Sample size clinical importance

Several specialized reviews on detection of QT liability in the clinical development phase have already been published and the reader is referred to these publications [63]. Guidelines of the International Society for Holter and Noninvasive Electrocardiology (IS H N E) for electrocardiographic evaluation of drug-related QT prolongation are also available [161]. The main issues related to measurement of the QT interval in clinical studies are summarized in Table 3.4. An important aspect is the calculation of sample size usually 40-60 subjects per treatment arm are required, implying high cost [162-164]. 

Suppose now that the trial in the example were a trial in which a difference of 0.5 mmol/1 was viewed as an important difference. Maybe this reflects the clinical relevance of such a difference or perhaps from a commercial standpoint it would be a worthwhile difference to have. Under such circumstances only having 62.3 per cent power to detect such a difference would be unacceptable this corresponds to a 37.7 per cent type II error, an almost 40 per cent chance of failing to declare significant differences. Well, there is only one thing you can do, and that is to increase the sample size. The recalculated values for power are given in Table 8.3 with a doubling of the sample size to 100 patients per group. 

We commonly refer to the level of effect to be detected as the cliniMlly relevant difference (crd) what level of effect is an important effect from a clinical standpoint. Note also that crd stands for commercially relevant difference it could well be that the decision is based on commercial interests. Finally crd stands for cynically relevant difference It does happen from time to time that a statistician is asked to do a sample size calculation, oh and by the way, we want 200 patients The issue here of course is budget and the question really is what level of effect are we able to detect with a sample size of 200 ... 

Olanzapine has shown encouraging results, in doses ranging from 2.5 to 20 mg/day, in two open trials involving a total of 30 adults with TS (Budman et al., 2001 Stamenkovic et al., 2000). A 52-week doubleblind crossover study of olanzapine (5 or 10 mg) versus pimozide (2 or 4 mg) in four adult patients with TS conducted by Onofrj et al. (2000) found olanzapine superior to pimozide in terms of tic reduction, sedation, and patient preference. The small sample size limits the clinical import of these findings. 

Overly complicated entrance criteria may be counterproductive, in that patients who have a classic presentation may be excluded for trivial reasons because they fail to meet one or more less-important criteria. This may result in too small a sample size and can lead to the inclusion of patients who technically fit the criteria but are clearly inappropriate. This problem is particularly true with an uncommon disorder and with patients who are difficult to enroll in clinical trials (e.g., acutely manic). 

Overly optimistic choice of treatment effect. As Machin and Campbell (2005) noted in the context of comparative clinical trials, researchers are often optimistic about the magnitude of the improvement of the new treatments over the standard. Since a larger estimated treatment effect leads to a smaller sample size being chosen, overestimating the estimated treatment effect may lead to a smaller but still clinically important effect not being detected, since the sample size adopted was too small to detect it. 

In particular, they do not convey the magnitude of a clinical effect. The size of the p-value is a consequence of two things the magnitude of the estimated treatment difference and its estimated variability (which is itself a consequence of sample size). Thus the p-value partially reflects the size of the experiment, which has no biological importance. The p-value also hides the size of the treatment, which does have major biological importance. 

Antibody arrays immobilized on glass surfaces mimic DNA microarrays in format and spot size. The biggest challenge in protein profiling using antibody microarrays is selection of validated antibodies that are useful in the desired sample environment. Many of the initial reports used antibody arrays assayed for cytokines because serum presents a relatively simple sample assay environment compared to tissue and also because there are numerous validated antibodies available for this clinically important set of proteins. Tissue and cell lysates present more complex assay environments with more opportunities for antibody cross-reactivity and other interferences which erode the biological meaningfulness of the data. 

Randomization will not inevitably result in an adequate balance of clinical characteristics and prognostic factors between the treatment groups in a trial, particularly if the sample size is relatively small. Details of the important clinical characteristics of the patients should. 

There are numerous possible explanations for why initial phar-macogenetic associations have failed to be replicated in subsequent studies. Outcome studies are particularly problematic in this field, because they almost uniformly lack statistical power owing to insufficient event rates (due to small sample size and/or short follow-up time). The choice of end point is also important, because studies more often than not note differences in clinical outcome end points without detecting differences in surrogates such as LVEF or heart rate [29, 41]. 

There are a number of values of the treatment effect (delta or A) that could lead to rejection of the null hypothesis of no difference between the two means. For purposes of estimating a sample size the power of the study (that is, the probability that the null hypothesis of no difference is rejected given that the alternate hypothesis is true) is calculated for a specific value of A. in the case of a superiority trial, this specific value represents the minimally clinically relevant difference between groups that, if found to be plausible on the basis of the sample data through construction of a confidence interval, would be viewed as evidence of a definitive and clinically important treatment effect. 

Another way of stating this is that, if the true difference in population means is as large as a specific value of A proposed as clinically important, we would like to find the sample size such that the null hypothesis would be rejected (1 -p)% of the time. The sample size must also be chosen so that a is maintained at an acceptably low value. 

Sample size estimation requires the input of a number of specialists involved with the development of new drugs. The estimate of the standard deviation can be informed by exploratory therapeutic trials of the same drug or by literature reviews of similar drugs. Synthesis of these data from a number of sources requires statistical and clinical judgments. As was seen in Figure 12.1 the estimate of the standard deviation has an important effect on the sample size. Study teams should understand the sources of variability in... 

A certain number of participants need to be recruited for any given trial. In Section 12.2 we discussed sample size estimation, which takes into account a number of considerations that are important not only to the statistician but also to the clinical scientist and the regulator. Once determined, the value produced by this process of estimation is incorporated into the study protocol. 

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