23 Novl CA- Co Studies

8/2/2019 23 Novl CA- Co Studies

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Dr. P. P. DokeProfessor, Department of Preventive and Social Medicine

MGM Medical College, Kamothe, Navi Mumbai

M.D., DNB., Ph.D., FIPHA


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The case-control study is an analyticepidemiologic research design in which thestudy population consists of groups whoeither have (cases) or do not have a particular

health problem or outcome (controls)

The investigator looks back in time tomeasure exposure of the study subjects. The

exposure is then compared among cases andcontrols to determine if the exposure couldaccount for the health condition of the cases


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Case-Referent

Case-Compeer

Retrospective ?


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Observational / Non-experimental Occasionally Exploratory Explanatory (Analytical)

Retrospective Effect to Cause Both Exposure & Disease have alreadyoccurred

Uses Comparison Group


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Consider some rare disease say some

cancer (leukemia)

Crude Annual Incidence = 3.4/100000 (< 15 years) Cohort Study: A year of observation on a million

children to identify 34 cases

Sample of 34 cases may be available in hospitals :

may be sub-divided in 2 or more exposurecategories

Easy to carry conduct case-control study


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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 hence suitable for

Chronic Diseases (Cancer / Cardiovascular

Diseases)


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RCT: Methodological Standard of ExcellenceHowever,

Case-Control; Not only SIMPLE to performbut 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 Research Question


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1. DirectionalityOutcome to exposure

2. TimingRetrospective for exposure, but case-

ascertainment can be either retrospectiveor concurrent

3. Sampling

Almost always on outcome, withmatching of controls to cases


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Not Exposed Exposed Not Exposed

Disease No Disease

CASES CONTROLS

Exposed


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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)


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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)


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Eligibility Criteria Inclusion/Exclusion criteria Ca-Co studies should be limited to incident cases :

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 atubal ligation are not likely to have recently used anycontraceptive method including IUDs


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Conceptual definition Obesity defined as body fat percentage > 33%

Operational definition Body Mass Index > 30


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Case definition should avoid misclassification For example:

Anemia was defined as Hemoglobin < 110gm/L as measured by WHO Colour Scale

WHO Colour Scale over-estimates thehemoglobin

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 diagnosisis generally imprecise


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A severe case definition may exclude peoplewho have been cured or who died ofdisease before the condition was severeenough to be labelled as case

Standard/consensus definitions if available,must be used For example,

Rheumatoid arthritis Rome criteria, NY criteria, 1987ARC criteria

Lack of agreement over definition may introducevariability in estimates of effect


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The issues of severity, diagnostic criteria andsubjectivity of criteria all lead to potential problemsof misclassification of cases

The researcher can choose between morerestrictive and inclusive definitions

Think in terms of sensitivity and specificity ofdefinition and its effect on validity, sample size,precision and power

It is observed 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)


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Hospitals (Multi-Centric Studies)

Community

Industrial Population


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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 andSn & Sp of test will decide who eventually becomes acase under operational definition

Selection bias ??


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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 usingestrogen


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1. Representativeness:Ideally, cases should be a

random sample of all cases ofinterest in the source population(e.g. from vital data, registry data).More commonly they are a selection

of available cases from a medicalcare facility. (e.g. from hospitals,clinics)


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2. Method of SelectionSelection may be from

incidence or prevalence case:

Incident cases are those derivedfrom ongoing-ascertainment ofcases over time.

Prevalent cases are derivedfrom a cross-sectional survey.


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Who is the best control? What universe

should controls come from?

If cases are a random sample of cases inthe population. Then controls should be a

random sample of all non-cases in the

population sampled at the same time.


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Comparability is more important thanrepresentativeness in the selection of controls

The control should be at risk of the disease The control should resemble the case in all

respects except for the presence of disease(and any as yet undiscovered risk factors for

disease)


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Usually, cases in a case-control

study are not a random sample of allcases in the population. And if so, the

controls must be selected in the same

way (and with the same biases) as the

cases.


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If follows from the above, that a pool ofpotential controls must be defined. This isa universe of people from whom controls

may be selected (study base).

Comparability vs. Representativeness


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1. The study base Source of case and the control should be the same

2. Deconfounding

3. Comparable accuracy Similar misclassification errors in cases & controls Same potential of recall bias in cases & control

4. Efficiency


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Hospital or clinic control Dead control

Controls with similar diseases

Peer or case-nominated (friend/neighbor)control

Population controls


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Readily available hence commonly used Main reasons to use hospital controls are

To select controls whose referral pattern is similarto cases

To obtain similar quality of examination For convenience

May not be representative of the population


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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 withliving controls and noticed that the deadcontrols smoked more cigarettes andconsumed more alcohol than living controls

Appropriateness depends on the exposurebeing studied


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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 ??


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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 standardizedpattern

Friend or neighbor control is a surrogate formatching on age, education, etc A quick way to find control

Bias is introduced if determinants of friendshipare associated with disease or exposure Friends share many risk behaviors


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Randomly drawn from population Truly representative of population

Ideal way of selecting controls

Practically, very difficult to carry out Study base ???


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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


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Statistical consideration When the number of subjects available in one group

(cases) is limited, an increase in the other groupincreases the study power

Gain in power is till the ratio of 4:1 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 toincrease the power


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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 controlgroup

If the measure of effect is similar when comparingcases with each control group Probably no biases (no surety)

If different measure of effect, then the bias is thereand the researcher can understand it


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MATCHING Purpose: To adjust - effects of relevant

confounders

Matching in Design - Accounted in Analysis

Misconception: The goal is to make thecase and control groups similar in allrespects, except for disease status

An Optimal Matching Scheme involves only

those variables which improve statisticalefficiency or eliminate bias from the effectof interest


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MATCHING Which variables are appropriate for matching?

Risk factors from prior work may be identifiedfor matching

Matching by interviewer or hospital may be usedto balance out the effects of interviewer andobserver errors

It is best to limit matching to basic descriptors

(age, sex, socio-economic status, etc) Non-modifiable risk factors

Use few matching factors


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MATCHINGOverzealous matching may have adverse

effects:

Matching on a strong correlate of theexposure, which is not an independent riskfactor for the outcome (overmatching) maylead to an underestimate of OR

Matching may lead to a false sense ofsecurity that a particular variable isadequately controlled


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1. Control selection is usually throughmatching.

Matching variables (e.g. age), andmatching criteria (e.g. within the same 5

year age group) must be set up in advance.


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2.Controls can be individually matched (most

common) or Frequency matched.

Individual matching: search for one (or more)controls who have the required matching

criteria, paired (triplet) matching is when thereis one (two) control (s) individually matched to

each cases.

Frequency matching: select a population ofcontrols such that the overall characteristics ofthe case, e.g. if 15% cases are under age 20,

15% of the controls are also


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3. Avoid over-matching, match only onfactors KNOWN to be cause of the disease.

4. Obtain POWER by matching MORE THANONE CONTROL per case. In general, N ofcontrols should be < 4, because there isno further gain of power above that.

5. Obtain Generalizability by matching bymatching more than one type of control.


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Various soft wares are available


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Questionnaires Records

Conversion tables/algorithms


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Questionnaire Question comprehension Information retrieval Response formulation and recording

Quality of exposure reports may beinfluenced by Type of respondent Administration of questionnaire Salience of exposure Way in which information is retrieved Ways in which responses are formulated and

recorded


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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 ormissing data


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FIRST: Select

CASES CONTROLS(With Disease) (Without Disease)

THEN: Were exposed a b

Measure

Exposure Were not exposed c d

TOTALS a + c b + d

Proportions a bExposed a + c b + d


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ac ad

Odds Ratio = =

b bcd

Risk = a

a + b= c

c + d

a b

c d

Case Control

E+

E-


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Case

Exposed UnexposedExposed Both Mixed

Controls

Unexposed Mixed Neither


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For one control

Case

Exposed UnexposedExposed r s

Controls

Unexposed t u

McNemar 2=(t+s)2/(t-s)


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Stroke Control totalHypertension 30 10 40

No hypertension 70 90 160

Total 100 100 200

30x9010x70

Odds ratio = = 3.86


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CaseHypertension No hypertension Total

Hypertension

Control

No hypertension

2 8 10

28 62 90

Total 30 70 100

McNemar 2=(t+s)2/(t-s) =(28+8)2/(28-8)= 34.61


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Advantages:1. Only realistic study design for

uncovering etiology in rare diseases2. Important in understanding newdiseases3. Commonly used in outbreaks

investigation4. Useful ifinducing period is long5. Relatively inexpensive


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1. Susceptible to bias if not carefully

designed

2. Especially susceptible to exposure

misclassification

3. Especially susceptible to recall bias

4. Restricted to single outcome

5. Incidence rates not usually calculate6. Cannot assess effects ofmatching

variables


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Dolls 1952 study of smoking and lungcancer. The problem was that the controlpopulation ( lung disease) was biased in

relation to the exposure. McMahons 1981 study of coffee and

pancreatic cancer. Problem was that someof the controls may have been biased in

relation to the exposure, because diseasesrelated to coffee were excluded from thecontrol series.


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1950s Cigarette smoking and lung cancer

1970s Diethyl stilbestrol and vaginal

adenocarcinoma

Post-menopausal estrogens and endometrial

cancer


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1980 s Aspirin and Reyes sydrome

Tampon use and toxic shocks syndrome

L-tryptopham and eosinophilia-myalgiasyndrome AIDS and sexual practices1990s Vaccine effectiveness Diet and cancer

Famous Examples and discoveries


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The odds ratio is a good estimate of the

relative risk when the disease is rare

(prevalence 1 controls

Statistical testing is by simple chi-square

(unmatched analysis) or by McNemars

chi- square (matched-pairs analysis) Can be extended to multiple strata

( Mantel-Haenzel chi-square)


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Thank you

23 Novl CA- Co Studies

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