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Table 3 Analyses of inter-cohort differences in BMI trajectories: assessment of Bayesian model complexity (effective number of parameters pD), and fit (deviance information criteria DIC) for each candidate model

From: Bayesian hierarchical piecewise regression models: a tool to detect trajectory divergence between groups in long-term observational studies

  Model Females PP p-val Males PP p-val
Unconditional A 26910 (2544) 0.72 197837(2223) 0.52
Birth cohort (int β0) B 26811 (2455) 0.70 19872 (2232) 0.70
Birth cohort (childhood slope β1) C 26759 (2489) 0.34 19849 (2175) 0.63
Birth cohort (Adulthood slope β2) D 26645 (2358) 0.67 19857 (2263) 0.68
Birth cohort (change point CP) E 26395 (2599) 0.60 19862 (2211) 0.63
Birth cohort (CP and β 2 ) F 26390 (2671) 0.49 19877 (2255) 0.43
Birth cohort (CP, β2 and β1) G 26783 (2775) 0.48 19945 (2342) 0.53
  1. Reported are: DIC (pD), and posterior predictive p-values (PP p-val). Best fitting models for each sex indicated in bold characters. (Convergence was not reached for the most complex model where all 4-trajectory parameters (i.e.β0, β1, β2, and CP) were adjusted for birth cohort effects)