Can anyone provide advice/ reference on dealing with the following issue:
In a community based mental health care intervention RCT study there are 3
equally important primary(?) outcomes namely :
symptom reduction, re-hospitalisation, quality of life.
Problem is being able to recruit to the study given limited resources (
financial and proximity to organising centre). If we take one outcome then
we can recruit a sample size which ensures adequate power. However,
statisticians tell us that increasing the number of outcomes requires
increased sample size ( the arguments are similar to those for Bonferroni
correction for multiple comparisons during data analysis, I understand) .
Reducing the number of outcomes meets the needs of the statisticians but
will ensure that research questions important to others remain unanswered.
One possible solution that has occurred to me is that we assume a
multivariate design/analysis ie these are all manifest measures of the
latent variable ' health gain ' . An additional benefit might be that
small, statistically insignificant changes across a range of measures might
hide a significant change on 'health gain'. Surely this is a problem faced
by anyone evaluating complex ( ie multidimensional) healthcare
interventions isn't it ?
Advice on this would be welcome from you methodological gurus out there.
Thanks
Dr. D. John Done,
Dept Psychology,
University of Hertfordshire,
Hatfield,
Herts
AL10 9AB
Tel: 01707 284638
Fax 01707 285073
"Its not about reductionism or antireductionism its about what works" Lewis
Wolpert 1997
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