In this paper, we propose a Bayesian clinical trial design that uses a Markov multistate model along with a power prior for the parameters which incorporates historical control data to estimate transition rates among two randomized groups as defined by the model.
Forty patients with type 2 diabetes, as defined by the American Diabetes Association criteria, were enrolled in a randomized, double-blind, double-dummy, parallel groups, placebo-controlled study.
The score for a candidate gene is defined by the maximum deviation between its weighted neighboring expression and the randomized expression.
The study consists of 4 parallel randomized phase II trials, defined by the 4 following histological subgroups: liposarcoma, leiomyosarcoma, synovial sarcoma and other sarcomas (according to local histology).
In a double blind placebo controlled randomized trial, 50 persons with MetS, as defined by the ATPIII criteria, received the study product or placebo for 8 weeks.
In the only available randomized controlled trials in this field, early versus late was defined by the urinary output versus biochemical values [ 5, 6] or by magnitude of urinary output [ 7].
The secondary aim was to test how remission, as defined by the PDAS, would perform as outcome measure when applied to the data from a large randomized controlled trial (RCT) in PD.
Three randomized levels of difficulty (levels dependent variable 2, 3, 4) were defined by the complexity of the pattern.
We included any published randomized clinical trial evaluating patients with moderate to severe COPD as defined by the Global Initiative on Obstructive Lung Disease GOLDD) classification, in line with the American Thoracic Society and European Respiratory Society (ATS/ERS).
Levels of evidence correspond to those defined by the AAP Steering Committee on Quality Improvement and Management 32 as follows: Level 1: Evidence is derived from well-designed randomized controlled trials or diagnostic studies on relevant populations.
