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Table 1 Individual growth models for longitudinal changes in physical healtha

From: Using individual growth model to analyze the change in quality of life from adolescence to adulthood

 

Unconditional Linear Model

Unconditional Non-linear Model

Gender

Psychiatric Disorder

 

Estimate (SE)

Estimate (SE)

Estimate (SE)

Estimate (SE)

Random Variance

Intercept

101.53 (8.14) ***

101.68 (8.11) ***

87.34 (7.39) ***

79.86 (7.09) ***

Linear Slope

0.30 (0.08) ***

0.31 (0.08) ***

0.30 (0.08) ***

0.28 (0.08) ***

Residual

130.45 (7.17) ***

129.36 (7.12) ***

128.50 (6.96) ***

128.71 (7.04) ***

Fixed Effects

    

Intercept

74.71 (0.44) ***

75.19 (0.52) ***

70.95 (0.59) ***

72.26 (0.60) ***

Age

-0.63 (0.04) ***

-0.59 (0.05) ***

-0.73 (0.06) ***

-0.67 (0.06) ***

Age2

 

-0.01 (0.01)

--

--

Gender

  

7.61 (0.84) ***

7.24 (0.81) ***

Gender × Age

  

0.25 (0.08) **

0.22 (0.08) **

Psychiatric Disorder

   

-5.95 (0.87) ***

Psychiatric Disorder × Age

   

-0.23 (0.11) *

Goodness of Fitb

    

Parameters

5

6

7

9

Raw Likelihood (-2LL)

17624.0

17627.0

17538.3

17485.8

X2

 

3.0

85.7 ***

138.2***

Degrees of Freedom

 

1

2

4

  1. Note. SE = standard error; LL = log likelihood.
  2. aAll parameter entries are maximum likelihood estimates fitted using SAS PROC MIXED.
  3. Age was centered at 23 years, Gender was coded 0 = Female, 1 = Male.
  4. Psychiatric disorder was coded 0 = no disorder, 1= disorder.
  5. bModels for non-linear, gender and psychiatric disorder are compared with the unconditional linear growth model.
  6. * p < 0.05; ** p < 0.01; *** p < 0.001