Case-Control Studies

1
Case-Control Studies

• Pradeep Deshmukh
• Professor
• Dr Sushila Nayar School of Public Health
• MGIMS, Sewagram

2
(No Transcript)
3
Definition.

• The case-control study is an analytic
  epidemiologic research design in which the study
  population consists of groups who either have
  (cases) or do not have a particular health
  problem or outcome (controls).
• The investigator looks back in time to measure
  exposure of the study subjects. The exposure is
  then compared among cases and controls to
  determine if the exposure could account for the
  health condition of the cases.

4
SYNONYMS

• Case-Referent
• Case-Compeer
• Retrospective

5
Characteristics

• Observational / Non-experimental
• Occasionally Exploratory
• Explanatory (Analytical)
• Retrospective
• Effect to Cause
• Both Exposure Disease have already occurred
• Uses Comparison Group

6
Why case-control design for study of rare
diseases?

• Consider some rare disease say some cancer
  (leukemia)
• Crude Annual Incidence 3.4/100000 (lt 15 years)
• Cohort Study A year of observation on a million
  children to identify 34 cases
• Sample of 34 cases Sub-divided in 2 or more
  exposure categories
• What about conducting case-control design?

7
Case-Control Studies for Diseases having long
induction period

• Advantageous Long induction period between the
  exposure and clinical onset of disease
• Cohort Study Waiting years for accrual of cases
• Case-Control Study Compress time
• Case-Control Studies Chronic Diseases (Cancer /
  Cardiovascular Diseases)

8
Case-control studies in hierarchy of designs

• RCT Methodological Standard of Excellence
• However,
• Ca-Co - Not only SIMPLE to perform but some times
  the ONLY approach to solve a problem.
• Philosophically no design is Gold Standard.
• Understand strengths and weaknesses .
• Select appropriate study design to address your
  RQ...

9
Progression of study design Clinical research

• Isolated Case Reports
• Case Series
• Cross-Sectional study
• Case-Control Study
• Cohort Study
• Randomized Clinical Trial
• Meta-Analysis

10
PROGRESSION OF STUDY DESIGN COMMUNITY RESEARCH

• Ecological Study
• Cross-Sectional Study
• Case-Control Study
• Cohort Study
• Randomized Community Trial
• Meta-Analysis
• EXAMPLE
• Lipid - Atherosclerosis Association

11
Lipid - Atherosclerosis Association

• Analysis of Death Rates from CAD according to per
  capita fat consumption in 20 countries ?
  Hypothesis of L-A association.
• CS Studies Framingham and Evans County Heart
  Studies (Dawber et al 1971, Cassel 1971)
• Case-Control Studies confirmed Association.
• Cohort Studies (Truett et al 1967, Tyroler et al
  1971)
• Community Based Controlled Trials of Lipid
  Reduction (Lipid Research Clinics Program)

12
Causation/causal association

• Criteria to be fulfilled
• Temporal association
• Strength of association effect of cessation
• Specificity of association
• Consistency of association
• Biological plausibility
• Coherence of association

13
The setting of case-control research

• Clinical
• Mechanisms of Disease Causation
• Community
• Population Health Impact of Exposure

14
Decision to conduct case-control research

• The characteristics of the exposure and disease
• The current state of knowledge Relationship
• The immediate goals of the study
• The research setting
• The resources available

15
Research questions

• Is OC use associated with MI in women?
• Is current IUD use associated with PID?
• Is OC use associated with the risk of breast
  cancer?
• Is age at first coitus associated with cervical
  cancer?
• Is legal abortion associated with placenta previa
  in a later pregnancy?

16
WHO ARE CASES?

• With a Specific Outcome
• Presence of Disease / Syndrome
• Complications / progression of Disease (Severe
  dehydration crisis)
• Death (Neonatal mortality)
• Serum cholesterol / Birth weight
• Delayed Immunization
• Early Initiation of Cigarette Smoking
• Adverse Reactions of Drugs / Vaccines (SIDS)
• Behavior (Juvenile Delinquency)
• Drug Resistance (MDR-TB)
• Couple as a case (Infertility)

17
Selection of cases (Definition)

• Diagnostic Criteria
• Risk of Disease Misclassification
• Continuous / Discrete Outcome Variable
• Relatively simple straightforward Children
  with cleft palates (physical examination)
• Sometimes difficult Hypertension
• Diagnosis Combination of methods
• Rationale / Logical
• Criteria Specific
• Operational versus Rigid
• Standard Definition (WHO, CDC, etc)
• Reference (growth references NCHS, CDC, New WHO)

18
Selection of cases (definition)

• Eligibility Criteria
• Inclusion/Exclusion criteria
• Ca-Co studies should be limited to incident cases
  (Sackett 1979)
• Exposures are presumably more recent and
  therefore more reliably recalled.
• Relatively homogeneous group
• Exclusion of prevalent cases Minimize the
  Selection Bias (Neyman Fallacy).
• Ex PID and IUD Use
• Women who are not sexually active or who have had
  a tubal ligation are not likely to have recently
  used any contraceptive method including IUDs

19
Case definition

• Conceptual definition
• Obesity defined as body fat percentage gt 33
• Operational definition
• Body Mass Index gt 30

20
Case definition Issues

• Case definition should avoid misclassification
• For example Sinha et al (2008)
• Anemia was defined as Hemoglobin lt 110 gm/L as
  measured by WHO Colour Scale
• WHO Colour Scale over-estimates the hemoglobin
• Misclassified cases with mild anemia
• Also, studying mild forms of cases, gives larger
  case group but misclassifies cases as non-cases
  OR non-cases as cases as early diagnosis is
  generally imprecise

21
Case definition Issues

• A severe case definition may exclude people who
  have been cured or who died of disease before the
  condition was severe enough to be labelled as
  case
• Standard/consensus definitions if available, must
  be used
• For example,
• Rheumatoid arthritis Rome criteria, NY
  criteria, 1987 ARC criteria
• Metabolic Syndrome ATP III, IDRF, and so on
• Lack of agreement over definition may introduce
  variability in estimates of effect

22
Case definition Issues

• The issues of severity, diagnostic criteria and
  subjectivity of criteria all lead to potential
  problems of misclassification of cases
• The researcher can choose between more
  restrictive and inclusive definitions
• Think in terms of sensitivity and specificity of
  definition and its effect on validity, sample
  size, precision and power
• Brenner and Savitz (1990) reported that
• Restrictive definition (less sensitive) leads to
  lack of precision and power by reducing sample
  size
• Broad criteria (less specificity) produce
  misclassification leading to biased measure of
  effect
• So, weigh validity - specificity over sensitivity
  (Restrictive definition over inclusive definition)

23
Sources of cases (Research Setting)

• Hospitals (Multi-Centric Studies)
• Community
• Industrial Population

24
Identification of cases Issues

• The goal is to
• Ensure that all true cases have an equal
  probability of entering the study and that no
  false cases enter
• Example Conceptual definition of HIV
• Factors affecting decision to test/access the
  test and Sn Sp of test will decide who
  eventually becomes a case under operational
  definition
• Selection bias ??

25
Biases

• Selection bias
• Unequal chance of getting into study
• Berksons bias
• Variable rate of hospitalization affecting case
  selection
• Neyman fallacy
• Incident case Vs prevalent case
• Detection bias
• Due to closer medical attention, detection of
  endometrial cancer was more in a group using
  estrogen

26
Selection of Controls

• The controls should come from the population at
  risk of the disease
• Men can not be controls for a gynecological
  condition
• The controls should be eligible for the
  exposure
• The controls should have same exposure rate as
  that of the population from where the cases are
  drawn

27
Wachlders four principles for selection of
controls

• The study base
• Source of case and the control should be the same
• Deconfounding
• Comparable accuracy
• Similar misclassification errors in cases
  controls
• Same potential of recall bias in cases control
• Efficiency

28
Types of Controls

• Hospital or clinic control
• Dead control
• Controls with similar diseases
• Peer or case-nominated (friend/neighbor) control
• Population controls

29
Hospital controls

• Readily available hence commonly used
• Main reasons to use hospital controls are
• To select controls whose referral pattern is
  similar to cases
• To obtain similar quality of examination
• For convenience
• May not be representative of the population

30
Dead controls

• Might use dead controls for dead cases
• In some situations, this might lead to use of
  surrogate informant
• The problem is the dead control is not
  representative of the living population
• McLaughlin compared dead controls with living
  controls and noticed that the dead controls
  smoked more cigarettes and consumed more alcohol
  than living controls
• Appropriateness depends on the exposure being
  studied

31
Controls with similar diseases

• Reasons
• To minimize the recall bias
• To minimize the interviewer bias
• To examine the specificity of an exposure for a
  particular type of cancer
• For practical but unspecified reasons
• Problem ??

32
Peer or case-nominated (friend/neighbor) control

• Neighborhood controls is used in two ways
• To refer to community or population controls
• To refer to controls selected from finite number
  of close neighbors
• Search starts from house of the case and
  door-to-door search conducted for eligible
  controls in a standardized pattern
• Friend or neighbor control is a surrogate for
  matching on age, SES, education, etc
• A quick way to find control
• Bias is introduced if determinants of friendship
  are associated with disease or exposure
• Friends share many risk behaviors

33
Population controls

• Randomly drawn from population
• Truly representative of population
• Ideal way of selecting controls
• Practically, very difficult to carry out
• Study base ???

34
Where to select controls from?

• Way the pros and cons
• Analyze the situation for bias being introduced
• If possible,
• select different sources of controls and compare
  with each other
• Compare the inferences drawn

35
Ratio of control to cases

• Statistical consideration
• When the number of subjects available in one
  group (cases) is limited, an increase in the
  other group increases the study power
• Gain in power is till the ratio of 41
• Thereafter, the gain is not substantial but cost
  increases
• When the study of power with equal allocation is
  as high as 0.9 or as low as 0.1, additional fails
  to increase the power

36
Ratio of control to cases

• Validity of inferences
• Even when there is no statistical need, more than
  one control may be recruited per case
• Enrolling two or more types of controls is a way
  of checking for biases introduced by choice of
  control group
• If the measure of effect is similar when
  comparing cases with each control group
• Probably no biases (no surety)
• If different measure of effect, then the bias is
  there and the researcher can understand it

37
MATCHING

• Purpose To adjust - effects of relevant
  confounders
• Matching in Design - Accounted in Analysis
• Misconception The goal is to make the case and
  control groups similar in all respects, except
  for disease status.
• An Optimal Matching Scheme involves only those
  variables which improve statistical efficiency or
  eliminate bias from the effect of interest.

38
MATCHING

• Which variables are appropriate for matching?
• Risk factors from prior work may be identified
  for matching
• Matching by interviewer or hospital may be used
  to balance out the effects of interviewer and
  observer errors
• It is best to limit matching to basic descriptors
  (age, race, sex, etc)
• Non-modifiable risk factors
• Use few matching factors

39
MATCHING

• Overzealous matching may have adverse effects
• Matching on a strong correlate of the exposure,
  which is not an independent risk factor for the
  outcome (overmatching) may lead to an
  underestimate of OR.
• Matching may lead to a false sense of security
  that a particular variable is adequately
  controlled.

40
Sample size

• Epi_info 6.04

41
Measurement of Exposure

• Questionnaires
• Records
• Conversion tables/algorithms

42
Measurement of exposure

• Questionnaire
• Question comprehension
• Information retrieval
• Response formulation and recording
• Quality of exposure reports may be influenced by
• Type of respondent
• Administration of questionnaire
• Salience of exposure
• Way in which information is retrieved
• Ways in which responses are formulated and
  recorded

43
Measurement of exposure

• Records
• Abstraction of data from record
• Quality control measures are important
• Careful design and testing of abstraction form
• Training and supervision of abstractors
• Priori definition of terms
• Specifications of rules for handling conflicting
  or missing data

44
Measurement of exposure

• Conversion tables/algorithm
• To obtain more specific exposure measure from
  questionnaire or record
• More in use now-a-days for dietary and
  occupational variables

45
Group work

• Three groups
• Design a case-control study

46
Analysis
47
Associations

• Use of tests of significance
• Estimation of Odds ratio and its confidence
  interval
• Attributable risk estimation

48
Tests of significance

• Unmatched study
• Matched study

49
Binary exposure without covariates
Exposure to fumes Headache present Headache absent Total
Factor present a10 b90 ab 100
Factor absent c50 d850 cd 900
Total ac60 bd940 n1000

• OR ad/bc
• SE(OR) eSqrt (1/a1/b1/c1/d)
• CI OR exp ( Z 1-a/2eSqrt (1/a1/b1/c1/d))

50
Binary exposure and categorical covariate

• Stratified analysis
• Calculate OR for each strata
• Mantel-Haenszel summary odds ratio
• å aidi/ni
• -------------------
• å bici/ni
• Logistic regression

51
Binary exposure with continuous covariate

• Consider height as covariate in a study where
  exposure is diabetes and outcome is MI
• Use of stratified analysis
• Huge number of strata
• Many of these strata will have zero frequency
• Solutions
• Form limited number of categories loss of some
  information
• Logistic regression misclassifying the
  functional form of relationship between covariate
  and outcome

52
Continuous exposure without covariate

• Divide the exposure variable in small number of
  categories
• For example, quintiles
• Logistic regression
• Good for assessing dose-response

53
Continuous exposure with covariates

• Categorize the exposure
• MH estimator
• Regression technique

54
Matched data
Exposure to fumes Headache present Headache absent Total
Factor present A B AB
Factor absent C D CD
Total AC BD ABCD

• A,B,C,D are number of pairs
• OR B/C
• SE (OR) e Sqrt (1/B1/C)
• CI OR exp ( Z 1-a/2Sqrt (1/B1/C))
• Association by McNemars c2 test (B-C)2/(BC)
• Regression Conditional regression

55
INTERPRETATION
OR1, ORlt1, ORgt1
OR Range Interpretation 0.0 - 0.3 Strong
Benefit 0.4 - 0.5 Moderate Benefit 0.6 -
0.8 Weak Benefit 0.9 - 1.1 No Effect 1.2 -
1.6 Weak Hazard 1.7 - 2.5 Moderate Hazard gt
2.6 Strong Hazard
56
Identification of confounding variables

• Statistically testing for association of
  potential confounder with disease and with
  exposure
• If crude OR differs from adjusted OR by a
  specified percentage points (15 or less), then
  the variable is regarded as confounder

57
Attributable risk estimation

• Also known as
• Etiologic fraction
• Excess fraction
• Population attributable risk percent
• Provides an estimate of proportion of cases that
  are related to a given exposure
• It is fraction of disease in population that
  might be avoided by eliminating the exposure to
  an etiologic agent
• It takes number of exposed individuals in the
  population into account
• Provides important information for public health
  action

58
ARP

• ARP (OR-1)/OR
• The proportion of total disease risk in exposed
  persons which may be attributed to their
  exposure.
• ORgt 1, ARP Range 0 - 1.
• OR 1, ARP 0
• If the OR is very large, much of the total
  disease risk in exposed persons may result from
  that exposure.

59
PARP

• Cole and MacMahon (1971)
• PARP p0(OR-1) / 1p0(OR-1)
• Taylor (1977)
• PARP 1-b(cd) /d(ab)
• Corresponds to the proportion of disease risk in
  all persons which may be attributed to the
  exposure under investigation.

60
EXAMPLE

• Intrauterine Irradiation Childhood Lukaemia
• OR 1.48
• ARP (1.48 -1) / 1.48 0.32
• One third of lukaemia in the irradiated children
  may be attributed in part to prenatal
  irradiation.
• PARP1-70 (45155) / 155(3070)1-0.90 0.10
• 10 of all childhood lukaemia may be attributed
  in part to intrauterine irradiation.

61
Example.
  HT NHT Total
HF diet 190 110  300
LF diet 90 220  310
  280   330 610
4.22    
3.46   5.18
62
Stratified analysis
    HT NHT Total
Male HF diet 130 90  
  LF diet 70 120  
        410
Female HF diet 60 20  
  LF diet 20 100  
        200
2.48  
2.03 3.04
15.00  
12.28 18.39
3.92  
63
Exercise on matched dataExercise using epi_info
64
Bias

• Selection
• Misclassification
• Differential
• Non-differential
• Confounding

65
SELECTION BIAS

• Berksons Paradox
• Neyman Fallacy
• Selective Referral
• Detection Bias
• Non-Response
• Length of Hospital Stay Bias
• Survival Bias

66
BERKSONS PARADOX
Berkson (1946) Hospital samples may
systematically differ from general populations
because of factors which influence the likelihood
of hospitalization. Hospital samples may
exhibit spurious associations between two
variables, even though these variables are
independently distributed in the general
population.
67
BERKSONS PARADOX

• Roberts Co-workers(1978)
• Respiratory and bone diseases
• OR (General Population) 1.06
• OR (Hospital Sample) 4.06
• Distorted medication-diseases associations
• Laxative use and arthritic disease
• OR (General Population) 1.48
• OR (Hospital Sample) 5.00

68
BERKSONS PARADOX

• Walter(1980) Minimization
• The exposure is not a direct cause of
  hospitalization.
• The case and control populations are mutually
  exclusive.

69
NEYMAN FALLACY
Neyman (1955) Prevalent Cases Distorted E-D
Associations If exposure is related to Disease
Prognosis. Ex Sex Colorectal Cancer IRM gtIRF
(Devesa Silverman 1978) SRF Longer (Koch et al
1982) Sample of Prevalent Cases Prop. Of F
Minimization Incident cases (Schesselman 1982)
70
SELECTIVE REFERRAL

• If cases within the population are differentially
  reported to study hospital.
• Tertiary Care Hospital Complicated/Severe D
  Which may differ etiologically from other cases.

NON-RESPONSE

• If enrolled subjects systematically differ from
  non-participants.
• Evaluate Comparability of participants
  non-participants.

71
DETECTION BIAS

• If exposure influences the likelihood of clinical
  recognition of the disease.
• Ex Exogenous Oestrogen- Endometrial Cancer
• E-DA may be partially attributed to preferential
  detection of D in exposed women.
• E -gt Dysfunctional Bleeding -gt Intra-abdominal
  Diagnostic Examination -gt Diagnosis of an
  Asymptomatic D.
• CaCo Studies should attempt to evaluate the
  extent to which exposure brings an otherwise
  asymptomatic cases to clinical detection.

72
LENGTH OF HOSPITAL STAY BIAS

• If cases are selected from a registry of current
  hospital patients, then cases who have been
  hospitalized for the longest period of time have
  a higher probability of being selected than cases
  admitted for minor conditions or cases who died
• These cases may have other diseases and
  conditions that may be related to the disease or
  exposure under study

73
SURVIVAL BIAS

• If only the survivors of the outcome are selected
  as cases and if survival is related to the
  exposure of interest

74
MISCLASSIFICATION BIAS
Non-Differential Misclassification The errors
in classification of one variable (E) do not
depend on the level of the other variable (D).
NDM Errors OR Differential
Misclassification The errors in classification
of one variable (E) depend on the level of the
other variable (D). DM Errors OR
75
NDM ERRORS

• Exposure Specification
• If E is not accurately assessed.
• Unacceptability Bias
• If E is a behaviour or characteristic which
  subjects are inclined to under-report.

76
DM ERRORS

• Recall (Anamnestic Bias)
• Protopathic Bias
• Interview Bias

77
CONFOUNDING BIAS

• If study effect is mixed with another effect.
• Confounder
• Extraneous to E-D association
• Predictive of D
• Unequally distributed between E groups.
• Ex Alcohol Oesophageal Cancer
• Confounder Smoking
• PI - CL - MG Example
• Control Design / Analysis

78
ADVANTAGES

• Easy to carry out
• Rapid (less time consuming)
• Less expensive
• Useful for rare diseases
• Useful for diseases with a long latent interval
• No risk to subjects
• Multiple exposures can be studied
• No attrition problem

79
DISADVANTAGES

• Susceptible for biases
• Selection of controls difficult
• Incidence (thereby RR) can not be calculated
• If disease is relatively common (gt 5 to 10), OR
  may not be reliable estimate of RR
• Other possible effects of exposure can not be
  studied
• Cause Vs Association

80
APPLICATIONS

• Evaluating Vaccine Effectiveness
• Evaluations of Treatment Program Efficacy
• Evaluation of Screening
• Outbreak Investigations
• Indirect Estimation in Demography
• Genetic Epidemiology
• Occupational Health Research
• Predictive Modeling