the effect of the covariate on outcome is more difficult. Here one typically reports the relative risk of a patient with a one-unit increase in the covariate. Most clinical investigators would rather have the continuous covariate converted into a binary covariate reflecting high and low risk values of the covariates.

To apply this approach the possible range of threshold values needs to be restricted. 5. Let C(γ ) be a standardized test statistics for the two sample problems with groups defined by the threshold parameter γ , then Lausen and Schumacher show that Max C(γ ) , γ ∈ [X(nε) , Xn(1−ε) ] ⇒ |W 0 (u)| , √ u∈[ε,1−ε] u(1 − u) sup as n → ∞. 20) where φ( ) is the standard normal density function. These results suggest the following corrected p-value approach. For all γ in the range [X(nε) , X(n(1−ε))] we compute either the Wald, score or likelihood ratio test pvalue.

P. -T. 5) Ln(2) % Continuous model Jespersen Cen Score Wald Like. 1 Like. 5) Ln(2) % Continuous model Jespersen Contal and O’Quigley Lausen and Schumacher Cen Score Wald Like. 1 Like. 8 200 0 20 40 98. 5) Ln(2) % Continuous model Cen Score Wald Like. 1 Like. 0 3. Extensions of Contal and O’Quigley’s approach In this section, we presented two extensions of Contal and O’Quigley’s correction. 1, is an extension of the approach to the accelerated failure time model with single continuous covariate.

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Advances in Survival Analysis by N. Balakrishnan, C.R. Rao


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