Study population homogeneity

Meta-analysis aims to increase the statistical power of the available evidence by combining the results of smaller trials together using specific statistical methods. The validity of the meta-analysis will depend on the quality of the evidence on which it is based and how homogeneous or comparable the samples are. Combining very heterogeneous study populations can lead to bias. 

Preliminary data from 43 patients analyzed for TS mRNA expression and 3 -UTR 6bp deletion suggest that patients homozygous for 6bp deletion (-6bp/-6bp) express threefold less TS mRNA than patients homozygous for the presence of the 6bp (+6bp/+6bp) ip = 0.017) (42,59,62). A recent study by Dotor et al. illustrated that in series of homogenously 5-FU treated patients, the presence of homozygous 3 -UTR 6bp deletion (-6bp/-6bp) appears to be a strong prognostic factor, which may be of benefit for at least 20% of the study population (63). 

The limitation of the data is that the study population from which the variance measure was derived was rather homogeneous, and the estimates were minimum requirements (Andersen et al., 2007a). 

Despite these limitations, studies on homogenates reflect the events in the intact cell and present advantages over histochemical methods, because reactions can be better quantitated and the medium is better controlled. However, in contrast to histochemical techniques, which provide information on individual cells, homogenate studies must deal with information gathered from a nonhomogeneous cellular population. This disadvantage can seldom be overcome. (There is an exception in liver, where reticuloendothelial cells can be separated from parenchymal cells.)... 

In practice, the kinetic pattern can sometimes be guessed from the shapes of S x) and M x) for the dose range studied. Thus, the experimentally determined value of x can be substituted directly into the inferred form of K(a ) to see whether the observed and predicted yield maxima agree. Any marked disagreement between the observed and predicted yield maxima would mean either that the kinetic pattern was not inferred correctly because the dose range studied was too limited or that the assumptions of population homogeneity and/or stochastic independence of mutation and killing do not hold for the system under consideration. 

Non-homogeneous CA. These are CA in which the state-transition rules are allowed to vary from cell to cell. The simplest such example is one where there are only two different rules randomly distributed throughout the lattice. Kauffman [kauff84] has studied the otlier extreme in whidi tlie lattice is randomly populated with all possible Boolean functions of k inputs. 

Non-Homogeneous CA a characteristic feature of all CA rules defined so far has been that of homogeneity - each cell of the system evolves according to the same rule 0. Hartman and Vichniac [hartSfi] were the first to systematically study a class of inhomogeneous CA (INCA), in which the state-transition rules are allowed to vary from cell to cell. The simplest such example is one where there are only two different 0 s, which are randomly distributed throughout the lattice. Kauffman has studied the other extreme in which the lattice is randomly populated with all 2 possible boolean functions of k inputs. The results of such studies, as well as the relationship with the dynamics of random, mappings, are covered in detail in chapter 8.3. 

A key element in planning and conducting clinical trials is to ensure that they have scientific validity and objectivity. This is particularly relevant with respect to Phase II and III studies, where it is desired to demonstrate a positive benefit to risk outcome. Responses to a drug among a patient population are rarely homogeneous and clear-cut. Thus, sound statistical principles must be applied in order to be able to distinguish significant effects from random events. 

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