1  Scope model by provisionally selecting a plausible number of classes based on available literature and the structure based on plausible clinical patterns.  Examine linearity of the shape of standardised residual plots for each of the classes in a model with no random effects. 
2  Refine the model from step 1 to confirm the optimal number of classes, typically testing K=1–7 classes.  Lowest Bayesian information criteria value. 
3  Refine optimal model structure from fixed through to unrestricted random effects of the model using the favoured K derived in step 2.  
4  Run model adequacy assessments as described in online supplementary table S3 including posterior probability of assignments (APPA), odds of correct classification (OCC) and relative entropy. 
APPA: average of maximum probabilities should be greater than 70% for all classes. OCC values greater than 5.0. Relative entropy values greater than 0.5.

5  Investigate graphical presentation 
Plot mean trajectories across time for each class in a single graph. Plot mean trajectories with 95% predictive intervals for each class (one class per graph). Plot individual class ‘spaghetti plots’ across time for a random sample.

6  Run additional tools to assess discrimination including Degrees of separation (DoS) and Elsensohn’s envelope of residuals 

7  Assess for clinical characterisation and plausibility. 
Tabulation of characteristics by latent classes. Are the trajectory patterns clinically meaningful? Perhaps, consider classes with a minimum percentage of the population. Are the trajectory patterns clinically plausible? Concordance of class characteristics with those for other wellestablished variables.

8  Conduct sensitivity analyses, for example, testing models without complete data at all time points.  General assessment of patterns of trajectories compared with main model. 