1. bookVolume 53 (2016): Issue 2 (December 2016)
Journal Details
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Journal
First Published
17 Aug 2013
Publication timeframe
2 times per year
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English
access type Open Access

A selection modelling approach to analysing missing data of liver Cirrhosis patients

Published Online: 10 Dec 2016
Page range: 83 - 103
Journal Details
License
Format
Journal
First Published
17 Aug 2013
Publication timeframe
2 times per year
Languages
English

Methods for dealing with missing data in clinical trials have received increased attention from the regulators and practitioners in the pharmaceutical industry over the last few years. Consideration of missing data in a study is important as they can lead to substantial biases and have an impact on overall statistical power. This problem may be caused by patients dropping before completion of the study. The new guidelines of the International Conference on Harmonization place great emphasis on the importance of carefully choosing primary analysis methods based on clearly formulated assumptions regarding the missingness mechanism. The reason for dropout or withdrawal would be either related to the trial (e.g. adverse event, death, unpleasant study procedures, lack of improvement) or unrelated to the trial (e.g. moving away, unrelated disease). We applied selection models on liver cirrhosis patient data to analyse the treatment efficiency comparing the surgery of liver cirrhosis patients with consenting for participation HFLPC (Human Fatal Liver Progenitor Cells) infusion with surgery alone. It was found that comparison between treatment conditions when missing values are ignored potentially leads to biased conclusions.

Keywords

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