Home > Research > Publications & Outputs > Combining Multiple Survival Endpoints within a ...

Electronic data

  • 11003699.pdf

    Final published version, 10.4 MB, PDF document

    Available under license: CC BY-ND

View graph of relations

Combining Multiple Survival Endpoints within a Single Statistical Analysis.

Research output: ThesisDoctoral Thesis

Unpublished

Standard

Combining Multiple Survival Endpoints within a Single Statistical Analysis. / Zain, Zakiyah.

Lancaster : Lancaster University, 2011. 260 p.

Research output: ThesisDoctoral Thesis

Harvard

Zain, Z 2011, 'Combining Multiple Survival Endpoints within a Single Statistical Analysis.', PhD, Lancaster University, Lancaster.

APA

Vancouver

Zain Z. Combining Multiple Survival Endpoints within a Single Statistical Analysis.. Lancaster: Lancaster University, 2011. 260 p.

Author

Zain, Zakiyah. / Combining Multiple Survival Endpoints within a Single Statistical Analysis.. Lancaster : Lancaster University, 2011. 260 p.

Bibtex

@phdthesis{1a8e7163622643a489d2365bfb1024fe,
title = "Combining Multiple Survival Endpoints within a Single Statistical Analysis.",
abstract = "The aim of this thesis is to develop methodology for combining multiple endpoints within a single statistical analysis that compares the responses of patients treated with a novel treatment with those of control patients treated conventionally. The focus is on interval-censored bivariate survival data, and five real data sets from previous studies concerning multiple responses are used to illustrate the techniques developed. The background to survival analysis is introduced by a general description of survival data, and an overview of existing methods and underlying models is included. A review is given of two of the most popular survival analysis methods, namely the logrank test and Cox's proportional hazards model. The global score test methodology for combining multiple endpoints is described in detail, and application to real data demonstrates its benefits. The correlation between two score statistics arising from bivariate interval-censored survival data is the core of this research. The global score test methodology is extended to the case of bivariate interval-censored survival data and a complementary log-log link is applied to derive the covariance and the correlation between the two score statistics. A number of common scenarios are considered in this investigation and the accuracy of the estimator is evaluated by means of extensive simulations. An established method, namely the approach of Wei, Lin and Weissfeld, is examined and compared with the proposed method using both real and simulated data. It is concluded that our method is accurate, consistent and comparable to the competitor. This study marked the first successful development of the global score test methodology for bivariate survival data, employing a new approach to the derivation of the covariance between two score statistics on the basis of an interval-censored model. Additionally, the relationship between the jackknife technique and the Wei, Lin and Weissfeld method has been clarified.",
keywords = "MiAaPQ, Statistics., Medicine.",
author = "Zakiyah Zain",
note = "Thesis (Ph.D.)--Lancaster University (United Kingdom), 2011.",
year = "2011",
language = "English",
publisher = "Lancaster University",
school = "Lancaster University",

}

RIS

TY - THES

T1 - Combining Multiple Survival Endpoints within a Single Statistical Analysis.

AU - Zain, Zakiyah

N1 - Thesis (Ph.D.)--Lancaster University (United Kingdom), 2011.

PY - 2011

Y1 - 2011

N2 - The aim of this thesis is to develop methodology for combining multiple endpoints within a single statistical analysis that compares the responses of patients treated with a novel treatment with those of control patients treated conventionally. The focus is on interval-censored bivariate survival data, and five real data sets from previous studies concerning multiple responses are used to illustrate the techniques developed. The background to survival analysis is introduced by a general description of survival data, and an overview of existing methods and underlying models is included. A review is given of two of the most popular survival analysis methods, namely the logrank test and Cox's proportional hazards model. The global score test methodology for combining multiple endpoints is described in detail, and application to real data demonstrates its benefits. The correlation between two score statistics arising from bivariate interval-censored survival data is the core of this research. The global score test methodology is extended to the case of bivariate interval-censored survival data and a complementary log-log link is applied to derive the covariance and the correlation between the two score statistics. A number of common scenarios are considered in this investigation and the accuracy of the estimator is evaluated by means of extensive simulations. An established method, namely the approach of Wei, Lin and Weissfeld, is examined and compared with the proposed method using both real and simulated data. It is concluded that our method is accurate, consistent and comparable to the competitor. This study marked the first successful development of the global score test methodology for bivariate survival data, employing a new approach to the derivation of the covariance between two score statistics on the basis of an interval-censored model. Additionally, the relationship between the jackknife technique and the Wei, Lin and Weissfeld method has been clarified.

AB - The aim of this thesis is to develop methodology for combining multiple endpoints within a single statistical analysis that compares the responses of patients treated with a novel treatment with those of control patients treated conventionally. The focus is on interval-censored bivariate survival data, and five real data sets from previous studies concerning multiple responses are used to illustrate the techniques developed. The background to survival analysis is introduced by a general description of survival data, and an overview of existing methods and underlying models is included. A review is given of two of the most popular survival analysis methods, namely the logrank test and Cox's proportional hazards model. The global score test methodology for combining multiple endpoints is described in detail, and application to real data demonstrates its benefits. The correlation between two score statistics arising from bivariate interval-censored survival data is the core of this research. The global score test methodology is extended to the case of bivariate interval-censored survival data and a complementary log-log link is applied to derive the covariance and the correlation between the two score statistics. A number of common scenarios are considered in this investigation and the accuracy of the estimator is evaluated by means of extensive simulations. An established method, namely the approach of Wei, Lin and Weissfeld, is examined and compared with the proposed method using both real and simulated data. It is concluded that our method is accurate, consistent and comparable to the competitor. This study marked the first successful development of the global score test methodology for bivariate survival data, employing a new approach to the derivation of the covariance between two score statistics on the basis of an interval-censored model. Additionally, the relationship between the jackknife technique and the Wei, Lin and Weissfeld method has been clarified.

KW - MiAaPQ

KW - Statistics.

KW - Medicine.

M3 - Doctoral Thesis

PB - Lancaster University

CY - Lancaster

ER -