Flow-stratified sampling is a new method for sampling water quality constituents such as suspended sediment to estimate loads. As with selection-at-list-time (SALT) and time-stratified sampling, flow-stratified sampling is a statistical method requiring random sampling, and yielding unbiased estimates of load and variance. It can be used to estimate event yields or to estimate mean concentratio...

1. Introduction. Until recently, statistical theory has been restricted to the design and analysis of sampling experiments in which the size and composition of the samples are completely determined before the experimentation begins. The reasons for this are partly historical, dating back to the time when the statistician was consulted , if at all, only after the experiment was over, and partly ...

In clinical research, the occurrence of certain events (e.g., adverse events, disease progression, relapse, or death) is often of particular interest to the investigators, especially in the area of cancer trials. In most situations, these events are undesirable and unpreventable. In practice, it would be beneficial to patients if the test treatment could delay the occurrence of such events. As ...

Monte Carlo sampling-based methods are frequently used in stochastic programming when exact solution is not possible. A critical component of Monte Carlo sampling-based methods is determining when to stop sampling to ensure the desired quality of the solutions. In this paper, we develop stopping rules for sequential sampling procedures that depend on the width of an optimality gap confidence in...

Stating a confidence interval is a traditional method of indicating the sampling error of a point estimator of a model’s performance measure. We propose a single dimensionless criterion, inspired by Schruben’s coverage function, for evaluating and comparing the statistical quality of confidenceinterval procedures. Procedure quality is usually thought to be multidimensional, composed of the mean...

A sample size calculation for logistic regression involves complicated formulae. This paper suggests use of sample size formulae for comparing means or for comparing proportions in order to calculate the required sample size for a simple logistic regression model. One can then adjust the required sample size for a multiple logistic regression model by a variance inflation factor. This method re...

Exact power and sample size calculation for bioequivalence studies with high order crossover designs using statistical software nQuery are presented. Such calculation can be very easily performed, and thus provides a convenient tool for practical usage.

BACKGROUND Central to the design of a randomised controlled trial is the calculation of the number of participants needed. This is typically achieved by specifying a target difference and calculating the corresponding sample size, which provides reassurance that the trial will have the required statistical power (at the planned statistical significance level) to identify whether a difference of...

In clinical research, parameters required for sample size calculation are usually unknown. A typical approach is to use estimates from some pilot studies as the true parameters in the calculation. This approach, however, does not take into consideration sampling error. Thus, the resulting sample size could be misleading if the sampling error is substantial. As an alternative, we suggest a Bayes...