Document Detail


Detecting systematic errors in multi-clinic observational data.
MedLine Citation:
PMID:  497346     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In multi-clinic studies it is hard to maintain a uniformly high quality of measurement and coding. Systematic errors almost always occur, in spite of the best of intentions and the most rigid protocols. It is the statistician's responsibility to plan for the detection of these errors, as well as to try to avoid them and not be misled by them. The practice of examining the univariate and multivariate sample frequency distributions of the variables under study, with an eye open for anything that looks puzzling, can be very helpful in detecting and trying to correct systematic errors that would bias the analysis. Examples are given from a 21-clinic study on pregnancy and child development.
Authors:
N Wermuth; W G Cochran
Related Documents :
25248206 - Prediction of heart failure decompensation events by trend analysis of telemonitoring d...
15119966 - An application of conditional logistic regression and multifactor dimensionality reduct...
23025156 - Contrasting predictions of low- and high-threshold models for the detection of changing...
9489966 - Validation of flow convergence region method in assessing mitral valve area in the cour...
22054046 - Effects of whole-body vibration on postural control in elderly: a systematic review and...
11536136 - Economic comparison between conventional and disposables-based technology for the produ...
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Biometrics     Volume:  35     ISSN:  0006-341X     ISO Abbreviation:  Biometrics     Publication Date:  1979 Sep 
Date Detail:
Created Date:  1980-01-19     Completed Date:  1980-01-19     Revised Date:  2007-11-15    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  UNITED STATES    
Other Details:
Languages:  eng     Pagination:  683-6     Citation Subset:  IM    
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Humans
Morbidity*
Statistics as Topic*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  Testing hypotheses in case-control studies--equivalence of Mantel-Haenszel statistics and logit scor...
Next Document:  Protein amino acid analysis by an isotope ratio gas chromatography mass spectrometry computer techni...