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Convergence of run replicates was confirmed by effective sample sizes (ESS) >1200 for all parameters and by visual inspection of traces within and between replicates in Tracer v1.5.
Each run was repeated three times to check for consistency of results, and T RACER v. 1.4 was used to assess whether the program had run for sufficiently long, as indicated by effective sample sizes of no less than 200, and trendlines with stable likelihood values.
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It is noteworthy that the exponential growth models did not fit the data sets, as indicated by effective sample size (ESS) scores <200 (data not shown).
Mixing was assessed by trend plots for estimates over the runs and by effective sample size (ESS) values.
The MCMC runs were repeated to refine skyline parameters and the convergence of the chains to the stationary distribution was confirmed by effective sample size (>100) for all parameters using TRACER (Rambaut et al. 2014).
As a concern for meta-analysis of diagnostic trials, publication bias was tested using the funnel plot and Deeks test [ 18], which was conducted by a regression of diagnostic log odds ratio against 1/sqrt effective sample size), weighting by effective sample size, with P < 0.10 for the slope coefficient indicating significant asymmetry.
Convergence statistics were monitored by effective samples sizes (ESS), in Tracer v1.5.
Sampling adequacy was diagnosed by examining effective sample sizes (ESS) using Tracer 1.5 [ 29]; the runs were terminated when all parameter values had ESS > 300.
These were then divided by the effective sample sizes, namely, the number of lung cancer patients who survived that month, to obtain the average monthly healthcare expenditures per case.
Convergence by the Markov chain simulations to stationary distribution was checked by monitoring multiple independent runs for each data set using different random number seeds (similar posterior distributions for each parameter across independent runs) and by assessing effective sample sizes (ESSs) values (ESS > 100), trendline plots, and swapping rates between chains over the course of the run.
We hypothesise that the behaviour of variances for the deep nodes are explained by the smaller effective sample sizes (ESS) obtained during the MCMC run.
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