[R] Comparing samples with widely different uncertainties

[Marc Schwartz]

On Aug 25, 2010, at 8:57 AM, Sandy Small wrote:

> Hi
> This is probably more of a statistics question than a specific R
> question, although I will be using R and need to know how to solve the
> problem in R.
> 
> I have several sets of data (ejection fraction measurements) taken in
> various ways from the same set of (~400) patients (so it is paired data).
> For each individual measurement I can make an estimate of the percentage
> uncertainty in the measurement.
> Generally the measurements in data set A are higher but they have a
> large uncertainty (~20%) while the measurements in data set Bare lower
> but have a small uncertainty (~4%).
> I believe, from the physiology, that the true value is likely to be
> nearer the value of A than of B.
> I need to show that, despite the uncertainties in the measurements
> (which are not themselves normally distributed), there is (or is not) a
> difference between the two groups, (a straight Wilcoxon signed ranks
> test shows a difference but it cannot include that uncertainty data).
> 
> Can anybody suggest what I should be looking at? Is there a language
> here that I don't know? How do I do it in R?
> Many thanks for your help
> Sandy


The first place that I would start is at Martin Bland's page pertaining to the design and analysis of measurement studies:

  http://www-users.york.ac.uk/~mb55/meas/meas.htm

The papers he co-authored with Doug Altman are the "go to" resource for this domain.

HTH,

Marc Schwartz