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A random coefficient growth curve analysis of mental development in low-birth-weight infants
R L Carter1, M B Resnick, M Ariet
1Department of Statistics, University of Florida, Gainesville 32610.
Insights
Low-birth-weight infants show cognitive development declines with age. Factors like race and mother's education impact this decline, but birth weight itself does not significantly alter the rate of cognitive development.
Area of Science:
- Pediatrics
- Developmental Psychology
- Biostatistics
Background:
- Longitudinal data analysis is crucial for understanding patient development over time.
- Assessing cognitive development in low-birth-weight infants requires specialized statistical approaches.
Purpose of the Study:
- To apply advanced growth curve analysis to longitudinal cognitive data in low-birth-weight infants.
- To examine the relationship between cognitive development (Bayley's Mental Development Index - MDI) and age across different demographic groups.
- To investigate the influence of covariates like birth weight, race, sex, and mother's education on cognitive trajectories.
Main Methods:
- Utilized statistical methods for growth curve analysis proposed by Vonesh and Carter.
- Analyzed repeated measures data on preschool cognitive development (MDI) in four race by sex groups of low-birth-weight infants.
- Tested assumptions required for the valid application of growth curve models.
Main Results:
- Significant declines in MDI with increasing age were observed in all infant groups.
- Birth weight influenced the overall MDI level but not the rate of decline.
- The rate of MDI decline was significantly associated with race and mother's education.
Conclusions:
- Despite risks associated with prematurity, developmental delays in low-birth-weight infants often emerge with age.
- Growth curve analysis provides a valid framework for studying cognitive development trajectories in this population.
- Further research is needed on alternative methods when growth curve assumptions are violated.
Abstract:
In many medical studies, longitudinal data are collected on each of a sample of patients. The objectives of such studies often are: to estimate and test bivariate or multivariate relationships within each of several groups of patients from these repeated measures data; to compare these relationships among groups; and to test for the effects of baseline covariates on the relationships. This paper illustrates the use of statistical methods for growth curve analysis recently proposed by Vonesh and Carter for achieving these goals by relating a measure of preschool cognitive development to age in four race by sex groups of low-birth-weight infants. Significant declines in Bayley's Mental Development Index (MDI) with increasing age were found in all groups. Birth-weight did not significantly influence the rate of decline but did influence the overall level of performance. Even so, in the group most comparable to Bayley's normative population, predicted MDI was near the norm even for extremely low-birth-weight infants (that is, 1000 grams). Although there is some risk of mental deficit associated with prematurity, eventual developmental delays in low-birth-weight infants frequently are acquired with age. The rate of decline in MDI was significantly associated with race and mother's education. Assumptions required for the valid application of these methods are discussed and tested in the setting of this applied problem. The assumptions appeared valid in this application. We conclude with a brief discussion of available alternatives when the assumptions are violated and point to areas for future research.