Abstract
In clinical studies, subjects or patients might be exposed to a succession of diagnostic tests or medication over time and interest is on determining whether there is progressive remission of conditions, disease or symptoms that have measured collectively as quality of life or outcome scores. In addition, subjects or study participants may be required, perhaps early in an experiment, to improve significantly in their performance rates at the current trial relative to an immediately preceding trial, otherwise the decision of withdrawal or dropping out is ineviTable. The common research interest would then be to determine some critical minimum marginal success rate to guide the management in decision making for implementing certain policies. Success rates lower than the minimum expected value would indicate a need for some remedial actions. In this article, a method of estimating these rates is proposed assuming the requirement is at the second trial of any particular study. Pairwise comparisons of proportions of success or failure by subjects is considered in repeated outcome measure situation to determine which subject or combinations is responsible for the rejection of the null hypothesis. The proposed method is illustrated with the help of a dataset on palliative care outcome scores (POS) of cancer patients.
Highlights
Research in many areas frequently involves study designs in which repeated measurements are obtained
Experimental units might be defined by families; responses are obtained from the members of each family
We proposed a method of pairwise comparisons in repeated measures that is suitable when interest is on testing whether the null hypothesis of no difference is rejected or accepted, but if the null hypothesis is rejected, which individual subjects or their combinations contributed to the rejection of the null hypothesis
Summary
Research in many areas frequently involves study designs in which repeated measurements are obtained. If the hypothesis of no improvement is rejected, there might exists some improvements in performance or increases in proportions of positive responses, one may search further to examine statistically any observed patterns in these increases, with a view to ascertaining which of the conditions or their combinations might have led to a rejection of the null hypothesis. Often interest in these situations may be in determining whether the subjects on the average successively improve their performance rather than in multiple comparisons of all the conditions.
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