Study design sample size estimation

Sample-size estimation therefore has an important ethical component. There are ethical issues involved in recruiting both too few and too many subjects (Matthews, 2006). Recruiting too few subjects means that the study may be underpowered and unable to detect a treatment effect of interest that actually exists. Such a design is scientifically inadequate to answer the research question of interest (i.e., to address the primary objective of the trial). It is also unethical. Subjects may have taken part in a study that did not have a chance of detecting a treatment effect that may have existed, and thus their expectation that participation may add to the knowledge base about the investigational drug was violated. 

Sample-size estimation can be performed for any study design. In each case, the respective formula will be used to estimate the sample size required (see Chow et al., 2003). For the formula used in the type of study design that we are using as our ongoing example, each of the variables we have discussed will have certain influences on the sample size, N, that will be given by the formula. These influences, i.e., their relationships with N given that all of the others remain the same, can be summarized as follows ... 

A sample-size estimation must be based on a specific objective in a clinical trial s study protocol. By the time sample-size estimation becomes particularly meaningful, i.e., in later-stage clinical trials designed to demonstrate efficacy, it is a very good idea to have a single objective (the primary objective) and a single... 

Sample-size estimation. A study design requires sufficient subjects but not an unnecessarily large subject sample. 

The topics of statistical power and sample size estimation are intimately related. A critical part of study design is deciding upon the number of participants that will be employed in the trial. Readers are referred to other sources for detailed discussion of these topics (e.g.. Turner 2010 Teare et al. 2014 Greene 2015 Jia and Lynn 2015) at this time, pragmatic descriptions are sufficient. 

FIGURE 37.1 A Une plot of the effect of sample size and intersubject variability on the precision (expressed as percent mean absolute error—%MAE) with which central volume of distribution (VI) was estimated in a simulated population pharmacokinetic study in which a balanced sampling design was used. 

In summary, the studies by Phillips et al. (1989a) and Taylor and Lawrence (1992) on apparent half-lives of PCB mixtures (Table 3-10) are in general agreement. These are well designed studies in two different occupational cohorts that are not limited by small sample size, short sampling intervals, or low initial body burdens. The main limitation of these studies was that congener specific PCB analysis was not conducted. Nevertheless, these studies provide the best estimates of the apparent half-lives of PCB mixtures following occupational exposure. 

On occasion, such a survey may indicate the need for a more formal epidemiological study. Skin complaints may, for example, be widespread but unusually hard to explain. Such investigations should never be undertaken by clinicians without previous epidemiological and statistical consultation. Coenraads and Nater (1987) have published a useful introduction to the problems that may arise, including true prevalence estimation, bias, confounding variables and sample size. Questions of disease definition and inter-observer variability are not necessarily familiar to clinicians, who may therefore need to seek epidemiological advice at the earliest opportunity. Questionnaires are frequently designed that ask for far more detail than can possibly be usefully analysed statistically, and they should always be piloted first in order to achieve validity. 

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