Treatment effects/differences sample size

We begin in Chapter 2 with a discussion of the chromatographic process, developing the separate concepts of (1) equilibrium distribution of sample between adsorbent and solvent (or gas) and (2) bed efficiency or theoretical plate number. These two factors are then related in a general way to the problem of separation, and the various techniques of adsorption chromatography are introduced in terms of the different separation problems they are intended to solve. Chapter 3 provides a general discussion of adsorption, emphasizing those fundamental concepts which will be necessary in the discussions of later chapters. The effect of sample size on separation is treated in Chapter 4, particularly the factors which affect isotherm linearity. Chapter 5 provides a complete treatment of bed efficiency in liquid-solid systems. The distribution of sample... 

Suppose that one is not convinced by the arguments above which tend to show that, for the sorts of sample size usually entertained for clinical trials, given a straight choice between pooling and not pooling, the former is preferable, but wishes to explore, as fully as possible, the extent to which treatment effects differ between the sexes. Another controversy is then raised, namely whether one should test for treatment-by-sex interaction or simply study the treatment effects separately for each sex. 

In pharmaceutical and medical device development, clinical trials are classified into four main phases designated with Roman numerals 1,11, III and lY The various phases of development trials differ in purpose, length and number of subjects involved. Phase I trials are conducted to determine safe dose levels of a medication, treatment or product (National Institutes of Health, 2002). The main purpose is often to determine an acceptable single dosage - how much can be given without causing serious side-effects. Phase I trials will also involve studies of metabolism and bioavailabity (Pocock, 1983). The sample size of a Phase 1 clinical trial is usually small, ranging from 10-80 subjects (National Institutes of Health, 2002 Pocock, 1983). 

The general rule is that the smaller the difference in effect to be detected between the two treatment groups, and the greater the variability in the measurement of the primary endpoint, the larger the sample size must be. Figure 6.5 gives an example of power curves, or statistical normogram, that relate sample size to size of effect to be detected. 

The aim of any clinical trial is to have low risk of Type I and II errors and sufficient power to detect a difference between treatments, if it exists. Of the three factors in determining sample size, the power (probability of detecting a true difference) is arbitrarily chosen. The magnitude of the drug s effect can be estimated with more or less accuracy from previous experience with drugs of the same or similar action, and the variability of the measurements is often known from published experiments on the primary endpoint, with or without the drug. These data will, however, not be available for novel substances in a new class and frequently the sample size in the early phase of development has to be chosen on an arbitrary basis. 

The sample size and patient-to-patient variability are the key elements of the noise. A small sample size and a large amount of patient-to-patient variability contribute to a large amount of noise. Both increasing the sample size and reducing the patient-to-patient variability will have the effect of reducing the noise and make it much easier to conclude that the treatments are different. 

This null hypothesis is saying that the treatment difference/effect is consistent. If the p-value from this test is significant then we talk in terms of having a significant treatment-hy-centre (or a treatment x centre) interaction. Power and sample size calculations (see later chapter on this topic) will have focused... 

As we have discussed, when conducting a sample-size estimation, the researcher has to choose values for a and p and has to come up with a standardized treatment effect, which is in turn the result of finding the best possible estimates of a clinically significant difference and its variation. What are the influences that lead the sponsor to choose certain values for a, p, and the standardized treatment effect ... 

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. 

Data may be converted to a normal distribution using log transforms, etc., as described in Chapter 5. Strongly positively skewed data are often converted to a normal distribution by log transformation. When this is done to allow analysis by a two-sample /-test, you should be aware that the 95 per cent Cl for the size of the treatment effect will estimate the ratio between the values of the endpoint under the two conditions instead of the absolute difference in the value. 

Symptom score responses were similar whether drug X was administered alone or in combination with interacting variable. A 25 % ( 8 %) mean decrease in overall exposure in the presence of food had no statistically significant effect on symptom scores. A sample size of at least 13 000 subjects/arm would have been needed to reach statistical significance using currently simulated treatment difference of 0.057. 

Clonazepam is widely used for the treatment of sleep disturbances related to post-traumatic stress disorder, despite very limited published data supporting its use for this indication. In a randomized, single-blind, placebo-controlled, crossover trial of clonazepam 1 mg at bedtime for 1 week followed by 2 mg at bedtime for 1 week in six patients with combat-related post-traumatic stress disorder there were no statistically significant differences between clonazepam and placebo (4). Adverse effects of clonazepam were generally mild and essentially indiscernible from those attributed to placebo. Only one patient elected to continue taking clonazepam at the end of the trial. The small sample size was a significant limitation of the study. 

---