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Epidemiologic Methods - Fall 2009
Where we have been:
Designing studies, measuring disease occurrence, andestimating associations
Lecture Title
1 Study Design
2 Measures of Disease Occurrence I
3 Measures of Disease Occurrence II
4 Measures of Disease Association I
5 Measures of Disease Association II
Where we are going:
Threats to validity in clinical research studies and how can they be prevented
6 Selection Bias
7 Understanding Measurement: Reproducibility & Validity Journal Club
8 Measurement Bias
9 Confounding and Interaction I: General Principles
10 Confounding and Interaction II: Assessing Interaction
11 Confounding and Interaction III: Stratified Analysis Journal Club
12 Journal Club
Bias in Clinical Research: General Aspects and Focus on Selection Bias
• Framework for understanding error in clinical research
– systematic error: threats to internal validity (bias)
– random error: sampling error (chance)
• Selection bias (a type of systematic error)
– by study design:• descriptive • case-control• cross-sectional• longitudinal studies (observational or experimental)
Clinical Research:SampleMeasure
(Intervene)Analyze
Infer• Inference
– Websters: the act of passing from sample data to generalizations, usually with calculated degrees of certainty
– All we can do is make educated guesses about the soundness of our inferences
– Those who are more educated will make better guesses
• Anyone can get an answer
• The challenge is to tell if it is correct
Disease
Exposure
+ -
+
-
REFERENCE/TARGET/SOURCE POPULATIONaka STUDY BASE STUDY SAMPLE
OTHER POPULATIONS
Two types of inferences
Disease
Exposure
+ -
+
-
San Franciscans, 20 to 65 years old
SAMPLE of San Franciscans, 20 to 65 yrs old
>65 years old in U.S.
20 to 65 year olds, in U.S., outside of San Francisco
20 to 65 year olds, in Europe
Disease
Exposure
+ -
+
-
REFERENCE/TARGET/SOURCE POPULATIONaka STUDY BASE STUDY SAMPLE
Most important inference is the
first one
Without an accurate first
inference, there is little point
considering the second
inference
Attempts in study design to enhance the second inference are
often in conflict with goal of making a sound first inference
• The goal of any study is make an accurate (true) inference, i.e.:
– measure of disease occurrence in a descriptive study
– measure of association between exposure and disease in an analytic study
• Ways of getting the wrong answer:
– systematic error; aka bias
• any systematic process in the conduct of a study that causes a distortion from the truth in a predictable direction
• captured in the validity of the inference
– random error; aka chance or sampling error
• occurs because we cannot study everyone (we must sample)
• direction is random and not predictable
• captured in the precision of the inference (e.g., SE and CI)
Error in Clinical Research
Good Validity
Good Precision
Poor Validity
Poor Precision
Validity and Precision: Each Shot at Target Represents a Study Sample of a
Given Sample Size
Validity and Precision
Poor Validity
Good Precision
Good Validity
Poor Precision
Validity and Precision
Poor Validity
Good Precision
Good Validity
Poor Precision
Systematic error (bias)
Random error
(chance)
Random error
(chance)
No
Systematic error
Performing an Actual Study: You Only Have One Shot
Field of “statistics” can tell you the random
error (precision)
with formulae for
confidence intervals
Only judgment can tell you about
systematic error
(validity)
Judgment requires
substantive and
methodologic knowledge
Disease
Exposure
+ -
+
-
REFERENCE/TARGET/SOURCE POPULATION
? INTERNAL VALIDITY
OTHER POPULATIONS
? EXTERNAL VALIDITY (generalizability)
STUDY SAMPLE
Two Types of Inferences
Correspond to Two Types of Validity
Two Types of InferencesCorrespond to Two Types of Validity
• Internal validity– Do the results obtained from the actual subjects accurately
represent the target/reference/source population?
• External validity (generalizability)– Do the results obtained from the actual subjects pertain to persons
outside of the source population?– Internal validity is a prerequisite for external validity
• “Validity” to us typically means internal validity– “Threat to validity” = threat to internal validity– Identifying threats to validity is a critical aspect of research
• The goal of any study is make an accurate (true) inference, i.e.:
– measure of disease occurrence in a descriptive study
– measure of association between exposure and disease in an analytic study
• Ways of getting the wrong answer:
– systematic error; aka bias
• a systematic process in the conduct of a study that causes a distortion from the truth in a predictable direction
• captured in the validity of the inference
– random error; aka chance or sampling error
• occurs because we cannot study everyone (we must sample)
• direction is random and not predictable
• captured in the precision of the inference (e.g., SE and CI)
Error in Clinical Research
MetLife Is Settling Bias Lawsuit
BUSINESS/FINANCIAL DESK August 30, 2002, Friday
MetLife said yesterday that it had reached a preliminary settlement of a class-action lawsuit accusing it of charging blacks more than whites for life insurance from 1901 to 1972.
MetLife, based in New York, did not say how much the settlement was worth but said it should be covered by the $250 million, before tax, that it set aside for the case in February.
“Bias” in Webster’s Dictionary1 : a line diagonal to the grain of a fabric; especially : a line at a 45° angle to the selvage often utilized in the cutting of garments for smoother fit2 a : a peculiarity in the shape of a bowl that causes it to swerve when rolled on the green b : the tendency of a bowl to swerve; also : the impulse causing this tendency c : the swerve of the bowl3 a : bent or tendency b : an inclination of temperament or outlook; especially : a personal and sometimes unreasoned judgment : prejudice
c : an instance of such prejudice
d (1) : deviation of the expected value of a statistical estimate from the quantity it estimates
(2) : systematic error introduced into sampling or testing
4 a : a voltage applied to a device (as a transistor control electrode) to establish a reference level for operation b : a high-frequency voltage combined with an audio signal to reduce distortion in tape recording
Bias of Priene (600 - 540 BC)
• One of the 7 sages of classical antiquity• Consulted by Croesus, king of Lydia,
about the best way to deploy warships against the Ionians
• Bias wished to avoid bloodshed, so he misled Croesus, falsely advising him that the Ionians were buying horses
• Bias later confessed to Croesus that he had lied.
• Croesus was pleased with the way that he had been deceived by Bias and made peace with the Ionians.
• Bias = deviation from truthBMJ 2002;324:1071
Classification Schemes for Error
• Szklo and Nieto– Bias
• Selection Bias• Information/Measurement Bias
– Confounding– Chance
• Other Common Approach– Bias
• Selection Bias• Information/Measurement Bias• Confounding Bias
– Chance
“BIG 4”
Sackett DL. Bias in analytic research. J Chron Dis 1979
selection biasmeasurement biasconfounding bias
vs.
popularity biascentripetal biasreferral filter biasdiagnostic access biasdiagnostic suspicion biasunmasking biasmimicry biasprevious opinion biasadmission biasprevalence-incidence biasdiagnostic vogue biasdiagnostic purity biasprocedure selection biasmissing clinical data biasnon-contemporaneouscontrol biasstarting time bias
volunteer biascontamination biaswithdrawal biascompliance biastherapeutic personality biasbogus control biasinsensitive measure biasunderlying cause biasend-digit preference biasapprehension biasunacceptability biasobsequiousness biasexpectation biassubstitution gamefamily information biasexposure suspicion biasrecall bias etc
Emerging Terminology: “Causal Research”
• Goal: Identify causal relationships
• 6 ways a statistical association can occur1. Chance
2. Selection bias
3. Measurement bias
4. Confounding
5. Reverse causation
6. True causal relationship
• Process of causal research: rule out the first 5
Selection Bias• Technical definition
– Bias that is caused when individuals have different probabilities of being included in the study according to relevant study characteristics: namely, the exposure and the outcome of interest
• Easier definition– Bias that is caused by some kind of systematic problem in the
process of selecting subjects initially or - in a longitudinal study - in the process that determines which subjects drop out of the study
• Problem caused by:
– Investigators: Faulty study design
– Participants: By choosing not to participate/ending participation
– (or both)
Selection Bias in a Descriptive Study
• Surveys re: 1948 Presidential election– various methods used to find subjects– largest % favored Dewey
• General election results– Truman beat Dewey
• Fault: Bad Study Design
• Ushered in realization of the importance of representative (random) sampling
N= 894 sample Actual vote
Yes 4,717,006 (55%)No 3,809,090 (45%)
The San Francisco Chronicle
Should Gov. Davis be recalled?
No, retain Davis39%
Yes, recall Davis57%
Undecided4%
Based on a survey conducted in English and Spanish among random samples of people likely to vote in California’s Oct. 7 recall election
Election polls provide rare opportunity to later look at
truth
SOURCE POPULATION
STUDY SAMPLE
Descriptive Study: Unbiased SamplingNo Selection Bias
Even dispersion of arrows
SOURCE POPULATION
STUDY SAMPLE
Descriptive Study: Biased SamplingPresence of Selection Bias
Uneven dispersion of arrows
e.g., Dewey backers were
over-represented
Leukemia Among Observers of a Nuclear Bomb Test
Caldwell et al. JAMA 1980• Smoky Atomic Test in Nevada• Outcome of 76% of troops at site was later found; occurrence
of leukemia determined
82% contacted by the investigators
18% contacted the investigators on their own
4.4 greater incidence of leukemia than those
contacted by the investigators
Fault: Study design (look back studies are inherently limited) + the participants
(especially who chose not to participate)
0.0
00
.05
0.1
00
.15
0.2
0P
rop
ort
ion
de
cea
sed
0 .5 1 1.5 2 2.5 3 3.5Time since initiation of antiretroviral therapy (years)
Mortality following initiation of antiretroviral therapy in Uganda
In the presence of 39% loss to follow-up at 3 years
Geng et al. JAMA 2008
0.0
00
.05
0.1
00
.15
0.2
0P
rop
ort
ion
de
cea
sed
0 .5 1 1.5 2 2.5 3 3.5Time since initiation of antiretroviral therapy (years)
Mortality following initiation of antiretroviral therapy in Uganda
Accounting for losses to follow-up by tracking down vital status of a sample of the lost in the community
Naive estimate
Corrected estimate
Selection bias
Disease
Exposure
+ -
+
-
SOURCE POPULATION
STUDY SAMPLE
Analytic Study: Unbiased SamplingNo Selection Bias
Given that a person resides in one of the 4 cells in the source population, the selection probability is the probability he/she will be
represented in that cell in the study sample.
For no selection bias to occur, selection probabilities cannot
differ according to both exposure and disease
Diseased
Exposed
+ -
+
-
SOURCE POPULATION
STUDY SAMPLE
Analytic Study: Biased SamplingPresence of Selection Bias
Unequal selection probability isolated to one cell:
Underestimate of Exposure Effect
Selection Bias in Case-Control Studies
Coffee and cancer of the pancreas MacMahon et al. N Eng J Med 1981; 304:630-3
Cases: patients with histologic diagnosis of pancreatic cancer in any of 11 large hospitals in Boston and Rhode Island between October 1974 and August 1979
What study base gave rise to these cases?
How should controls be selected?
Selection Bias in a Case-Control Study
Coffee and cancer of the pancreas MacMahon et al. N Eng J Med 1981; 304:630-3
Controls: • Other patients without pancreatic cancer under the care of the
same physician of the cases with pancreatic cancer.
• Patients with diseases known to be associated with smoking or alcohol consumption were excluded
207 275
9 32
Case Control
Coffee: > 1 cup day
No coffee
OR= (207/9) / (275/32) = 2.7 (95% CI, 1.2-6.5)
Coffee and cancer of the pancreasMacMahon et al., (N Eng J Med 1981; 304:630-3)
216 307
Biased?
Relative to the true study base that gave rise to the cases, the:
Controls were: • Other patients under the care of the same physician at the time of
an interview with a patient with pancreatic cancer
Most of the MDs were gastroenterologists whose other patients were likely advised to stop using coffee
• Patients with diseases known to be associated with smoking or alcohol consumption were excluded
Smoking and alcohol use are correlated with coffee use; therefore, sample is relatively depleted of coffee users
Conclusion: Controls vastly depleted of coffee users compared to true study base
Fault: Investigators (Poor study design)
Cancer No cancer coffee
no coffee
SOURCE POPULATION
STUDY SAMPLE
Case-control Study of Coffee and Pancreatic Cancer: Selection Bias
Bias: overestimate effect of coffee in
causing cancer
1410
8284
Case Control
Coffee: > 1 cup day
No coffee
OR= (84/10) / (82/14) = 1.4 (95% CI, 0.55 - 3.8)
Coffee and cancer of the pancreas:Use of population-based controls
•Gold et al. Cancer 1985
Disease
Exposure
+ -
+
-
SOURCE POPULATION
STUDY SAMPLE
Equal selection probability in all 4 cells:
No Selection Bias
Selection Bias in a Cross-sectional Study: Presence of exposure and disease at outset invites selection bias
Disease
Exposure
+ -
+
-
SOURCE POPULATION
STUDY SAMPLE
Unequal selection probability:
Overestimate of Effect
Selection Bias in a Cross-sectional Study: Presence of exposure and disease at outset invites selection bias
Disease
Exposure
+ -
+
-
SOURCE POPULATION
STUDY SAMPLE
Unequal selection probability:
Underestimate of Effect
Selection Bias in a Cross-sectional Study: Presence of exposure and disease at outset invites selection bias
Disease
Exposure
+ -
+
-
SOURCE POPULATION
STUDY SAMPLE
Unequal selection probability:
Overestimate of Effect
Selection Bias in a Cross-sectional Study: Presence of exposure and disease at outset invites selection bias
Disease
Exposure
+ -
+
-
SOURCE POPULATION
STUDY SAMPLE
Unequal selection probability:
Underestimate of Effect
Selection Bias in a Cross-sectional Study: Presence of exposure and disease at outset invites selection bias
Disease
Exposure
+ -
+
-
SOURCE POPULATION
STUDY SAMPLE
Typically you don’t know the selection probabilities
Selection Bias in a Cross-sectional Study: Presence of exposure and disease at outset invites selection bias
?
? ?
?
History of Heart Attack
Hyper-lipidemia
+ -
+
-
SOURCE POPULATION
STUDY SAMPLE
Selection Bias in a Cross-sectional Study: Effect of Non-Responders
Austin, AJE 1981Survey of S. California adults
OR observed = 3.6
25 347
45 2312
Overall 82% Response?
?
?
?
History of Heart Attack
Hyper-lipidemia
+ -
+
-
SOURCE POPULATION
Investigators made the extra effort to track down and question the initial non-
responders
Selection Bias in a Cross-sectional Study: Effect of Non-Responders
Austin, AJE 1981Survey of S. California adults
OR true = 3.3
2807100%100%
63
30100%
401100%
CORRECTED STUDY SAMPLESelection
probability
History of Heart Attack
Hyper-lipidemia
+ -
+
-
SOURCE POPULATION
STUDY SAMPLE
Investigators made the extra effort to track down and question the initial non-
responders
Selection Bias in a Cross-sectional Study: Effect of Non-Responders
83% 87%
83%72%Austin, AJE 1981Survey of S. California adults
OR biased = 3.6
OR true = 3.325 347
45 2312
2807100%100%
63
30100%
401100%
CORRECTED STUDY SAMPLE
Response % Selection bias
Effect of unequal response probabilities in a cross-sectional study
Group Exposure Outcome
Bias in OR due to non-
response
Men Family h/o MI Heart failure +63%
Hypertension Stroke -32%
Women Family h/o stroke Stroke +59%
Family h/o diabetes Stroke -34%
Austin, AJE 1981Survey of S. California adults
Fault: The Participants(Study design is fine)
Another Mechanism for Selection Bias in Cross-sectional Studies
• Finding a diseased person in a cross-sectional study requires 2 things:– the disease occurred in the first place– person survived long enough to be sampled
• Any factor found associated with a prevalent case of disease might be associated with disease development, survival with disease, or both
• Assuming goal is to find factors associated with disease development (etiologic research), bias in prevalence ratio occurs any time that exposure under study is associated with survival with disease
Cross-Sectional Study Design
Selection Bias in a Cross-Sectional Study• Is glutathione S-transferase class deletion (GSTM1-null) polymorphism
associated with increased risk of breast cancer?
• With prevalent breast cancer, an association with GSTM1-null is seen depending upon the number of years since diagnosis
• But not with brand new incident diagnoses
Kelsey et al. Canc Epi Bio Prev 1997
4 - 8 yrCancer
Nocancer
GSTM1-null
52 126
GSTM1-positive
39 119
OR = 1.3
CancerNo
cancer
GSTM1-null
119 115
GSTM1-positive
121 124
OR = 1.08
<4 yrCancer
Nocancer
GSTM1-null
44 126
GSTM1-positive
43 119
OR = 0.97
>8 yrCancer
Nocancer
GSTM1-null
44 126
GSTM1-positive
21 119
OR = 2.0
Breast Cancer
GSTM1
+ -
null
SOURCE POPULATION
STUDY SAMPLE
Cross-sectional study of GSTM1 polymorphism and breast cancer
pos.
Bias: overestimate effect of GSTM-1 null polymorphism in causing breast
cancer
Fault: Study design
Selection Bias: Cohort Studies/RCTs
• Among initially selected subjects, selection bias “on the front end” less likely to occur compared to case-control or cross-sectional studies
– Reason: study participants (exposed or unexposed; treatment vs placebo) are selected (theoretically) before the outcome occurs
Disease
Exposure
+ -
+
-
SOURCE POPULATION
STUDY SAMPLE
Cohort Study/RCTAt the outset, since disease has not occurred yet among initially selected subjects, there is typically no opportunity for disproportionate sampling with respect to exposure and disease. (We cannot yet draw the 4 arrows)
Disease
Exposure
+ -
+
-
SOURCE POPULATION
STUDY SAMPLE
Cohort Study/RCT All that is sampled is exposure status (the
“margins”)
Even if disproportionate sampling of exposed or
unexposed groups occurs, it will not result in selection bias when forming measures of
association
A + B
C + D
a + b
c + d
Selection Bias: Cohort Studies
• Selection bias can occur on the “front-end” of the cohort if diseased individuals are unknowingly entered into the cohort
• e.g.:
– Consider a cohort study of effect of exercise on all-cause mortality in persons initially thought to be completely healthy.
– If some participants were enrolled had undiagnosed cardiovascular disease and as a consequence were more likely to exercise less, what would happen to the measure of association?
Death No death
exercise
no exercise
SOURCEPOPULATION
STUDY SAMPLE
Cohort Study of Exercise and Survival
Selection bias will lead to spurious protective effect of exercise (assuming truly no effect)
Selection Bias: Cohort Studies/RCTs
• Most common form of selection bias does not occur with the process of initial selection of subjects
• Instead, selection bias most commonly caused by forces that determine length of participation (who ultimately stays in the analysis; losses)
– When those lost to follow-up have a different probability of the outcome than those who remain (i.e. informative censoring) in at least
one of the exposure groups
AND
– Rate of informative censoring differs across exposure groups
• Selection bias results
Selection Bias: Cohort Studies
e.g., Cohort study of progression to AIDS: IDU vs homosexual men
All the ingredients are present:
• Informative censoring is present– getting sick is a common reason for loss to follow-up
– persons who are lost to follow-up have greater AIDS incidence than those who remain (i.e., informative censoring)
• Informative censoring is differential across exposure groups– IDU more likely to become lost to follow-up - at any level of feeling sick
– i.e., the magnitude of informative censoring differs across exposure groups (IDU vs homosexual men)
• Result: selection bias -- underestimates the incidence of AIDS in IDU relative to homosexual men
Effect of Selection Bias in a Cohort Study
Survival assuming no informative censoring and no difference between IDU and homosexual men
Effect of informative censoring in IDU group
Effect of informative censoring in homosexual men group
Time
Pro
bab
ilit
y of
bei
ng
AID
S-f
ree Selection
bias
AIDS No AIDS
IDU
Homo-sexual men
SOURCE POPULATION
STUDY SAMPLE
Cohort Study of HIV Risk Group and AIDS Progression
Selection bias will lead to spurious underestimation of AIDS incidence in both exposure groups, more so in IDU group
Fault: The Participants(Study design is fine)
Effect of losses to follow-up in a cohort study
Bisson, PLoSOne, 2008
Naively Ignoring Losses
Tracking Down Vital Status on Losses
Determinants of survival after initiation
of antiretroviral therapy in Africa
Selection Bias in a Randomized Clinical Trial
• If randomization is performed correctly, then selection bias on the “front-end” of the study (i.e., differential inclusion of diseased individuals between arms) is not possible (other than by chance)
– even if diseased individuals are unknowingly included, randomization typically ensures that this occurs evenly across treatment groups
Selection Bias in a Clinical Trial
• Losses to follow-up are the big unknown in clinical trials and the major potential cause of selection bias
• e.g., Assume that:– a symptom-causing side effect of a drug is more common in
persons “sick” from the disease under study– occurrence of the side effect is associated with more losses to
follow-up • Then:
– Compared to placebo, drug treatment group would be selectively depleted of the sickest persons (i.e., informative censoring)
– Would make drug treatment group appear better
Effect of Selection Bias in an RCT
Survival assuming no informative censoring and no difference between drug and placebo
Effect of informative censoring in drug group
Time
Pro
bab
ilit
y of
non
-d
isea
se
Managing Selection Bias
• Prevention and avoidance are critical– Unlike confounding where there are solutions in the analysis of
the data, once the subjects are selected and their follow-up occurs, there are usually no easy fixes for selection bias
• In case-control studies:– Follow the study base principle
• In cross-sectional studies:– Strive for high response percentages– Be aware of how exposure in question affects disease survival
• In longitudinal studies (cohorts/RCTs):– Screen for occult disease/precursors at baseline– Avoid losses to follow-up – Consider approaches to tracking down the lost