A quick search gave these three abstracts, number 2 explains nicely what the
Neyman bias is.
Also-a web search shows that this term is often used , but not explained...
e.g. at the CEBM site: http://cebm.jr2.ox.ac.uk/docs/studies.html#cross
1. 81264754: Smith MW. The case control or retrospective study in
retrospect. J Clin Pharmacol 1981; 21: 269-74.
The epidemiologic case control design is described background
information on its resurgence in popularity despite historical criticisms by
its detractors. Weaknesses charged to the design by Feinstein and others such
as its potential for recall bias in the use of retrospective data. Neyman's
case selection bias, and the diagnostic suspicion bias are analyzed and shown
to affect both the cohort incidence design and the clinical trial design as
well as the case control.
2. 88244856: Detsky AS, O'Rourke K, Corey PN, Johnston N, Fenton S,
Jeejeebhoy KN. The hazards of using active clinic patients as a source of
subjects for clinical studies. J Gen Intern Med 1988; 3: 260-6.
The authors describe and empirically demonstrate a form of bias that
results from deriving subjects for clinical studies from available patients
currently being followed in specific disease clinics instead of inception
cohorts (patients enrolled at a uniform and early point in their disease).
They
label this effect "clinic patient bias." It is a variation of
prevalence-incidence (Neyman) bias in that it also results from the time gap
between the onset of a specific characteristic (a risk factor, exposure or
disease) and enrollment in the study, causing selective exclusion of fatal or
short episodes, or mild or silent cases. Clinic patient bias may distort an
estimate of relative risk in either direction. The empirical example is
derived
from a study of risk factors for developing complications such as peritonitis
among end- stage renal disease patients treated with continuous ambulatory
peritoneal dialysis (CAPD). The use of available clinic patients rather
than an
inception cohort (patients newly beginning CAPD) resulted in the demonstration
of false apparent risk relationships for two variables: the calendar date when
patients began CAPD (with those enrolled at an earlier time appearing to be at
lower risk), and serum albumin level at the start of CAPD (with those having
lower albumin levels appearing to be at higher risk). This example
demonstrates
one of the potential hazards of using active or available clinic patients as a
source of subjects for clinical studies.
3. 99163261: Alagumalai S, Keeves JP. Distractors--can they be biased
too?
J Outcome Meas 1999; 3: 89-102.
Numerous work has been done on item bias and differential item
functioning. Although there is some research on distractor analysis, no
detailed study has been attempted to examine the way distractors in an item
function, with regards to comparing distractor performance. This paper
examines
how distractors function differentially and compares various methods for
identifying this. The Pearson chi-square, likelihood ratio chi-square and
Neyman weighted least squares chi- square tests are some of these methods.
Possible causes of distractor bias are discussed with illustrations from a
physics problem-solving scale.At 11.05.99 09:09 +0100, john geddes wrote:
>
>can anyone help with this request?
>cheers
>john
>>DEAR ANDRE,
>>
>>
>>CAN YOU TELL ME WHAT IS THE NEYMAN BIAS (IS A DISADVANTAGE OF
>>CROSS-SECTIONAL SURVEYS)?
>>
>>THANKS FOR YOUR TIME
>>EDUARDO
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|