Hostname: page-component-8448b6f56d-t5pn6 Total loading time: 0 Render date: 2024-04-24T19:55:11.600Z Has data issue: false hasContentIssue false

A Method for Analysing Assessments of Symptom Change

Published online by Cambridge University Press:  29 January 2018

Alistair E. Philip*
Affiliation:
Medical Research Council’, Unit for Research on the Epidemiology of Psychiatric Illness, Edinburgh University Department of Psychiatry, Edinburgh, 10

Extract

In studies which attempt to assess the efficacy of some new treatment it is customary to make a formal assessment of the relevant behaviour or symptomatology using standardized inventories, checklists, symptom rating scales or ad hoc ratings of variables considered to be important by the clinician-researcher. Ratings made at the beginning and end of treatment are compared for groups of patients using improvement scores, arbitrary cut-off points and other devices aimed at circumventing the statistical problems arising from the non-normal distributions of most rating scales. Present practice favours the use of some nonparametric statistic in these comparisons. The aim of this paper is to present a method which facilitates the analysis of ratings made on more than two occasions, allowing a trend analysis to be carried out without making assumptions about the distribution of scores. The method also allows the clinician-researcher to make a statistically-based decision regarding the efficacy of the treatment in question for individual patients.

Type
Research Article
Copyright
Copyright © Royal College of Psychiatrists, 1969 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ferguson, G. A. (1965). Nonparametric Trend Analysis. Montreal: McGill University Press.Google Scholar
Kendall, M. G. (1948). Rank Correlation Methods. London: Griffin.Google Scholar
Page, E. B. (1963). ‘Ordered hypotheses for multiple treatments: a significance test for linear ranks.’ J. Amer. statist. Ass., 58, 216–30.CrossRefGoogle Scholar
Pearson, E. S., and Hartley, H. O. (1966). Biometrika Tables for Statisticians, Vol. 1. Cambridge: Cambridge University Press.Google Scholar
Siegel, S. (1956). Nonparametric Statistics for the Behavioural Sciences. New York: McGraw-Hill.Google Scholar
Submit a response

eLetters

No eLetters have been published for this article.