In meta-analysis, between-study heterogeneity indicates the presence of effect-modifiers and has implications for the interpretation of results in cost-effectiveness analysis and decision making. A distinction is usually made between true variability in treatment effects due to variation in patient populations or settings and biases related to the way in which trials were conducted. Variability...

Distributed learning is an effective way to analyze big data. In distributed regression, a typical approach is to divide the big data into multiple blocks, apply a base regression algorithm on each of them, and then simply average the output functions learnt from these blocks. Since the average process will decrease the variance, not the bias, bias correction is expected to improve the learning...

All experimental assaydata contains error, but themagnitude, type, andprimaryoriginof this error is o en not obvious. Here, we describe a simple set of assay modeling techniques based on the bootstrap principle that allow sources of error and bias to be simulated and propagated into assay results. We demonstrate how deceptively simple operations—such as the creation of a dilution series with a ...

We derive the maximum bias functions of the MM -estimates and the constrained M -estimates or CM -estimates of regression and compare them to the maximum bias functions of the S -estimates and the τ -estimates of regression. In these comparisons, the CM -estimates tend to exhibit the most favorable bias-robustness properties. Also, under the Gaussian model, it is shown how one can construct a C...