Transcript No Slide Title

Epidemiology and Biostatistics 679: Clinical Epidemiology
June 6-29, 2005
Instructors:
Dr. Jean Bourbeau ([email protected])
Dr. Dick Menzies ([email protected])
Dr. Kevin Schwartzman (course coordinator;
[email protected])
Research Offices:
Respiratory Epidemiology and Clinical Research Unit
Montreal Chest Institute K1
3650 St. Urbain
Course Objectives
• The general objective of this 3-credit course is to provide
students with a basic understanding of the methods of
epidemiology, as applied to clinical practice and clinical
research.
• Specifically, we will address key principles of testing and
measurement in the clinical context, as well as study
design, analysis, and inference in the clinical research
setting.
• Students will be encouraged to apply concepts covered
in the course to their own areas of interest.
Course Materials
• Textbook: Fletcher, Clinical Epidemiology: The
Essentials, 3rd edition
• Course pack with supplemental readings from
McGill bookstore
• Lecture notes, handouts, assignments from
course website (www.mcgill.ca/epibiostat/summer/courses)
• Journal articles on-line from Health Sciences
Library (www.health.library.mcgill.ca)
Format
• Ten classroom sessions, from 1:30-4:45
Mondays, Wednesdays, and Fridays for
four weeks (no class June 24 and July 1)
• Attendance at all sessions is mandatory.
• Students will be divided into teams of 3-4,
for purposes of assignments and
presentations (8 groups total)
Assignments
•
•
•
Before each lecture, an assignment
addressing key points of that day’s lecture will
be distributed.
During each classroom session, one team will
give an oral presentation outlining its answers
to the assignment on the topic of that day’s
lecture. Over the month, all students will be
expected to present in this fashion.
The written assignments must be handed in (1
per team) at the beginning of the following
lecture.
Assignments
• For lecture 1 (today: diagnostic tests and
screening) the oral presentation of the
assignment will be during lecture 2, with the
written assignment due at the beginning of
lecture 3
• For lecture 2 (Wednesday, June 8:
measurement issues) the oral presentation will
also be during that class, with the written
assignment due at the beginning of lecture 3
• After that, there will be one oral presentation per
classroom session, with the written assignment
due at the beginning of the following session
Assignments
•
•
•
•
The assignments will include questions about papers
from the medical literature, which reflect issues
addressed in the lectures
With the exception of assignments 1 and 2, these
papers will be selected by the group responsible for
each oral presentation, and identified ahead of time so
that all students in the class use the same paper.
Papers should be available on-line through the health
sciences library
For example, the students responsible for the oral
presentation on cohort studies will select a paper
reporting a cohort study of interest to them.
Assignments
•
•
For the final assignment, each group will hand
in a summary (maximum 2 pages doublespaced) of an original proposed research
protocol, addressing a clinical research
question which group members consider
relevant.
Further details on content and format will be
provided in class. These summaries will be
presented by the groups in class on Monday,
June 27 and handed in that day.
Final Exam
• A written final exam, in short-answer
format, will be administered in class on
Wednesday, June 29.
Grading
•
•
•
•
•
•
Written homework assignments (8):
Oral presentation of homework assignment:
Written protocol summary:
Oral presentation of protocol summary:
Final exam:
Class participation:
TOTAL
20%
10%
20%
10%
30%
10%
100%
Academic Integrity
• It is understood that assignments submitted by groups of
students will include contributions of all group members;
for such assignments, a single copy submitted with all
group members’ names will be sufficient.
• However, we expect that each group will submit its own
assignment, written separately from those of other
groups.
• The same holds true for the protocol summaries.
• Where assignments cite others’ research work,
appropriate references must be provided.
• Direct quotes from other writers should be indicated by
quotation marks.
Academic Integrity
III. ACADEMIC OFFENCES
The integrity of University academic life and of the degrees the University confers is dependent upon
the honesty and soundness of the teacher- student learning relationship and, as well, that of the
evaluation process. Conduct by any member of the University community that adversely affects this
relationship or this process must, therefore, be considered a serious offence.
15 Plagiarism
(a) No student shall, with intent to deceive, represent the work of another person as his or her own in
any academic writing, essay, thesis, research report, project or assignment submitted in a course or
program of study or represent as his or her own an entire essay or work of another, whether the
material so represented constitutes a part or the entirety of the work submitted.
(b) Upon demonstration that the student has represented and submitted another person’s work
as his or her own, it shall be presumed that the student intended to deceive; the student shall bear the
burden of rebutting this presumption by evidence satisfying the person or body hearing the case that
no such intent existed, notwithstanding Article 22 of the Charter of Student Rights.
(c) No student shall contribute any work to another student with the knowledge that the latter may
submit the work in part or whole as his or her own. Receipt of payment for work contributed shall be
cause for presumption that the student had such knowledge; the student shall bear the burden of
rebutting this presumption by evidence satisfying the person or body hearing the case that no such
intent existed (notwithstanding Article 22 of the Charter of Students’ Rights).
Downloaded and excerpted from A Handbook on Student Rights and Responsibilities, 2003, p. 17.
Available on-line at http://upload.mcgill.ca/secretariat/greenbookenglish.pdf
Additional information is available at www.mcgill.ca/integrity/
#
Date
Topics
Instructor(s)
1
Mon June 6
Introduction, course overview
Diagnostic tests, screening, prevention
All
Schwartzman
2
Wed June 8
Measurement issues: precision, validity,
responsiveness; clinical scales/scores
Bourbeau
3
Fri June 10
From clinical observations to research: hierarchy Menzies
of study designs
Planning and designing a first study
Dr. S. Dial, MUHC
4
Mon June 13
Measures of disease occurrence, association;
descriptive, cross-sectional and ecologic studies
Menzies
5
Wed June 15
Cohort studies, survival analysis, selection bias
Menzies
6
Fri June 17
Clinical trials
Bourbeau
7
Mon June 20
Case-control studies
Beginning your own clinical research
Peer review process; protocol assignment
Schwartzman/
Menzies
8
Wed June 22
Confounding, matching; analysis
Inference and hypothesis testing
Schwartzman
Fri June 24
HOLIDAY—NO CLASS
9
Mon June 27
Protocol summary presentations
Exam review
All
10
Wed June 29
Final exam
All
Lecture 1
Topic: DIAGNOSTIC TESTS AND SCREENING
Objectives
Students will be able to:
1. Define and calculate the following:
Sensitivity, specificity, positive and negative predictive
values of diagnostic tests
2. Illustrate the influence of prevalence and/or pre-test
probability on predictive values
3. Define pre- and post-test probabilities in terms of Bayes’
theorem and likelihood ratios
4. Identify key elements of screening programs and
evaluations of their impact
5. Describe the impact of misclassification on results of
clinical research studies
Diagnostic Tests and Screening
Readings:
• Fletcher, chapters 1 (Introduction), 3
(Diagnosis), 8 (Prevention)
• Barry MJ, Prostate-specific antigen testing for
early diagnosis of prostate cancer, N Engl J Med
2001; 344:1373-1377 [Clinical Practice]
• Hamm CW et al, Emergency room triage of
patients with acute chest pain by means of rapid
testing for cardiac troponin T or troponin I, N
Engl J Med 1997; 337:1648-53 (for assignment)
Diagnostic Tests
Tests as diagnostic aids and screening tools - key element
of clinical medicine and public health.
• Electrocardiogram, cardiac enzymes for diagnosis of
myocardial infarction
• Murphy’s sign (right upper abdominal tenderness on
inspiration) in diagnosis of acute cholecystitis
• Pap smear for detection of cervical cancer
Also essential in many epidemiologic studies where
diagnostic criteria and/or tests are used to establish
exposure, outcome status.
Goal is to minimize misclassification; yet some
misclassification may be inevitable for logistical reasons
Diagnostic Tests and Screening—Slide 2
Definitive diagnosis/classification may be difficult or
impossible to obtain.
“Gold standard” may be expensive, inappropriate (e.g.
autopsy based) or unsuitable (e.g. clinical follow-up
when immediate decision required).
Tests may serve as surrogates but this requires that
they be appropriately validated against a suitable
gold standard - and that their properties be
documented.
Diagnostic Tests and Screening--Slide 3
We will focus largely on the situation where the
diagnosis/outcome and the test result are both
dichotomous, i.e.
Disease: Present vs. absent
Test:
Positive vs. negative
We need to know how well the test separates those who
have the disease of interest from those who do not.
Diagnostic Tests and Screening-- Slide 4
We can use a 2x2 table to describe the various
possibilities:
Test +
Test True positive rate
Disease +
True +
False -
Disease False +
True -
= P(T+ D+)
= TP/(TP+FN)
= Sensitivity: The probability that a diseased individual
will be identified as such by the test
Diagnostic Tests and Screening-- Slide 5
Test +
Test -
Disease +
True +
False -
Disease False +
True -
True negative rate = P(T- D-)
= TN/(TN+FP)
= Specificity: The probability that an individual without
the disease will be identified as such by the test
Diagnostic Tests and Screening-- Slide 6
Complementary probabilities:
False negative rate = FN/(TP+FN) = P(T- D+)
= 1-sensitivity
False positive rate = FP/(TN+FP) = P(T+ D-)
= 1-specificity
Diagnostic Tests and Screening-- Slide 7
Example:
A researcher develops a new saliva pregnancy test.
She collects samples from 100 women known to be
pregnant by blood test (the gold standard) and 100
women known not be pregnant, also based on the
same blood test.
The saliva test is “positive” in 95 of the pregnant
women. It is also “positive” in 15 of the non-pregnant
women. What are the sensitivity and specificity?
Diagnostic Tests and Screening-- Slide 8
Pregnant
Saliva +
Saliva Totals
95
5
100
Non-pregnant
15
85
100
Sensitivity = TP/(TP+FN) = 95/100 = 95%
Specificity = TN/(TN+FP) = 85/100 = 85%
Totals
110
90
200
Diagnostic Tests and Screening-- Slide 9
Is it more important that a test be sensitive or specific?
• It depends on its purpose. A cheap mass screening
test should be sensitive (few cases missed). A test
designed to confirm the presence of disease should
be specific (few cases wrongly diagnosed).
• Note that sensitivity and specificity are two distinct
properties. Where classification is based on an
cutpoint along a continuum, there is a tradeoff
between the two.
Diagnostic Tests and Screening-- Slide 10
Example:
The saliva pregnancy test detects progesterone.
A refined version is developed.
Suppose you add a drop of indicator solution to
the saliva sample. It can stay clear (0
reaction) or turn green (1+), red (2+), or black
(3+).
(For purposes of discussion we will ignore
overlapping colors)
Diagnostic Tests and Screening-- Slide 11
The researcher conducts a validation study and finds
the following:
Pregnant
Non-pregnant
Totals
Saliva 3+
Saliva 2+
Saliva 1+
Saliva 0
85
10
3
2
5
10
17
68
90
20
20
70
Totals
100
100
200
Diagnostic Tests and Screening-- Slide 12
The sensitivity and specificity of the saliva test will
depend on the definition of “positive” and “negative”
used.
• If “positive”  1+, sensitivity = (85+10+3)/100 = 98%
specificity = 68/100 = 68%
• If “positive”  2+, sensitivity = (85+16)/100 = 95%
specificity = (68+17)/100 = 85%
• If “positive” = 3+, sensitivity = 85/100 = 85%
specificity = (68+17+10)/100 = 95%
Diagnostic Tests and Screening-- Slide 13
The choice of cutpoint depends on the relative adverse
consequences of false-negatives vs. false-positives.
If it is most important not to miss anyone, use
sensitivity and  specificity.
If it is most important that people not be erroneously
labeled as having the condition, use  sensitivity and
 specificity.
Diagnostic Tests and Screening-- Slide 14
In practice, the clinician or researcher needs to know
how to interpret test results without the simultaneous
gold standard measurement.
(If you already know the “gold standard” result, why
would you obtain the other test?)
Hence we need to know:
1. How likely is a patient to have the condition of
interest, given a “positive” test result?
This is P(D+ T+), or the positive predictive value of the
test [=TP/(TP+FP)]
2. How likely is a patient not to have the condition of
interest, given a “negative” test result?
This is P(D- T-), or the negative predictive value of the
test [=TN/(TN+FN)]
Diagnostic Tests and Screening-- Slide 15
Key point: The positive and negative predictive values
depend on the pretest probability of the condition
of interest - in addition to the sensitivity and
specificity of the test.
This pretest probability is often the prevalence of the
condition in the population of interest.
But it can also reflect restriction of this population based
on clinical features and/or other test results.
For example, the pretest probability of pregnancy will be
very different among young women using oral
contraceptives from that among sexually active
young women using no form of contraception.
Diagnostic Tests and Screening-- Slide 16
Example: The saliva pregnancy test is administered 30 days
after the first day of the last menstrual period to two
groups of women who have thus far “missed” a period.
Group 1: 1000 sexually active young women using no
contraception. Pretest probability of pregnancy 40%
(hypothetical)
Based on sensitivity of 95%, expected TP = 400 x 0.95 = 380
expected FN = 400-380 = 20
Based on specificity of 85%, expected TN = 600 x 0.85 = 510
expected FP = 600-510 = 90
Test +
Test Totals
Pregnant
380
20
400
Non-pregnant
90
510
600
Totals
470
530
1000
Diagnostic Tests and Screening-- Slide 17
Positive predictive value = TP = 380/470 = 81%
TP+FP
In this context, a woman with a positive saliva test has
an 81% chance of being pregnant.
Negative predictive value = TN = 510/530 = 96%
TN+FN
In this context, a woman with a negative saliva test has
a 96% chance of not being pregnant (and a 4%
chance of being pregnant)
Diagnostic Tests and Screening-- Slide 18
Group 2: 1000 oral contraceptive users - pretest probability
of pregnancy = 10% (hypothetical)
Test +
Test Totals
Pregnant
95
5
100
Using sensitivity = 95%,
Using specificity = 85%,
Non-pregnant
135
765
900
Totals
230
770
1000
expected TP = 0.95 x 100 = 95
expected FN = 100-95 = 5
expected TN = 0.85 x 900 = 765
expected FP = 900-765 = 135
Diagnostic Tests and Screening-- Slide 19
In this context, positive predictive value is only
95/230 = 41%
[TP/(TP+FP)]
Negative predictive value is [TN/(TN+FN)]
= 765/770 = 99%
Diagnostic Tests and Screening-- Slide 20
In which situation is the saliva test more helpful?
Group 1:
Test +: 81% probability of pregnancy
Pretest probability 40%
Test -: 4% probability of pregnancy
Group 2:
Test +: 41% probability
Pretest probability 10%
Test -: 1% probability
Diagnostic Tests and Screening-- Slide 21
• Note that the same test would likely be used and
interpreted very differently in these two contexts.
• This does not imply any difference in the
characteristics of the test itself, i.e. sensitivity and
specificity are not altered by the pretest probability of
the condition of interest.
• Test are most useful when the pretest probability is in
a middle range. They are unlikely to be useful when
the pretest probability is already very high or low.
Diagnostic Tests and Screening-- Slide 22
Deriving predictive values (post-test probabilities) using a
2x2 table:
1. Fill in totals with/without disease based on pretest
probabilities. In general these depend on external
information about the population of interest and cannot be
extrapolated from a validation study.
2. Fill in the positives and false negatives using sensitivity.
- TP = Number with disease x sensitivity
- FN = Number with disease x (1-sensitivity)
2. Fill in true negatives and false positives using specificity.
- TN = Number free of disease x specificity
- FP = Number free of disease x (1-specificity)
4. Calculate PPV = TP/(TP+FP)
Calculate NPV = TN/(TN+FN)
Diagnostic Tests and Screening-- Slide 23
Bayes’ theorem:
Allows us to calculate revised (“posterior” or post-test)
probabilities, based on “prior” (pretest) probabilities
and new information (here, test results).
General form:
P(B A) = P(A B) x P(B)
P[(A B) x P(B)] + [P(A B) x P(B)]
Note that B corresponds to “Not B”, so P(B) = 1 - P(B)
Diagnostic Tests and Screening-- Slide 24
For positive predictive value,
P (D+ T+) = P (T+ D+) x P(D+)
[P(T+ D+) x P(D+)] + [P(T+ D-) x P(D-)]
Note this is identical to TP
TP+FP
Lecture 17 - DTESTS - Slide 25
For negative predictive value,
P(D- T-) = P(T- D-) x P(D-)
[P(T- D-) x P(D-)]+[P(T- D+)xP(D+)]
which is equal to TN
TN+FN
Diagnostic Tests and Screening-- Slide 26
Example:
What would be the positive and negative predictive
values for the saliva pregnancy test if the pretest
probability of pregnancy is 20%?
(sensitivity = 95%, specificity = 85%)
P(pregnant T+) = P(T+ pregnant) x P(pregnant)
[P(T+ pregnant)xP(pregnant)]+[P(T+ not
pregnant)xP(not pregnant)]
=
0.95 x 0.2
=
0.19
= 0.61 or 61%
(0.95x0.2)+(0.15x0.8) 0.19+0.12
Diagnostic Tests and Screening - Slide 27
P(not pregnant T-) = P(T- not pregnant)xP(not pregnant)
[P(T- not pregnant)xP(not pregnant)]+
[P(T- pregnant)xP(pregnant)]
=
0.85 x 0.8
=
0.68
= 0.99 or 99%
(0.85x0.8)+(0.05x0.2) 0.68+0.01
Diagnostic Tests and Screening - Slide 28
Likelihood Ratios
• An alternative way of developing post-test
probabilities (predictive values)
• Relationship between pre- and post-test odds,
where
• Odds = [probability of x]/[1-probability of x]
– If pre-test probability of pregnancy is 20%, then odds
of pregnancy = 0.2/(1-0.2) = 0.25
– Odds of no pregnancy = 0.8/(1-0.8) = 4 [the
reciprocal]
• Probability = [odds of x]/[1+odds of x]
– If prior odds of pregnancy = 0.25, then pre-test
probability of pregnancy = 0.25/(1+0.25) = 0.2
Diagnostic Tests and Screening - Slide 29
Likelihood Ratios
• Post-test odds = pre-test odds x likelihood
ratio, where
• Likelihood ratio =
[P test result│condition of interest]
[P test result│no condition of interest]
Diagnostic Tests and Screening - Slide 30
Likelihood Ratios
• Pregnancy example, saliva test as before
– Prior odds 0.25 (20% pre-test probability)
– Sensitivity 95%, specificity 85%
• Post-test odds with positive test
= 0.25 x (0.95/0.15)
= 0.25 x 6.33 = 1.58
• Post-test probability = 1.58/(1+1.58) = 61%
• This approach can be particularly useful for tests
with multiple categories, and for serial testing
Diagnostic Tests and Screening-- Slide 31
Pitfalls in assessments of diagnostic test
performance
• Importance of pretest probability, as discussed.
• Pretest probability (and predictive values) cannot
ordinarily be extrapolated from a validation study,
since the proportions with and without disease are
determined by the investigator - unless there is truly
random sampling that reflects the context in which
the test will be applied.
Diagnostic Tests and Screening - Slide 32
Was the test applied in a consistent fashion to all
members of the validation sample?
e.g. was test interpretation properly blinded?
(unrelated to “true” presence or absence of disease
or clues to it)
Was the gold standard applied in a consistent fashion to
all members of the validation sample?
(again, blinded application not related to results of
test(s) being evaluated)
Diagnostic Tests and Screening-- Slide 33
Example: New diagnostic tests for pulmonary embolism
“Positive” results
confirmed by pulmonary angiography
(an invasive test with some risk)
“Negative” results
confirmed by clinical follow-up, i.e.
does the patient return with further symptoms or signs?
- this condition can resolve spontaneously and not recur
Diagnostic Tests and Screening-- Slide 34
Result: Good documentation of true and false positives
Overestimate true negatives, underestimate false
negatives
 sensitivity of test overestimated
specificity of test also overestimated
Diagnostic Tests and Screening-- Slide 35
Importance of the sample used for test validation:
• What was the spectrum of the condition evaluated?
• How similar is this to the situation in which the test
will be used?
Example: saliva pregnancy test
Imagine that test hinges on ability to detect
progesterone, a hormone where the level increases
as pregnancy progresses
• If the test is validated by comparing women who are
3 months pregnant with young, non-pregnant women,
it will perform very well as progesterone levels are
very high by 3 months.
Diagnostic Tests and Screening - Slide 36
• On the other hand, the sensitivity may be much lower
if the pregnant group consists of women who are only
1 month after their last menstrual period.
• Conversely, the estimated specificity of the test will
be higher if the comparison group has very low
progesterone levels (e.g. postmenopausal women).
Diagnostic Tests and Screening-- Slide 37
You would reject results of a validation study involving
women who are 3 months pregnant, or women who
are postmenopausal
• by 3 months, pregnancy is usually relatively obvious
by history and thus is unlikely to be the situation
where the test will be used.
• the test would never be administered to postmenopausal women!
Diagnostic Tests and Screening-- Slide 38
So:
Sensitivity and specificity estimates do not depend on
the prevalence of the condition in question.
BUT their values and their validity depend on the
context in which they were obtained, vis-a-vis the
context in which they will be used.
This in turn will affect positive and negative predictive
values, quite apart from the prevalence/prior
probability of the condition.
Diagnostic Tests and Screening - Slide 39
Misclassification
The use of an imperfect diagnostic test leads to
misclassification (assigning individuals to the wrong
category). In research studies, it is most often nondifferential.
• That is, the probability of misclassification is not
associated with the exposure or intervention under
study.
• For example, the use of an imperfect cardiac enzyme
assay to define myocardial infarction in a primary
prevention study with a novel anti-platelet agent.
• Another example: ascertaining the development of
HIV infection based on a saliva test, comparing
injection drug users who do vs. who do not clean
their needles (in a cohort study).
Diagnostic Tests and Screening-- Slide 40
• The effect of nondifferential misclassification is to
dilute any association which may be present, i.e. the
effect measure is biased toward the null value.
• Consider the extreme case where the cardiac
enzyme assay is no better than flipping a coin. Then
no effect of the antiplatelet drug will be detected,
even if it is truly very beneficial.
• If the degree of misclassification is known, then
corrected 2x2 tables and parameter estimates can be
derived.
Diagnostic Tests and Screening-- Slide 41
Differential misclassification implies that measurement
error is associated with study group membership, i.e.
it operates differentially between groups.
For example, imagine that the antiplatelet drug directly
interferes with the cardiac enzyme assay, leading to
underestimation of enzyme levels.
Here, the drug may appear to be protective even if in
reality, it is no better than placebo.
Hence depending on the specific circumstances,
differential misclassification may lead to under- or
overestimation of the true association between
exposure and outcome.
Screening
• “The identification of an unrecognized disease or risk factor by…[a]
procedure that can be applied rapidly.” (Fletcher, p. 167)
• Screening is relevant only if disease is relatively common, testing is
sensitive, specific, and cost-effective, and early treatment improves
outcomes
Sensitivity may be calculated by
• Detection method:
Cases found by screening
Cases found by screening + those identified during followup of screened
persons (interval cases)
• Incidence method:
Incidence among unscreened - interval incidence among screened
Incidence among unscreened
Incidence method accounts for “overdiagnosis” of abnormalities that
are not clinically important, e.g. prostate cancer
Diagnostic Tests and Screening-- Slide 42
Biases in performance of screening tests
(Does screening lead to better survival?)
1. Lead time bias
The earlier in its natural history an ultimately fatal disease is
detected, the longer will be the survival from the time of
diagnosis, even if there is no difference in treatment effect.
e.g.
2 years
3 years
Disease
Detectable
develops
by screening
5 years
Clinical
symptoms
Death
If 2 persons A+B develop the same disease at the same
age but person A is diagnosed by screening, person A will
live 3 more years than person B from time of diagnosis,
even if neither is treated, though the chronological survival
is equivalent
Diagnostic Tests and Screening-- Slide 43
2. Length bias
The probability of detecting a disease during its
preclinical period is proportional to the length of that
period, which is inversely proportional to the rate of
disease progression.
Hence cases diagnosed by screening may be
“destined” for a more favourable evolution, regardless
of treatment.
Diagnostic Tests and Screening-- Slide 44
3. Overdiagnosis bias (a variant of length bias; courtesy
of Dr. W. Black)
Screening may detect disease that would never have
become clinically detectable, e.g. remains stable or
regresses spontaneously.
It may also detect disease that would not have
contributed to the patient’s death e.g. competing
mortality risks among smokers with early-stage lung
cancer, or men with early-stage prostate cancer
detected by PSA screening.
Diagnostic Tests and Screening-- Slide 45
4.
Compliance bias
•
Persons who comply with a screening intervention may
be healthier—on average--and have healthier
behaviours than non-compliers.
Also likely to be healthier than an unscreened “control
group,” which implicitly includes a mixture of persons
who would and would not have complied, had they
been offered screening.
Leads to biases in observational (non-randomized)
studies, and with analyses limited to “compliers” within
randomized trials.
Relevance of “intent to screen” analyses.
•
•
•