Bayesian inference has the advantage of formally incorporating prior beliefs about the effect of an intervention into analyses of treatment effect through the use of prior probability distributions or "priors".
This paper presents a hybrid classical and Bayesian procedure for choosing an estimate of the treatment effect to be detected in a clinical trial that formally integrates prior information into this aspect of trial design.
By using a hybrid classical and Bayesian approach, we are able to formally integrate prior information on the uncertainty and variability of the treatment effect into the design of the study, mitigating the risk that the power calculation will be overly optimistic while maintaining a frequentist framework for the final analysis.
Liver and adrenal gland metastases should be formally assessed prior to chemotherapy (standard).
In addition, systems-based analyses may be performed, which more formally integrates prior knowledge via ontologies into the identification and testing of relevant associations (58– 61).
Whilst the questionnaire was not formally validated prior to the study, its design was discussed with our clinical audit department and then reviewed in the initial stages of the project.
The above methods allow for simultaneous inferences to be made for all unknown parameters, which take full advantage of all the information contained in the data, as well as formally incorporated prior information, when available.
This group met formally once prior to the start of the study, once approximately mid-way through the first trial, and is scheduled to meet again in early 2015 to review the results of both trials.
Inter-rater reliability was enhanced by development and use of a standardised training programme, with mastery being formally assessed prior to data collection, to reduce the possibility of information bias.
However, in order to effectively capture the properties that emerge in a complex disease, we need analytical methods that provide a robust framework to formally integrate prior knowledge of the biological attributes with the experimental data.
