Sensitivity analysis for the generalized shared-parameter model framework

ABSTRACT

In this paper, we assess the effect of tuberculosis pericarditis treatment (prednisolone) on CD4 count changes over time and draw inferences in the presence of missing data. We accounted for the missing data and performed sensitivity analyses to assess robustness of inferences, from a model that assumes that the data are missing at random, to models that assume that the data are not missing at random. Our sensitivity approaches are within the shared-parameter model framework. We implemented the approach by Creemers and colleagues to the CD4 count data and performed simulation studies to evaluate the performance of this approach. We also assessed the influence of potentially influential subjects, on parameter estimates, via the global influence approach. Our results revealed that inferences from missing at random analysis model are robust to not missing at random models and influential subjects did not overturn the study conclusions about prednisolone effect and missing data mechanism. Prednisolone was found to have no significant effect on CD4 count changes over time and also did not interact with anti-retroviral therapy. The simulation studies produced unbiased estimates of prednisolone effect with lower mean square errors and coverage probabilities approximately equal the nominal coverage probability.

KEYWORDS:

• Generalized shared-parameter model
• global influence
• longitudinal studies
• missing at random
• not missing at random
• sensitivity analysis
• simulation studies
• tuberculosis pericarditis

Acknowledgments

We thank South African Centre for Epidemiological Modelling and Analysis (SACEMA) for funding the project. Thank you Mayosi Research Group, Department of Medicine, University of Cape Town for providing the data for the study. We thank the Newton Fund, The Academy of Medical Sciences, UK for partial funding.

Disclosure statement

The author declares that he has no competing interests.

Availability of data and materials

We do not have permission to distribute the data.

Additional information

Funding

This work was supported by the National Research Foundation of South Africa [91016] andUniversity of Cape Town