Statistical Approaches to Assess the Effects of Disease on Neurocognitive Function Over Time

Abstract

Introduction: Assessment of the effects of disease on neurocognitive outcomes in children over time presents several challenges. These challenges are particularly pronounced when conducting studies in low-income countries, where standardization and validation is required for tests developed originally in high-income countries. We present a statistical methodology to assess multiple neurocognitive outcomes over time. We address the standardization and adjustment for age in neurocognitive testing, present a statistical methodology for development of a global neurocognitive score, and assess changes in individual and global neurocognitive scores over time in a cohort of children with cerebral malaria.

Methods: Ugandan children with cerebral malaria (CM, N = 44), uncomplicated malaria (UM, N = 54) and community controls (N = 89) were assessed by cognitive tests of working memory, executive attention and tactile learning at 0, 3, 6 and 24 months after recruitment. Tests were previously developed and validated for the local area. Test scores were adjusted for age, and a global score was developed based on the controls that combined the assessments of impairment in each neurocognitive domain. Global normalized Z-scores were computed for each of the three study groups. Model-based tests compare the Z-scores between groups.

Results: We found that continuous Z-scores gave more powerful conclusions than previous analyses of the dataset. For example, at all four time points, children with CM had significantly lower global Z-scores than controls and children with UM. Our methods also provide more detailed descriptions of longitudinal trends. For example, the Z-scores of children with CM improved from initial testing to 3 months, but remained at approximately the same level below those of controls or children with UM from 3 to 24 months. Our methods for combining scores are more powerful than tests of individual cognitive domains, as testing of the individual domains revealed differences at only some but not all time points.

Keywords: Cumulative; Development; Global score; Longitudinal data analysis; Malaria; Neurocognitive; Normalization.

Figure 1

The Working Memory (Sequential Processing) Score collected at baseline versus age. The line indicates the loess fit using a span of 1. The disease groups are CM = cerebral malaria, UM = uncomplicated malaria, and CC = community controls.

Figure 2

Histograms of the residuals derived from the age adjustment of the Tactile-based Learning Score, before and after log function transformation.

Figure 3

Longitudinal estimates from the linear mixed effects model, black = CM, red = UM, and blue = CC.