1. Revising FDA’s “Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests”
FDA/Industry Statistics Workshop
September 28-29, 2006
Kristen Meier, Ph.D.
Mathematical Statistician, Division of Biostatistics
Office of Surveillance and Biometrics
Center for Devices and Radiological Health, FDA

2. Outline Background of guidance development Overview of comments
STARD Initiative and definitions
Choice of comparative benchmark and implications
Agreement measures – pitfalls
Bias
Estimating performance without a “perfect” [reference] standard - latest research
Reporting recommendations

3. Background
Motivated by CDC concerns with IVDs for sexually transmitted diseases
Joint meeting of four FDA device panels (2/11/98): Hematology/Pathology, Clinical Chemistry/Toxicology, Microbiology and Immunology
Provide recommendations on “appropriate data collection, analysis, and resolution of discrepant results, using sound scientific and statistical analysis to support indications for use of in vitro diagnostic devices when the new device is compared to another device, a recognized reference method or ‘gold standard,’ or other procedures not commonly used, and/or clinical criteria for diagnosis”

4. Statistical Guidance Developed
“Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests: Draft Guidance for Industry and FDA Reviewers”
issued in Mar. 12, 2003 with a 90-day comment period
for all diagnostic products not just in vitro diagnostics
only addresses diagnostic devices with 2 possible outcomes (positive/negative)
does not address design and monitoring of clinical studies for diagnostic devices

5. Dichotomous Diagnostic Test Performance
Study Population
TRUTH
Truth Truth
New Test+ TP (true+) FP (false+)
Test Test FN (false ) TN (true)
estimate:
sensitivity (sens) = Pr(Test+|Truth+)  100%×TP/(TP+FN)
specificity (spec) = Pr(Test|Truth)  100%×TN/(FP+TN)
“Perfect” test: sens=spec=100% (FP=FN=0)

6. Example Data: 220 Subjects
TRUTH Imperfect Standard
 
New New
Test  Test 
total total
Unbiased Estimates Biased* Estimates
Sens 86.3% (44/51) % (40/44)
Spec 99.4% (168/169) % (171/176)
* Misclassification bias (see Begg 1987)

7. Recalculation of Performance Using “Discrepant Resolution”
STAGE 1 – retest discordants STAGE 2 – revise 2x2*
using a “resolver” test based on resolver result
Imperfect Standard Resolver/imperfect std.
 “+” “”
New (5+, 0) New
Test  (1+, 3) Test 
total total
“sens” % (40/44)  % (45/46)
“spec” % (171/176)  100% (174/174)
*assumes concordant=“correct”

8. Topics for Guidance Realization:
Problems are much larger than “discrepant resolution”
2x2 is an oversimplification, but still useful to start
Provide guidance:
What constitutes “truth”?
What to do if we don’t know truth?
What name do we give performance measures when we don’t have truth?
Describing study design: how were subjects, specimens, measurements, labs collected/chosen?

9. Comments on Guidance
FDA received comments from 11 individuals/organizations:
provide guidance on what constitutes “perfect standard”
remove “perfect/imperfect standard” concept and include and define “reference/non-reference standard” concept (STARD)
reference and use STARD concepts
provide approach for indeterminate, inconclusive, equivocal, etc… results
minimal recommendations
discuss methods for estimating sens and spec when a perfect [reference] standard is not used
cite new literature
include more discussion on bias, including verification bias
some discussion added, add more references
add glossary

10. STARD Initiative
STAndards for Reporting of Diagnostic Accuracy Initiative
effort by international working group to improve quality of reporting of studies of diagnostic accuracy
checklist of 25 items to include when reporting results
provide definitions for terminology

11. STARD Definitions Adopted
Purpose of a qualitative diagnostic test is to determine whether a target condition is present or absent in a subject from the intended use population
Target condition (condition of interest) – can refer to a particular disease , a disease stage, health status, or any other identifiable condition within a patient, such as staging a disease already known to be present, or a health condition that should prompt clinical action, such as the initiation, modification or termination of treatment
Intended use population (target population) – those subjects/patients for whom the test is intended to be used

12. Reference Standard (STARD)
Move away from notion of a fixed, theoretical “Truth”
“considered to be the best available method for establishing the presence or absence of the target condition…it can be a single test or method, or a combination of methods and techniques, including clinical follow-up”
dichotomous - divides the intended use population into condition present or absent
does not consider outcome of new test under evaluation

13. Reference Standard (FDA)
What constitutes “best available method”/reference method?
opinion and practice within the medical, laboratory and regulatory community
several possible methods could be considered
maybe no consensus reference standard exists
maybe reference standard exists but for non-negligible % or intended use population, the reference standard is known to be in error
FDA ADVICE:
consult with FDA on choice of reference standard before beginning your study
performance measures must be interpreted in context: report reference standard along with performance measures

14. Benchmarks for Assessing Diagnostic Performance
NEW: FDA recognizes 2 major categories of benchmarks
reference standard (STARD)
non-reference standard (a method or predicate other than a reference standard; 510(k) regulations)
OLD: “perfect standard” and “imperfect standard”, “gold standard” – concepts and terms deleted
Choice of comparative method determines which performance measures can be reported

15. Comparison with Benchmark
If a reference standard is available: use it
If a reference standard is available, but impractical: use it to the extent possible
If a reference standard is not available or unacceptable for your situation: consider constructing one
If a reference standard is not available and cannot be constructed, use a non-reference standard and report agreement

16. Naming Performance Measures: Depends on Benchmarks
Terminology is important – help ensure correct interpretation
Reference standard (STARD)
a lot of literature on studies of diagnostic accuracy (Pepe 2003, Zhou et al. 2002)
report sensitivity, specificity (and corresponding CIs), predictive values of positive and negative results
Non-reference standard (due to 510(k) regulations)
report positive percent agreement and negative percent agreement
NEW: include corresponding CIs (consider score CIs)
interpret with care – many pitfalls!

17. Agreement Study Population Non-Reference Standard +  New Test+ a b
+ 
New Test a b
Test Test c d
Positive percent agreement (new/non ref. std.) =100%×a/(a+c)
Negative percent agreement (new/non ref. std.)=100%×d/(b+d)
[overall percent agreement=100%×(a+d)/(a+b+c+d)]
“Perfect” new test: PPA≠100% and NPA≠100%

18. Pitfalls of Agreement
agreement as defined here is not symmetric: calculation is different depending on which marginal total you use for the denominator
overall percent agreement is symmetric, but can be misleading (very different 2x2 data can give the same overall agreement
agreement ≠ “correct”
overall agreement, PPA and NPA can change (possibly a lot) depending the prevalence (relative frequency of target condition in intended use population)

19. Overall Agreement Misleading
Non-Ref Non-Ref Standard Standard
 
New New
Test  Test 
total total
overall agreement = 96.5% ((40+512)/572))
PPA = 67.8% (40/59) PPA = 97.6% (40/41)
NPA = 99.8% (512/513) NPA = 96.4% (512/531)

20. tests agree and are wrong for 6+1 = 7 subjects
Agreement ≠ Correct
Original data: Non-Reference Standard
+ 
New
Test  4 171
Stratify data above by Reference Standard outcome
Reference Std + Reference Std 
Non-Ref Std Non-Ref Std
 
New New
Test  Test 
tests agree and are wrong for 6+1 = 7 subjects

21. Bias Unknown and non-quantified uncertainty
Often existence, size (magnitude), and direction of bias cannot be determined
Increasing overall number of subjects reduces statistical uncertainty (confidence interval widths) but may do nothing to reduce bias

22. Some Types of Bias error in reference standard
use test under evaluation to establish diagnosis
spectrum bias – do not choose the “right” subjects”
verification bias – only a non-representative subset of subjects evaluated by reference standard, no statistical adjustments made to estimates
many other types of bias
See Begg (1987), Pepe (2003), Zhou et al. (2002)

23. Estimating Sens and Spec Without a Reference Standard
Model-based approaches: latent class models and Bayesian models. See Pepe (2003), and Zhou et al. (2002)
Albert and Dodd (2004)
incorrect model leads to biased sens and spec estimates
different models can fit data equally well, yet produce very different estimates of sens and spec
FDA concerns & recommendations:
difficult to verify that model and assumptions are correct
try a range of models and assumptions and report range of results

24. Reference Standard Outcomes on a Subset
Albert and Dodd (2006, under review)
use info from verified and non-verified subjects
choosing between competing models is easier
explore subset choice (random, test dependent)
Albert (2006, under review)
estimation via imputation
study design implications (Albert, 2006)
Kondratovich (2003; 2002-Mar-8 FDA Microbiology Devices Panel Meeting)

25. Practices to Avoid
using terms “sensitivity” and “specificity” if reference standard is not used
discarding equivocal results in data presentations and calculations
using data altered or updated by discrepant resolution
using the new test as part of the comparative benchmark

26. External validity
A study has high external validity if the study results are sufficiently reflective of the “real world” performance of the device in the intended use population

27. External validity FDA recommends
include appropriate subjects and/or specimens
use final version of the device according to the final instructions for use
use several of these devices in your study
include multiple users with relevant training and range of expertise
cover a range of expected use and operating conditions

28. Reporting Recommendations
CRITICAL - need sufficient detail to be able to assess potential bias and external validity
just as (more?) important as computing CI’s correctly
see guidance for specific recommendations

29. References
Albert, P. S. (2006). Imputation approaches for estimating diagnostic accuracy for multiple tests from partially verified designs. Technical Report 042, Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute (
Albert, P.S., & Dodd, L.E. (2004). A cautionary note on the robustness of latent class models for estimating diagnostic error without a gold standard. Biometrics, 60, 427–435.
Albert, P. S. and Dodd, L. E. (2006). On estimating diagnostic accuracy with multiple raters and partial gold standard evaluation. Technical Report 041, Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute (
Begg, C.G. Biases in the assessment of diagnostic tests. Statistics in Medicine 1987;6:
Bossuyt, P.M., Reitsma, J.B., Bruns, D.E., Gatsonis, C.A., Glasziou, P.P., Irwig, L.M., Lijmer, J.G., Moher, D., Rennie, D., & deVet, H.C.W. (2003). Towards complete and accurate reporting of studies of diagnostic accuracy: The STARD initiative. Clinical Chemistry, 49(1), 1–6. (Also appears in Annals of Internal Medicine (2003) 138(1), W1–12 and in British Medical Journal (2003) 329(7379), 41–44)

30. References (continued)
Bossuyt, P.M., Reitsma, J.B., Bruns, D.E., Gatsonis, C.A., Glasziou, P.P., Irwig, L.M., Moher, D., Rennie, D., deVet, H.C.W., & Lijmer, J.G. (2003). The STARD statement for reporting studies of diagnostic accuracy: Explanation and elaboration. Clinical Chemistry, 49(1), 7–18. (Also appears in Annals of Internal Medicine (2003) 138(1), W1–12 and in British Medical Journal (2003) 329(7379), 41–44)
Lang, Thomas A. and Secic, Michelle. How to Report Statistics in Medicine. Philadelphia: American College of Physicians, 1997.
Kondratovich, Marina (2003). Verification bias in the evaluation of diagnostic devices. Proceedings of the 2003 Joint Statistical Meetings, Biopharmaceutical Section, San Francisco, CA.
Pepe, M. S. (2003). The statistical evaluation of medical tests for classification and prediction. New York: Oxford University Press.
Zhou, X. H., Obuchowski, N. A., & McClish, D. K. (2002). Statistical methods in diagnostic medicine. New York: John Wiley & Sons.