screening. outline screening basics evaluation of screening programs
TRANSCRIPT
Outline
• Screening basics
• Evaluation of screening programs
Where are we going today?
• Definition of screening?• Whether it is always beneficial?• Types of bias in screening?• Principles for the development of screening.
– The test: Validity, LR, Multiple testing, ROC curve, Kappa
– The disease;
• Evaluation of a screening program
Where are we?
• Definition of screening?• Whether it is always beneficial?• Types of bias in screening?• Principles for the development of screening.
– The test: Validity, LR, Multiple testing, ROC curve, Kappa
– The disease;
• Evaluation of a screening program
Screening Basics
• What does “screening” mean?
• What do we screen for (objective)?
• What makes a disease an appropriate target for screening?
• What makes a test a good screening test?
Levels of Prevention(Mausner and Kramer 1985)
• Primary Prevention - Prevention of the occurrence of disease (reduce incidence of disease)
• Secondary Prevention - Early detection and prompt treatment of disease for cure, to slow progression, to prevent complications, or to limit disability (reduce prevalence of disease)
• Tertiary Prevention - Limitation of disability and rehabilitation where disease has already occurred and left residual damage
Natural History of Disease
The Pre-Clinical Phase (PCP) is• the period between when early detection
by screening is possible and when the clinical diagnosis would usually be made.
Pathology begins
Disease detectable Normal Clinical Presentation
Pre-Clinical Phase
Pre-clinical Phase
Objective and Definition
• Objective: to reduce mortality and/or morbidity by early detection and treatment.
• Definition: Asymptomatic individuals are classified as either unlikely or possibly having disease.
Trisha Greenhalgh, BMJ 1997;315:540-543 (30 August)
Where are we?
• Definition of screening?• Whether it is always beneficial?• Types of bias in screening?• Principles for the development of screening.
– The test: Validity, LR, Multiple testing, ROC curve, Kappa
– The disease;
• Evaluation of a screening program
Principles for the development of screening;
1. The condition screened for is an important cause of morbidity, disability, or mortality.
2. The natural history of the disease is sufficiently well known.
3. The test must have high levels performance.4. The test must be acceptable to the target
population and their health care providers, and appropriate follow-up of positive findings must be ensured.
Sam Shapiro in Epidemiology and Health Services
Consequence of a screening test:
• Beneficence
• Non-beneficence
• Do harm;
Clofibrate in US
Labeling effect; Social psychology
Biases in assessing efficacy of screening
• Two major biases affect these data:
– lead time bias
– length bias
Where are we?
• Definition of screening?• Whether it is always beneficial?• Types of bias in screening?• Principles for the development of screening.
– The test: Validity, LR, Multiple testing, ROC curve, Kappa
– The disease;
• Evaluation of a screening program
Lead Time
Lead time = amount of time by which diagnosis is advanced or made earlier
Pathology begins
Disease detectable Normal Clinical Presentation
Lead Time
Screen
Lead time bias
• We think early detection has increased survival– in fact all it has done is increase the time the
patient is aware of his disease!
– treatment could even hasten death and it might appear survival is longer post diagnosis!!
• Cannot just look at survival time post diagnosis.
Lead-time Bias
Length bias
Survival due to screening and treatment may be over rated because screening will tend to discover more slow-growing disease.
Length-time Bias
Biologic onset
Usual time of diagno-sis
Severe clinical illness (eg metasta-ses)
Death from the disease
First detect-able by screen-ing test
Suppose there are two subtypes of the disease:
Type 1: fast progression
Biologic onset
Usual time of diagnosis
Severe clinical illness (eg metastases)
Death from the disease
First detectable by screening test
Type 2: slow progression
Biologic onset
Usual time of diagno-sis
Severe clinical illness (eg metasta-ses)
Death from the disease
First detect-able by screen-ing test
Type 1
Biologic onset
Usual time of diagnosis
Severe clinical illness (eg metastases)
Death from the disease
First detectable by screening test
Type 2
Length of time in pre-clinical phase longer in Type 2 than in Type 1
Biologic onset
Usual time of diagno-sis
Severe clinical illness (eg metasta-ses)
Death from the disease
First detect-able by screen-ing test
Type 1
Biologic onset
Usual time of diagnosis
Severe clinical illness (eg metastases)
Death from the disease
First detectable by screening test
Type 2
Periodic screening will tend to detect more of Type 2, as these have longer “exposure” in the critical interval for screening.
Biologic onset
Usual time of diagno-sis
Severe clinical illness (eg metasta-ses)
Death from the disease
First detect-able by screen-ing test
Type 1
Biologic onset
Usual time of diagnosis
Severe clinical illness (eg metastases)
Death from the disease
First detectable by screening test
Type 2
But look!! Type 2 individuals have a longer survival time from time of diagnosis than do Type 1.
• Without screening, suppose type 1 and type 2 were equal fractions of the population– average survival time is 50:50 mixture of the short
and long survival times.
• With screening, the screen-detected population has a higher fraction of type 2 (slow) individuals– mix will be proportional to ratio of the two intervals– suppose it is 70:30 in favor of long interval
• average survival time will be longer in screen detected individuals!
Length bias
Length bias
• Even if the treatment tended to be harmful and shorten life, because more longer interval individuals tend to be detected by screening, the screening program will appear to be effective!!
Where are we?
• Definition of screening?• Whether it is always beneficial?• Types of bias in screening?• Principles for the development of screening.
– The test: Validity, LR, Multiple testing, ROC curve, Kappa
– The disease;
• Evaluation of a screening program
Principles for the development of screening;
1. The test must have high levels performance.2. The condition screened for is an important cause
of morbidity, disability, or mortality. 3. The natural history of the disease is sufficiently
well known.4. The test must be acceptable to the target
population and their health care providers, and appropriate follow-up of positive findings must be ensured.
Sam Shapiro in Epidemiology and Health Services
Characteristics of Test
• Safety
• Cost
• Acceptability
• Validity
• Reliability
• Validity of test is shown by how well the test actually measures what it is supposed to measure. Validity is determined by the sensitivity and specificity of the test.
• Reliability is based on how well the test does in use over time - in its repeatability.
Characteristics of Test
Characteristics of Test: Validity
• Sensitivity is the ability of a screening procedure to correctly identify those who have the disease-- the proportion of persons with the disease who have a positive test result
• Specificity is the ability of a screening procedure to correctly identify the percentage of those who do not have the disease--the proportion of persons without the disease who have a negative test result
Sensitivity and Specificity
True positive
A
False positive
B
False negative
C
True negative
D
Disease Present Absent
Positive
Negative
TestResult
Sensitivity = A / (A+C)
Specificity = D / (B+D)
Sensitivity and Specificity of Breast Cancer Screening Examination
True positive
A = 132
False positive
B = 983
False negative
C = 45
True negative
D = 63650
Breast CancerPresent Absent
Positive
Negative
Screening Test
Sensitivity=132/(132+45)=74.6%
Specificity=63650/(63650+983)=98.5%
Predictive Values
• Predictive Value Positive – Probability that a person actually has the disease given a positive screening test
• Predictive Value Negative – Probability that a person is actually disease-free given a negative screening test
Predictive Values
True positive
A
False positive
B
False negative
C
True negative
D
Disease Present Absent
Positive
Negative
TestResult
Sensitivity = A / (A+C)
Specificity = D / (B+D)
PPV = A / (A+B)
NPV = D / (C+D)
Predictive Values of Breast Cancer Screening Examination
True positive
A = 132
False positive
B = 983
False negative
C = 45
True negative
D = 63650
Breast CancerPresent Absent
Positive
Negative
Screening Test
Sensitivity=132/(132+45)=74.6%
Specificity=63650/(63650+983)=98.5%
PPV=132/(132+983)=11.8%
NPV=63650/(63650+45)=99.9%
Effect of Prevalence on Predictive Value Positive with Constant Sensitivity and Specificity
Prevalence PV+ (%) Sensitivity Specificity
(%) (%) (%)
0.1 1.8 90 95
1.0 15.4 90 95
5.0 48.6 90 95
50.0 94.7 90 95
Do not testDo not treat
Test and treat, based on the
basis of the result
Do not test
Get on the
treatmentPretest Probability of disease
Test-treatment threshold
Bayesian Approach (probabilities)
Prior ideaof effect
Study Results:effect size
Final Conclusion:Study + prior effect
PretestProbabilityof disease
TestResult
PosttestProbabilityof disease
Clinical Reasoning (probabilities)
0.3 13 64
Probability of Breast Ca (%)
Beforemammogram
Aftermammogram
AfterFNA
Sensitive test Specific test
Likelihood ratio of a positive test
• How much more likely is a positive test to be found in a person with the condition than in a person without it?
• sensitivity/ (l-specificity)
Likelihood Ratios
Disease
PresentAbsent
TestPositiveab
Negativecd
Se= a a + c
Sp= d b + d
LR+ = [a/a+c] [b/b+d] probability of + test in non-diseased
LR+=1 = no value
= probability of + test in diseased
Likelihood Ratios
Disease
PresentAbsent
TestPositiveab
Negativecd
Se= a a + c
Sp= d b + d
LR+ = [a/a+c] [b/b+d] probability of + test in non-diseased
LR+=1 = no value
= probability of + test in diseased
LR+>1 = valuable
Bayesian Approach
Prior ideaof effect
Study Results:effect size
Final Conclusion:Study + prior effect
PretestProbabilityof disease
TestResult
PosttestProbabilityof disease
PretestProb.of disease
LR+X = PosttestProb.of disease
Where are we?
• Definition of screening?• Whether it is always beneficial?• Types of bias in screening?• Principles for the development of screening.
– The test: Validity, LR, Multiple testing, ROC curve, Kappa
– The disease;
• Evaluation of a screening program
Influence of prevalence on predictive values
0102030405060708090
100
2 10 25 50 75 90 98
PPV
NPV
Multiple testing
• Serial (Sequential)
• Simultaneous (Parallel)
ROC Curves
(PD- x CFP) / PD- x CFN)
Comparing two tests
Prior probability
Pos
teri
or p
roba
bili
ty
Where are we?
• Definition of screening?• Whether it is always beneficial?• Types of bias in screening?• Principles for the development of screening.
– The test: Validity, LR, Multiple testing, ROC curve, Kappa
– The disease;
• Evaluation of a screening program
Reliability
Percent agreement
Cohen’s Kappa
• Reported in 1960
• Kappa corrects for the chance agreement that would be expected to occur if the 2 classifications were completely unrelated
N° = Observed number of agreement
Ne= Number of agreement expected to occur by chance alone
Varies from -1 to 1
K =N° - Ne
1 - Ne
Kappa
Population One (Prevalence = 0.05)Table for true positives
Observer A Positive Negative
Positive 36 9 45
Negative 4 1 5
40 10 50
Observer B
From Szklo and Nieto, 2000
Interpretation of Kappa
Below 0.0 Poor
0.00 - 0.20 Slight
0.21 - 0.41 Fair
0.42 - 0.60 Moderate
0.61 - 0.80 Substantial
0.81 - 1.00 Almost PerfectLandis & Koch (1977a)
Interpretation of Kappa
Where are we?
Principles for the development of screening;
1. The test must have high levels performance.2. The condition screened for is an important cause
of morbidity, disability, or mortality. 3. The natural history of the disease is sufficiently
well known.4. The test must be acceptable to the target
population and their health care providers, and appropriate follow-up of positive findings must be ensured.
Sam Shapiro in Epidemiology and Health Services
Where are we?
• Definition of screening?• Whether it is always beneficial?• Types of bias in screening?• Principles for the development of screening.
– The test: Validity, LR, Multiple testing, ROC curve, Kappa
– The disease;• Evaluation of a screening program
Characteristics of Disease
• Serious
• Treatable
• Pre-clinical detectable period
• Early treatment is better than late
• Prevalent
Characteristics of Disease
• Why do we screen for serious disease?– why we screen for Phenyl ketonuria?
• Why do we screen for treatable disease?– why don’t we screen for pancreatic cancer?
Characteristics of Disease
• Why do we screen for diseases with a pre-clinical detectable period?– Ex: HIV vs. Legionella
• Why do we screen for diseases where early treatment is better than late?– Ex: breast cancer
Characteristics of Disease
• Why do we screen for prevalent disease?
– Prevalence affects predictive value
• Tayssachs
• Breast cancer
Characteristics of Disease
– The more prevalent a condition, the
fewer false positive tests there will be
– The less prevalent a condition, the fewer
false negative tests there will be
– No matter how good the test is!
Why do we test for prevalent diseases?
Although the combination ELISA/Western Blot test for HIV has extremely high sensitivity and specificity, predictive value is dependent on prevalence.
High risk population: prevalence = 40%
PPV = 0.985 NPV = 0.993
Low risk population: prevalence = 0.01%PPV = 0.0098 NPV = 0.999
Principles for the development of screening;
1. The test must have high levels performance.2. The condition screened for is an important cause
of morbidity, disability, or mortality. 3. The natural history of the disease is sufficiently
well known.4. The test must be acceptable to the target
population and their health care providers, and appropriate follow-up of positive findings must be ensured.
Sam Shapiro in Epidemiology and Health Services
Where are we?
• Definition of screening?• Whether it is always beneficial?• Types of bias in screening?• Principles for the development of screening.
– The test: Validity, LR, Multiple testing, ROC curve, Kappa
– The disease;• Evaluation of a screening program
• What are the costs of
– treatment for the disease? stage by stage
– a false negative? how are cases usually found? Does missing the case
on screen mean it is missed forever?
– false positive? risks of confirmatory test psychological risks / harms: worry, etc. labeling
Evaluation of Screening
Characteristics of Test
• Safety
• Cost
• Acceptability
• Validity
• Reliability
Evaluation of Screening Outcomes
a) Study Designs• RCT
– Compare disease-specific cumulative mortality rate between those randomized (or not) to screening
– Eliminates confounding and lead time bias– But, problems of:
• Expense, time consuming, logistically difficult, ethical concerns, changing technology.
Evaluation of Screening Outcomes
a) Study Designs• Observational Studies
– Cohort:• Compare disease-specific cumulative mortality rate
between those who choose (or not) to be screened
– Case-control:• Compare screening history between those with advanced
disease (or death) and healthy.
– Ecological:• Compare screening patterns and disease experience (both
incidence and mortality) between populations
Problems with Observational Studies
• Confounding due to health awareness - screenees are more healthy (selection bias)
• More susceptible to effects of lead-time bias and length-biased sampling
• Poor quality, often retrospective data
b) Measures of Effect of Screening Disease-specific Mortality Rate (MR)
the number of deaths due to disease
Total person-years experience
– The only gold-standard outcome measure for screening
– NOT affected by lead time,
Evaluation of Screening Outcomes
NBSS1 (National Breast Screening Study), Canada 1980
– Age at entry: 40 to 49. – Randomization: Individual volunteers, with names entered
successively on allocation lists. Although criticisms of the randomization procedure have been made, a thorough independent review found no evidence of subversion and that subversion on a scale large enough to affect the results was unlikely.
– Sample size: 25,214 study) and 25,216 control. – Cause of death attribution: Death certificates, with review of
questionable cases by a blinded review panel. Also linked with the Canadian Mortality Data Base, Statistics Canada.
– Follow-up duration: 13 years. – Relative risk of breast cancer death, screening versus control
(95% CI): 0.97 (0.74-1.27).
Where are we?
• Definition of screening?• Whether it is always beneficial?• Types of bias in screening?• Principles for the development of screening.
– The test: Validity, LR, Multiple testing, ROC curve, Kappa
– The disease;• Evaluation of a screening program
Session objectives
• Screening basics
• Evaluation of screening programs