External validity: to what populations do our study results apply?

Name: External validity: to what populations do our study results apply?
Uploaded: 2017-07-16T14:08:16+00:00
Duration: PTM21S54
Channel: Greyson Bourne
Description: External validity: to what populations do our study results apply?

External validity: to what populations do our study results apply?
Epidemiology matters: a new introduction to methodological foundations Chapter 12

Epidemiology Matters – Chapter 1
Seven steps Define the population of interest Conceptualize and create measures of exposures and health indicators Take a sample of the population Estimate measures of association between exposures and health indicators of interest Rigorously evaluate whether the association observed suggests a causal association Assess the evidence for causes working together Assess the extent to which the result matters, is externally valid, to other populations Epidemiology Matters – Chapter 1

Generalizability or external validity refers to our capacity to generalize our results beyond our study sample Epidemiology Matters – Chapter 12

Question: How can we assess the extent to which results of a study are applicable in populations outside of the underlying population base of particular study? Answer: Think through characteristics of population of interest and determine how robust study findings might be across populations with similar or different characteristics. Epidemiology Matters – Chapter 12

Validity, four stages Introduction to external validity Prevalence of component causes Causation and study design External versus internal validity Randomized control trials Representative samples Summary Epidemiology Matters – Chapter 12

Four types of validity Measurement validity Statistical conclusion validity Internal validity External validity Epidemiology Matters – Chapter 12

1. Measurement validity An association cannot be valid beyond the study sample unless it is valid within the study sample Accuracy and precision are key measurements Have we measured what we wanted to measure? Epidemiology Matters – Chapter 12

2. Statistical conclusion validity
Is the association observed due to chance? We assess this via confidence intervals around estimates of association to describe role of sampling variability We aim to rule out the potential that our results arose by chance in the sampling process from an underlying population of interest Epidemiology Matters – Chapter 12

3. Internal validity Assessment of non-comparability between exposed and non-exposed in any study Epidemiology Matters – Chapter 12

4. External validity Explore external validity after assessing and ensuring Measurement validity Statistical conclusion validity Internal validity Epidemiology Matters – Chapter 12

External validity External validity: the applicability of study findings beyond the study sample In an epidemiologic study we Identify a population of interest Sample from population – random or purposive Conduct study Sample result should reflect underlying association in population of interest Therefore, identifying population of interest is central to exploring external validity once we have our findings Epidemiology Matters – Chapter 12

Prevalence of component causes
To understand external validity we must understand the prevalence and distribution of component causes across populations. Epidemiology Matters – Chapter 12

Prevalence of component causes, example
Question: Does exposure to ambient air pollution cause lung cancer? Component cause A: Ambient air pollution and smoking; therefore, smoking will cause lung cancer only among individuals exposed to ambient air pollution Component cause B: Genetics Epidemiology Matters – Chapter 12

Exposed genetic, diseased
Non-diseased Diseased Exposed genetic, diseased Exposed genetic, diseased, exposed air pollution, smoker Non-exposed Exposed air pollution Smoker Exposed air pollution, diseased, smoker Epidemiology Matters – Chapter 12

Prevalence of component causes example lung cancer
Population 1 Black = exposed to air pollution Dots = genetically determined Hat = smoker Exposed to air pollution and genetic (regardless of disease status and smoking status) 2 Un-exposed to air pollution and exposed to genetic (regardless of disease status and smoking status) 2 Exposed to air pollution and smoker (regardless of disease status and genetic status) 6 Un-exposed to air pollution and smoker (regardless of disease status and genetic status) 6 Epidemiology Matters – Chapter 12

Prevalence of component causes example lung cancer
Population 2 Black = exposed to air pollution Dots = genetically determined Hat = smoker Exposed to air pollution and genetic (regardless of disease status and smoking status) 2 Un-exposed to air pollution and exposed to genetic (regardless of disease status and smoking status) 2 Exposed to air pollution and smoker (regardless of disease status and genetic status) 3 Un-exposed to air pollution and smoker (regardless of disease status and genetic status) 3 Epidemiology Matters – Chapter 12

Prevalence of component causes example lung cancer risk difference
Population 1 Population 2 Black = exposed air pollution Dots = genetically determined Hat = smoker Exposed diseased = 6 = 60% risk Unexposed diseased = 2 = 20% risk Risk difference: 60% – 20% = 40% Interpretation: 40 cases of lung cancer are associated with ambient air pollution per 100 exposed Exposed diseased = 3 = 30% risk Unexposed diseased = 2 = 20% risk Risk difference: 30% – 20% = 10% Interpretation: 10 cases of lung cancer are associated with ambient air pollution per 100 exposed Epidemiology Matters – Chapter 12

Prevalence of component causes example lung cancer interpretation
Two studies asked the same question Both are internally valid studies because the exposed and unexposed are comparable on genetic determinism Population 1: the causal effect is a risk difference of 40% Population 2 : the causal effect is a risk difference of 10% Why do these two causal effects differ? Prevalence of people exposed to ambient air pollution is the same in both studies Prevalence of genetic determinism is same in both studies The reason that these two causal effects diverge is the different prevalence of smoking between the two populations Epidemiology Matters – Chapter 12

Prevalence of component causes example lung cancer interpretation
When two causes interact the measure of association for the effect of one cause on the outcome will differ across levels of the second cause Air pollution example Ambient air pollution and smoking are causal partners within the same sufficient cause Prevalence of one of them (smoking) influences the causal effect of the other (ambient air pollution) on the outcome (lung cancer) We would therefore expect the causal effect of ambient air pollution on lung cancer to differ across population where prevalence of smoking also differs Therefore, there is no one causal effect for all populations; the causal effect is dependent on prevalence of component causes in each population Therefore, the result from one study will be externally valid to populations in which the distribution of component causes of exposure is similar to the study sample Epidemiology Matters – Chapter 12

Causation and study design
The magnitude of an association will be applicable beyond our study to the extent that the distribution of causal partners of exposure is similar in the population If we want to identify a cause of disease, should it be a cause absolutely and in all types of populations? Epidemiology Matters – Chapter 12

Causation and study design
Cause: a factor that was necessary for that disease to occur in an individual at that time; most causes are insufficient and unnecessary in isolation Causal effect: epidemiology studies populations; therefore we focus on the effect of causes We document an association between those who embody cause (exposed) and those who do not (unexposed); this is context specific and dependent on prevalence of component causes Therefore, understanding a cause in context of causal partners is central to theory, design, and analysis Epidemiology Matters – Chapter 12

External vs. internal validity
Internal validity is a prerequisite to external validity To achieve internal validity we need to design a study with narrow population of interest and minimize non-comparability The resulting sample may not reflect broader swath of population beyond underlying population of interest The more narrow a sample becomes - due to strict inclusion and exclusion criteria for internal validity - the less external validity it may have if causal partners of exposure have differing prevalence in study compared with other populations Epidemiology Matters – Chapter 12

External vs. internal validity
Balancing external and internal validity is a a trade off To build a scientific argument for causal effect of exposure on outcome, we select study design and assess internal validity of causal question After causal effect of exposure is established in narrow population, we expand the causal question to ask How often? Among whom? Under what conditions? Epidemiology Matters – Chapter 12

An example, RCT Question: Does weight-loss drug reduce obesity among school-age children? Study details: Recruit children for randomized drug trial with body mass index 25< (BMI) < 40 Exclude children with diabetes Parents must be fully participatory (monitoring children’s drug regime and attend study clinic once per week) Baseline survey and monthly measurements Children randomized to receive weight loss drug or placebo Follow-up over two years Study results: Mean BMI among drug group declines 31.5 to 26.7 (4.8 points BMI) Mean BMI among placebo group declines from 31.4 to 28.5 (2.9 points BMI) Reduction of 1.9 (95% CI 0.9 – 2.9) more points of BMI in drug group than placebo group Conclusion: Weight-loss drug reduced obesity among school-age children Epidemiology Matters – Chapter 12

An example, RCT Questions to ask about external validity Are these results externally valid to a broader population all overweight children in Farrlandia? What about overweight children in other places? What information would we need to know in order to inform this issue? Epidemiology Matters – Chapter 12

An example, RCT Are we confident of a causal effect in the study?
Can only be externally valid if internally valid Good reason to conclude that results obtained are approximation of causal effect of drug for population Consider characteristics of population from which participants were drawn Good adherers to study protocol Diabetes-free Actively participating parents There is evidence that drug is effective in reducing BMI Consider characteristics of larger population to assess external validity Does action of drug interact with other factors? Do other factors have a different prevalence in general the general population of overweight children who may be prescribed the drug?

Example, representative sample
Question: Do sales tax on sugar-sweetened beverages reduce obesity among children aged 7 to 13? Study details: Enumerate all school-age children in Farrlandia Take random sample of 1,000 eligible Farrlandians who are between age 7 to 13 and live in Farrlandia Measure BMI before tax goes into effect Measure BMI after tax across a two-year period Study results: Mean BMI school-age children prior to the tax = 26.7 (95% C.I ) Mean BMI of school-age children was 24.3 (95% C.I ) two years after tax Conclusion: Tax lowered mean BMI among school-age children Epidemiology Matters – Chapter 12

Snowtown is considering a similar tax Are Farrlandian results externally valid to Snowtown? What information would we need to know about Farrlandians and Snowtownians? Epidemiology Matters – Chapter 12

Are we confident of a causal effect in the study? Internal validity: If school lunches changed to healthier offerings during study period we would not make a causal claim that tax reduced BMI What are potential causal partners of soda tax? External validity Soda availability is component cause Is the distribution of causal partners similar across populations? Soda is plentiful in Farrlandia Soda is hard to find in Snowtown Epidemiology Matters – Chapter 12

Seven steps Define the population of interest Conceptualize and create measures of exposures and health indicators Take a sample of the population Estimate measures of association between exposures and health indicators of interest Rigorously evaluate whether the association observed suggests a causal association Assess the evidence for causes working together Assess the extent to which the result matters, is externally valid, to other populations Epidemiology Matters – Chapter 1

epidemiologymatters.org Epidemiology Matters – Chapter 1

External validity: to what populations do our study results apply?

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