Three main points to be covered

• Nature, weakness, and (sometime) strength of studies using group-level observations

• Cohort study as gold standard and its assumptions and limitations

• Concept of the study base linking case-control design to the cohort design

Studies making observations on groups of individuals vs. individuals

• Studies using group level data are usually called ecological studies

• Two main weaknesses:– ecological fallacy– very limited control of confounding

• One (sometime) strength:– some exposures may be best measured at area

or group level

Example from Szklo and Nieto of grouped datafrom cohorts in the Seven Countries Study

Ecological Fallacy

• Cannot tell whether the relationship between the predictor and the outcome at the group level holds at the individual level

• In this example: Are the individuals in the cohorts eating more saturated fat the same individuals experiencing more CHD deaths?

• Sometimes called confounding at the group level

Confounding in group data

• If no ecological fallacy, still left with possible confounding: some 3rd variable causing increase in CHD deaths and also related to consumption of fat (eg, exercise)

• Difficult to control for because measures may not be available

• Even if data available, don’t know relationship of confounding variable to other two variables at individual level

Example of the potential strength of measures at group level: Effect of Floods

in Bangladesh in 1988 on Children

• Children 2 - 9 years samples 6 months before flood and 5 months after

• Outcomes: Enuresis and aggressive behavior• Individual level predictor: individual danger of

drowning• No association seen at individual level• At group level, before and after flood

comparison showed significant difference

Situations where group level variables may be better

• Exposures without much within group variability or a threshold effect (eg, salt consumption in U.S.)

• Herd immunity in studying infectious disease (vaccination levels may be more informative than individual behavior)

• Exposures that have powerful effects at group level (Bangladesh flood -- may also be example of a threshold effect)

Ecological Studies: Summary

• As text emphasizes, common view that they are only hypothesis-generating is inadequate

• Weakest design for establishing causality but has a role because inexpensive and easy to do

• For some situations and kinds of data may actually be superior

• Some variables can only be measured at group level (policies and laws, environment)

Cohort Study Design

• Mimics individual’s progress through life and accompanying disease risk

• Gold standard because exposure/risk factor is observed before the outcome occurs

• Randomized trial is a cohort design with exposure assigned rather than observed

• Other study designs can be understood by how they sample the experience of a cohort

Cohort study designcensored observations = losses to follow-up

Minimum loss to follow-up (1%)

Time of Cohort Follow-up vs. Time when measurements made

• Concurrent cohorts give most control because measurements are made at the same time as cohort assembly and follow-up (most texts call these prospective cohorts)

• Non-concurrent cohorts rely on obtaining measurements made in the past (most texts call these retrospective cohorts)

• Mixed cohorts obtain some measures made in the past and rest at same time as follow-up

Selecting a non-concurrent cohort from a current administrative data base

• Not a cohort study if you sample persons currently in the data base in order to insure retrospective data from past years– cross-sectional sample – no loss to follow-up by definition

• Must sample individuals from some baseline in the past in the data base– ascertain outcome, losses to follow-up from that

time forward

Non-concurrent cohort study cannot be defined by presence at end of follow-up

Not thecohort

This is thecohort

Main Threats to Validity of a Cohort Study

• Subjects lost during follow-up

– Number of losses is less important than how losses are related to outcome and risk factor

• Ascertainment of outcome– Ascertainment should be complete and unbiased with respect to

risk factor

Subjects lost during follow-up

• If losses are random, only power is affected

• If disease incidence important, losses related to outcome bias results

• If association of risk factor to disease is focus, losses bias results only if related to both outcome and the risk factor

• If losses introduce bias in the outcome, the censoring is called informative censoring

Crucial issue is who is leaving cohort: what bias do thecensored observations (losses to follow-up) introduce?

Same issue with ascertainment of outcome events.

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Two Cohort Studies of HCV/HIV Coinfection and Risk of AIDS

• Swiss HIV Cohort• 3111 patients, ‘96-’99• At least two visits• Med. follow-up 28 mos• HCV+ more rapid disease

progression• Adj RH = 1.7 (95% CI =

1.3 - 2.3)• No loss to follow-up info (Greub, Lancet, 2000)

• Johns Hopkins Cohort• 1955 patients, ‘95-’01• At least two visits• Med. follow-up 25 mos• HCV not associated with

disease progression• Adj RH = 1.0 (95% CI =

0.9 - 1.2)• No loss to follow-up info (Sulkowski, JAMA, 2002)

Case-Control Design: Concept of the Study Base

• Study Base = the population that gave rise to the cases (Szklo and Nieto call it the “reference population”)

• Key concept that shows the link between case-control design and cohort design

• Case-control design using the study base concept is most easily understood in the setting of a cohort study

Nested Case-Control Study within a Cohort Study Study Base = Cohort

Controls Sampled each time a Case is diagnosed = Incidence Density

Nested Case-control Study• In text example, 4 cases occur at 4 different

points in time giving rise to 4 risk sets of cases and controls

• Controls for each case are selected at random in each risk set from cohort subjects under follow-up at the time

• It follows from the random selection, that a control can later become a case

• Results can be just as valid as using entire cohort; gives unbiased estimate of rate ratio

Definition of a Primary Study Base

• Primary Study Base = population that gives rise to cases that can be defined before cases appear by a geographical area or some other identifiable entity like a health delivery system

• Nested within a cohort is a special case

Examples of Primary Study Bases

• Residents of San Francisco during 2001

• Members of the Kaiser Permanente system in the Bay Area during 2001

• Military personnel stationed at California bases during 2001

Example of Case-Control Incidence Density Sampling in a

Primary Study Base

• Use cancer registry covering San Francisco County to identify all new cases of glioma during a defined time period

• At time each new glioma case is reported, randomly sample two controls from current residents of San Francisco

Incidence Density Sampling in a Primary Study Base (e.g., San Francisco County)

New residents

Nested case-control in an open cohort with new subjects entering

PrimaryStudyBase

Case-Control Incidence Density Sampling in a Primary Study Base

• Same as nested case-control sampling in a cohort study with exception that in-migration of new persons requires one additional assumption

• Just as losses to the study base should not bias the results, additions to the study base should not introduce bias

Case-Based Case-Control Study: The Secondary Study Base

• Secondary Study Base = population that gave rise to cases, identified after cases diagnosed; those persons who would have been among the cases if they had developed the disease during the time period of study

• Start with a cases and then attempt to identify hypothetical cohort that gave rise to them

Primary vs. Secondary Base

• Main problem with a primary base is often ascertainment of all cases

• Main problem with a secondary base is the definition of the base

Case-Based Case Control Studies and the Secondary Study Base

• Source of cases is often one or more hospitals or other medical facilities

• Problem is identifying the population who would come to those institutions if they were diagnosed with the disease

• Careful consideration has to be given to factors causing someone to show up at that institution with that diagnosis

Case-control study starting with a sampleof cases and identifying secondary study base

Sampling can be incidence density just as in primary study base

Secondarystudy base

Case-Based Case Control Studies

• Example: glioma cases seen at UCSF

• Difficult because referrals come from many areas

• One possible control group might be UCSF patients with a different neurologic disease

• Patients from a similar tertiary referral clinic are another possible control group

Text example of case-based case-control design shows sampling prevalent controls

SecondaryStudyBase

Cross-Sectional Study Design

Case-based design using prevalent cases: essentially same as cross-sectional design

Example of case-based design using prevalent cases

• Sampling glioma patients under treatment in a hospital during study period

• Poor survival so patients in treatment will over-represent those who live longest

• Nature of bias variable and not predictable

Study base and case-control design

Critical features of best case-control design:

- cases need to consist of all, or a random sample, of subjects in the study base experiencing the outcome

- controls need to consist of a sample of the study base that can be used to estimate the distribution of the exposure (risk factor) in the base

Summary Points

• Ecological studies weak in showing cause but have some valuable features

• Nature, not the size, of losses to follow-up crucial in cohort studies

• Key to case-control design is specifying and sampling the study base

• Case-control results can be as valid as cohort results if study properly designed and measurements made without bias