The models included outcome data from two time periods for each participant (pre-program and post-program) along with baseline and time dependent covariates identified from the block models (sex, marital status, aerobic exercise score, cognitive symptom management, self-rated health).
The quality model includes: outcome quality (outcomes essential to achieve), process quality (activities necessary to perform in order to achieve the defined outcomes) and structural quality (the physical attributes necessary to achieve the goal, e.g. equipment, laws or staff).
The data imputation models included the outcome variables (survival time, vital status and cause of death, and an interaction term between survival time and outcome), smoking status and smoking exposure variables among smokers (total, intensity and duration), all potential confounders above, alcohol use, and a few additional variables (weight and height, clinical tumor size).
Results of the models including this outcome and its best functional predictor (the WOMAC score) are shown in Table 6.
The unadjusted models included each outcome with the following covariates: age, sex, BMI, education, marital and employment status, smoking status, smokeless tobacco use, physical activity, duration of living in the city, family history of diabetes, and wealth index.
In order to adjust for the inevitable selection bias associated with missing data in molecular pathology studies (Hoppin et al, 2002), we used multiple imputation (MI) to resolve missing values for all the variables included in multivariate models including an outcome indicator variable in the model (Moons et al, 2006), generating 50 data sets as previously described (Ali et al, 2011).
Can the model be broadened successfully to include outcomes that patients and families value, such as long-term cognitive function, physical independence, and quality of life?
Models including cardiovascular outcomes (24-h ambulatory blood pressure monitoring and 24-h ambulatory ECG recordings) were further adjusted, one at a time, for FPG or HbA1c.
This study established a Two-Level Logistic Regression Model, with the individual (including outcome measure and individual variables) as level-1 and the community as level-2.
In addition, the second and third models include whether the pregnancy was intended while the third model further includes the outcome of the pregnancy.
