[15], 36 (51%) completed phase I (14 days in length) and 26 (37%) completed phase II (21 days in length after phase I completion). Based on this attrition rate, but recognizing the shorter duration of our study, we expect that approximately 60% of our patients will complete cycle I, 50% will complete cycle II, and 45% will complete cycle III. If we assume no period effect or treatment×time interaction, then simulations of size N=10,000 in Stata (Statacorp, College Station, TX) programmed to model the repeated measures design, attrition rates, and s b 2 and s w 2 variances above, then n=70 patients need to be recruited

to detect a 2 cm change in NRS Inhibitors,research,lifescience,medical scores between treatments with 80% power at the 5% level of significance. With n=70, we expect 31 patients will complete all 3 cycles, 4 will complete cycles I and II, 7 will complete cycle I, and 28 will fail to complete any cycles. In contrast, a conventional RCT with similar attrition would require 120 patients. Data analysis Data preparation and descriptive reporting will Inhibitors,research,lifescience,medical follow that recommended by the CONSORT statement [25]. For each cycle, data from day 1 will be discarded to allow Inhibitors,research,lifescience,medical for a wash-out period, and data from days 2 and 3 data will be analysed. All patients with at least one completed treatment cycle will be included in analyses.

An effect size will then be calculated between active medication cycles and placebo, thus providing a population measure of effect commensurate with an RCT. Both individual and population treatment differences will be estimated using hierarchical Bayesian methods and employing noninformative priors using the methods described in Zucker et al. [33], and Schluter and

Ware Inhibitors,research,lifescience,medical [34]. The likelihood Inhibitors,research,lifescience,medical distributions for each model will be assessed for violations and data transformations undertaken, where necessary. Conventional burn-in click here periods, model convergence and stability diagnostics, and residual checks will be employed [35]. WinBUGS [35] will be used for the Bayesian analysis. To describe participants’ overall response, three types of Bayesian results will be presented: (i) the mean of the posterior distribution of the mean difference between placebo and stimulant scores, which gives the best estimate of the overall effect size difference between treatments; Mannose-binding protein-associated serine protease (ii) the associated 95% credible region, which give intervals of uncertainty (in this case the 2.5 and 97.5 percentile) of the posterior distributions used in (i); and (iii) the posterior probability of the mean difference that stimulant scores were better than placebo scores, which describes the likelihood that the patients will favour the active treatment in future cycles [34]. A patient will be defined to be a ‘responder’ when these estimated values exceed predefined threshold values [34].