Sample size calculation issues

Also, the analysis plan should identify the statistical methods that will be used and how hypotheses will be tested (e.g., a p value cutoff or a confidence interval for the difference that excludes 0). And the plan should prespecify whether interim analyses are planned, indicate how issues of multiple testing will be addressed, and predefine any subgroup analyses that will be conducted. Finally, the analysis plan should include the results of power and sample size calculations. 

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 ... 

A very unsatisfactory feature of conventional approaches to sample size calculation is that there is no mention of cost. This means that for any two quite different indications with the same effect size, that is to say the same ratio of clinically relevant difference to standard deviation, the sample size would be the same whatever the cost or difficulty of recruiting and treating patients. This is clearly illogical and trialists probably manage this issue informally by manipulating the clinically relevant difference in the way discussed in Section 13.2.3. Clearly, it would be better to include the cost explicitly, and this suggests decision-analytic approaches to sample size determination. There are various Bayesian suggestions and these will be discussed in the next section. 

Zhang J, Machado SG (2008) Statistical issues including design and sample size calculations in thorough QT/QTc studies. J Biopharm Stat 18 451 67... 

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]. 

The book is aimed at those who have to use statistics, but have no ambition to become statisticians per se. It avoids getting bogged down in calculation methods and focuses instead on crucial issues that surround data generation and analysis (sample size estimation, interpretation of statistical results, the hazards of multiple testing, potential abuses, etc.). In this day of statistical packages, it is the latter that cause the real problems, not the number-crunching. 

As a final result the producer issues a material certified for a number of parameters, based on the minimum sample size determined in the homogeneity study. This information must be given to the end user in the form of a certificate. The certificate or the accompanying report must also state the uncertainty and the way it is calculated. 

In many cases one can only simulate very small sample sizes and the imposed temperature gradients can lead to deviations from the linear response. To account for these issues, simulations are usually done for samples of different length and then extrapolated to determine k for a sample infinite length or micron scale [18,47]. The values of k calculated by the equilibrium MD approach was used to check the consistency of the MD simulations results and adjust the length of simulated sample in [16]. 

It is important in any discussion of residue limits to understand that limits for a cleaning process may be expressed in different ways. This includes the limit of the residue in the subsequently manufactured product, the limit of the residue on the cleaned equipment surfaces, and the limit of the residue in the analyzed sample. These are all related, but they are usually different numbers. For an active ingredient in the cleaning of a finished drug product, the limit in the next product is usually calculated based on application of a safety factor (usually 0.001 or lower) to the minimum daily dose of that active in the maximum daily dose of the subsequently manufactured product. The active or level of active in the subsequently manufactured product is irrelevant unless there is information about unusual deleterious interactions. This calculation is also independent of manufacturing issues such as batch size and equipment surfaces areas, and can be calculated solely on information about the dosing of the two products as follows ... 

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