BaSiS guidelines for reporting Bayesian analyses Draft, 13 September 2001	Item #	Describe	Reported On page #
METHODS	h	h	h
Research question and Statistical model	1	The research questions and the statistical model used in the analysis including: (i) the likelihood of the observed data, (ii) the structure of higher levels of the model, in the case of hierarchical models, (iii) the choice of prior distributions used in the analysis and the rationale for this choice, including a description of methods of elicitation, if such methods were used.	h
Computational approach	3	The algorithmic approach to posterior computations including: (i) checks for convergence, if MCMC was used, (ii) methods for generating posterior summaries, (iii) software used in the analysis and how it was validated, in the case of non-publicly available software.	h
Model checks and sensitivity analysis	3	The approaches used to check model fit and to carry out any sensitivity analyses.	h
h	h	h	h
RESULTS	h	h	h
Describe posterior distribution of parameters and other quantities of interest.	4	Summaries (numerical and/or graphical) of the posterior distribution of model parameters and other quantities of interest,  which can include: (i) posterior mean, standard deviation and quantiles for parameters of interest, (ii) shape of the posterior densities of individual parameters, (iii) appropriate joint posterior probability intervals in the case of multiple comparisons, such as when ranks are reported, (iv) Bayes factors.	h
Model checks and sensitivity analysis	5	Findings of these analyses and implications for study results.	h
h	h	h	h
DISCUSSION	6	Interpretation of the results.	h
h	7	Possible limitations of the analysis	h