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Table 3 List of the APC models fitted and selection of the best models. Quality of fit and contribution of each new variable added to the model are presented.

From: Time trends in exposure of cattle to bovine spongiform encephalopathy and cohort effect in France and Italy: value of the classical Age-Period-Cohort approach

   Adjustment of the models Estimate of the effect of the covariates  
No. Model Residual deviance df p-value* Comparison with model Difference of deviance Difference of df p-value** 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 Age-Drift 344.3 200 0.000 1 647.5 1 0.000 Drift(1)
3a Age-Cohort 68.0 167 1 2 276.3 33 0.000 Non-linear cohort effect
3b Age-Period 336.4 194 0.000 2 7.9 6 0.246 Non-linear period effect
4 Age-Cohort-Period(2) 55.5 161 1 3a 12.5 6 0.051 Period effect (non-linear + linear)
  Italy         
0 Null 347.4 91 0.000      
1 Age 224.9 81 0.000 0 122.5 10 0.000 Age
2 Age-Drift 101.4 80 0.053 1 123.6 1 0.000 Drift(1)
3a Age-Cohort 49.6 64 0.907 2 51.8 16 0.000 Non-linear cohort effect
3b Age-Period 90.3 73 0.082 2 11.1 7 0.134 Non-linear period effect
4 Age-Cohort-Period(2) 32.5 57 0.996 3a 17.1 7 0.017 Period effect (non-linear + linear)
  1. * goodness of fit test, ** log-likelihood ratio test (α = 5%), for p > 0.05 the effect of the variable is non-significant
  2. df, degree of freedom
  3. (1) linear effect of the period and cohort combined
  4. (2) period is the last covariate entered in the model