Other ways to improve the efficiency of analyzing many QTL effects with Bayesian model selection include specifying prior inclusion probability for epistasis and using Metropolis/Hastings algorithm to perform fast sampling for binary indicator (Yi et al., 2007).
Nevertheless, for the purposes of the present meta-analysis, we reduced the effective sample size for binary outcomes and increased the standard deviations for continuous outcomes on the basis of the design factor of 1.12 (based on an intracluster correlation of 0.01 and 13 clusters).
Paired-samples tests for binary results (McNemar) were used to compare the bedside evaluations before and after the pedagogical intervention, and the Spearman ρ value was used to measure reliability between the judges and the ICDSC designer and between the judge and the nurse.
To assess the type I error rate, we set βtreat to 0. To assess power, we set βtreat to give 80% power based on the specified sample size (for binary and time-to-event outcomes, we powered the study based on reducing, rather than increasing, the number of events).
In order to make R n 0 P m − 1, A n 0 ′, B n 0 ′ closer to R n 0 P m − 1, A n 0 ∗, B n 0 ∗, we now use the randomized rounding (RR) algorithm, where N s samples of binary values are generated for variables A n 0, B n 0 by a randomized process with probability A n 0 ∗, B n 0 ∗.
Frequentist sample size determination for binary outcome data in a two-arm clinical trial requires initial guesses of the event probabilities for the two treatments.
The experimental design has two noteworthy features: a representative sample of binary choice problems (for fair comparison across theories) and a lottery set with a small number of outcomes and probabilities (for ease of non-parametric estimation).
All regressions were run at the individual level using probability weighting for sample inclusion, binary indicators for year and region, and clustering of standard errors on CBSA to allow for the most conservative estimates possible.
Correlation-based distance and the Euclidean distances cluster six out of eight tissues correctly, despite small sample size, and distance measures for binary transformed expression values cluster four tissues, indicating some loss of signal due to discretization.
Code has been written to perform random sampling from the best-fit Beta (for binary nodes) and Dirichlet distributions for each node using the Scythe Statistical Library (Pemstein et al. 2007).
