To study the effect of the two different interventions in the linear regression analysis, we computed two dummy variables to capture the effect of the three-category group variable.
An overall linear trend over the period of data collection and an interaction between the intervention factor and the linear trend (to allow the intervention to influence any underlying trend and to allow a gradual impact of the introduction of the intervention) were also included.
To evaluate the effect of the intervention, linear regression models were constructed with the change scores as dependent variable, with an indicator variable for the intervention (intervention community versus control community) as the effect measure.
Table 3 provides least square means by intervention status over time from the linear mixed models for the primary and secondary outcomes and corresponding P values.
To evaluate the effects of the intervention, longitudinal linear and logistic regression analysis will be used.
We evaluated the change in daily mean traditional stove use after introduction of the intervention using linear mixed models to partition the between- and within-household variance components and to calculate the intraclass correlation coefficient (ICC, the proportion of variability explained by between subject differences).
Results showed decline for all symptoms in the intervention group (linear trends ps<.05), whereas stress management skills remained stable.
17 18 Nevertheless, effects of nutritional interventions on linear growth (upper bound of 95% CI +0.79 Z-scores) 19 fall far short of the median growth deficits observed in Sub-Saharan Africa and Southeast Asia, which are on the order of –2.0 Z-scores.
We first evaluated the bivariate associations between the average number of patients starting HAART per month and the presence/absence of the CD4 nurse intervention using linear regression.
The first question is whether WASH and nutritional interventions can prevent linear growth faltering in the first 2 years of life.
