AJR
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Jarvik, J. G.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Jarvik, J. G.
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati  
What's this?

The Research Framework

Jeffrey G. Jarvik1

1 Departments of Radiology, Neurosurgery and Health Services, and the Center for Cost and Outcomes Research, University of Washington, Seattle, WA.



View larger version (11K):

[in a new window]
  		Fig. 1. —Diagram shows that test sensitivity focuses on first column of two-by-two table. Sensitivity equals A / (A + C), or number of patients with true-positive (TP) findings divided by all patients with positive reference test findings. + = positive test result, - = negative test result, FP = false-positive, FN = false-negative, TN = true-negative.

 


View larger version (12K):

[in a new window]
  		Fig. 2. —Diagram shows how test specificity focuses on second column of two-by-two table. Test specificity equals D / (B + D), or number of patients with true-negative (TN) findings divided by all patients with negative findings on reference test. + = positive test result, - = negative test result, TP = true-positive, FP = false-positive, FN = false-negative.

 


View larger version (10K):

[in a new window]
  		Fig. 3. —Predictive values are calculated from table rows rather than table columns. Positive predictive value equals A / (A + B), or number of true-positive (TP) findings divided by number of all patients with positive findings on index test. + = positive test result, - = negative test result, FP = false-positive, FN = false-negative, TN = true-negative.

 


View larger version (11K):

[in a new window]
  		Fig. 4. —Negative predictive value is calculated from second row of table and equals D / (C + D), or number of true-negative (TN) findings divided by number of all patients with negative index test results. + = positive test result, - = negative test result, TP = true-positive, FP = false-positive, FN = false-negative.

 

Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati    What's this?



HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Copyright © 2001 by the American Roentgen Ray Society.