 
Adjustment of the models

Estimate of the effect of the covariates
 

No.

Model

Residual deviance

df

pvalue*

Comparison with model

Difference of deviance

Difference of df

pvalue**

Tested effect



France
        
0

Null

1958.7

232

0.000
     
1

Age

991.8

201

0.000

0

966.9

31

0.000

Age

2

AgeDrift

344.3

200

0.000

1

647.5

1

0.000

Drift^{(1)}

3a

AgeCohort

68.0

167

1

2

276.3

33

0.000

Nonlinear cohort effect

3b

AgePeriod

336.4

194

0.000

2

7.9

6

0.246

Nonlinear period effect

4

AgeCohortPeriod^{(2)}

55.5

161

1

3a

12.5

6

0.051

Period effect (nonlinear + linear)


Italy
        
0

Null

347.4

91

0.000
     
1

Age

224.9

81

0.000

0

122.5

10

0.000

Age

2

AgeDrift

101.4

80

0.053

1

123.6

1

0.000

Drift^{(1)}

3a

AgeCohort

49.6

64

0.907

2

51.8

16

0.000

Nonlinear cohort effect

3b

AgePeriod

90.3

73

0.082

2

11.1

7

0.134

Nonlinear period effect

4

AgeCohortPeriod^{(2)}

32.5

57

0.996

3a

17.1

7

0.017

Period effect (nonlinear + linear)

 * goodness of fit test, ** loglikelihood ratio test (α = 5%), for p > 0.05 the effect of the variable is nonsignificant
 df, degree of freedom
 ^{(1)} linear effect of the period and cohort combined
 ^{(2)} period is the last covariate entered in the model