5.1 study design overview

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Study design overview

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Study design outline• Big picture• Overview of designs

– Experimental design• Randomized controlled trial

– Observational designs• Cohort• Case-control• Cross-sectional• Ecological

• Summary

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Big picture• Critical part of epidemiology involves designing

studies to capture the data we need to estimate measures of disease and measures of association

• All designs aim to capture the effects of different exposures over time in populations

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Big picture• In randomized controlled trials exposures are

assigned randomly to individuals or groups and they are followed over time for outcomes

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Big picture• For most epidemiologic questions

randomized controlled trials are not ethical or feasible

• Rely on observational designs– Include individuals or groups that experience a range

of exposures and follow them for outcomes

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Big picture• Recall counterfactual framework illustrated by an

“ideal experiment”• a hypothetical study which, if we could actually

conduct it, would allow us to infer causality– Population experiences one exposure and observed

for outcome over a given time period– Roll back the clock– Change the exposure but leave everything else the

same, observe for outcome over the same time period– Compare the outcomes under both exposures: this is

the causal effect

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Big picture• In reality cannot observe counterfactual

exposures and outcomes• In epidemiology we use aspects of study design

and tools in analysis of study data to approximate counterfactual comparisons

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Big picture• Designs to be discussed

– Randomized controlled trials– Cohort studies– Case-control studies

• Nested case control• Case cohort• Case crossover

– Cross-sectional studies– Ecological studies

• Starting with an overview of study design• Moving into detailed discussion of each design

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• Summary

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Experimental design• Trials are experiments with human subjects• Researchers allocate the exposure• Allocation is typically done with

randomization

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Experimental design• Why randomization?

– Many extraneous characteristics, known as confounders in epidemiology, will affect risk/rate of the outcome

• Extraneous = factors that affect the outcome but are not the exposure of interest for a given research question

– Randomizing the exposure means that as sample size approaches infinity, the confounders will be randomly distributed between the exposed and unexposed groups

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Experimental design• Randomization and counterfactuals

– In a counterfactual experiment the same population would be observed exposed and unexposed and thus the distribution of confounders would be the same for the exposed and unexposed risks/rates

– In a randomized actual experiment, the group randomized to exposure should have the same distribution of confounders (whether measured or not) as the group randomized to no exposure (as sample size approaches infinity)

– The exposed and unexposed groups serve as reasonable estimates of the counterfactual outcomesfor each other – they are exchangeable

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Experimental design• In an infinite sample, any difference in the outcome

between the exposed and unexposed groups can be attributed to a causal treatment effect– However, other concerns remain including compliance with the

assigned treatment, loss to follow-up– In finite samples (any real trial), confounders still have to be considered

and handled in analysis

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• Summary

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Observational designs• Observational designs can be used when people

experience different levels of an exposure of interest

• However, exposures are not experienced randomly

• People who are exposed likely differ from those who are unexposed by other factors that also affect disease, i.e., confounders

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Observational designs• Example: study of the association between

socioeconomic status and birth outcomes– Poorer women are different from wealthier women

beyond their differences in wealth

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Observational designs• Example: study of traffic related air pollution on

asthma among children– Children living near freeways are different from

children not near freeways beyond the differences in air pollution

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Observational designs• Observational designs and counterfactuals

– In a counterfactual experiment the same population would be observed exposed and unexposed and thus the distribution of confounders would be the same for the exposed and unexposed risks/rates

– In an observational study, the group experiencing exposure will most likely not have the same distribution of confounders (whether measured or not) as the group not experiencing exposure

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Observational designs– The exposed and unexposed groups are not

exchangeable– For measured confounders we can address this issue

in design or analysis in most instances– For unmeasured confounders we incur bias (a topic

we began earlier in course – essentially a systematic difference between causal effect and association observed)

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Observational designs• Any difference in the outcome between the

exposed and unexposed groups includes any causal effect of the exposure, and effects of confounders– Other concerns also remain including loss to

follow-up– To identify the causal effect of an exposure

confounders have to be considered and handled in analysis

– Identifying a causal effect is thus more challenging with observational data

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Cohort studies• Basic approach

– Identify groups defined by an exposure of interest (exposed vs unexposed, or more gradations)

– Follow the groups over time and document risks/rates of disease

– Compare risks/rates in exposed vs unexposed or across exposure groups

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Case-control studies• Basic approach

– Design that captures the experience of a cohort (whether an actual cohort study or a more theoretical “study base”) by collecting data from a subset of the cohort

– Cases of disease are identified and their exposure status is determined (numerator of risks or rates)

– Control group is sampled to accurately capture the exposure experience in the source population that gave rise to the cases – i.e., the underlying cohort

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Case-control studies– Variations on the case-control design allow estimation of different measures of

association from the underlying cohort (e.g., OR may estimate risk ratio or rate ratio depending on exact design)

• Selection by outcome status

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Cross-sectional studies• Basic approach

– Design that examines a population at one point in time, selected without regard to exposure or disease status

– Exposure and disease ascertained simultaneously– Typically measure disease prevalence– Often a representative sample of a population of

interest

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Ecological studies• Basic approach

– Design that examines exposure and disease at the level of populations instead of individuals (populations defined by schools, neighborhoods, cities, nations, work places etc)

– Variables can be:• Aggregate – rate of disease, median income• Environmental – air pollution• Global - laws

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• Summary

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Summary• While there are a variety of designs, they all

include elements of– Capturing some aspect of a population experiencing

different exposures over time to quantify relations between exposures and disease

– Design aspects to deal with the issues of confounding, other biases and random error to get as close as possible to estimating a causal effect

• We will begin now with details on cohort study design

Health & Medicine

5.1 study design overview