<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

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

Department of Statistics, Gauhati University,

Department of Biometrics, Chiltern Clinical Research Ltd,

Biometrical Letters

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Methods for dealing with missing data in clinical trials have received increased attention from the regulators and practitioners in the pharmaceutical industry...

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

Publié en ligne: 10 déc. 2016

Pages: 83 - 103

DOI: https://doi.org/10.1515/bile-2016-0007

Mots clés
selection model, model for end-stage liver disease, missing not at random

© 2016 Dilip C. Nath et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Publié en ligne: 10 déc. 2016

Pages: 83 - 103

DOI: https://doi.org/10.1515/bile-2016-0007

Mots clésselection model, model for end-stage liver disease, missing not at random

© 2016 Dilip C. Nath et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
selection model, model for end-stage liver disease, missing not at random