Design and analysis for three level hierarchical structures with count response

In numerous intervention studies and education field trials, random assignment to treatment occurs in clusters rather than at the level of observation. This departure of random assignment of units may be due to logistics, political feasibility, or ecological validity. Data within the same cluster or grouping are often correlated. Application of traditional regression techniques, which assume independence between observations, to clustered data produce consistent parameter estimates. However such estimators are often inefficient as compared to methods which incorporate the clustered nature of the data into the estimation procedure (Neuhaus 1993).1 Multilevel models, also known as random effects or random components models, can be used to account for the clustering of data by estimating higher level, or group, as well as lower level, or individual variation. Designing a study, in which the unit of observation is nested within higher level groupings, requires the determination of sample sizes at each level. This study investigates the design and analysis of various sampling strategies for a 3-level repeated measures design on the parameter estimates when the outcome variable of interest follows a Poisson distribution. ^ Results study suggest that second order PQL estimation produces the least biased estimates in the 3-level multilevel Poisson model followed by first order PQL and then second and first order MQL. The MQL estimates of both fixed and random parameters are generally satisfactory when the level 2 and level 3 variation is less than 0.10. However, as the higher level error variance increases, the MQL estimates become increasingly biased. If convergence of the estimation algorithm is not obtained by PQL procedure and higher level error variance is large, the estimates may be significantly biased. In this case bias correction techniques such as bootstrapping should be considered as an alternative procedure. For larger sample sizes, those structures with 20 or more units sampled at levels with normally distributed random errors produced more stable estimates with less sampling variance than structures with an increased number of level 1 units. For small sample sizes, sampling fewer units at the level with Poisson variation produces less sampling variation, however this criterion is no longer important when sample sizes are large. ^ 1Neuhaus J (1993). “Estimation efficiency and Tests of Covariate Effects with Clustered Binary Data”. Biometrics , 49, 989–996^

Factors that affect non-adherence to first line antiretroviral medications among HIV infected children and adolescents in Botswana

The aims of the study were to determine the prevalence of and factors that affect non-adherence to first line antiretroviral (ARV) medications among HIV infected children and adolescents in Botswana. The study used secondary data from Botswana-Baylor Children's Clinical Center of Excellence for the period of June 2008 to February 10th, 2010. The study design was cross-sectional and case-comparison between non-adherent and adherent participants was used to examine the effects of socio-demographic and medication factors on non-adherence to ARV medications. A case was defined as non-adherent child with adherence level < 95% based on pill count and measurement of liquid formulations. The comparison group consisted of children with adherence levels ≥95%.^ A total of 842 participants met the eligibility criteria for determination of the prevalence of non-adherence and 338 participants (169 cases and 169 individuals) were used in the analysis to estimate the effects of factors on non-adherence. ^ Univariate and multivariable logistic regression were used to estimate the association between non-adherence (outcome) and socio-demographic and medication factors (exposures). The prevalence of non-adherence for participants on first line ARV medications was 20.0% (169/842).^ Increase in age (OR (95% CI): 1.10 (1.04–1.17) p = 0.001) was associated with nonadherence, while increase in number of caregivers (OR (95% CI): 0.72 (0.56–0.93) p = 0.01) and increase in number of monthly visits (OR (95% CI): 0.92 (0.86–0.99) p = 0.02), were associated with good adherence in both the unadjusted and the adjusted models. For the categorical variables, having more than two caregivers (OR (95% CI): 0.66 (0.28–0.84), p = 0.002) was associated with good adherence even in the adjusted model. ^ Conclusion. The prevalence of non-adherence to antiretroviral medicines among the study population was estimated to be 20.0%. In previous studies, adherence levels of ≥ 95% have been associated with better clinical outcomes and suppression of virus to prevent development of resistance. Older age, fewer numbers of caregivers and fewer monthly visits were associated with non-adherence. Strategies to improve and sustain adherence especially among older children are needed. The role of caregivers and social support should be investigated further.^