## Contemporaneous Independent Treatment Allocation

Taves (1974) has described a study design that requires an independent coordinator who allocates each patient, as he/she is recruited, to one or other treatment group. The independent coordinator allocates each patient so as to minimize the difference between the two treatment groups, according to prospectively defined patient characteristics, e.g. age, sex, genotype, disease state or stage, or concomitant therapy. This allocation is therefore also based upon the accumulating characteristics of the treatment groups, as has developed during the study to date. Patients are therefore not allocated to a treatment group by the chance of a randomization schedule.

Bias in minimization trials can be avoided when three conditions are met. First, those performing the clinical trial itself, i.e. administering test medications and measuring end-points, should be double-blind and unaware of which treatment the patient has received. Second, the independent coordinator need only allocate patients to anonymous groups A or B, and the study pharmacist need be the only person who knows which treatments these codes represent. Third, the criteria for which

Figure 11.2 Illustration of the Armitage 'sequential analysis' study design. Patients are paired, and one of each pair receives each alternative treatment. If the patient receiving treatment A does better than the one receiving treatment B (A > B), then the line moves upwards; vice versa, if the patient receiving B does better than the one receiving A (B > A), then the line moves downward. If the treatments cannot be distinguished within a pair of patients, then the line moves horizontally. The critical boundaries (broken lines) are computed from prospective measures of a and (3 (e.g. p = 0.05 and 80% power, respectively). The technique derives from an engineering control chart and, once again, can be adapted to more sophisticated forms, including limits on the study size for indeterminate results

Figure 11.2 Illustration of the Armitage 'sequential analysis' study design. Patients are paired, and one of each pair receives each alternative treatment. If the patient receiving treatment A does better than the one receiving treatment B (A > B), then the line moves upwards; vice versa, if the patient receiving B does better than the one receiving A (B > A), then the line moves downward. If the treatments cannot be distinguished within a pair of patients, then the line moves horizontally. The critical boundaries (broken lines) are computed from prospective measures of a and (3 (e.g. p = 0.05 and 80% power, respectively). The technique derives from an engineering control chart and, once again, can be adapted to more sophisticated forms, including limits on the study size for indeterminate results the treatment groups should be balanced must be prospectively identified and rigidly adhered to, using a recorded, quantitative system of scoring the factors.

In its simplest form, this class of minimization designs usually results in treatment groups of nearly equal size. By equitably assigning patients to three or more treatment groups, and yet having identical treatments for two or more of these, unbalanced sample sizes can be created. This is of use when, for example, it may be desirable to expose fewer patients to placebo than to active therapy, especially when conducting a trial of compounds whose properties are fairly well known or may be predicted with some confidence.

Note that minimization trials can only alter power calculations when assumptions of the size of worthwhile differences in effect are also pro-spectively defined. For example, from a clinical point of view, a small-sized improvement in outcome (perhaps a few per cent of patients more than that observed for placebo treatment) may be viewed as very worthwhile in an extremely heterogenous patient population when subjected to multivariate analysis (this is common in large, simple studies; see below). On the other hand, when designing a minimization study, the assumption is that the treatment groups will be devoid of relevant differences in baseline characteristics, and therefore clinical significance might only be assumed to follow from a large-sized difference in patient response. The size of the difference that is assumed to be of interest, as it increases, may compensate for the reduction in variability amongst study group samples, and thus have less than expected impact on the sample sizes needed to conduct the clinical trial.

Minimization designs are probably under-used by the pharmaceutical industry. This approach is not well-designed for pivotal clinical trials, or for diseases with large numbers of prognostic factors, where, in any case, large numbers of patients are needed for a tolerability database. If the controlled clinical trial is a gold standard, then it would be wrong to assert that the independent treatment allocation design is the 'platinum standard' (pace Treasure and MacRae 1998). The interested reader is referred to a good published example (Kallis et al 1994), and to more detailed statistical treatments (Pocock and Simon 1975; Freedman and White 1976).

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