Bias in Clinical Research: Measurement Bias Measurement bias in descriptive studies Measurement bias...
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Transcript of Bias in Clinical Research: Measurement Bias Measurement bias in descriptive studies Measurement bias...
Bias in Clinical Research: Measurement Bias
• Measurement bias in descriptive studies
• Measurement bias in analytic studies
– Misclassification of dichotomous exposure & outcome variables
• non-differential misclassification
• differential misclassification
• magnitude and direction of bias
– Misclassification of interval scale variables
– Advanced topics (mention only)
• misclassification of multi-level categorical variables
• misclassification of confounding variables
• back-calculating to the truth
Measurement Bias
• Definition– bias that is caused when any measurement
collected about or from subjects is not completely valid (accurate)
• any type of variable: exposure, outcome, or confounder
– aka: misclassification bias; information bias (text); identification bias
• misclassification is the immediate result of an error in measurement
Misclassification of Dichotomous Variables: Terms Related to Measurement Validity
• Sensitivity
– the ability of a measurement to identify correctly those who
HAVE the characteristic (disease or exposure) of interest.
• Specificity
– the ability of a measurement to identify correctly those who
do NOT have the characteristic of interest
• Applies to any dichotomous variable, not just diagnoses
Gold Standard Present Absent
Your Present a b Measurement Absent c d
Sensitivity = a/(a+c) Specificity = d/(b+d)
Positive predictive value = a/(a+b)
Negative predictive value = d/(c+d)
Causes for Misclassification• Questionnaire problems
– inaccurate recall– socially desirable responses– ambiguous questions– under or overzealous interviewers
• Biological specimen collection– problems in specimen collection or processing or storage
• Biological specimen testing– inherent limits of detection– faulty instruments
• Data management problems in coding• Design or analytic problems
– incorrect time period assessed– lumping of variables (composite variables)
SOURCE POPULATION = CALIFORNIA
STUDY SAMPLE = PRE-ELECTION POLL(Field Poll)
Descriptive Study: Measurement Bias
Deukmejian
Bradley +7%
1982 California Governor Election
SOURCE POPULATION = CALIFORNIA
STUDY SAMPLE = PRE-ELECTION POLL(Field Poll)
Descriptive Study: Measurement Bias
“Bradley Effect” = Respondents who favored Deukmejian sought to avoid appearing racist and hence did not
state their true choice during polling
Deukmejian
Deukmejian 49% Bradley +7%
Bradley 48%
1982 California Governor Election
SOURCE POPULATION
STUDY SAMPLE
Contrast with Selection Bias
Uneven dispersion of arrows
e.g., Dewey backers were
over-represented
Diseased
Exposed
+ -
+
-
SOURCE POPULATION
STUDY SAMPLE
Non-Differential Misclassification of Exposure: Imperfect Sensitivity
Problems with sensitivity in the measurement of exposure - independent of disease status
e.g., case-control study
exposure = alcohol abuse
Evenly shaded arrows =
non-differential
Non-differential Misclassification of Exposure
Truth: No misclassification (100% sensitivity/specificity)
Exposure Cases ControlsYes 50 20No 50 80
OR= (50/50)/(20/80) = 4.0
Presence of 70% sensitivity in exposure classification
Exposure Cases ControlsYes 50-15=35 20-6=14No 50+15=65 80+6=86
OR= (35/65)/(14/86) = 3.3
Effect of non-differential misclassification of dichotomous exposures: Bias the OR toward the null value of 1.0
Diseased
Exposed
+ -
+
-
SOURCE POPULATION
STUDY SAMPLE
Non-Differential Misclassification of Exposure: Imperfect Specificity
Problems with specificity of exposure measurement - independent of disease status
e.g., exposure = self-reported second-hand smoke exposure
Non-differential Misclassification of Exposure
Truth: No misclassification (100% sensitivity/specificity)
Exposure Cases ControlsYes 50 20No 50 80
OR= (50/50)/(20/80) = 4.0
Presence of 70% specificity in exposure classification
Exposure Cases ControlsYes 50+15=65 20+24=44No 50-15=35 80-24=56
OR= (65/35)/(44/56) = 2.4
Effect of non-differential misclassification of dichotomous exposures: Bias the OR toward the null value of 1.0
Diseased
Exposed
+ -
+
-
SOURCE POPULATION
STUDY SAMPLE
No misclassification
e.g., exposure = self-reported second-hand smoke exposure
50
50
20
80 OR = 4.0
Diseased
Exposed
+ -
+
-
SOURCE POPULATION
STUDY SAMPLE
Non-Differential Misclassification of Exposure: Imperfect Specificity
e.g., exposure = self-reported second-hand smoke exposure
OR = 2.4
65
50 35
44
80 56
differences become blurred
Diseased
Exposed
+ -
+
-
SOURCE POPULATION
STUDY SAMPLE
Non-Differential Misclassification of Exposure: Imperfect Specificity and Sensitivity
Problems with sensitivity - independent of disease status
Problems with specificity - independent of disease status
Non-Differential Misclassification of Exposure: Imperfect Sensitivity and Specificity
Exposure Cases ControlsYes 80 50No 20 50 True OR = (80/20) / (50/50) = 4.0
True Cases Controls Distribution exp unexp exp unexp (gold standard) 80 20 50 50
Study distribution: Cases ControlsExposed 56 6 62 35 15 50Unexposed 24 14 38 15 35 50
sensitivity 0.70 0.70 0.70 0.70 or specificity
Exposure Cases ControlsYes 62 50No 38 50 Observed OR = (62/38) / (50/50) = 1.6
SOURCE POPULATION
STUDYSAMPLE
Sensitivity = 0.7
Specificity = 0.7
Non-Differential Misclassification of Exposure: Imperfect Sensitivity and Specificity
Exposure Cases ControlsYes 80 50No 20 50 True OR = (80/20) / (50/50) = 4.0
True Cases Controls Distribution exp unexp exp unexp (gold standard) 80 20 50 50
Study distribution: Cases ControlsExposed 72 4 76 45 10 55Unexposed 8 16 24 5 40 45
sensitivity 0.90 0.80 0.90 0.80 or specificity
Exposure Cases ControlsYes 76 55No 24 45 Observed OR = (76/24) / (55/45) = 2.6
SOURCE POPULATION
STUDYSAMPLE
Sensitivity = 0.9
Specificity = 0.8
Non-Differential Misclassification of Exposure: Imperfect Sensitivity & Specificity and Uncommon Exposure
Exposure Cases ControlsYes 50 20No 500 800 True OR = (50/500) / (20/800) = 4.0
True Cases Controls Distribution exp unexp exp unexp (gold standard) 50 500 20 800
Study distribution: Cases ControlsExposed 45 100 145 18 160 178Unexposed 5 400 405 2 640 642
sensitivity 0.90 0.80 0.90 0.80 or specificity
Exposure Cases ControlsYes 145 178No 405 642 Observed OR = (145/405) / (178/642) = 1.3
SOURCE POPULATION
STUDYSAMPLE
e.g. radon exposure
Sensitivity = 0.9
Specificity = 0.8
Non-differential Misclassification of Exposure: Magnitude of Bias on the Odds Ratio
True OR=4.0
2.20.0770.900.90
2.80.200.900.90
3.00.3680.900.90
1.90.200.600.90
3.20.200.950.90
1.90.200.850.60
2.60.200.850.90
Observed ORPrev of Exp in controls
SpecificitySensitivity
Bias as a function of non-differential imperfect sensitivity and specificity of exposure measurement
0.9
0.7
0.5
Sensitivity of exposure measurement
Specificity of exposure measurement
Copeland et al. AJE 1977
True OR = 2.67
Prevalence of exposure in controls = 0.2
Ap
par
ent
Od
ds
Rat
io
2.8
2.5
2.2
1.9
1.6
1.3
1.0
.50 .55 .60 .65 .70 .75 .80 .85 .90 .95 1.00
Bias as a function of non-differential imperfect sensitivity and specificity of exposure measurement
0.9
0.7
0.5
Sensitivity of exposure measurement
Specificity of exposure measurement
Copeland et al. AJE 1977
True OR = 2.67
Prevalence of exposure in controls = 0.2
Ap
par
ent
Od
ds
Rat
io
2.8
2.5
2.2
1.9
1.6
1.3
1.0
.50 .55 .60 .65 .70 .75 .80 .85 .90 .95 1.00
When does OR fall
below 2?
Non-Differential Misclassification of Exposure in a Cohort Study: Effect of Sensitivity, Specificity and Prevalence of Exposure
All RR < 8
If Pe >.25, ↑ Sn. influ.
Dependence upon Pe
Flegal et al. AJE 1986
True Risk Ratio = 10
App
aren
t Ris
k R
atio
U = sensitivity; V = specificity
Non-Differential Misclassification of Exposure: Rules of Thumb Regarding Sensitivity & Specificity
Exposure Cases ControlsYes 50 100No 50 300 True OR = (50/50) / (100/300) = 3.0
SOURCE POPULATION
Sens + Spec = 1 gives OR = 1 (no effect)
Sensitivity Specificity Observed OR
0.8 1.0 2.6
0.8 0.8 1.9
0.4 0.6 1.0
0.4 0.4 0.82
0 0 0.33
Sens + Spec >1 but <2 gives attenuated effect
Sens + Spec < 1 gives reversal of effect
Coding error
Diseased
Exposed
+ -
+
-
SOURCE POPULATION
STUDY SAMPLE
Non-Differential Misclassification of Outcome
Problems with outcome sensitivity -independent of exposure status
Problems with outcome specificity - independent of
exposure status
Evenly shaded arrows =
non-differential
Bias as a function of non-differential imperfect sensitivity and specificity of outcome measurement in a cohort study
Sensitivity of outcome measurement0.9
0.7
0.5
Specificity of outcome measurementCopeland et al. AJE 1977
True risk ratio = 2.0
Cumulative incidence in unexposed = 0.05
Steep bias with change in
specificity
Relatively less influence
from sensitivity
App
aren
t Ris
k R
atio
Non-Differential Misclassification of Outcome: Effect of Incidence of Outcome
Copeland et al. AJE 1977
Specificity of outcome measurement
0.2 0.1
0.1 0.05
0.05 0.025
Cumulative incidence of outcome
Exposed Unexposed
True risk ratio = 2.0
Sensitivity of outcome measurement held fixed = 0.9
App
aren
t Ris
k R
atio
Special Situation In a Cohort or Cross-sectional Study
Misclassification of outcome• If specificity of outcome measurement is 100%• Any degree of imperfect sensitivity, if non-differential, will not
bias the risk ratio or prevalence ratio• e.g.,
• Risk difference, however, is changed by a factor of (1 minus sensitivity), in this example, 30% (truth=0.1; biased = 0.07)
DiseaseNoDisease
Exposed 20 80 100Unexposed 10 90 100
2.0
1001010020
ratio )prevalence (or Risk
DiseaseNoDisease
Exposed 20-6=14 80+6=86100Unexposed 10-3=7 90+3=93100
2.0
1007
10014
ratio )prevalence (or Risk
Truth
70% sensitivity
When specificity of outcome is 100% in a cohort or cross-sectional study
Sensitivity of outcome measurement0.9
0.7
0.5
Specificity of outcome measurementCopeland et al. AJE 1977
True risk ratio = 2.0
Cumulative incidence in unexposed = 0.05
App
aren
t Ris
k R
atio
When specificity of outcome measurement is 100% in a cohort or cross sectional study
• Worth knowing about when choosing outcomes, such as cutoffs for continuous variables on ROC curves
• Choosing most specific cutoff (or 100% cutoff) will lead to least biased ratio measures of association
0.0 0.1 0.2 0.3 0.4 0.5
0.5
0.6
0.7
0.8
0.9
1.0
1.0 0.9 0.8 0.7 0.6 0.5
Sen
sit
ivit
y
1 - Specificity
00.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
OD: 0.06 Specificity: 84 % Sensitivity: 100 %
OD: 0.19 Specificity: 95 % Sensitivity: 94 %
OD: 0.49 Specificity: 100 % Sensitivity: 74 %
0.0 0.1 0.2 0.3 0.4 0.5
0.5
0.6
0.7
0.8
0.9
1.0
1.0 0.9 0.8 0.7 0.6 0.5
Sen
sit
ivit
y
1 - Specificity
00.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
OD: 0.06 Specificity: 84 % Sensitivity: 100 %
OD: 0.19 Specificity: 95 % Sensitivity: 94 %
OD: 0.49 Specificity: 100 % Sensitivity: 74 %
OD: 0.06 Sensitivity = 100%OD: 0.06 Specificity = 84%
OD: 0.19 Sensitivity = 94%OD: 0.19 Specificity = 95%
OD: 0.49 Sensitivity = 74%OD: 0.49 Specificity = 100%
1.0 0.9 0.8 0.7 0.6 0.5
Specificity
Efficacy of a pertussis vaccine
• Outcome: Cough > 5 days– No. of events: 2672 (and apparently lots of “power”)– Result: No significant difference between groups
• Outcome: Cough + microbiologic pertussis confirmation– No. of events: 10– Result: rate ratio = 0.08 (92% vaccine efficacy) (95% CI = 0.01 to 0.68)
• Acellular vaccine vs. control (hepatitis A vaccine) for the prevention of pertussis in adults (Ward et al. NEJM 2005)
Group No. of subjects Person-years Pertussis vaccine 1391 2421 Control 1390 2444
Pervasiveness of Non-Differential Misclassification
• Direction of this bias is towards the null
• Therefore, called a “conservative” bias
• Goal, however, is to get the truth
• Consider how much underestimation of effects must be occurring in research
• How many “negative” studies are truly “positive”?
Differential Misclassification of ExposureWeinstock et al. AJE 1991• Nested case-control study in Nurses Health Study cohort
• Cases: women with new melanoma diagnoses
• Controls: women w/out melanoma - by incidence density sampling
• Measurement of exposure: questionnaire about self-reported
“tanning ability”; administered shortly after melanoma development
MelanomaNoMelanoma
No tan to light tan 15 77Med to dark tan 19 157
1.6
157771915
OR
• Question asked after diagnosis
• Question asked before diagnosis (NHS baseline)
MelanomaNoMelanoma
No tan to light tan 9 79Med to dark tan 25 155
0.7
15579259
OR
MelanomaNoMelanoma
No tan to light tan 15 77Med to dark tan 19 157
1.6
157771915
OR
Virtually unchanged
Substantially changed
Melanoma
Tanningability
+ -
No
Yes
SOURCE POPULATION
STUDY SAMPLE
“Tanning Ability” and Melanoma:
Differential Misclassification of Exposure
Imperfect specificity of exposure measurement - mostly in cases
Bias away from the null
leading to spurious
association
Congenital Malformation
Exposed
+ -
+
-
SOURCE POPULATION
STUDY SAMPLE
Differential Misclassification of Exposure:
Exposures During Pregnancy and Congenital Malformations
Cases more likely than controls to remember a variety of exposures
Cases might be more likely than controls to falsely state a
variety of exposures
Uneven shading of arrows =
differential
Differential Misclassification of Exposure: Magnitude of Bias on the Odds Ratio
True OR=3.9
Exposure Classification
Sensitivity Specificity
Cases Controls Cases Controls OR
0.90 0.60 1.0 1.0 5.79
0.60 0.90 1.0 1.0 2.22
1.0 1.0 0.9 0.70 1.00
1.0 1.0 0.7 0.90 4.43
Prevalence of Exposure in Controls = 0.1
Misclassification of Dichotomous Exposure or Outcome: Summary of Effects
Misclassification Effect on Ratio Measure of Association
Non-differential Exposure Towards null Outcome Towards null*
Differential
Exposure Away or towards null Outcome Away or towards null
*Exception: When specificity is 100%, no effect on risk ratio or prevalence ratio regardless of sensitivity
Relating Last Week to This Week:Relating Reproducibility/Validity of Individual
Measurements to Measurement Bias in Inferences in Analytic Studies
• Validity– How sensitivity and specificity of a measurement
results in measurement bias covered in prior slides
• How about reproducibility? – Recall that a measurement with imperfect
reproducibility will lack perfect validity --unless it is repeated many many times
Reproducibility and Validity of a Measurement
With only one shot at the measurement, most of the time you will be off the center of the target
GoodB-Ball
PoorB-Ball
>6 ft 10 30 40 +1 10 +3 30<6 ft 10 50 60 10 +1 50 +5
20 80 100 20 80
P
GoodB-Ball
PoorB-Ball
>6 ft 10 32 42<6 ft 10 48 58
20 80 100
Truth = Prevalence Ratio= (10/40) / (10/60) = 1.5
Observed = Prevalence Ratio = (10/42) / (10/58) = 1.38
10% Misclassification
Imperfect reproducibility leads
to 90% sensitivity and 90% specificity of
height measurement –non-differential with respect to outcome
Relating the Reproducibility and Validity of Measurements to Measurement Bias in Analytic Studies – Interval Scale Variables
Validity (Systematic error)• Result moves systematically up or down scale by given factor or absolute
difference• e.g., systematic error in an interval scale outcome variableMean Ratio of Means Difference in Means
Truth Exposed 100
Unexposed 50 2 50
Measurement off by factor of 10 Exposed 1000
Unexposed 500 2 500
Measurement off by difference of 10 Exposed 110
Unexposed 60 1.83 50
Bias depending
upon measure of association
Relating the Reproducibility and Validity of Measurements to Measurement Bias in Analytic Studies – Interval Scale Variables
Reproducibility (Random error)
e.g., random error in a predictor variableAssuming:
• Exposure is normally distributed with variance, 2True
• Random error is normally distributed with variance, 2E
• Then, the observed regression coefficient is equal to the true regression coefficient times:
• i.e., the greater the measurement error, the greater the attenuation (bias) towards the
null (e.g., if ICC is 0.5, the measure of association is halved)
22
2
ETrue
True
(i.e. reproducibility, the intraclass correlation coefficient)
Truth and Error
Truth
Advanced Topics• Misclassification of multi-level categorical variables
– some of the rules change regarding direction of bias
• Misclassification of confounding variables– net result is failure to fully control (adjust) for that variable (left with
“residual confounding”)– measures of association may be over or under-estimated
• Back-calculating to unbiased results (Quantitative bias analysis)– thus far, truth about relationships have been assumed– in practice, we just have observed results– when extent of classification errors (e.g., PPV, NPV, sensitivity &
specificity) are known, it is possible to back-calculate to truth– if exact classification errors are not known, it is possible to perform
sensitivity analyses to estimate a range of study results given a range of possible classification errors
Poor Reproducibility
Poor Validity
Good Reproducibility
Good Validity
Managing Measurement Bias
• Prevention and avoidance are critical– study design phase is critical; little to be done after study over
• Become an expert in the measurement of your primary variables
• For the other variables, seek out the advice of other experts
• Optimize the reproducibility/validity of your measurements!