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Fig. 7 Goodness of fit assessment for the allometric models by land use class for each of the three levels of model complexity analysed.

Based on the time-rescaling theorem, we can transform a point process into a Poisson process with unit rate which is appropriate for goodness of fit assessment [ 36].

Goodness of fit assessment showed no significant difference between the models and the observed data, confirming a good fit of both model to the data (p = 0.43 for Table 4 and p = 0.46 for Table 5).

For each model, the odds ratios (ORs) linked to PF predictors and their respective significances were determined and the Hosmer and Lemeshow test for goodness of fit assessment of models was performed.

This work does not intend to dictate rules on model goodness-of-fit assessment.

The Hosmer Lemeshow goodness-of-fit assessment of the final model reported a p value of 0.90.

As with the ECFP4 fingerprint, goodness-of-fit assessment was performed for all fingerprints and the assumptions of each fitted model were assessed with the Hosmer-Lemenshow test.

In addition, we present grid methods for goodness-of-fit assessment and for classification performance evaluation.

Multicollinearity, intervariable correlation, the Hosmer Lemeshow goodness-of-fit assessment, and the effect of each risk factor on the outcome were considered.

The goodness-of-fit assessment demonstrated that the predicted probability of achieving NEDA status was similar to the observed probability of achieving NEDA status, suggesting that this was an appropriate choice of model.

Details of goodness-of-fit assessment are provided in online supplementary section 5. Power analyses were conducted by simulation to determine the minimum detectable effect sizes given our model and sample size.