Randomization models are useful in supporting the validity of linear model analyses applied to data from a clinical trial that employed randomization via permuted blocks. Here, a randomization model for clinical trials data with arbitrary randomization methodology is developed, with treatment effect estimators and standard error estimators valid from a randomization perspective. A central limit...

BACKGROUND The decision to randomize by clusters of subjects such as a classroom or clinic versus individual randomization where some contamination may occur is examined within the framework of sample size issues. Estimates for background rates and intraclass correlations are also provided for adolescent tobacco and alcohol outcomes derived from a recent study using cluster randomization. MET...

We deal with randomness quantifiers and concentrate on their ability to discern the hallmark of chaos in time series used in connection with pseudo-random number generators (PRNGs). Workers in the field are motivated to use chaotic maps for generating PRNGs because of the simplicity of their implementation. Although there exist very efficient general-purpose benchmarks for testing PRNGs, we fee...

Markov models are commonly used to asses the dependability/performability of fault-tolerant systems. Computation of many dependability/performability measures for repairable fault-tolerant systems requires the transient analysis of irreducible Markov models. Examples of such measures are the unavailability at time and the -expected interval unavailability at time . Randomization (also called un...

In this paper we develop a variant, regenerative randomization with Laplace transform inversion, of a previously proposed method (the regenerative randomization method) for the transient analysis of rewarded continuous time Markov models. Those models find applications in dependability and performability analysis of computer and telecommunication systems. The variant differs from regenerative r...

BACKGROUND Re-randomization trials allow patients to be re-enrolled for multiple treatment episodes. However, it remains uncertain to what extent re-randomization improves recruitment compared to parallel group designs, or whether treatment estimates might be affected. METHODS We evaluated trials included in a recent Cochrane review of granulocyte colony-stimulating factors for patients with ...

Outcome-adaptive randomization allocates more patients to the better treatments as the information accumulates in the trial. Is it worth it to apply outcome-adaptive randomization in clinical trials? Different views permeate the medical and statistical communities. We provide additional insights to the question by conducting extensive simulation studies. Trials are designed to maintain the type...

Randomization is described by Fisher (1935) as the reasoned basis for inference about the effectiveness of treatments. Fisher advocated both using randomization in designing experiments and using “randomization inference” to analyze experiments that have been randomized. Randomization inference is inference that assumes only the physical act of randomization for its validity. It provides exact,...

BACKGROUND A well designed randomized clinical trial rates as the highest level of evidence for a particular intervention's efficacy. Randomization, a fundamental feature of clinical trials design, is a process invoking the use of probability to assign treatment interventions to patients. In general, randomization techniques pursue the goal of providing objectivity to the assignment of treatmen...