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Implications of missing data in tuberculosis non-inferiority clinical trials

Rehal, Sunita; (2018) Implications of missing data in tuberculosis non-inferiority clinical trials. Doctoral thesis (Ph.D), UCL (University College London). Green open access

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Abstract

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.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Implications of missing data in tuberculosis non-inferiority clinical trials
Event: UCL (University College London)
Open access status: An open access version is available from UCL Discovery
Language: English
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology
URI: https://discovery.ucl.ac.uk/id/eprint/10059380
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