Pitt Logo LinkContact Us

A Wilcoxon-type Statistic for Repeated Binary Measures with Dropouts and Possible Multiple Outcomes

Elci, Okan Umit (2011) A Wilcoxon-type Statistic for Repeated Binary Measures with Dropouts and Possible Multiple Outcomes. Doctoral Dissertation, University of Pittsburgh.

[img] PDF - Primary Text
Restricted to University of Pittsburgh users only until 31 January 2016.

Download (705Kb) | Request a copy

    Abstract

    In clinical trials, we often compare two treatment groups using repeated binary measures over time. In such trials, we may encounter missing observations, adverse side effects, or non-responsiveness to therapy which for ethical reasons, may result in increased medical intervention beyond the protocol therapy. We developed a family of statistical tests based on the Wilcoxon statistic which orders the vectors of repeated binary observations and events where the ordering is determined by 'clinical relevance'. For some scenarios, clinically meaningful ordering of the vectors may be defined by a natural algorithm, while for other scenarios the ordering is obtained from a group of clinicians. We present the statistical development of the proposed method, effects of the variability of rankings among clinicians, examples of the application of the proposed method using data from a clinical trial on otitis media, and simulation studies comparing the statistical power of the proposed method to more traditional methods of analysis. Our simulation studies indicate that the proposed method is competitive with and, for some scenarios, is preferable to the traditional methods. Although the proposed method is not applicable to every situation, we believe that for some diseases and scenarios, this simple method is noteworthy in the sense that it can be adjusted to extremely complex situations if vectors can be hierarchically ordered in a reasonable fashion, it can be focused on alternatives that have high clinical relevance, and it can be readily adapted to accommodate non-protocol 'outcomes' and missing data. The public health relevance of this study is that clinically meaningful results can be targeted in clinical trials.


    Share

    Citation/Export:
    Social Networking:

    Details

    Item Type: University of Pittsburgh ETD
    ETD Committee:
    ETD Committee TypeCommittee MemberEmail
    Committee ChairRockette, Howard Eherbst@pitt.edu
    Committee MemberMazumdar, Satimaz1@pitt.edu
    Committee MemberAnderson, Stewart Jsja@pitt.edu
    Committee MemberSereika, Susan Sssereika@pitt.edu
    Title: A Wilcoxon-type Statistic for Repeated Binary Measures with Dropouts and Possible Multiple Outcomes
    Status: Unpublished
    Abstract: In clinical trials, we often compare two treatment groups using repeated binary measures over time. In such trials, we may encounter missing observations, adverse side effects, or non-responsiveness to therapy which for ethical reasons, may result in increased medical intervention beyond the protocol therapy. We developed a family of statistical tests based on the Wilcoxon statistic which orders the vectors of repeated binary observations and events where the ordering is determined by 'clinical relevance'. For some scenarios, clinically meaningful ordering of the vectors may be defined by a natural algorithm, while for other scenarios the ordering is obtained from a group of clinicians. We present the statistical development of the proposed method, effects of the variability of rankings among clinicians, examples of the application of the proposed method using data from a clinical trial on otitis media, and simulation studies comparing the statistical power of the proposed method to more traditional methods of analysis. Our simulation studies indicate that the proposed method is competitive with and, for some scenarios, is preferable to the traditional methods. Although the proposed method is not applicable to every situation, we believe that for some diseases and scenarios, this simple method is noteworthy in the sense that it can be adjusted to extremely complex situations if vectors can be hierarchically ordered in a reasonable fashion, it can be focused on alternatives that have high clinical relevance, and it can be readily adapted to accommodate non-protocol 'outcomes' and missing data. The public health relevance of this study is that clinically meaningful results can be targeted in clinical trials.
    Date: 31 January 2011
    Date Type: Completion
    Defense Date: 27 May 2010
    Approval Date: 31 January 2011
    Submission Date: 29 November 2010
    Access Restriction: 5 year -- Restrict access to University of Pittsburgh for a period of 5 years.
    Patent pending: No
    Institution: University of Pittsburgh
    Thesis Type: Doctoral Dissertation
    Refereed: Yes
    Degree: PhD - Doctor of Philosophy
    URN: etd-11292010-102813
    Uncontrolled Keywords: binary outcomes; Longitudinal data; natural algorithm; ordering; Wilcoxon test statistic
    Schools and Programs: Graduate School of Public Health > Biostatistics
    Date Deposited: 10 Nov 2011 15:06
    Last Modified: 15 May 2012 12:07
    Other ID: http://etd.library.pitt.edu/ETD/available/etd-11292010-102813/, etd-11292010-102813

    Actions (login required)

    View Item

    Document Downloads