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Table 1 Objectives, hypotheses, measures, and methods of analysis

From: The CANadian Pediatric Weight Management Registry (CANPWR): Study protocol

Objective Hypothesis Outcome measure (C = Continuous; B = Binary) Methods of analysis
Primary Change in BMI z-score will be influenced by child/youth, family, and program characteristics consistent with our theoretical model BMI z-score (C) Hierarchical/multilevel modeling
Document changes in anthropometric, lifestyle, behavioural, and obesity-related co-morbidities in children enrolled in Canadian pediatric weight management programs over a three-year period
Secondary Change in cardiometabolic health outcomes will be influenced by child/youth, family, and program characteristics consistent with our theoretical model Systolic and diastolic blood pressure (C) Hierarchical/multilevel modeling
1) Document changes in anthropometric, lifestyle, behavioural, and obesity-related co-morbidities in children enrolled in Canadian pediatric weight management programs over a three-year period;
Blood glucose (Fasting & 2 hr post glucose load) (C)
Total cholesterol/HDL-C ratio (C)
Triglyceride (C)
Fitness (C)
Quality of Life (C)
Lifestyle behaviours (C)
2) Characterize the individual-, family-, and program-level determinants of change in anthropometric and obesity-related co-morbidities; Individual-, family-, and program-level determinants will be identified that predict sustainability of change from years 1 – 3. BMI z-score (C) Hierarchical/multilevel modeling
3) Examine the individual-, family-, and program-level determinants of program attrition. Individual-, family-, and program-level determinants will differentiate those who dropped out of the program Drop out from the program between enrollment and 1 year (B) Hierarchical/multilevel modeling
Logistic regression
Exploratory analyses We will identify interaction terms between some individual, family and program determinants All outcomes Hierarchical/multilevel modeling
Identify what works best for what groups of individuals or families
Sensitivity analyses As above All outcomes 1) Analysis with multiple imputation
1) Imputation methods
2) All outcomes analyzed simultaneously to account for correlation among them 2) MANCOVA
3) GEE
3) Serial correlation of all outcomes over time    
  1. MANOVA: multivariate analysis of covariance.
  2. GEE: Generalized estimating equations.