Impact of informative censoring on estimated treatment effects for confirmed disability worsening in multiple sclerosis clinical trials
department or school: Science, Faculty of
department or school: Statistics, Department of
data provider: DSpace
degree campus: UBCV
creator: Lobay, Rachel
issued: 2020-08-24T15:25:15Z
issued: 2020
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: In multiple sclerosis (MS) clinical trials, any elevation of a patient’s baseline Expanded Disability Status Scale (EDSS) score at a follow-up visit that is sustained or increased at the next visit is known as confirmed disability worsening (CDW). It is not clear how to handle patients who have an initial EDSS elevation (known as initial disability worsening (IDW)) immediately preceding dropout or at the last visit of the trial as they lack a confirmatory visit. Our objective was to assess the impact of different approaches to impute those missing CDW statuses on the estimated treatment effect in a time to first CDW analysis. Guided by a MS clinical trial dataset of 718 patients, we constructed a larger simulated dataset where all patients had EDSS scores from baseline to the last study visit. From this complete dataset, we obtained several samples of 300 patients and imposed dropouts on them by using probabilities that depended on whether IDW occurred at the immediately preceding visit. Then, 3 investigations were conducted to ascertain in what circumstances and to what extent different strategies for the imputation of the CDW statuses led to clearly different treatment effect results. First, we investigated the impact of magnifying the probabilities of dropout alone. In our next 2 investigations, we magnified the dropouts in conjunction with magnifying the treatment effect by modifying the EDSS data simulation procedure. In the first, we used Markov chain models to simulate the EDSS data, while in the second we incorporated more patient-specific information in the simulation through use of mixed-effects proportional odds models. We found that magnifying the dropouts alone had little impact on the estimated treatment effects. In contrast, under extreme treatment and dropout factors, the imputation strategies that accounted for treatment group led to estimated treatment effects that were clearly stronger than those from the other strategies in both the Markov chain …
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/75627
note: Impact of Informative Censoring on Estimated Treatment Effects for Confirmed Disability Worsening in Multiple Sclerosis Clinical Trials by Rachel Lobay B.Sc., Thompson Rivers University, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Statistics) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) August 2020 © Rachel Lobay, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Impact of Informative Censoring on Estimated Treatment Effects for Confirmed Disability Worsening in Multiple Sclerosis Clinical Trials submitted by Rachel Lobay in partial fulfillment of the requirements for the degree of Master of Science in Statistics. Examining Committee: John Petkau, Statistics Supervisor Lang Wu, Statistics Supervisory Committee Member iii Abstract In multiple sclerosis (MS) clinical trials, any elevation of a patient’s baseline Expanded Disability Status Scale (EDSS) score at a follow-up visit that is sustained or increased at the next visit is known as confirmed disability worsening (CDW). It is not clear how to handle patients who have an initial EDSS elevation (known as initial disability worsening (IDW)) immediately preceding dropout or at the last visit of the trial as they lack a confirmatory visit. Our objective was to assess the impact of different approaches to impute those missing CDW statuses on the estimated treatment effect in a time to first CDW analysis. Guided by a MS clinical trial dataset of 718 patients, we constructed a larger simulated dataset where all patients had EDSS scores from baseline to the last study visit. From this complete dataset, we obtained several samples of 300 patients and imposed dropouts on them by using probabilities that depended on whether IDW occurred at the immediately preceding visit. Then, 3 investigations were…
has type: Thesis/Dissertation
date issued: 2020-11
is shown at: 10.14288/1.0392924
language: eng
degree discipline: Statistics
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/75627