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The analysis was implemented with the R package "ordinal" (version 2012.09-11) using the "clmm2" function, and maximum likelihood estimates of the parameters were approximated using the adaptive Gauss–Hermite quadrature method with 20 quadrature nodes.

The 95% confidence intervals for the optimized parameters were approximated using a residual bootstrap method developed before.

Posterior distributions of parameters were approximated using two independent MCMC analyses of 107 steps each, following a discarded burn-in of 106 steps.

Posterior distributions of parameters were approximated using two independent MCMC analyses of 100,000,000 generations each.

Posterior distributions of parameters were approximated using three independent MCMC analyses of 20,000,000 generations with 10% burn-in.

Posterior distributions of parameters were approximated using two independent Markov chain Monte Carlo analyses of twenty million generations followed by a discarded burnin of 2,000,000 generations (10%).

For all analyses, posterior distributions of parameters were approximated using two independent MCMC analyses of 100,000,000 generations with 20% burn-in.

Tumour volumes were approximated using the formula width × length)/2.

Codon frequencies were approximated using the F3x4 calculation.

The uncertainty in parameter estimates for each simulated dataset was approximated using the Hessian matrix (Fisher Information Matrix) using modifications of a script from the δaδi user group.

In some parameter regimes, the NB distribution can be approximated using the normal distribution and, in these cases, maximum likelihood fit might also well approximate the best fit obtained from our rigorous model.