The minimum clinically important difference (MCID) between treatments is recognized as a key concept in the design and interpretation of results from a clinical trial. Yet even assuming such a difference can be derived, it is not necessarily clear how it should be used. In this paper, we consider three possible roles for the MCID. They are: (1) using the MCID to determine the required sample si...

Before-and-after safety studies are becoming more desirable in lieu of traditional cross-sectional studies in establishing crash modification factors, especially after the introduction of the first version of the Highway Safety Manual (2010). We present a simple method for estimating necessary sample sizes to obtain a target precision or power when the effect is represented as a proportional ch...

In comparing or assessing methods of treatment it is vital that the appropriate number of patients is selected in order to ensure that the conclusions drawn are statistically viable. This annotation describes the relevance of a statistical power analysis in the context of hypothesis testing to the determination of the optimal sample size of a study. The power of the test indicates how likely it...

This procedure computes power and sample size for non-inferiority tests in two-sample designs in which the outcome is a continuous normal random variable. Measurements are made on individuals that have been randomly assigned to one of two groups. This is sometimes referred to as a parallel-groups design. This design is used in situations such as the comparison of the income level of two regions...

(a) larger sample size so that the result is more precise, appropriate subgroup analysis is more feasible, and there is a lower risk of an apparently "negative" result when the treatment is, in truth, effective; (b) quicker results before people lose scientific and commercial interest in the treatment and before it is modified or the theoretical indications for it are changed; (c) wider dissemi...

Very often statisticians have to deal with the problem of sample size calculation. However this is one of the most difficult questions to be answered since it depends on many considerations, assumptions and restrictions on the real problem being solved. In this paper we develop some methods, based on credible intervals, to calculate sample sizes for proportions and correlations.

We simulated multistate capture histories (CHs) by varying state survival (ϕ), detection (p) and transition (ψ), number of total capture occasions and releases per capture occasion and then modified these scenarios to mimic false rejection error (FRE), a common misidentification error, resulting from the failure to match samples of the same individual. We then fit a multistate model and estimat...

We developed a new analytic proof and conducted Monte Carlo simulations to assess the effects of methodological and statistical artifacts on the relative accuracy of intercept- and slope-based test bias assessment. The main simulation design included 3,185,000 unique combinations of a wide range of values for true intercept- and slope-based test bias, total sample size, proportion of minority g...

While manual tracing is the method of choice in measuring hippocampal volume, its time intensive nature and proneness to human error make automated methods attractive, especially when applied to large samples. Few studies have systematically compared the performance of the two techniques. In this study, we measured hippocampal volumes in a large (N = 403) population-based sample of individuals ...

The importance of power and sample size estimation for study design and analysis.