Assignment 1
Article: Availability of Reproductive Health Care Services at Schools and Subsequent Birth Outcomes Among Adolescent Mothers.
Madkour AS, Xie Y, Harville EW
J Sch Health. 2016 Jul;86(7):488-94. doi: 10.1111/josh.12399.
The unit of clustering: The unit of clustering is high school: Public, private and feeder high schools were included
The hypothesized effects: The investigators hypothesized that young women who attended schools offering reproductive health care services on-site would evidence better birth outcomes in subsequent pregnancies compared with young women who attended schools without those services.
The level at which the exposure is measured: The exposure (on site reproductive health care services) is measured as a characteristic of the cluster(structural) i.e. presence or absence of on site reproductive health care services coded as follows
1 = provided on site,
0 = not provided on-site
Reproductive health care services were defined as diagnostic screening (including but not limited to STDs), treatment for STD, family planning counseling, and prenatal/postpartum health care
The statistical model used to estimate the effect: They used multilevel random intercept linear regression analyses, which was appropriate in this case.
Level 1: school level characteristics
level 2: individual characteristics
Analyses began by examining univariate distributions and bivariate relationships. Proportions and means of individual-level and school-level characteristics were calculated. Bivariate associations between each covariate and birth outcomes were assessed with a series of bivariate random intercept linear regression models. They then entered all variables into a multilevel model simultaneously (1 model for each outcome).
Describe whether there are any other statistical models that might be appropriate and whether they would be preferable (e.g., GEE vs mixed).
- they could have used mixed effects models as these can handle a wide variety of data structures e.g. time within adolescents within schools. These methods are also able to give additional information about correlation and are more robust than GEE when you have missing data
I am not too sure but generalized estimating equations (GEE) may be an option as well (to a lesser extent). GEE ae good when you have only one level of clustering e.g pregnant adolescents. In this case we have adolescents only clustering within schools. In general GEE would require a fairly large number of clusters (ideally > 50) and works best when the number of clusters is greater than the number of observations per cluster – in this case – there are 107 clusters of schools , each with ~11 individuals per cluster and so this method would work pretty well
-