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st: Incorporating error into a prevalence measure
From
Fred Wolfe <[email protected]>
To
[email protected]
Subject
st: Incorporating error into a prevalence measure
Date
Sat, 18 Dec 2010 08:16:08 -0600
After reviewing Stata programs and presentations relating to
misclassification and error, and searching on the web, I have not been
able to solve this problem. I wonder if any listers might be able to
help.
A soon to be published paper defines a binary outcome, cpesr, as being
present when 4 characteristics are present:
mdtjnt28 <= 1 [a continuous variable with a range of 0-28] +
mdsjnt28 <= 1 [a continuous variable with a range of 0-28] +
glb <= 1 <= 1 [a continuous variable with a range of 0-10] +
(sex==0 & esr <30) | (sex == 1 & esr <20) [esr is a continuous
variable with a range of 0-150, sex is 1 for male, 0 for female)]
I calculate this as follows:
gen cpesr = mdtjnt28 <=1 & mdsjnt28 <=1 & glb <=1 & ((sex==0 & esr
<30) | (sex == 1 & esr <20)) if !mi(mdtjnt28, mdsjnt28, glb, esr)
. ci cpesr if ruse
Variable | Obs Mean Std. Err. [95% Conf. Interval]
-------------+---------------------------------------------------------------
cpesr | 1478 .0568336 .0060243 .0450165 .0686506
However, mdtjnt28, mdsjnt28 and glb have reliabilities < .9, and above
results fail to account for reduced reliability.
My question is how I can incorporate the reliability information into
the estimate of cpesr to provide more accurate confidence intervals.
Thanks,
Fred
--
Fred Wolfe
National Data Bank for Rheumatic Diseases
Wichita, Kansas
NDB Office +1 316 263 2125 Ext 0
Research Office +1 316 686 9195
[email protected]
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