CASE CONTROL STUDY

Presented by : Dr. Radhika ManiyarPost Graduate student Public Health DentistryM. R. Ambedkar Dental College

Contents :

• Introduction• Aims of epidemiology• Epidemiologic methods• Definition of case control study• Framework of the study• Basic steps of the study• The three principles• Selection of cases & controls• Control sampling strategies• Matching• Measurement of exposure• Analysis• Variants of case control study• Bias in case control study• Advantages & disadvantages

INTRODUCTION• Epidemiology is the basic science of preventive &

social medicine.• It has evolved rapidly during the past few decades.• Its ramifications cover not only study of disease

distribution & causation (thereby prevention), but also health and health- related events occuring in human population.

• Epidemiology is derived from the word epidemic, epi = among; demos = people; logos = study.

• The study of distribution and determinants of the health related events in specified population and application of this study to control of health problems (by John M. Last)

• A science concerned with the study of factors influencing the occurrence and distribution of disease defect disability or death in a group of individuals. (by Clarke EG)

AIMS OF EPIDEMIOLOGY• To describe the distribution and magnitude of health

and disease problems in human population• To identify the etiological factors In the pathogenesis. • To provide data essential to the planning,

implementation and evaluation of services for prevention, control and treatment of diseases, and to the setting up of priorities among those services

EPIDEMIOLOGIC METHODS

DESCRIPTIVE EPIDEMIOLOGY• Usually the first phase of an epidemiologic

investigation.• Concerned with observing the distribution of disease

or health- related characteristics in human populations & identifying the characteristics with which the disease in question seems to be associated.

• Describing the disease by time, place & person.

ANALYTICAL EPIDEMIOLOGY• Second major type of epidemiological studies.• The object is to test the hypothesis.• One can determine : whether or not a statistical

association exists between a disease & a suspected factor and if exists, the strength of association.

Case control study Cohort study

CASE CONTROL STUDY

• Also called as “retrospective study”.• It is a common first approach to test causal

hypothesis.• It possesses three distinct features:1. Both exposure and outcome (disease) have occurred

before the start of the study.2. The study proceeds backwards from effect to cause.3. It uses a control or comparison group to support or

refute an inference.

DEFINITION

• The observational epidemiologic study of persons with the disease of interest and a suitable control group of persons without the disease. The relationship of an attribute to the disease is examined by comparing the diseased and non-diseased with regard to how frequently the attribute is present.

• John M. Last, Dictionary of Epidemiology

• It involves two populations- cases and controls.

• Unit is the individual rather than the group.

• The focus is on the disease or some other health

problem that has already developed.

• They are commonly referred to as “comparison

studies.”

FRAMEWORK OF THE STUDYSuspected or risk

factorsCase

(disease present)Control

(disease absent)

present a b

absent c d

a + c b + d

BASIC STEPS IN THE STUDY

SELECTION OF CASES & CONTROLS

MATCHING MEASUREMENT OF EXPOSURE

ANALYSIS & INTERPRETATIO

N

THREE PRINCIPLES :

• The three principles in case control designs:1. The study base principle2. The deconfounding principle3. The comparable accuracy principle

The concept of the “study base”• Definitions of the “study base” concept (first introduced by

Olli Miettinen)• The aggregate of total population-time in which cases occur• The members of the underlying cohort or source

population** (from which the cases are drawn) during the time period when cases are identified

• **The source population may be defined directly, as a matter of defining its membership criteria; or the definition may be indirect, as the catchment population of a defined way of identifying cases of the illness. [Source: Miettinen OS, 2007]

The study base principle• The study base principle goal is to sample controls

from the study base in which the cases arose• Controls serve as the proxy for the complete study

base• Controls should be representative of the person-time

distribution of exposure (exposure prevalence) in the study base (i.e. be representative of the study base)

• Controls should be selected independent of the exposure.

Types of study base: primary

Primary study base :• The base is defined by the population experience

that the investigator wishes to target• The cases are subjects within the base who develop

disease• Generally implies that all cases are identifiable

(although not all are necessarily used)

Types of study base: secondary

• Secondary study base :• Cases are defined before the study base is identified• The study base then is defined as the source of the

cases; controls are people who would have been recognized as cases if they had developed disease

The deconfounding principle

• The study base principle guides the selection of who can be entered into the study

• The deconfounding principle deals with the problems created when the exposure of interest is associated with other possible risk factors. These other risk factors are unmeasured since measured confounders could be handled in the analysis.

• Confounders in one study base may not necessarily be confounders in another study base

• Confounding by a factor is (theoretically) eliminated by eliminating variability in that factor.

• For example, if gender is a possible confounder, selecting only men or only women completely eliminates the variability of gender.

The comparable accuracy principle• Comparable accuracy principle : The accuracy of the measurement

of the exposure of interest in the cases should be the same as that in the controls

• Example: in a study of the effect of smoking on lung cancer it would not be appropriate to measure smoking with urine nicotine levels in the cases and with questionnaires in the controls

• Example: in a study of a fatal disease, it is suspect to measure an exposure by questioning the relatives of diseased cases but questioning the actual controls

• Bias caused by differential errors in the measurement of cases and controls should be eliminated (e.g. use the same measurement tools in the same way for cases and controls).

• Summary• If the principles of study base comparability,

deconfounding, and comparable accuracy are followed, then any effect detected in a study should (hopefully!) not be due to:

• Differences in the ways cases and controls are selected from the base (selection bias)

• Distortion of the true effect by unmeasured confounders (confounding bias)

• Differences in the accuracy of the information from cases and controls (information bias)

SELECTION OF CASES & CONTROLS1. Selection of cases: it involves defining the case and

determining the source of cases.

• DIAGNOSTIC CRITERIA : As the cause and effect has already occurred, the proper diagnosis of the disease under investigation is necessary in the selection of a case. Once the diagnostic criteria is established, it should not be changed or altered till the end of the investigation.

• ELIGIBILITY CRITERIA- Also, the eligibility of the case precludes that the

diagnosed disease should be fresh (new) within a specified period of time.

• Old or advanced stages of the disease should not be used (prevalent cases).

• Incident cases are preferable to prevalent cases for reducing (a) recall bias and (b) over-representation of cases of long duration

Source of the cases Comes from two sites: hospitals and general population.

• Hospitals provide a convenient way to categorize the population and the sample can be drawn.

• In general population, all the cases of the study disease have to be necessarily within the same geographic area. The entire population or a sample can be drawn from it.

2. Selection of controls:

• Controls should be selected from the same population -the source population (i.e. study base) -that gives rise to the study cases. If this rule cannot be followed, there needs to be solid evidence that the population supplying controls has an exposure distribution identical to that of the population that is the source of cases, which is a very stringent demand that is rarely demonstrable.

• Controls should be selected independently of their exposure status, in that the sampling rate for controls should not vary with exposure.

Control sampling strategies• Cumulative sampling (i.e. traditional case-control design):

from those who do not develop the outcome until the end of the study period (i.e. from the “survivors” or prevalent cases)

• Case-cohort design(case-base; case-referent) sampling: from the entire cohort at baseline (start of the follow-up period; when cohort is established)

• Incidence density case control design (risk-set sampling): throughout the course of the study, from individuals at risk (“risk-set”) at the time each case is diagnosed

Cumulative sampling case control study

Case-cohort study

• Selection of cases :• Because of the cohort nature of this design, it should be

possible to include all the cases (or an appropriate random sample of them)

• Selection of controls :• All or random sample from among those in the baseline

cohort.• Same set of controls can be used for several case-control

studies (for various outcomes)

• This does include some who later become cases

Density case control studies

INCIDENCE DENSITY CASE CONTROL STUDY

• Select one or more controls from disease-free (at risk) members of the source cohort at the ‘instantaneous’ time at which each case occurs.

• The probability of control selection is proportional to the total person-time at risk.

• Establish the source cohort and identify cases• Determine the date on which the first case occurred• Identify all cohort members (including cases) who were

disease free (at risk) at that date (risk set)• Randomly select one (or more) controls from the risk set.• Repeat steps 1-3 for 2nd, 3rd, .... last case.

• Source of controls :

• Population controls• Hospital or disease registry controls• Controls from a medical practice• Friend controls• Relative controls

• Population control :

• When a population roster (sampling frame) is available, the selection of population controls is simplest.

• Census lists• Birth certificates• Electoral rolls • Some possible approaches when no roster is available:• Random digit dialing• Neighborhood controls

• Advantages and disadvantages of population controls

• Neighborhood and Friend Controls. • For the former method, a census is taken of all households in the

immediate geographic area of the case and these are approached in a random order until a suitable control is found.

• Care must be taken to ensure that the control was resident at the same time the case was diagnosed. Even with these precautions, neighborhood sampling may yield biased controls for hospital based studies since it will not be guaranteed that the control would have been ascertained as a case if ill, thus violating the study-base principle (Wacholder et al. 1992b).

• Neighborhood controls are also susceptible to overmatching due to their similarity to the cases on factors associated with exposure that are not risk factors for disease.

• These same difficulties confront the use of friend controls, whereby a random selection is taken from among a census of friends provided by each case.

• The primary advantage of friend controls would be a low level of nonresponse.

Relative controls

If cases are dead, what about controls?• Main argument for choosing dead controls is to enhance

comparability• Dead people are not in the study base for cases, since

death will preclude the occurrence of any further disease• Choosing dead controls may misrepresent the exposure

distribution in the study base if the exposure causes or prevents death in a substantial number of people

• If live controls are used for dead cases, then proxy respondents can be used for live controls as well

How Many Controls per Case?How Many Control Groups?• For a fixed number of study subjects, statistical power

for testing the null hypothesis is optimized by having equal numbers of cases and controls.

• With a fixed number of cases, the proportion of the maximum precision (unlimited controls) that is reached is approximately: r/(r+1), where r is the ratio of controls to cases

• If r = 4 (4:1 matching) precision is 4/(4+1) = 0.80• > 4 controls per case of little additional statistical

value

• multiple control groups were recommended by Dorn (1959) to improve the case control study so that it would “provide a more valid basis for generalization”.

• As explained by Hill (1971) “If a whole series of control groups, e.g., of patients with different diseases, gives much the same answer and only the one affected group differs, the evidence is clearly much stronger than if the affected group differs from merely one other group.”

• Similar informal arguments have been put forward in favor of multiple control groups as a means of addressing the possible biases that may be associated with the use of any one of them (Ibrahim and Spitzer 1979).

MATCHING• An important consideration is to ensure comparability

within the cases and controls. This involves the process of “matching”.

• Matching is defined as the process by which we select controls in such a way that they are similar to cases with regard to certain pertinent selected variables which are known to influence the outcome of disease and which if not adequately matched for comparability, could distort or confound the results.

• The term “confounding factor” is defined as one which is associated with both exposure and disease, and is distributed unequally in study and control groups.

• More specifically, a confounding factor is one that although associated with exposure under investigation is itself, independently of any such association, a risk factor for the disease.

• Let us suppose that we are interested in examining the relationship between current use of oral contraceptives and ovarian cancer.

• In this example, it is appropriate to match on age, since age is associated with the exposure of interest (current oral contraceptive use) and is an independent risk factor for ovarian cancer. In other words, age is a confounding factor.

• Failure to match, or otherwise control, for age would result in a biased assessment of the effect of oral contraceptive use.

Problems with Matching

Practical problems with Matching :• If matching done for too

many characteristics, difficult or impossible to find an appropriate control.

Conceptual problem :• Once matched controls

to cases according to a given characteristic, we cannot study that characteristic.

• We do not match on any variable that we may wish to explore in our study.

• Overmatching • Overmatching refers to matching on a factor that is not a

confounder of the disease exposure association.• The most serious type of overmatching occurs when one

matches on a factor that is both affected by exposure and a cause of disease.

• E.g. If the effect of anti-hypertensive medication on the risk of myocardial infarction was being investigated, for example, yet cases and controls were matched on blood pressure measurements taken after treatment commenced, the data would be completely useless for estimation of treatment effect

• Ignoring the matching in the analysis would only compound the error by driving the odds ratio even closer towards unity.

MATCHING PROCEDURES• Matching may be of two types :• Group matching : consists of selecting the controls in

such a manner that the proportion of controls with a certain characteristic is identical to the proportion of cases with the same characteristic.

• Individual matching : in this approach, for each case selected, a control is selected who is similar to the case in terms of the specific variables of concern.

MEASUREMENT OF EXPOSURE• Information about the exposure should be obtained

from both the cases and controls in the same manner.• This may be achieved by:1. Interviews2. Questionnaires3. Studying past records like hospital or employment

records etc.

ANALYSIS• This is the final step in a case control study, and it

provides:1. Exposure rates among cases and controls to the

suspected factor and2. Estimation of disease risk associated with exposure.

EXPOSURE RATES• A case control study provides a direct estimation of

the exposure rates to a suspected factor in disease and non disease groups.

• The significance of measuring the exposure rates lies in estimating the probability of associating the disease and the factor under study

Exposure Rates:Cases = a/(a+c) =33/35 =94.2%Controls =b/(b+d) =55/82= 67.0%

P<0.001 ( highly significant)

CasesLung Cancer

Present

ControlsLung Cancer

AbsentSmoking

(less than 5 Cigarettes a day)

33(a)

55(b)

Non Smokers 2(c)

27(d)

35 (a+c)

82(b+d)

ESTIMATION OF RISK• The second analytical step is estimation of disease risk

associated with exposure.

• It should be noted that if the exposure rate was 94.2% in study group, it does not mean that 94.2% of those smoked would develop lung cancer

• Estimation of risk: it is obtained by an index termed as “relative risk” or “risk ratio”, which is defined as the probability of an event(developing a disease) occurring in exposed people compared to the probability of the event in non-exposed people, or the as the ratio of the two probabilities.

• Relative risk = risk in exposed / risk in non-exposed.

• As obviously, the case control study does not provides actual incidence from which relative risk can be calculated directly, because there is no appropriate denominator or population at risk, to calculate these rates.

• In general, the relative risk can be exactly determined only from a cohort study.

ODDS RATIO• It is a measure of the strength of the association

between risk factor and its outcome. It is closely related to relative risk.

• The determination of odds ratio is based on 3 assumptions:

1. The disease to be investigated must be relatively rare or a chronic disease.

2. The cases must be representative of those with the disease and

3. The controls must be representative of those without the disease

CONCEPT OF ODDS• Probability of winning (p) = 60%• Probability of losing (1-p) = 40%• Odds of winning = probability of winning /

probability of losing = 60 / 40 = 1.5:1

Odds of an event can be defined as the ratio of the number of ways the event can occur to the number of ways the event cannot occur.

• Odds Ratio (OR) • Compares the odds of exposure among those with

disease to the odds of exposure among those without the disease.

• Does not compare the incidence of disease between groups.

Cases (with disease) Controls (without disease)

Exposed a b

Not exposed c d

Total a + c b + d

Proportions exposed a/a + c b/ b + d

Odds of a case being exposed = a:c or a/cOdds of a control being exposed = b:d or b/d

Odds ratio = odds that cases were exposed odds that controls were exposed = ad/bc

• Interpretation of the Odds Ratio• OR = 1: no association between outcome and exposure

(same odds of exposure in cases and controls = same odds of disease in exposed vs. unexposed)

• OR >1: exposure is associated with increased risk for outcome (greater odds of exposure in cases than controls = greater odds of disease in exposed vs. unexposed) Harmful Effect

• OR <1: exposure is associated with reduced risk for outcome (lower odds of exposure in cases than controls = lower odds of disease in exposed vs. unexposed) Protective Effect

• Always consider the confidence interval!

VARIANTS OF CASE CONTROL STUDY• Nested case control studies• Case cohort studies• Density case control studies• Cumulative (“epidemic”) case control studies• Case-only, case-specular, & case-crossover studies• Two- stage sampling• Case control studies with prevalent cases.

Nested case control study• It is a hybrid design in which a case control study is

nested in a cohort study.• In this type of study, a population is identified and

followed over time.• At the time population is identified, baseline data are

obtained from interviews, blood tests and other ways.• The population is then followed for a period of years.• For most of the diseases that are studied, a small

percentage of study participants manifest the disease, whereas most do not.

• A case control study is then carried out using persons in whom the disease developed(cases) and a sample of those in whom the disease did not develop(controls).

• Advantages :• Recall bias is eliminated.• We know that risk factor has preceded the disease.• economical

Case only study• There are a number of situations in which cases are the

only subjects used to estimate or test hypotheses about effects.

• E.g. it is sometimes possible to employ theoretical considerations to construct a prior distribution of exposure in source population & use this distribution in place of an observed control series.

• Such situations arise naturally in genetic studies, in which basic laws of inheritance may be combined with certain assumptions to derive a population or parental specific distribution of genotypes.

Case specular study• A type of case only study that obtains the actual

distribution of exposure among the dwellings of the cases & a reflected or ‘specular’ exposure distribution which is what the exposure distribution would have been if the dwellings had been placed on the opposite side of the street.

• From these two distributions (i.e. the actual & specular distribution) & including the rare disease assumption, a relative risk estimate for the effect of exposure can be calculated.

Case crossover study• This design is useful when the risk factor/exposure is

transient. • Each case serves as its own control, i.e the study is self

matched. For each person, there is a 'case window', the period of time during which the person was a case, and a 'control window', a period time associated with not being a case.

• Risk exposure during the case window is compared to risk exposure during the control window.

• Advantages of Case crossover• Efficient – self matching• Efficient – select only cases• Can use multiple control windows for one case window• Disadvantages of Case crossover• Information bias – inaccurate recall of exposure during

control window (can be overcome by choosing control window to occur after case window)

• Requires careful selection of time period during which the control window occurs (circumstance associated with the control window should be similar to circumstances associated with case window; e.g., traffic volume)

• Requires careful selection of the length and timing of the windows

BIAS IN CASE CONTROL STUDY

• The concept of bias is the lack of internal validity or incorrect assessment of the association between an exposure and an effect in the target population.

• Confounding bias• Selection bias• Information bias

Confounding bias • Confounding bias: non matching of cases and controls

create this bias. It can be eliminated by careful matching of both the groups.

Selection bias• The error introduced when the study population does

not represent the target population.• It can be introduced at any stage of a research study

design : 1. Inappropriate definition of the eligible population,2. lack of accuracy of sampling frame, 3. Uneven diagnostic procedures in the target population4. implementation.

Inappropriate definition of the eligible population• Ascertainment bias : • It is produced when the kind of patients gathered does not represent

the cases originated in the population.• Healthcare access bias: • when the patients admitted to an institution do not represent the

cases originated in the community. This may be due: to the own institution if admission is determined by the interest of health personnel on certain kind of cases (popularity bias), to the patients if they are attracted by the prestige of certain clinicians (centripetal bias), to the healthcare organisation if it is organised in increasing levels of complexity (primary, secondary, and tertiary care) and ‘‘difficult’’ cases are referred to tertiary care (referral filter bias), to a web of causes if patients by cultural, geographical, or economic reasons show a differential degree of access to an institution (diagnostic/treatment access bias)

• Neyman bias: (synonyms: incidence-prevalence bias, selective survival bias) when a series of survivors is selected, if the exposure is related to prognostic factors, or the exposure itself is a prognostic determinant, the sample of cases offers a distorted frequency of the exposure.

• Lets suppose that a case-control study is carried out to study the relation between tobacco smoking and acute myocardial infarction (AMI), being cases interviewed one week after the coronary attack. If smoker patients with AMI die more frequently, the leaving cases will show lower frequency of smoking, undervaluing the association between smoking and AMI.

• It has been shown that the bias occurs only if the risk factor influences mortality from the disease being studied

• Inclusion bias: produced in hospital based case-control studies when one or more conditions of controls are related with the exposure. The frequency of exposure is higher than expected in the reference group, producing a toward the null bias.

• Exclusion bias: when controls with conditions related to the exposure are excluded, whereas cases with these diseases as comorbidities are kept in the study. This was the explanation given for the association between reserpine and breast cancer: controls with cardiovascular disease (a common comorbidity and related to the use of reserpine) were excluded but this criterion was not applied to cases, thus yielding a spurious association between reserpine and breast cancer.

• Berkesonian bias: termed after Dr. Joseph Berkson who recognized this problem. It arises due to the different rates of admission to hospitals for peoples with different diseases, leading to bias in cases and control selection.

• Detection bias :if exposure influences the diagnosis of the disease, detection bias occurs. Particular types of this bias are:

• exposure can be taken as another diagnostic criterion (diagnostic suspicion bias).

• Exposure can trigger the search for the disease; for instance, benign anal lesions increases the diagnosis of anal cancer.

• Exposure may produce a symptom/ sign that favours diagnosis (unmasking-detection signal-bias) or a benign condition close clinically to the disease (mimicry bias)

Information bias• Information bias occurs during data collection.• Misclassification bias : It is originated when sensitivity

and/or specificity of the procedure to detect exposure and/or effect is not perfect, that is, exposed/diseased subjects can be classified as nonexposed/ non-diseased and vice versa. Given that perfect tools to gather data are very uncommon most studies must assume a certain degree of misclassification. Random error also can produce it. This implies that random errors in data entry/capture, missing data, end digit preference (rounding to 5 or 0), frequently unavoidable, also introduce misclassification.

• There are two major types of misclassification bias:• Differential misclassification bias: when

misclassification is different in the groups to be compared; for example, in a case-control study the recalled exposure is not the same for cases and controls.

• Non-differential misclassification bias: when the misclassification is the same across the groups to be compared, for example, exposure is equally misclassified in cases and controls.

• The most common biases producing misclassification are:

• Observer/interviewer bias: the knowledge of the hypothesis, the disease status, or the exposure status (including the intervention received) can influence data recording (observer expectation bias). The means by which interviewers can introduce error into a questionnaire include administering the interview or helping the respondents in different ways (even with gestures), putting emphases in different questions, and so on. A particular situation is when the measure of an exposure influences its value (for example, blood pressure) (apprehension bias).

• Recall bias: if the presence of disease influences the perception of its causes (rumination bias) or the search for exposure to the putative cause (exposure suspicion bias), or in a trial if the patient knows what they receive may influence their answers (participant expectation bias). This bias is more common in case-control studies, in which participants know their diseases.

Advantages 1. Relatively easy to carry out.2. Rapid and inexpensive.3. Require comparatively few subjects.4. Particularly suitable to investigate rare diseases.5. No risk to subjects.6. Allows study of different etiological factors.7. Risk factors can be identified.8. No attrition problems, because case control do not

require follow up of individuals into future.9. Ethical problems are minimal.

Disadvantages 1. Problems of bias relies on memory or past records, the

accuracy of which may be uncertain; validation of information obtained is difficult or sometimes impossible.

2. Selection of an appropriate control group may be difficult.3. We cannot measure incidence, and can only estimate the

relative risk.4. Do not distinguish between causes and associated factors.5. Not suited to the evaluation of therapy or prophylaxis of

disease.6. Another major concern is the representativeness of cases and controls.

• Be aware that the term case-control study is frequently misused. All studies which contain cases and controls are not case-control studies. One may start with a group of people with a known exposure and a comparison group (control group) without the exposure and follow them through time to see what outcomes result, but this doe not constitute a case control study.

• Case-control studies are sometimes less valued for being retrospective. However, they can be a very efficient way of identifying an association between an exposure and an outcome.

• Sometimes they are the only ethical way to investigate an association. If care is taken with definitions, selection of controls, and reducing the potential for bias, case-control studies can generate valuable information.

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19th ed. Bhanot publishers,65-75.2. Gordis L. Epidemiology 1st ed. W.B. Saunders

company,114 – 1673. Rothman et al. Modern Epidemiology. 3rd edition,

111-1284. Schulz et al . Case control studies: research in

reverse Lancet 2002; 359 : 431-355. Miguel R, Javier L. Bias, J Epidemiol Community

Health 2004;58:635–641.