StatTools : Sequential Paired Difference Analysis Program

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Related Links:
Sequential Analysis Introduction and Explained Page
Sequential Paired Preference Analysis Program Page
Sequential Paired Difference Analysis Program Page

Sequential Analysis Paired Difference Paired Preference References
General discussions on sequential analysis are presented in the Sequential Analysis Introduction and Explained Page and quality statisitcs in the Quality Statistics Explained Page , and they are not repeated here.

This page discusses paired sequential comparisons that were developed in the late 1950s and 1960s by Armitage. The methods were particularly suitable to support medical research comparing efficacies of different treatment or medications, but is also useful in quality control.

The model uses paired comparisons, where two treatments are administered to either the same individual or a paired of matched subjects, and the differences between the pair is then used for analysis.

The data is analysed after the results from each pair is available, and one of 3 decisions are made. These are to conclude the study and reject the null hypothesis (significant difference exists), to accept the null hypothesis (significant difference does not exist), or to defer any decision and collect more data.

In his book, Armitage presented 3 models, the paired preference, the paired difference, and the paired follow up (survival). StatTools presents two of these models, that of preference and paired differences.

Common Terms and Abbreviations α : , also represented as alpha, or p, is the probability of Type I Error. Commonly, p<0.05 or p<0.01 is used as the criteria to reject the null hypothesis. Please note that the default setting is the one tailed test, where the test is whether tmt1 is better than tmt2, or visa versa, but not both. Where the null hypothesis is for both directions, the two tail model is required, and the α value entered should be halved. α(two tail) and 2α(one tail) produces the same results.

β : is the probability of Type II Error. Commonly, β<0.2 is used at the planning stage to determine stopping borders for sequential analysis.

Power : is 1 - β, a concept intuitively easier to understand, and represents the ability to detect a difference, if its really there. A power of 0.8 (80%) is usually used as this is the same as β=0.2. In Armitage's text book however, a power of 0.9 or 0.95 are often presented.

In the results, the positions of the decision lines, depends on whether the effect size is positive (grp1>grp2), of negative (grp1<grp2)