Use this URL to cite or link to this record in EThOS:
Title: Implications of missing data in tuberculosis non-inferiority clinical trials
Author: Rehal, Sunita
ISNI:       0000 0004 7660 7888
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2018
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
Non-inferiority designs have been increasingly used in randomised clinical trials in recent years. However, there remain several key issues with this design that can have important implications for the primary analysis and its interpretation. Specifically, choosing the population for inclusion in the primary analysis and how to deal with missing values, remains unclear. This thesis tackles three related methodological issues in tuberculosis (TB) clinical trials: (i) a lack of clear guidance on design and reporting; (ii) the need for a valid approach to missing data and (iii) how to perform sensitivity analysis. First, widely available guidance documents on non-inferiority trials are critiqued, highlighting differences in recommendations between them on fundamental issues. These differences are reflected in inconsistent reporting from a systematic review we conducted, and make suggestions for improvements. Second, using data from two recent TB non-inferiority trials, we compare and contrast (i) different imputation approaches, (ii) inverse probability weighting with marginal models, and (iii) multi-state Markov models, for handling missing outcome data under the missing at random assumption. We find a form of multiple imputation is the best practical approach. Third, we explore sensitivity analysis to the missing at random assumption, and show how a "reference based" method provides an accessible, practical approach. In conclusion, more appropriate guidelines and analyses for non-inferiority trials in TB are needed, and some proposals are made to this end. Based on these findings, it is proposed that missing data in TB non-inferiority trials should be handled using the "two-fold" multiple imputation algorithm for imputing the missing data. By imputing the data in this way uses all the information available and allows for the trials defined primary outcome to be determined for each patient. Following this, reference based sensitivity analysis should be utilised.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available