Issues in case-control studies Internal Medicine Samsung Medical Center Sungkyunkwan University...
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Issues in case-control studies Internal Medicine Samsung Medical Center Sungkyunkwan University School of Medicine Kwang Hyuck Lee [email protected][email protected]
Issues in case-control studies Internal Medicine Samsung
Medical Center Sungkyunkwan University School of Medicine Kwang
Hyuck Lee [email protected][email protected]
Presenters Name Date Case-control study historical synonyms
Retrospective study Trohoc study Case comparison study Case compeer
study Case history study Case referent study 3
Slide 4
Presenters Name Date Case Control Study Disease YesNo
ExposedYesA1A1 B1B1 NoA0A0 B0B0 Case Control
Slide 5
Presenters Name Date , , , , *, *, ** , , *, **
Slide 6
(LDLT) : 50% . LDLT .
Slide 7
2006 1 2008 12 duct to duct (hepaticojejunostomy ) ,
Slide 8
group LDLT group ( : AST>80, ALT>80, ALP>250 or
bilirubin>2.2) Group A : ERCP Vs ERCP Group B : Vs Group C : CT
ERCP Vs
Slide 9
n=46 23 7 5 3 3 5 n=74 58 13 3 LDLT patients during 3years :
n=213 need ERCP stricture leakage stone Patients with LFT elevation
: n=120 not need ERCP rejection infection HCC viral reactivation
vessel stenosis etc Analysis group B Analysis group A Analysis
group C CT(-) need ERCP : 32 CT(-) not need ERCP : 40
Slide 10
Case-Control Study or not?
Slide 11
Presenters Name Date 11
Slide 12
Presenters Name Date 12
Slide 13
Presenters Name Date Brock MV, et al. N Engl J Med
2008;358:900-9 13
Slide 14
Presenters Name Date Conducting case-control studies Case and
Control selection Exposure measurement Odds ratio
Slide 15
Presenters Name Date Research New Question ?? Method Clinical
study Translational study Laboratory study Clinical study
Observational studies Case-control study Vs Cohort study Randomized
controlled trial
Slide 16
Presenters Name Date Why case-control studies? New question of
interest Cohort study with the appropriate outcome or exposure
ascertainment does NOT exist Need to initiate a new study Do you
have the time and/or resources to establish and follow new cohort?
16
Slide 17
Presenters Name Date Case control study ?? High cholesterol
Myocardial infarction MI (+) case MI (-) control Cholesterol level
Result Negative Positive 17
Slide 18
Presenters Name Date Impetus for case-control studies :
EFFICIENCY May not have the sufficient duration of time to see the
development of diseases with long latency periods. May not have the
sufficiently large cohort to observe outcomes of low incidence.
NOTE: Rare outcomes are not necessary for a case-control study, but
are often the drive. 18
Slide 19
Presenters Name Date 19
Slide 20
Presenters Name Date Efficiency of case-control study Do
maternal exposures to estrogens around time of conception cause an
increase in congenital heart defects? Assume RR = 2, 2-sided =
0.05, 90% power Cohort study: If I 0 = 8/1000, I 1 = 16/1000, would
need 3889 exposed and 3889 unexposed mothers Case-control study: If
~30% of women are exposed to estrogens around time of conception,
would need 188 cases and 188 controls Schlesselman, p. 17 20
Slide 21
Presenters Name Date Strengths of case-control study Efficient
typically: Shorter period of time Not as many individuals needed
Cases are selected, thus particularly good for rare diseases
Informative may assess multiple exposures and thus hypothesized
causal mechanisms 21
Slide 22
Presenters Name Date Learning objectives Exposure Selection of
cases and controls Bias Selection, Recall, Interviewer, Information
Odds ratios Matching Nested studies Conducting a case-control study
DCR Chapter 8 22
Slide 23
Presenters Name Date Exposure ascertainment examples Active
methods Questionnaire (self- or interviewer- administered)
Biomarkers Passive methods Medical records Insurance records
Employment records School records 23
Slide 24
Presenters Name Date Exposure ascertainment issues Establish
biologically relevant period Measurement occurs once at current
time Repeated exposure Previous exposure Measure of exposure occurs
after outcome has developed Possibility of information bias
Possibility of reverse causation (outcome influences the measure of
exposure) 24
Slide 25
Presenters Name Date Is it possible in case-control study?
relevant period 25 Yesterday smoking and radiation Cancer risk
Slide 26
Presenters Name Date Information bias: recall bias Mothers of
babies born with congenital malformations more likely to recall
(accurately or over-recall) events during pregnancy such as
illnesses, diet, etc. 26
Slide 27
Presenters Name Date Possibility of reverse causation High
cholesterol Myocardial infarction MI (+) case MI (-) control
Cholesterol level Result ? MI Cholesterol level decrease Measure
cholesterol after MI 27
Slide 28
Presenters Name Date Case selection basic tenets Eligibility
criteria Characteristics of the target and source population
Diagnostic criteria Definition of a case: misclassification
Feasibility 28
Slide 29
Presenters Name Date Source populations samples Health
providers: clinics, hospitals, insurers Occupations: work place,
unions Surveillance/screening programs Laboratories, pathology
records Birth records Existing cohorts Special interest groups:
disease foundations or organizations 29
Slide 30
Presenters Name Date Incident versus prevalent cases Incident
cases: All new cases of disease cases (that become diagnosed) in a
certain period Prevalent cases: All current cases regardless of
when the case was diagnosed 30
Slide 31
Presenters Name Date Incident Vs Prevalence Do the cases
represent all incident cases in the target population?
Exposuredisease association Vs Exposuresurvival association 31
Slide 32
Presenters Name Date Prevalence cases 32 Disease only A (causal
factor) 1-month survival A+B (protective factor) 1-year survival
A+C (protective factor)10-year survival Patient A: A11 month
Patient B: A1+B1 year Patient C: A1+C 10 years Prevalence cases
A1,B,C : Causes intervention of B or C Survival
Slide 33
Presenters Name Date Disease severity Which stage is chosen for
a case? Early stage onlyProgression not always Late stage
onlyInfluence of severity Increase sample size for stratification
33
Slide 34
Presenters Name Date Early stage only Case selection was done
in prevalent cases of thyroid cancer Case: small thyroid cancer
Control: normal population Determined the differences Clinical
meaning of this study if there is no difference of survival between
them 34
Slide 35
Presenters Name Date Late stage only difficult diagnosis 35
Pancreatic cancer Vs. Weight Cases: late stage pancreatic cancer
Low weight due to Cancer progression Conclusion low weight
pancreatic cancer Increase sample size for stratification
Slide 36
Presenters Name Date Selection bias Selection of cases
independent of exposure status Related to severity Related to
hospitalization or visiting 36
Slide 37
Presenters Name Date Example selection bias (1) Hypothesis
Common cold Asthma Setting Patients in Hospital Truth Common cold:
aggravating factor not causal factor No different incidence of
asthma according to common cold Common cold (+) aggravation
hospital visit Common cold (-) no symptoms no visit 37
Slide 38
Presenters Name Date 38 TotalCommon cold in society Patients in
hospital Common cold in hospital Asthma1000105010
General2000002000100020 (10+ alpha) Cause positiveCause negative
Case (asthma)1040 Control149 Odds ratio = (1X49)/(4X1) Example
selection bias (2)
Slide 39
Presenters Name Date Case and Control selection 39 Same
distribution of risk factors ??
Slide 40
Presenters Name Date Guallar E, et al. N Engl J Med
2002;347:1747-54 40
Slide 41
Presenters Name Date Selection of controls basic tenets Same
target population of cases Confirmation of lack of outcome/disease
Selection needs to be independent of exposure 41
Slide 42
Presenters Name Date Controls in case-control studies Should
have the same proportion of exposed to non-exposed persons as the
underlying cohort (source population) Should answer yes to: If
developed disease of interest during study period, would they have
been included as a case? 42
Slide 43
Presenters Name Date Selecting controls Same as case source
Characteristics 1.Convenient 2.Most likely same target population
3.Rule out outcome avoids misclassification 4.Similar factors
leading to inclusion into source population 5.Sometimes impractical
Examples Breast cancer screening program Confirmed breast cancer
cases No breast cancer controls Same hospital as case series
Similar referral pattern examine by illness types Pediatric clinics
Geographic population Other special populations (e.g., occupational
setting) 43
Slide 44
Presenters Name Date Source for controls Geographic population
Roster needed Probability sampling Neighborhood controls Random
sample of the neighborhood Friends and family members
Hospital-based control 44
Slide 45
Presenters Name Date Selection of controls: Friends or family
members Friends or family members Ask each case for list of
possible friends who meet eligibility criteria Randomly select
among list Type of matching - will be addressed later Concerns: May
inadvertently select on exposure status, that is, friends because
of engaging in similar activities or having similar
characteristics/culture/tastes over-matching 45
Slide 46
Presenters Name Date Am J Epidemiol 2004;159:915-21 46
Slide 47
Presenters Name Date Selection of controls Hospital or
clinic-based Strengths Ease and accessibility Avoid recall bias
Concerns Section bias: exposure related to the hospitalization A
mixture of the best defensible control Referral pattern Same Or not
47
Slide 48
Presenters Name Date Diet pattern: Colon cancer (GI referral
center) Case: (+) Control: (-) : . Control: (+) 48
Slide 49
Presenters Name Date Guallar E, et al. N Engl J Med
2002;347:1747-54 49
Slide 50
Presenters Name Date Weakness of Case-Control Studies Time
period from which the cases arose Survival factor, Reverse
causation Biologically relevant period Only one outcome measured
Susceptibility to bias Separate sampling of the cases and controls
Retrospective measurement of the predictor variables 50
Presenters Name Date Case and Control selection 52 Same
distribution of risk factors ??
Slide 53
Presenters Name Date Selection of cases Case selection in
hospitals Alcohol Hip fractures: All visit hospitals IUD abortion 1
st abortion: Some visit but others not Women with IUD in general
population more frequently visit clinics 53 Disease No disease
Exposed Non-exposed Target population Disease No disease Exposed
Non-exposed Study sample a AB b C c D d
Slide 54
Presenters Name Date 1 st abortion: 3% rate and no relation of
IUD IUD: frequent visit General population IUD(+) 1000 970/30
IUD(-) 9000 8730/270 Hospital population IUD (+) 90% 873/27 IUD (-)
45% 4050/120 54 casecontrol Yes10 No90 100 casecontrol Yes18 No82
100 Control: general population difference due to frequent visit
Control: Hospital population theoretically same unless this control
group has higher abortion rates due to other problems Control
mixture: both
Slide 55
Presenters Name Date Actual situation Limited cases Selection
bias from control selection 55
Slide 56
Presenters Name Date 56
Slide 57
Presenters Name Date Nomura A, et al. N Engl J Med
1991;325:1132-6 57
Slide 58
Presenters Name Date Selection bias in nested case-control
study Controls were excluded if they had had gastrectomy or history
of peptic ulcer disease Controls with a cardiovascular disease or
cancer at baseline or during follow-up were excluded Disease No
disease Exposed Non- exposed Target population Disease No disease
Exposed Non- exposed Study sample a AB b C c D d 58
Slide 59
Presenters Name Date 59
Slide 60
Presenters Name Date MacMachon B, et al. N Engl J Med
1981;304:630-3 60
Slide 61
Presenters Name Date MacMachon B, et al. N Engl J Med
1981;304:630-3 61
Slide 62
Presenters Name Date MacMachon B, et al. N Engl J Med
1981;304:630-3 62
Slide 63
Presenters Name Date Selection bias in case-control study
Controls were largely patients with diseases of the
gastrointestinal tract Control patients may have reduced their
coffee intake as a consequence of GI symptoms Disease No disease
Exposed Non- exposed Target population Disease No disease Exposed
Non- exposed Study sample a AB b C c D d 63
Slide 64
Presenters Name Date 64
Slide 65
Presenters Name Date Antunes CMF, et al. N Engl J Med
1979;300:9-13 65
Slide 66
Presenters Name Date Antunes CMF, et al. N Engl J Med
1979;300:9-13 66 Non-GY Control 6.0 GY Control 2.1
Slide 67
Presenters Name Date Criticisms of prior case-control studies
Diagnostic surveillance bias Women on estrogens are evaluated more
intensively they are more likely to be diagnosed and to be
diagnosed at earlier stages Women with asymptomatic cancer who
receive estrogens are more likely to bleed and to be diagnosed
Antunes CMF, et al. N Engl J Med 1979;300:9-13 67
Slide 68
Presenters Name Date To avoid selection bias in case-control
studies Selection of cases Types of cases selected (non-fatal,
symptomatic, advanced) Response rates among cases Relation of
selection to exposure Are exposed cases more (or less) likely to be
included in the study? Selection of controls Type of controls
(general population, hospital, friends and relatives) For hospital
controls, diseases selected as control conditions Response rate
among controls Relation of selection to exposure Are exposed
controls more (or less) likely to be included in the study? Similar
response rates in cases and controls do NOT rule out selection bias
68
Slide 69
Presenters Name Date 69
Slide 70
Presenters Name Date Recall issues All information in
case-control studies is historic, so if relying on reporting by
participants, accuracy depends on recall Concerns: Do cases recall
prior events differently from controls? Mindset of someone with
disease : Is there something that I did that may have caused the
disease? Recall Bias (Information Bias) 70
Slide 71
Presenters Name Date Recall bias example Mothers of babies born
with congenital malformations more likely to recall (accurately or
over-recall) events during pregnancy such as illnesses, diet, etc.
71
Slide 72
Presenters Name Date 72
Slide 73
Folic acid and neural tube defects Figure 1: Features of neural
tube development and neural tube defects. Botto et el. Neural tube
defects. NEJM 1999. (28 th days after fertilization)
Slide 74
Background and Aim A reduced recurrent risk of neural tube
defects among women receiving muti-vitamin supplements containing
folic acid. Most of NTDs are de-novo; less than 10% of NTDs are
recurrent. First occurrence of only NTDs and periconceptional
folate supplements
Slide 75
Study population Case NTDs Control Other major malformations
due to recall bias Subjects with oral clefts were excluded because
vitamin supplementation has been hypothesized to reduce the risk:
selection bias Pregnant women Target Source Study
Slide 76
Overall data 76 Folate (+) OR = 0.6 (0.4 0.8)
Slide 77
Recall Bias: Previous knowledge 77
Slide 78
Recall Bias quantification CaseControlORIn this study 1000
Recall rate real5008000.625Control 75% all4006000.667Case 80%0.6
Prev known4506000.750Case 90%0.8 Prev unknown3756000.625Case 75%0.4
78
Slide 79
Presenters Name Date Recall bias assessment / avoidance Check
with recorded information, if possible Use objective markers or
surrogates for exposure careful of markers that are affected by
disease Ask participant to identify which factor(s) are important
for disease Build in false risk factor to test for over- reporting
Use controls with another disease 79
Slide 80
Study population Case NTDs Control Other major malformations
due to recall bias Subjects with oral clefts were excluded because
vitamin supplementation has been hypothesized to reduce the risk:
selection bias Pregnant women Target Source Study
Slide 81
Selection bias If oral clefts were included in control group,
control with exposure (lack of vitamin supplement or folate intake)
increased. As B number increases, the probability of rejecting null
hypothesis decreases. CaseControl Exposure (+)AB Exposrue (-)CD
Exposure: lack of folate intake Cleft = intake of vitamin
Slide 82
Methods Periconceptional folic acid exposure was determined by
Interview with study nurses Demographic Health behavior factors
Reproductive history Family history of birth defects Occupation
Illnesses (chronic and during pregnancy) Use of alcohol, cigarettes
and medications Vitamin use during the 6 months before the last LMP
through the end of pregnancy Semi-quantitative food frequency
questionnaire Knowledge of vitamins and birth defects
Presenters Name Date Interviewer bias Differential interviewing
of cases and controls, i.e., may probe or interpret responses
differently Interviewer Bias (Information Bias) 84
Slide 85
Presenters Name Date Interviewer bias avoidance / assessment
Self-administered instruments (prone to more non-response)
Standardized instruments Computerized instruments (CADI, ACASI)
Avoid open-ended questions but rather use questions with each
possible response elicited Training Masking interviewers to
research question Masking interviewers to case/control status Same
interviewers for cases and controls 85
Slide 86
Presenters Name Date Odds ratio Disease YesNo ExposedYesA1A1
B1B1 NoA0A0 B0B0
Slide 87
Presenters Name Date Example: CHD and Diabetes CHD YesNo
DiabetesYes18365 No575735 No units! 87
Slide 88
Presenters Name Date Some properties of odds ratios Null value:
OR = 1 OR >= 0 (cannot be negative) Multiplicative scale (be
careful with plots) Use logistic regression to estimate
multivariate adjusted odds ratios in case- control studies 88
Slide 89
Presenters Name Date Odds ratios and the rare disease
assumption With incidence density sampling (represents underlying
cohort at time of case) and sampling of cases and controls
independent of exposure: OR IR With outcomes of very low incidence
in the underlying cohort and sampling of cases and controls
independent of exposure: OR RR Higher incidence increases the bias
away from the null 89
Slide 90
Presenters Name Date 90
Slide 91
Presenters Name Date Matching Individual matching Frequency
matching Stratified matching Nested study Case-control study
Case-cohort study 91
Slide 92
Presenters Name Date Siegel DS, et al. Blood 1999;93:51-4
Matching in cohort study example 92
Slide 93
Presenters Name Date Matching in case-control studies
individual matching Pairing or grouping controls to case by known
risk factors in the design phase, i.e., when selecting controls In
protocol, define matching characteristics and their boundaries
Dichotomous or categorical: self-explanatory (e.g., sex, race,
blood type, disease stage) Continuous: can be exact, or typically a
window (e.g., age 5 years, CD4 cell count 50 cells) For each
recruited case, search in control source population for the
person(s) who meet the matching criteria Select 1 or more of them
at random 93
Slide 94
Presenters Name Date Odds ratio matched pairs Case Control #
pairs A 1 B 1 n 11 A 1 B 0 n 10 A 0 B 1 n 01 A 0 B 0 n 00 N = total
# pairs N pairs = N cases and N controls 2 N people 94
Slide 95
Presenters Name Date Antunes CMF, et al. N Engl J Med
1979;300:9-13 95
Slide 96
Presenters Name Date Frequency matching Select cases Examine
distribution of potential confounder (matching variable) Select
controls so that they have same distribution of the potential
confounder Conduct stratified analyses or regression to control for
the induced selection bias 96
Slide 97
Presenters Name Date Stratified sampling alternative to
matching Decide up front what distribution of cases and controls
according to confounder is desired Select cases and controls so
that expectations are met Selection of controls does not depend on
cases being selected first Note that distribution of confounder is
not the distribution one may see among all cases in the population
97
Slide 98
Presenters Name Date Stratified sampling example Want 50%
females in 100 cases and controls 50 female cases and 50 male cases
50 female controls and 50 male controls In the study period, 175
incident male cases and 75 incident female cases occur As they
occur, enroll cases until 50 are recruited in each stratum
Throughout study period, enroll 50 male and 50 female controls
98
Slide 99
Presenters Name Date Matching limitations Cannot examine the
independent effect of matched variable on outcome Cases are
controls are balanced for the matched factor May be costly to
perform May inadvertently match On the exposure itself or its
surrogate On a factor in the causal pathway On a factor that is
affected by the outcome Matching on an exposure-related factor but
not a disease determinant may reduce the statistical efficiency
(matched cases and controls with same exposure are not used in
matched analysis) Logistical complexity of matching 99
Slide 100
Presenters Name Date Matching strengths Costs of finding a
matched control may < costs of performing tests to assess
confounding < costs of recruiting additional controls to yield
enough persons across entire range of confounding variable
Particularly useful when distribution of confounders is very
different in cases and controls Increases amount of
information/subject Matching yields same ratio of cases and
controls according to distribution of matched variable 100
Slide 101
Presenters Name Date Nested studies In an existing cohort study
New questions arise Need efficient method to use existing
information Do not want to conduct methods on entire cohort, due to
limited resources Nest a study without sacrificing validity and too
much precision Some nesting options: Case-cohort Sub-cohort
Case-control 101
Slide 102
Presenters Name Date 102 Nested Case-Control and Case- Cohort
Studies Case-comparison studies Use all cases or representative
subset as of date of analysis Comparison group : Cohort member for
all nested designs Study DesignComparison Case-controlEvent-free
member at time of cases event (incidence density sampling)
Case-cohortMembers of subcohort, selected at random from cohort at
time of enrollment, at risk at time of cases event= In the
subcohort riskset
Slide 103
Presenters Name Date Full Cohort Events: A 1 1 2 S1 S6 S3,S8 At
risk: N 8 6 4 S1,S2,S3,S4,S5,S6,S7,S8 S3,S4,S5,S6,S7,S8 S3,S4,S7,S8
10 20 30 35 S1 S2 S3 S4 S5 S6 S7 S8 103
Slide 104
Presenters Name Date 104 Case-cohort study
Slide 105
Presenters Name Date Nested case-control study Events: A 1 1 2
S1 S6 S3,S8 At risk: N 8 6 4 S1,S2,S3,S4,S5,S6,S7,S8
S3,S4,S5,S6,S7,S8 S3,S4,S7,S8 10 20 30 35 S1 S2 S3 S4 S5 S6 S7 S8
Potential controls: S2,S3,S4,S5,S6,S7,S8 S3,S4,S5,S7,S8 S4,S7
105
Slide 106
Presenters Name Date 106 A cohort study 3 events or cases occur
among 8 people, of whom 5 are ever exposed Exposed are solid lines,
unexposed are dashed Dots are events Time Persons
Slide 107
Presenters Name Date 107 A nested case-control study Compare 3
cases to 3 non-cases (at event time) among cohort members Time
Persons Incidence Density Sampling
Slide 108
Presenters Name Date 108 A case-control study Compare 3 cases
to 3 non-cases (at event time) among cohort members but what is the
cohort? They arise from some underlying cohort!! Time Persons
Incidence Density Sampling
Slide 109
Presenters Name Date Designing a case-control study Overview I
What is the research question? In what target population? What
source(s) will be used? How long will recruitment take? What is the
definition of the cases? What confirmation is needed? Is
screening/additional testing necessary? Will prevalent cases be
used? Does exposure influence the disease prognosis? What is the
underlying cohort? How many cases are seen per year in the source?
109
Slide 110
Presenters Name Date What are the eligibility criteria for
controls? What source(s) will be used to identify controls? Do they
represent the same underlying cohort as the cases? What
confirmation is needed? Is screening/additional testing necessary?
Sampling methods? Will the controls be selected throughout the
study period? Can they be selected as cases if they later develop
disease? Do additional sources need to be used? For both cases and
controls, does exposure status affect: inclusion in source
populations or participation? 110 Designing a case-control study
Overview II
Slide 111
Presenters Name Date Are there known confounders? Should
matching be used? What methods will be used to recruit cases and
controls? What methods will be used to obtain information about
exposures and potential confounders? Active / Passive? Are the
methods of data collection objective and independent of
case/control status? What methods are in-place to avert and monitor
differential recall by case/control status if interviewing is
involved? If study involves personnel-administered data collection,
are the personnel masked to case-control status? 111 Designing a
case-control study Overview III
