CASE CONTROL STUDY – DESIGN, CONDUCT AND ANALYSIS

Dr. C . RAMESH

ASSOCIATE PROFESSORDEPARTEMENT OF EPIDEMIOLOGY AND BIOSTATISTICS

KIDWAI MEMORIAL INSTITUTE OF ONCOLOGYHOSUR ROAD, BANGALORE

Mobile: 98454 62496

“ Epidemiology is the simplest and most direct method of

studying the causes of disease in humans and many major

contributions have been made by studies that have

demanded nothing more than ability to count, to think

logically & have an imaginative idea ”

-- SIR RICHARD DOLL

EPIDEMIOLOGY

Study of disease & Health in Human Population Pathological Process State of well being - Study of distribution of disease & its Determinants

Descriptive Analytical

Aims of Epidemiologic Research

Describe Explain Predict Control

Health Etiology Disease DistributionStatus occurrence of Disease

Utility of cancer registry data

A well organized and continuing Cancer Registry can serve as an efficient tool for

1. Medical Audit – Hospital Performance

2. Planning Hospital facilities

3. Evaluation of Patient Care

4. Research – Epidemiology, Clinical trials, Prognostic factors, etiology, cancer control

5. Education – Public and Professional

Epidemiological Study Design

Main types of epidemiological study design Intervention (Experimental studies)

– Clinical Trials– Field Trials

• Individual level• Aggregated level (community trials)

Observational (Non-experimental studies)– Cohort studies– Case-control studies– Cross sectional surveys– Routine data-based studies

• Individual level• Aggregated level (ecological studied)

CASE – CONTROL STUDY

Exposed Cases

Unexposed

Exposed

Unexposed

Direction of inquiry

Study Populn

Controls

Case – Control study

1. Directionality Outcome to exposure

2. Timing Retrospective for exposure

Case ascertainment either retrospective or

concurrent

3. Sampling Almost always on outcome with matching controls to

cases

HISTORICAL NOTE

Very few Studies prior to 1920

1. LANE-CLAYPTON (1926) : Reproductive factors in Breast Cancer (Method for selecting matched controls)

2. Cornfield (1951) : Showed that RR can be estimated from either a Case control or cohort

3. Cornfield (1954) : Direct Standardization to control extraneous factors in the Analysis

4. Mantel – Haenzel (1959) : Estimation of RR from Stratified data and gave Chi Square for association

5. Cornfield (1962) : Introduced the multiple logistic function (for analysis of factors related to CHD in Framingham Heart Study)

Case Control Study Important Discoveries

– 1950’s : Cigaratte smoking and lung cancers

– 1970’s : DES and Vaginal adeno Ca.

– 1980’s : Asprin and Rayes Sydrome

: Tampon use and Toxic shock syndrome

: L.tryptophan and eosinophilia – myalgia Syndrome

: AIDs and Sexual Practices

– 1990’s : Vaccine effectiveness

: Diet and Cancers

Case Control Study

ADVANTAGES

Well suited for Rare disease / Long Latency Quick to mount and conduct, inexpensive Requires comparatively few subjects Existing records occasionally be used

– no risk to subjects Allows study of multiple factors

Case Control StudyDISADVANTAGES Relies on Recall records for past exposures Valid information difficult/impossible Control of extraneous factors – incomplete Selection of appropriate comparison group difficult Rates of disease in E & E individuals cannot be Determined. Detailed study of mechanism rarely possible.

Useful in the study of exposures that cannot be randomized for logistic or ethical reasons (ex. Water hardness, alcohol consumption during pregnancy)

Case Control Study

Characteristics of Cases

1. Representativeness

Ideally, cases are random sample of all cases of interest in the source population (eg. Registry data)

Commonly, they are selection of available cases from Hospital

CASE SELECTION

Group of individuals who have disease which is in as far as possible a homogenous etiologic entity

Incident Prevalent Decedant

Usually used Only advantage Occasionally Readily Used in Prelim. Available Study based on Medi. Rec.

Diseases manifested by sudden death

Case Control Study

Selection of Controls

Most important and most difficult task

• No single type suitable for all studies• No firm criteria for an acceptable group

Case Control Study

Characteristics of Control Who is the best control ?

What universe should controls come from ?

If cases are random sample of cases in the popln.,

controls should be a random sample of all non cases

in the popln. Sampled at the same time

Case Control Study

Qualities needed in controls1. Comparability More important than

representative ness

2. Should be at Risk of the disease

3. Resemble case in all respects except for presence of disease (and any as yet undiscovered risk factor for disease)

Case Control Study

No. of Control Group:

1. One control group best suited to needs of particular study

2. II group if I group has known or suspected deficiency which can be offset by II Group

3. Stay within the bounds of 4:1 Small in statistical power as ratio greater than 4

Case Control Study

SOURCE OF CONTROLS

Hospital Patients General Population Restricted Popn. Group (Neighbourhood, associate or Relative of cases)

HOSPITAL CONTROLS

* Readily available may be in hosp. for* Have time to spare & co-operative condition which has* Have ‘mental set’ similar to casesetiological features* Similar to cases w.r.t determinants in common (include

of Hospitalization diff Dignostic Category)

Case Control Study

General Population Control

Comparability when 1. Extremely expensive,Popn. Based cases time consumingselected

2. Often not co-operativeEven with Hosp. cases - Response is poorCausative factors are not 3. Factors may be present Inordinately prevalent leading to seeking Med.Care

Matching

Refers to selection of Comparison series (controls)

Effects on study efficiency Not on validity

Individual Matching Principles - Identical

Frequency Matching Purpose

Control confounding Increase information per obsern.in the post stratification Analysis

Stratification Matching

Excess results in Over matching

Data Analysis

Purpose: 1. Assess random variation 2. Control confounding

3. Evaluation of interaction

Data Editing Data Reduction Effect estimation

- Accuracy - Distn of obsern Testing of Stat. Hyp.- Consistency - Contingency Tables - point estimate- Completeness for key factors - Interval Estimates

Regression Method For M.V.Analysis

Case Control Study – Data Analysis

Depends on the design of the study

Unmatched or frequency matched studies

Individual matched studies

VALIDITY

Lack of Systematic Error

Inference on actual Inference on subjectssubjects outside study Population(Internal Validity) (External Validity)

Many biases * Distinction often difficult

Unmatched (and frequency matched studies)

Exposed Unexposed Total

Cases a b n1

Controls c d no

Total m1 m0

Odds of exposure in the cases = a/b

Odds of exposure in the controls= c/d

Odds (of exposure) ratio = (a/b) / (c/d) = ad / bc

Example for Unmatched Analysis SCHOOLING

Never (E+) Ever (E-) Total

Cx. Cancers 119 317 436Controls 68 319 387Total 187 636 823

O.R = (119/317)/(68/319)= 1.76

Chi.Square = 11.04, p = 0.0009

95 % CI can be estimated using S.E of the logaritham of an odds ratio (OR).

S.E ( in OR) = Sqrt [1 + 1 + 1 + 1 ] a b c d

95 % C.I = (1.24,2.46)

Individual Matched Studies (Paired – Analysis)

Controls

Cases Exposed Unexposed total

Exposed r s aUnexposed t u b

Total c d N/2

N = Total number of Paired individualMatched O.R = s / t (Provided t <> 0)

Example of Matched Study

Controls

Cases Exp. Unexp. Total

Exp. 468 87 555 Unexp. 73 4 77

Total 541 91 632 (N/2)

Matched O.R = 87/73=1.19McNeman’s Chi-Square =1.23, p=0.2795 % , CI=(0.86, 1.65)

Case Control Study

The OR is good estimate for the RR when disease is rare ( prevalence < 20 %)

Can be extended to N > 1 Controls Statistical testing is by simple Chi-square (unmatched

analysis) or by McNemar’s Chi-square (matched pair analysis)

Can be extended to multiple strata using M-H Chi-square.(M-H Chi square gives a weighted average of the OR’s in different strata, where those from larger strata are given more weight)

Regression Modelling

Can be used to adjust for the effect of confounders Dependent (out come variable) =

Function [Independent (explanatory variable)] Main advantage – It does not require us to define,

which independent variable is the exposure and which ones are the potential confounders, since all independent variables are treated in the same way

Case Control Study

Logistic Regression: Suitable for unmatched (frequency matched) case control studies.

Conditional L.R : suitable for individually matched Case Control Studies

Adjusted Vs. Crude RR

Stratum 1 Stratum 2 Crude

C & No Intn. 1.02 3.00 4.00

No C & No intn. 1.83 1.83 1.83

No C & Intn. 0.82 0.85 4.00

Strong Intn. But 1.10 9.00 4.00

C.irrelevant

COST OF CASE CONTROL STUDIES

Data Collection Phase - 75 % of the total cost of study

Planning - 10 %Analysis - 15 %

Evaluation of an optimal allocation procedure based on the relative cost of Cases and Controls reduced the total

study cost by at most 2%

(ERICA BRITTAIN ET.AL, AJE)

Case Control StudyCONCLUSION

RETROSPECTIVE CASE CONTROL STUDY

Important Research Strategy commonly encountered in Medical literature

When thoughtfully designed, carefully executed - Provides important clinical information

“Backward Logic” accompanied by several methodological hazards.

Conflicting or incorrect conclusions - Directly attributed to methodological deficiencies.

Case Control Study

Difference between Bias and ConfoundingBias creates an association that is not true,

confounding describes an association that is true but potentially misleading

GOOD STUDY DESIGN PROTECTS AGAINST ALL FORMS OF ERROR

Thank you