Application of sensitivity analysis to incomplete longitudinal CD4 count data

ABSTRACT

In this paper, we investigate the effect of tuberculosis pericarditis (TBP) treatment on CD4 count changes over time and draw inferences in the presence of missing data. We accounted for missing data and conducted sensitivity analyses to assess whether inferences under missing at random (MAR) assumption are sensitive to not missing at random (NMAR) assumptions using the selection model (SeM) framework. We conducted sensitivity analysis using the local influence approach and stress-testing analysis. Our analyses showed that the inferences from the MAR are robust to the NMAR assumption and influential subjects do not overturn the study conclusions about treatment effects and the dropout mechanism. Therefore, the missing CD4 count measurements are likely to be MAR. The results also revealed that TBP treatment does not interact with HIV/AIDS treatment and that TBP treatment has no significant effect on CD4 count changes over time. Although the methods considered were applied to data in the IMPI trial setting, the methods can also be applied to clinical trials with similar settings.

KEYWORDS:

• Local influence
• missing at random and not missing at random
• missing completely at random
• sensitivity analysis
• selection model
• stress-testing

Acknowledgements

We thank the Mayosi Research Group, Department of Medicine, University of Cape Town for providing the data for the study. The authors would like to thank the following for their invaluable comments: Prof James Carpenter of London School Hygiene and Tropical Medicine, UK and Prof Jane Hutton, University of Warwick, Department of Statistics, UK. The authors would also like to thank Suzie Cro of London School of Hygiene and Tropical Medicine, UK, for making software available as well as offering valuable suggestions for software's implementation.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Freedom Gumedze  http://orcid.org/0000-0003-4387-8844

Additional information

Funding

AI would like to thank South African Center for Epidemiological Modeling and Analysis (SACEMA) for funding the project. The authors would also like to thank The Academy of Medical Sciences and the The National Research Foundation of South Africa (Grant No. 91016) for partially funding this research.