Dickinson_outcome {cvcrand}

Simulated individual-level binary outcome and baseline variables for study 1 in Dickinson et al (2015)

Description

At the end of the study, the researchers will have ascertained the outcome in the 16 clusters. Suppose that the researchers were able to assess 300 children in each cluster. We simulated correlated outcome data at the individual level using a generalized linear mixed model (GLMM) to induce correlation by including a random effect. The intracluster correlation (ICC) was set to be 0.01, using the latent response definition provided in Eldrige et al. (2009). This is a reasonable value of the ICC for population health studies (Hannan et al. 1994). We simulated one data set, with the outcome data dependent on the county-level covariates used in the constrained randomization design and a positive treatment effect so that the practice-based intervention increases up-to-date immunization rates more than the community-based intervention. For each individual child, the outcome is equal to 1 if he or she is up-to-date on immunizations and 0 otherwise.

Note that we still categorize the continuous variable of average income to illustrate the use of cvcrand on multi-category variables, and we truncated the percentage in CIIS variable at 100

Format

A data frame with 4800 rows and 7 variables:

county

the identification for the county

location

urban or rural

inciis

percentage of children ages 19-35 months in the Colorado Immunization Information System (CIIS)

uptodateonimmunizations

percentage of children already up-to-date on their immunization

hispanic

percentage of population that is Hispanic

incomecat

average income categorized into tertiles

outcome

the status of being up-to-date on immunizations

References

Dickinson, L. M., B. Beaty, C. Fox, W. Pace, W. P. Dickinson, C. Emsermann, and A. Kempe (2015): Pragmatic cluster randomized trials using covariate constrained randomization: A method for practice-based research networks (PBRNs). The Journal of the American Board of Family Medicine 28(5): 663-672

Eldridge, S. M., Ukoumunne, O. C., & Carlin, J. B. (2009). The Intra Cluster Correlation Coefficient in Cluster Randomized Trials: A Review of Definitions. International Statistical Review, 77(3), 378-394.

Hannan, P. J., Murray, D. M., Jacobs Jr, D. R., & McGovern, P. G. (1994). Parameters to aid in the design and analysis of community trials: intraclass correlations from the Minnesota Heart Health Program. Epidemiology, 88-95. ISO 690