Longitudinal designs and their statistical analysis in pediatric pulmonary research

Modern statistical techniques for the analysis of longitudinal data in biomedical research

Longitudinal study designs in biomedical research are motivated by the need or desire of a researcher to assess the change over time of an outcome and what risk factors may be associated with the outcome. The outcome is measured repeatedly over time for every individual in the study, and risk factors may be measured repeatedly over time or they may be static. For example, many clinical studies involving chronic obstructive pulmonary disease (COPD) use pulmonary function as a primary outcome and measure it repeatedly over time for each individual. There are many issues, both practical and theoretical, which make the analysis of longitudinal data complicated. Fortunately, advances in statistical theory and computer technology over the past two decades have made techniques for the analysis of longitudinal data more readily available for data analysts. The aim of this paper is to provide a discussion of the important features of longitudinal data and review two popular modern statistical techniques used in biomedical research for the analysis of longitudinal data: the general linear mixed model, and generalized estimating equations. Examples are provided, using the study of pulmonary function in cystic fibrosis research.

Response to tube breathing in preterm infants with apnea

In order to analyze the effects of maturity and apnea frequency on the respiratory control of preterm infants, we studied their responses to tube breathing using tubes equivalent to two anatomical dead spaces. Ventilation during tube breathing was expressed as a percentage above baseline and compared to an "expected" value calculated from the volume of the added tube. Twenty-seven preterm infants (median birthweight, 1.14 kg.; and gestational age, 29 weeks) were studied on 86 occasions. The percentage of "expected" ventilation increased with post-conceptional age (r = 0.48, slope = 3.12, P less than 0.0005), from a mean of 73% at 26 weeks up to 104% at 36 weeks. Using multiple regression analysis, neither postnatal age nor apnea frequency had any effect once allowance had been made for post-conceptional age. Although the respiratory adaptation of the most immature infants was poor, this study suggests that infants with apnea show no gross deficit in respiratory control, compared to those without apnea.