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Sample Actual vote
Gallup Poll*
*Based on a national survey conducted Oct. 15 -17 in likely voters
N = 2263 N = 125,040,818
ObamaMcCain Other
ObamaMcCain Other
50% 46% 4%
50% 46% 4%
53% 46% 1%
53% 46% 1%
Which was present in the sample?
Selection bias�
� Measurement bias
� Sampling error
Perhaps we should conduct our elections by sampling?
Perhaps we should conduct our elections by sampling?
Confounding and Interaction I
Confounding: one of the central problems in observational clinical research
– What is it? What does it do? What is its origin?
– What kind of variables act as confounders?
– Which variables are not confounders (colliders and intermediary variables)?
– Use of causal diagrams (DAGs) to conceptualize confounding and guide us for what to adjust for
» Emphasis on specifying the research question and understanding the underlying biological/clinical/behavioral system
» Confounding is a substantive, not statistical issue
Matches and Lung Cancer
A tobacco company researcher believes that
exposure to matches is a cause of lung cancer
He conducts a large case-control study to test this hypothesis
Exposure odds ratio = (820/180) / (340/660) = disease odds ratio
OR = 8.8
95% CI (7.2, 10.9)
Should we remove matches from the environment?
LungCancer
No LungCancer
Matches 820 340No matches 180 660
Smoking, Matches, and Lung Cancer
Lung Ca No Lung CaMatches 820 340No Matches 180 660
Lung CaNo
Lung CAMatches 810 270No Matches 90 30
Stratified
Crude
Non-SmokersSmokers
OR crude
OR CF+ = ORsmokers OR CF- = ORnon-smokers
Stratification produces two 2-by-2 tables
In each stratum, all subjects are homogeneous with respect to smoking
We have adjusted or controlled for smoking
ORcrude = 8.8 (7.2, 10.9)
ORsmokers = 1.0 (0.6, 1.5)
ORnon-smoker = 1.0 (0.5, 2.0)
ORadjusted = 1.0 (0.5, 2.0)
Lung CaNo
Lung CAMatches 10 70No Matches 90 630
Confounding: Smoking, Matches, and Lung Cancer
Illustrates how confounding can create an apparent effect even when there is no actual true effect
– Can also be opposite: confounding can mask an effect when one is truly present
Proper terminology
– In the relationship between matches and lung cancer, smoking is a confounding factor or a confounder
– Smoking confounds the relationship between matches and lung cancer
In clinical research, confounding has a very specific meaning
Estes continues to be confounding puzzle Ray RATTO
SHAWN ESTES seemed loath to analyze his own performance last night, for fear that people would see the first three innings and use them to obscure the last four.
But that's what made his outing so perfectly Estes-like -- an ongoing argument with himself that he eventually won.
Well, an argument in which he held his own and his teammates won for him in the bottom of the ninth.
Ramon Martinez lined a game-tying single with two outs, and Jeff Kent followed two batters later with a bases-loaded walk off Juan Acevedo to give the Giants a 2-1 victory against Colorado and move them to within 4 1/2 games of division leader Arizona. It was in many ways an eye-opening victory for a team that hadn't had one of this type for a while.
Finding: “After an initial course of post-exposure prophylactic (PEP) medication following a sexual exposure to HIV infection, gay men reported a decrease in the practice of high-risk behavior over the following year.”
Reviewer: “Perhaps the men simply withheld the real amount of high-risk behavior they had in order to be eligible for future courses of PEP. How do you account for this confounding?”
Lung Ca No Lung CaSmoking 900 300No Smoking 100 700
Lung CaNo
Lung CASmoking 810 270No Smoking 10 70
Stratified
Crude
Matches Absent
Matches Present
OR crude
OR CF+ = ORmatches
Lung CaNo
Lung CASmoking 90 30No Smoking 90 630
OR CF+ = OR no matches
ORcrude = 21.0 (16.4, 26.9)
ORmatches = 21.0 (10.7, 41.3)
ORno matches = 21.0 (13.1, 33.6)
The study is not over!
To be complete, you decide to examine the relationship between smoking and lung cancer independent from the use of matches.
Confounding: Smoking, Matches, and Lung Cancer
Interpretation?
What is the effect of matches on the relationship between smoking and lung cancer?
Matches have no effect on the relationshipMatches have no effect on the relationship
Effect of matches could have been predicted based on matches — lung cancer relationship
– Illustrates one important component in the requirements of a confounder
(aka a confounding factor) - A confounder must be causally related to
the outcome
Confounding: Examples of Magnitude and Direction
OR Crude OR CF+ OR CF- Type of Confounding
4.0 2.0 2.0 Positive 4.0 1.0 1.0 Positive 0.2 0.9 0.9 Positive 4.0 4.0 4.0 No confounding 4.0 8.0 8.0 Negative 1.0 3.0 3.0 Negative 0.9 0.2 0.2 Negative 4.0 0.5 0.5 Qualitative (reversal of
effect)
Disease No DiseaseExposedUnexposed
Disease No DiseaseExposedUnexposed
Disease No DiseaseExposedUnexposed
Stratified (adjusted)
Crude (unadjusted)
Potential Confounder
Absent
Potential Confounder
Present
OR crude
OR CF+ OR CF-
Nightlights
Let there be light!Let there be light!
Nightlights and Myopia
Quinn et al. Nature 1999
Prevalence Ratio =
Myopia No MyopiaNight light 79 153No night light 17 155
5.6) to2.1 :CI (95% 4.3
1721723279
Insert picture with nightlight off
Lights are off and the stumbling around begins.
Lights are off and the stumbling around begins.
Nightlights and Myopia
Two subsequent studies found no association and explained the prior result by confounding
– Zadnik et al. and Gwiazda et al. Nature, 2000
Child’s MyopiaChild’s Myopia
Night LightNight Light
??
How might confounding account for this finding?
Child’s MyopiaChild’s Myopia
Night LightNight Light
Parental Myopia
Parental Myopia XX
Positive or negative
confounding?
Positive or negative
confounding?
PositivePositive
Insert picture with nightlight on again
Let there be light (again)!Let there be light (again)!
AZT to Prevent HIV After Needlesticks
Case-control study of whether post-exposure AZT use can prevent HIV seroconversion after needlestick (NEJM 1997)
CrudeHIV No HIV
AZT 8 131No AZT 19 189
27 320 347
ORcrude = 0.61
(95% CI: 0.26 - 1.4)
Interpretation?
Could confounding be present?
Interpretation?
Could confounding be present?
HIVHIV
AZT UseAZT Use
??
HIVHIV
AZT UseAZT Use
Severity of
Exposure
Severity of
Exposure
??
Positive or negative
confounding?
Positive or negative
confounding?
Adjustment for Confounder
Potential confounder: severity of exposure
Minor Severity Major
Severity
Crude
Stratified
HIV No HIVAZT 8 131No AZT 19 189
27 320 347
HIVNo
HIVAZT 0 91No AZT 3 161
3 252 255
ORcrude =0.61
HIVNo
HIVAZT 8 40No AZT 16 28
24 68 92
ORadjusted = 0.30
(95% CI: 0.12 – 0.79)
Negative Confounding
Confounding by Indication
Classification Schemes for Error in Clinical Research
Szklo and Nieto
– Bias
» Selection Bias
» Information/Measurement Bias
– Confounding
– Chance
Other Common Approach
– Bias
» Selection Bias
» Information/Measurement Bias
» Confounding
– Chance
Counterfactuals: Conceptualizing Why Confounding Occurs
Night lights and myopia
Ideal study: evaluate children exposed to night lights for several years and directly compare them to the SAME children not exposed to night lights
– Result (e.g. risk ratio) is called the “Effect Measure” of night lights
– Assuming no measurement error, the “effect measure” must be true.
However, since time has passed and children are older it is impossible to assess them without night lights
Hence, the ideal is “counterfactual” – contrary to the fact. It is unobservable. It cannot happen.
Exposed to
night lights
Exposed to
night lights
Unexposed to
night lights
Unexposed to
night lights
timetime
Counterfactuals: Conceptualizing Why Confounding Occurs
Gender and heart disease
Ideal study: evaluate men for several years for occurrence of heart disease; compare them directly to SAME individuals who are now women
However, you cannot change a man into a woman and you cannot go back in time
The “effect measure” is preposterous. It cannot be observed. It is counterfactual.
menmen
womenwomen
timetime
Counterfactuals: Conceptualizing Why Confounding Occurs
Nights and Myopia
Because we cannot perform the counterfactual ideal (SAME population studied under 2 conditions), we must evaluate TWO distinct populations (exposed to a night light and unexposed) to study the problem
– Result (e.g. risk ratio): a “measure of association”
The TWO distinct populations may be subject to different influences OTHER than just the night light
If these influences cause the disease under study, any difference in the risk ratio between the SAME population study (effect measure) and the TWO population study (measure of association) is what is known as confounding
Confounding occurs because of a mixing of effects
Exposed to
night lights
Exposed to
night lights
Unexposed to
night lights
Unexposed to
night lights
timetime
Other
influences
Other
influences
Striving for the Counterfactual
In the real (observable) world
All of our strategies in analytic studies are striving to simulate the counterfactual
We strive for our TWO distinct populations (exposed and unexposed) to be “exchangeable”
Whenever the TWO distinct populations are “non-exchangeable”, confounding will be present
Our strategies to manage confounding are attempts to make our populations exchangeable
Back to the Observable (Factual) World: Criteria for Confounding
Confounding occurs because of mixing between exposures of interest and unwanted extraneous effects
Extraneous effects are termed confounders
Criteria for a confounder
– Must be causally associated with the outcome,
or be a surrogate for a causally related variable
– Must be associated with the exposure under
study, but cannot be caused by the exposure
– Must not be on the causal pathway under study
(i.e., must not be an intermediary variable)
CC??
EE
DD
Causal Diagrams -- DAGsCausal Diagrams -- DAGs DAGs = directed acyclic graphs; aka chain graphs
Consist of nodes (variables) and arrows
“Directed”: all arrows have one-way direction and depict causal relationships
“Acyclic”: there is never a complete circle (i.e. no factor can cause itself)
Better than the rough criteria for confounding when planning studies and analyses
Identifies pitfalls of adjusting and not adjusting for certain variables
Frontier of epidemiologic theory
Research Question: Does E cause D?
Research Question: Does E cause D?
Forces investigator to conceptualize system
CC ??
EE
DD
Confounding in a DAGConfounding in a DAG
Confounding occurs if there is a factor C that is a “Common Cause” of both E and D
Confounding occurs if there is a factor C that is a “Common Cause” of both E and D
C is the genesis of a “backdoor path” to E and D
Adjusting/controlling for C closes the backdoor paths; eliminates confounding
Lung CancerLung
Cancer
MatchesMatches
SmokingSmoking
??
Smoking is a “common cause” of matches and lung cancer.
It therefore confounds the relationship (positive CF)
Controlling for smoking blocks the paths and unconfounds relationship
Smoking is a “common cause” of matches and lung cancer.
It therefore confounds the relationship (positive CF)
Controlling for smoking blocks the paths and unconfounds relationship
RQ: Do matches cause lung cancer?
RQ: Do matches cause lung cancer?
Birth DefectsBirth
Defects
Multi-vitamin
Use
Multi-vitamin
Use
History of birth
defects
History of birth
defects ??
Genetic factor is the “common cause” but cannot be measured or adjusted for
Genetic factor is the “common cause” but cannot be measured or adjusted for
Genetic Factor (not measured)
Genetic Factor (not measured)
Adjusting for history of birth defects, which can be measured, blocks the path between genetic factor and MVI use, and prevents confounding
Adjusting for history of birth defects, which can be measured, blocks the path between genetic factor and MVI use, and prevents confounding
Threat: negative confounding
Threat: negative confounding
Hernan AJE 2002Hernan AJE 2002
SeriousHead Injury
SeriousHead Injury
Use of Helmets in Motorcyclists
Use of Helmets in Motorcyclists
Safety-oriented
Personality (not
measured)
Safety-oriented
Personality (not
measured)
??Safe
Driving Practices
Safe Driving
Practices
Threat: positive confounding
Threat: positive confounding
Adjusting for safe driving practices, which can (theoretically) be measured, blocks path from safety-oriented personality to head injury
Adjusting for safe driving practices, which can (theoretically) be measured, blocks path from safety-oriented personality to head injury
Attraction of DAGs Abstract: The Criteria
– Must be causally associated with the outcome, or be a surrogate for a causally related variable
– Must be associated with the exposure under study, but cannot be caused by the exposure
– Must not be on the causal pathway under study (i.e. must not be an intermediary variable)
More tangible: DAGs
– Draw the system
– Look for “common causes” of exposure and disease
Birth DefectsBirth Defects
Multi-vitamin
Use
Multi-vitamin
Use
??
Genetic Factor (not measured)
Genetic Factor (not measured)
History of birth
defects
History of birth
defects
The Challenge
DAGs provide the framework
However, to identify the confounders, you need to be a subject matter expert
Sexual Activity
?
Mortality
RQ: Does sexual activity cause greater lifespan?
RQ: Does sexual activity cause greater lifespan?
Self-reported General Health
Unknown biologic factor(s)
(not measured)
Sexual Activity
?
Mortality
RQ: Does sexual activity cause greater lifespan?
RQ: Does sexual activity cause greater lifespan?
Ca channel Blockers
GI Bleeding
?
RQ: Do Calcium channel blockers cause GI bleeding?
RQ: Do Calcium channel blockers cause GI bleeding?
Coronary Artery
Disease
Other Meds (e.g.,
aspirin)
Ca channel Blockers
GI Bleeding
?
RQ: Do Calcium channel blockers cause GI bleeding?
RQ: Do Calcium channel blockers cause GI bleeding?
Birth DefectsBirth
Defects
Folate Intake
Folate Intake
??
What should we do with stillbirths (spontaneous
abortions)?
What should we do with stillbirths (spontaneous
abortions)?
RQ: Does lack of folate cause birth defects?RQ: Does lack of folate cause birth defects?
Stillbirths are associated with folate intake, even among infants without birth defects: OR = 0.50
Stillbirths are associated with birth detects: OR = 15.22
Stillbirths are not on the causal pathway between folate and birth defects
In the past, other investigators have commonly adjusted for stillbirths in analyses, or have limited analyses to live births.
Should we adjust for stillbirths here?
Slone Epidemiology Unit Birth Defects Study
Hernan AJE 2002Hernan AJE 2002
Adjustment for Stillbirths
Stillbirth No stillbirth
Crude
Stratified
Defect No Defect Good folate 43 239 Low folate 194 704 237 943 1180
ORcrude = 0.65
(95% CI 0.45 – 0.95)
ORadjusted = 0.80
(95% CI: 0.53 – 1.2)
Apparent positive confounding
Public health implication: No reason for women to supplement diet with folate
Defect
No Defect
Good folate 19 8 Low folate 100 46 119 54 173
Defect
No Defect
Good folate 24 231 Low folate 94 658 118 889 1007
Slone Epidemiology Unit Birth Defects Study
Hernan AJE 2002Hernan AJE 2002
Birth DefectsBirth
Defects
Folate Intake
Folate Intake
StillbirthsStillbirths ??
RQ: Does lack of folate intake cause birth defects?
RQ: Does lack of folate intake cause birth defects?
Use of DAGs to Identify What is Not Confounding
Stillbirths are a “common effect” of both the exposure and disease – not a common cause.
Common effects are called “colliders”
Adjusting for colliders OPENS paths. Will actually result in bias. It is harmful.
Stillbirths are a “common effect” of both the exposure and disease – not a common cause.
Common effects are called “colliders”
Adjusting for colliders OPENS paths. Will actually result in bias. It is harmful.
Hernan AJE 2002Hernan AJE 2002
Birth DefectsBirth
Defects
Multi-vitamin
use
Multi-vitamin
use
Maternal Weight Gain
Maternal Weight Gain ??
No common causes for exposure and disease
No common causes for exposure and disease
DAGs to Identify What is Not Confounding
Maternal weight gain is a collider
Adjusting for colliders will OPEN the path. Will actually result in bias. It is harmful.
Maternal weight gain is a collider
Adjusting for colliders will OPEN the path. Will actually result in bias. It is harmful.
Behavioral factors (not measured)
Behavioral factors (not measured)
Genetic Factor (not measured)
Genetic Factor (not measured)
Hernan AJE 2002Hernan AJE 2002
DAGs Force Investigators to First Conceptualize the System
Study of sunlight exposure & melanoma
A college intern is given a dataset and asked to estimate relationship between sunlight exposure and melanoma – adjusted for “everything”
He analyzes the data and finds that gum chewing is associated with melanoma and associated with sunlight exposure
After adjusting for gum chewing there is an appreciable difference between the crude and adjusted measure of association
Should gum chewing be controlled for?
No. Just by chance alone there can be the appearance of confounding
Based on our a priori understanding of the role of gum chewing (in melanoma), it is more likely that chance – as opposed to truth -- is causing appearance of confounding
Controlling for a variable should only be done if there is a strong subject matter evidence.
i.e. If it is not in your DAG, don’t control for it.
Rules for Reading DAGs
A path is blocked if
– a collider (“common effect”) is present, which has not been adjusted for (by stratification, mathematical regression or other techniques)
Or
– a non-collider (“common cause”) is adjusted for
To prevent confounding, block all of the paths
FolateFolate
Birth defects
Birth defects
StillbirthsStillbirths ??
NightlightsNightlights
Child’s Myopia
Child’s Myopia
Parental Myopia
Parental Myopia
??
Rules for Reading DAGs
A path is open if
– A collider (“common effect”) is adjusted for
Or
– a non-collider (“common cause”) is not adjusted for
Open paths produce bias
FolateFolate
Birth defects
Birth defects
StillbirthsStillbirths ??
NightlightsNightlights
Child’s Myopia
Child’s Myopia
Parental Myopia
Parental Myopia
??
What other variables are NOT Confounders? “Must not be on the causal pathway under study
(i.e. must not be an intermediary variable)”
A variable that you are conceiving as an intermediate step in the causal path under study between the exposure in question and the disease is not a confounding variable.
EE
DD
factor Ifactor I
Despite being associated with both exposure and outcome,
Factor I is not a confounder
It is on the pathway under
study.
It is an intermediary
variable
Despite being associated with both exposure and outcome,
Factor I is not a confounder
It is on the pathway under
study.
It is an intermediary
variable
CCR5 and HIV Disease Progression
CCR5 (receptor)
defect
CCR5 (receptor)
defect
AIDSAIDS
How should CD4 count be handled in assessing the association between CCR5 defect status and progression in HIV disease to AIDS?
How should CD4 count be handled in assessing the association between CCR5 defect status and progression in HIV disease to AIDS?
??
CCR5: the human cellular receptor for HIV –found on CD4 cells
Genetic defects in CCR5 now described
CD4 count potent predictor of time-to-AIDS
CCR5: the human cellular receptor for HIV –found on CD4 cells
Genetic defects in CCR5 now described
CD4 count potent predictor of time-to-AIDS
CD4 count
CD4 count
CCR5 and HIV Disease Progression
CCR5 (receptor)
defect
CCR5 (receptor)
defect
AIDSAIDS
How should CD4 count be handled in assessing the association between CCR5 defect status and progression in HIV disease to AIDS?
How should CD4 count be handled in assessing the association between CCR5 defect status and progression in HIV disease to AIDS?
CD4 countCD4 count
CCR5: the human cellular receptor for HIV –found on CD4 cells
Genetic defects in CCR5 now described
CD4 count potent predictor of time-to-AIDS
CCR5: the human cellular receptor for HIV –found on CD4 cells
Genetic defects in CCR5 now described
CD4 count potent predictor of time-to-AIDS
It depends upon the research question
CCR5 defectCCR5 defect
? [Other mechanisms]
? [Other mechanisms]
? [CD4 count]? [CD4 count]
AIDSAIDS
#1: Do CCR5 defects reduce progression to AIDS, irrespective of mechanism?
#1: Do CCR5 defects reduce progression to AIDS, irrespective of mechanism?
CCR5 defectCCR5 defect
Low CD4 countLow CD4 count
AIDSAIDS
Do not adjust for CD4 count !
Do not adjust for CD4 count !
AIDS No AIDS Defect No defect
AIDS No AIDS Defect No defect
AIDS No AIDS Defect No defect
High CD4 countHigh CD4 count
CD4 countCD4 count
Do Adjust ! Do Adjust !
#2: Do CCR5 defects reduce progression to AIDS, independent of CD4 count?
#2: Do CCR5 defects reduce progression to AIDS, independent of CD4 count?
RQ 1: What if you did adjust for CD4 count?
CCR5 defectCCR5 defect
AIDSAIDS
#1: Is CCR5 associated with progression to AIDS, irrespective of mechanism?
#1: Is CCR5 associated with progression to AIDS, irrespective of mechanism?
Low CD4 countLow CD4 count
AIDS No AIDS Defect No defect
AIDS No AIDS Defect No defect
AIDS No AIDS Defect No defect
High CD4 countHigh CD4 count
If “via CD4 count” was only pathway, no effect for CCR5 would be observed after stratification
If “via CD4 count” was only pathway, no effect for CCR5 would be observed after stratification
? [CD4 count]? [CD4 count]
Taylor et al. JAIDS 2003
CCR5 defectCCR5 defect
Other mechanism
Other mechanism
#2 #2
??
CD4 countCD4 count
AIDSAIDS
#1#1
CCR5 defectCCR5 defect
??
AIDSAIDS
CD4 not adjusted
for
CD4 not adjusted
for
CD4 countCD4 count
CD4 adjusted for
CD4 adjusted for
Crude (unadjusted) association:
- rate ratio: 0.71
Crude (unadjusted) association:
- rate ratio: 0.71
Stratified (adjusted) by CD4 count
-rate ratio: 0.93;
-Conclude: no mechanism other than via CD4
Stratified (adjusted) by CD4 count
-rate ratio: 0.93;
-Conclude: no mechanism other than via CD4
Hep B and C virus
infection
Hep B/C are not “common causes” but they do form another extraneous path from IDU to mortality; adjusting for Hep B/C blocks the path
IDU
Early Mortality
? [via bacterial infections]
RQ: Does injection drug use (IDU) cause earlier mortality independent of its effect on hepatitis infections?
RQ: Does injection drug use (IDU) cause earlier mortality independent of its effect on hepatitis infections?
Poor Diet
Poverty
Mortality
? [access to care]
RQ: Does poverty cause early mortality independent of effects on diet?
RQ: Does poverty cause early mortality independent of effects on diet?
Adjust for diet to
remove the extraneous
pathway
Adjust for diet to
remove the extraneous
pathway
Exercise and Coronary Heart Disease
When evaluating the relationship between exercise and CHD, what should be done with HDL cholesterol?
ExerciseExercise
Coronary Heart
Disease
Coronary Heart
Disease
??HDL
cholesterol
HDL cholesterol
RQ: Does exercise prevent coronary heart disease?
It depends on the pathway under investigation
If interest is in a pathway other than through HDL, then HDL should be adjusted for
Termed the “direct effect, independent of HDL”
ExerciseExercise
CADCAD
[not yet specified
mechanism]
[not yet specified
mechanism]HDLHDL ??
Adjust for HDL to remove the
extraneous pathway
Adjust for HDL to remove the
extraneous pathway
Exercise and CAD
If no particular mechanistic pathway is being studied
e.g., Does exercise influence CAD risk in a newly studied population (elderly Asians)?
Here, HDL as well as a variety of other mechanistic explanations are on the pathway in question
Therefore, HDL is an intermediary variable.
ExerciseExercise
CADCAD
Do not adjust for
HDL
Do not adjust for
HDL [HDL . .+. . other mechanisms][HDL . .+. . other mechanisms]
DAGs point out special issue when estimating direct effects
RQ: Does aspirin prevent CHD in a pathway other than through platelet aggregation
– Assumes no common cause of platelet agg. and D
Would be correct to adjust
But if
– Assume common cause (e.g., genetic component)
– Need other statistical methods to resolve
AspirinAspirin
Coronary Heart Disease
Coronary Heart Disease
Platelet Aggregation
Platelet Aggregation ??
AspirinAspirin
Coronary Heart Disease
Coronary Heart Disease
Platelet Aggregation
Platelet Aggregation ??
Genetic factors (not measured)
Genetic factors (not measured)
Would be incorrect to
adjust OR not to adjust for
platelet aggregation
Would be incorrect to
adjust OR not to adjust for
platelet aggregation
Cole and Hernan IJE 2002Cole and Hernan IJE 2002
When Planning a Study, Which Factors Should be Measured as Potential
Confounders or Extraneous Pathways?
Draw a DAG
With previously studied exposures-diseases:
– consider/measure any factor for which prior evidence indicates is a confounder
» e.g., effect of diet on CAD?
must deal with smoking as potential confounder
When studying new exposures for which little is known:
– plan on measuring ALL factors associated with the disease
– i.e. If you don’t, you may regret it later
Confounding can be dealt with in the analysis phase of a study but NOT if the factor is not measured
Seeking cause of high Marin cancer rates Activists canvass residents to search for trends
Thousands of volunteers scattered across Marin County under baleful skies Saturday in an unprecedented grassroots campaign against the region's soaring cancer rate.
Armed with surveys, some 2,000 volunteers went door to door in every neighborhood in the county . . . . The volunteers hope to collect enough money to hire an epidemiologist . . .