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Course overview, the diagnostic process, and measures of interobserver agreement
Thomas B. Newman, MD, MPH
September 18, 2008
Overview Administrative stuff Overview of the course The diagnostic process Interobserver agreement
– Continuous variables– Categorical variables
• Concordance• Kappa
– Regular– Weighted
Administrative stuff Introductions Basic structure of course
– New material each week in lecture– Read material before lecture if possible– HW on that material due the following week in
section– Exceptions:
• No class October 9• Penultimate class 12/4 – Chapter 12 (Challenges for
EBD) and course review: pass out take-home exam; no HW on Ch 12
• Last lecture 12/11: review of take-home exam Lectures: mixture of PPT and Whiteboard
– How many want paper copies of PPT slides?
SECTIONS
Section assignments: Click ROSTER on Epi 204 website
Section rooms: Click SCHEDULE on website
Faculty will rotate; students, rooms and TA's will be constant for the quarter
Homework Required – key way of learning material Which problems are assigned announced
in SECTION and (later) posted on web Not graded if late, but can still be turned in;
answers on web Use fresh sheets of paper with your name
on each, not syllabus pages, not e-mail. (You can download and word-process if you want, but print a copy unless section leader prefers electronic.)
Will be graded by section leaders and returned the following week
Getting help Classmates, then section leaders, then
faculty Ambiguous/confusing problems – send
e-mail to section leader or me– Unless you indicate otherwise, we will
assume we can cc the whole class when we respond if we think question is of general interest
Textbook
TBN and MAK have almost finished a book, “Evidence-based Diagnosis” (Cambridge University Press, 2009)
Other texts listed in on web Copies of other books in bookstore and on
reserve in the library and available for browsing here
Grading, honor code, etc. Worst HW score dropped; all other HW count
equally 2/3 Homework avg + 1/3 final examination
OR 1/3 Homework avg + 2/3 final examination, whichever is better
Try all problems on your own first; OK to help each other with HW but– Acknowledge help– Write answer in own words
Do not collaborate on final exam Honor code taken seriously
Course overview Diagnosis
– Theory– Inter-rater reliability– Dichotomous tests– Multilevel tests– Studies of tests– Combining tests
Screening and prognostic tests Treatments: randomized trials Alternatives to randomized trials P-values and confidence intervals; Bayes' theorem Clinicians and probability
Diagnostic process Why do we want to assign a name to
this person’s illness? Different reasons lead to different
classification schemes
Examples Acute nephrotic syndrome Acute leukemia Attention deficit disorder Dysuria worth a course of antibiotics SLUBI=Self-limited undiagnosed benign
illness
Simplified Generic Decision Problem
Patient either has the disease or not If D+, net benefit of treatment If D-, better not to treat (“Treat” could include doing more tests)
Simplifying assumptions (often wrong) Test results are dichotomous
– Most tests have more than two possible answers
Disease states are dichotomous– Many diseases occur on a spectrum– There are many kinds of “nondisease”
Evaluating diagnostic tests
Reliability Accuracy Usefulness
Today we do reliability
Types of variables
Categorical– Dichotomous – 2 values– Nominal – no intrinsic ordering – Ordinal – intrinsic ordering
Continuous (infinite number of values) vs Discrete (limited number)
Measuring interobserver agreement for categorical variables
Gallop heard by Observer B
No gallop heard by Observer B
Total, Observer A
Gallop heard by Observer A 20 15 35No gallop heard by Observer A 10 55 65Total, observer B 30 70 100
What is agreement?
Concordance rate
What percent of the time do the 2 observers agree (exactly)
Advantage: easy to understand Disadvantage: may be misleading if
observers agree on prevalence of abnormality
Concordance rate problem
Gallop heard by Observer B
No gallop heard by Observer B
Total, Observer A
Gallop heard by Observer A 0 5 5No gallop heard by Observer A 5 90 95Total, observer B 5 95 100
Unbalanced Disagreement
Lesion # RATER A RATER B
1 S S
2 S S
3 S M
4 S M
5 S M
6 M M
7 M L
8 L L
9 L L
10 L L
BA S M L Total
S 2 2 1 5M 0 0 2 2L 0 0 3 3
Total 2 3 6
What is going on here? Look for lack of balance
above and below diagonal Results when observers
have different thresholds
Definition of Kappa The amount of agreement beyond what
would be expected by chance* Formula:
Practice– Obs = 90%, Exp = 80%, K =– Obs = 70%, Exp = 60%, K =– Obs = 60%, Exp = 70%, K =
*Given the observed marginals
Observed agreement – Expected agreement
1 – Expected agreement
Calculation of Expected Agreement from Marginals
Gallop heard by Observer B
No gallop heard by Observer B
Total, Observer A
Gallop heard by Observer A 20 15 35No gallop heard by Observer A 10 55 65Total, observer B 30 70 100
GCS Eye opening- Observed
Doc #2None To Pain To
CommandSpontaneous Total
None 11 2 0 4 17To Pain 4 1 2 0 7
To Command 0 3 8 3 14Spontaneous 2 1 7 68 78Total 17 7 17 75 116
Emergency Physician #2
GCS Eye Opening: Expected
Doc #2None To Pain To
CommandSpontaneous Total
None 2.5 1 2.5 11 17To Pain 1 0.4 1 4.5 7
To Command 2.1 0.8 2.1 9.1 14Spontaneous 11.4 4.7 11.4 50.4 78Total 17 7 17 75 116
Emergency Physician #2
17 x 78/116 = 1326/116 = 11.4
Why does multiplying row total by column total and dividing by N give you the expected agreement?
Weighted Kappa Weighted kappa
– Linear– Quadratic– Custom
Real-life illustration: Rating of neurological examination Types of weights, Stata illustration.
. tab ex1 ex2
. kap ex1 ex2, w(w)
. kap ex1 ex2, w(w2) (See Appendix 2.1)
What does observed Kappa depend upon?
How well people agree SPECTRUM within classifications
– E.g., re the abnormal ones VERY abnormal?– Difficult cases can be excluded or over-sampled
PREVALENCE of classifications by the various observers (and whether they agree on prevalence)
Chance (random error; people can get lucky/unlucky)
Weighting scheme used
Wireless Internet Access
Key is n2xa8!wr