Issues in case-control studies

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Issues in case-control studies. Internal Medicine Samsung Medical Center Sungkyunkwan University School of Medicine Kwang Hyuck Lee [email protected] Issues in case-control studies. Eliseo Guallar , MD, DrPH [email protected] Juhee Cho, M.A., Ph.D. [email protected]
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  • Issues in case-control studies
  • Internal Medicine Samsung Medical CenterSungkyunkwanUniversity School of [email protected]
  • Issues in case-control studies EliseoGuallar, MD, [email protected] Juhee Cho, M.A., Ph.D. [email protected] Case-control study – historical synonyms
  • Retrospective study
  • Trohoc study
  • Case comparison study
  • Case compeer study
  • Case history study
  • Case referent study
  • Case Control Study Control Case 생체 간이식 후 간수치 상승 환자에서 담도 협착의 조기 발견과 관련된 요인 오초롱, 이광혁, 이종균 , 이규택 , 권준혁*,조재원*, 조주희** 성균관대학교 의과대학, 삼성서울병원 소화기내과, 이식외과*, 암교육센터** 연구목적
  • 생체간이식(LDLT) 후 발생하는 담도 합병증
  • 가장 좋은 치료인 내시경적 치료 성공률 : 50% 전후
  • 담도 합병증을 조기에 발견하여 내시경적 배액술을 시행하면 성공률이 높다.
  • LDLT 후 간 기능 이상 소견을 보이는 환자 중에 담도 합병증을 예측할 수 있는 요인을 찾고자 하였다.
  • 대상 및 방법
  • 기간 및 대상 환자
  • 2006년1월부터2008년12월 생체간이식을 받은 환자
  • 수술 후 회복된 간기능이 다시 악화되었던 환자
  • duct to duct 문합 환자만 포함(hepaticojejunostomy 환자는 제외)
  • 조사한 항목
  • 기저질환, 증상
  • 간기능 검사
  • 수술기록
  • 영상의학검사
  • 분석 group
  • LDLT 후 간수치가 재상승한 환자를 대상으로 group 을 나눔 (상승 기준 :AST>80, ALT>80, ALP>250 or bilirubin>2.2)
  • Group A
  • : ERCP가 필요한 환자 Vs ERCP 필요하지 않은 환자
  • Group B
  • : 문합부담도협착 환자 Vs 거부반응 환자
  • Group C
  • : CT 상 협착소견이 없었던 환자 중에
  • ERCP가 필요한 환자 Vs 필요하지 않은 환자
  • LDLT patients during 3years : n=213 Patients with LFT elevation : n=120 Analysis group A Analysis group B Analysis group C CT(-) need ERCP : 32 CT(-) not need ERCP : 40 Case-Control Study or not? Brock MV, et al. N Engl J Med 2008;358:900-9 Conducting case-control studies
  • Case and Control selection
  • Exposure measurement
  • Odds ratio
  • Research
  • New Question ??
  • Method
  • Clinical study
  • Translational study
  • Laboratory study
  • Clinical study
  • Observational studies
  • Case-control study Vs Cohort study
  • Randomized controlled trial
  • 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?
  • Case control study ??
  • High cholesterol  Myocardial infarction
  • MI (+) case
  • MI (-) control
  • Cholesterol level
  • Result
  • Negative
  • Positive
  • 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.
  • 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 α = 0.05, 90% power
  • Cohort study: If I0 = 8/1000, I1 = 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 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
  • 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 Exposure ascertainment – examples
  • Active methods
  • Questionnaire (self- or interviewer- administered)
  • Biomarkers
  • Passive methods
  • Medical records
  • Insurance records
  • Employment records
  • School records
  • 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)
  • Is it possible in case-control study? – relevant period Yesterday smoking and radiation  Cancer risk 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. Possibility of reverse causation
  • High cholesterol  Myocardial infarction
  • MI (+) case
  • MI (-) control
  • Cholesterol level
  • Result ?
  • MI  Cholesterol level decrease
  • Measure cholesterol after MI
  • Case selection – basic tenets
  • Eligibility criteria
  • Characteristics of the target and source population
  • Diagnostic criteria
  • Definition of a case: misclassification
  • Feasibility
  • 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
  • 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
  • Incident Vs Prevalence
  • Do the cases represent all incident cases in the target population?
  • Exposure–disease association Vs Exposure–survival association
  • Prevalence cases
  • Disease
  • only A (causal factor) 1-month survival
  • A+B (protective factor) 1-year survival
  • A+C (protective factor) 10-year survival
  • Patient A: A1 1 month
  • Patient B: A1+B 1 year
  • Patient C: A1+C 10 years
  • Prevalence cases  A1,B,C : Causes intervention of B or C ↓↓Survival Disease severity
  • Whichstage is chosen for a case?
  • Early stage only Progression not always
  • Late stage only Influence of severity
  • Increase sample size for stratification
  • 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
  • Late stage only – difficult diagnosis
  • 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 Selection bias
  • Selection of cases independent of exposure status
  • Related to severity
  • Related to hospitalization or visiting
  • 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
  • Example selection bias (2) Odds ratio = (1X49)/(4X1) Case and Control selection Study population Source population Target population Same distribution of risk factors ?? Guallar E, et al. N Engl J Med 2002;347:1747-54 Selection of controls – basic tenets
  • Same target population of cases
  • Confirmation of lack of outcome/disease
  • Selection needs to be independent of exposure
  • 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?
  • Selecting controls – Same as case source Characteristics
  • Convenient
  • Most likely same target population
  • Rule out outcome – avoids misclassification
  • Similar factors leading to inclusion into source population
  • 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)
  • Source for controls
  • Geographic population
  • Roster needed
  • Probability sampling
  • Neighborhood controls
  • Random sample of the neighborhood
  • Friends and family members
  • Hospital-based control
  • 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”
  • Am J Epidemiol 2004;159:915-21 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
  • Referralpattern
  • Same
  • Or not
  • Diet pattern: Colon cancer
  • 소화기 암 전문 병원 (GI referral center)에서 연구를 수행함
  • Case : 소화기 클리닉의대장암 (+)
  • Control : 호흡기 클리닉의대장암 (-)
  • 소화기 클리닉: 대기실 소화기 암 관련 음식 정보
  • 호흡기 클리닉
  • 두 군 간에 차이는 질환의차이가 아니라 클리닉의차이를 반영할 수도 있다.
  • Control:소화기클리닉의 위암 (+)
  • Guallar E, et al. N Engl J Med 2002;347:1747-54 Weakness of Case-Control Studies
  • Timeperiod 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
  • Issues in case-control studies EliseoGuallar, MD, [email protected] Juhee Cho, M.A., Ph.D. [email protected] Case and Control selection Study population Source population Target population Same distribution of risk factors ?? Selection of cases
  • Case selection in hospitals
  • Alcohol  Hip fractures: All visit hospitals
  • IUD  abortion
  • 1stabortion: Some visit but others not
  • Women with IUD in general population more frequently visit clinics
  • Target population Study sample Disease No disease Disease No disease A B a b Exposed Non-exposed Exposed Non-exposed C D c d 1st 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
  • 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
  • Actual situation Limited cases Selection bias from control selection Nomura A, et al. N Engl J Med 1991;325:1132-6 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
  • Target population Study sample Disease No disease Disease No disease A B a b Exposed Non-exposed Exposed Non-exposed C D c d MacMachon B, et al. N Engl J Med 1981;304:630-3 MacMachon B, et al. N Engl J Med 1981;304:630-3 MacMachon B, et al. N Engl J Med 1981;304:630-3 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
  • Target population Study sample Disease No disease Disease No disease A B a b Exposed Non-exposed Exposed Non-exposed C D c d Antunes CMF, et al. N Engl J Med 1979;300:9-13 Non-GY Control 6.0 GY Control 2.1 Antunes CMF, et al. N Engl J Med 1979;300:9-13 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 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
  • 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) 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. 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. (28th days after fertilization) Background and Aim
  • A reducedrecurrent 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 periconceptionalfolate supplements
  • Study population Pregnant women
  • 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
  • Target Source Study Overall data Folate(+) OR = 0.6 (0.4 – 0.8) Recall Bias: Previous knowledge Recall Bias quantification 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 Study population Pregnant women
  • 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
  • Target Source Study Cleft = ↓intake of vitamin 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.
  • Exposure: lack of folate intake 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
  • Confounding Exposure ↓ Folate intake Outcome ↑ NTDs Confounding Alcohol Interviewer bias Differential interviewing of cases and controls, i.e., may probe or interpret responses differently  Interviewer Bias (Information Bias) 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 Odds ratio Example: CHD and Diabetes No units! 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
  • 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
  • Matching
  • Individual matching
  • Frequency matching
  • Stratified matching
  • Nested study
  • Case-control study
  • Case-cohort study
  • Matching in cohort study – example Siegel DS, et al. Blood 1999;93:51-4 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
  • Odds ratio – matched pairs Case Control # pairs A1 B1 n11 A1 B0 n10 A0 B1 n01 A0 B0 n00 N = total # pairs N pairs = N cases and N controls  2 N people Antunes CMF, et al. N Engl J Med 1979;300:9-13 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
  • 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
  • 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
  • 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 matc
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