Epidemiological Methods in Behavioral, Social and biomedical Research Matthew J. Mimiaga, ScD, MPH Associate Professor Psychiatry, HARVARD MEDICAL SCHOOL Epidemiology, Harvard School of Public Health

Methods in Epidemiology Study Design Establishing causality Overview of Talk Methods in Epidemiology Study Design Establishing causality Longitudinal analysis – conceptual understanding Case study – Explore CSA data

Methods in Epidemiology

Definition of epidemiology “The study of the distribution and determinants of health related states or events in specified populations and the application of this study to control health problems” - James Last A Dictionary of Epidemiology

Prevalence and incidence Case An instance of a disease or health condition in an individual Incidence Number of new cases of a disease or health condition in a population over a given period of time Prevalence Number of new and existing cases of a disease or health condition in a population during a point in time (“snapshot”)

Distinguishing between incidence and prevalence Prevalence includes both old and new cases and is usually expressed as a percentage Incidence includes only NEW cases and is expressed as the number of cases per population per year Time period and population must be specified

Prevalence Prevalence of colds in my class at HSPH Number of cases (people with colds) = 3 Population of class = 30 Prevalence = 3/30 Expressed as a percentage = 3/30 X 100 =10%

Incidence Number of cases of newly diagnosed HIV infection in random-city in 2003 is 900 Population of the city is 100 000 Incidence of HIV is 900 per 100 000 in 2003 in random-city

Risk and risk factors Risk factors are factors that increase the probability that a disease will occur Risk factors could be Environmental Due to one’s behavior lifestyle Genetic Ask for examples

Differentiating between risk and causation Risk is about probability or likelihood Causation is about “certainty” Identifying a risk may be the first step to understanding causation eg smoking and lung cancer

Types of risk Absolute risk Relative risk Attributable risk

Measures of risk – absolute risk Number of cases in a defined population Similar to incidence If 100 people are infected with HIV in a town of 1000 people, the absolute risk of HIV in the town is 100 per 1000 But the people in the town have different lifestyles, genes, living conditions which absolute risk does not take note of….

Measures of risk – relative risk Going back to our example, we could divide the population of the town into injecting drug users (IDUs) and non-injecting drug users (non-IDUs) Count the number of cases of HIV in IDUs and count the number in non-IDUs Relative risk (risk ratio) is the ratio between the two, i.e., risk in the exposed / risk in the unexposed

Relative risk In our example, say there were 400 IDUs in the town, and 80 of them were diagnosed with HIV in the year of our study. The risk of HIV in IDUs is therefore 80/400 = 0.2 There are 20 diagnoses of HIV in the non-IDU population of 600, so the risk of HIV in non-IDUs is 20/600 = 0.033 The relative risk is therefore 0.2 divided by 0.033 =6.06

Attributable risk Difference between risk in the exposed and risk in the unexposed Risk in exposed minus risk in unexposed From our example the attributable risk for HIV in the town is: 0.2-0.033=0.167 In fact AR shows which proportion of the disease in exposed subjects is due to exposure

Study Design

Study Design The experiment: the “Gold Standard” Key elements: Random assignment: assigning cases (individuals or organizations) to groups for the purpose of making a comparison Intention to treat: compare subjects assigned to intervention versus control Blinding: ideally subjects and assessors are blinded with regard to treatment of assignment Pre-test and post-test

Study Design Randomized Control Trial: classical experimental design. A complete experiment with all 3 elements (randomization, ITT, pre- and post- test) Quasi-experimental design: Subjects not randomly assigned but part of a natural or social experiment

Study Design Observational Studies: Be clear about the study design and aim: exploratory, descriptive, explanatory What is the time dimension/sample Cross-sectional: Which components are retrospective? Cumulative? When do current exposures reflect long-term exposures? How common is the condition? Cohort studies: Follow a group of people with some common experience (birth, neighborhood, occupational, nurses) – could be cross-sectional or not

Establishing Causality

Causal Thinking A fairly simple notion of cause:   x causes y means:   a change in x produces a change in y

Some rules for causal diagrams 1) Time runs from left to right on the page 2) Causally prior variables have arrows running from them to affected variables x y Time

3) You can have two causally prior variables (x1 and x2) that both are hypothesized to have an effect on y, measured temporally at the same time, and make no assumptions about causality between them: we assume they are associated => a double-headed arrow: x1 y x2

  4) You can have a mediating variable (e.g. m in these diagrams): Model 1: x ==> m ==> y Model 2: x y m

Notice the very different assumptions of these two models: The first model is a causal chain (see e.g. Duncan). x impacts y only via m. In the second model, there are direct and indirect effects of x on y and x on y via m.

So For Causal Inference (see Stinchcomb): 1) Variation in independent variable (i.e., variation cannot be explained with a constant) 2) Covariation between x and y 3) Clear causal direction (i.e., temporality) 4) Nonspuriousness (i.e., no confounding)

My perspective - the most important points: You cannot prove causality, you can only provide evidence in support of causal relations; It is a very important part of science to specify a hypothesis and test it against data. Again, you can’t prove causality, but you can provide evidence in support of hypotheses. The cause doesn’t have to be something concrete or observable If you buy into this summary, you buy into “normal science”

As an alternative to this, one could… Begin research with data, not theory and hypotheses; Look for associations of variables with other variables of interest; Find an association; Develop a hypothesis and theory to explain the observed association.

  The clear hazard of this approach is that your research may be based on chance and fluctuations in relationships due to chance.

On the other hand… If one is testing another hypothesis, and a strong association emerges that leads to some new hypotheses….and you can replicate this relationship in another study….such exploratory research could be useful. This happens all the time!

Counterfactuals From Shadish, Cook, Campbell   “In an experiment, we observe what did happen when people received a treatment. The counterfactual is knowledge of what would have happened to those same people if they simultaneously had not received treatment. An effect is the difference between what did happen and what would have happened. We cannot directly observe a counterfactual”

Longitudinal Analysis

Features of Longitudinal Data Defining feature: repeated observations on individuals, allowing the direct study of change over time Primary goal: to characterize the change in response and factors that influence change over time With repeated measures on the individual, one can capture within-individual change Note: measurements are commensurate, i.e., the same variable is measured repeatedly

Features of Longitudinal Data Cont. Obtain more precise estimates and SEs Requires somewhat more sophisticated stat techniques because the repeated observations are usually (+) correlated Sequential nature of the measures implies that certain types of correlation structures are likely to arise E.g., measurements taken closer together will be more highly correlated than measurements taken further apart Correlation must be accounted for in order to obtain valid inferences

Empirical Observations about Correlations in Longitudinal Data Correlations are positive Correlations decrease with increasing time separation Correlations between repeated measures rarely approach zero Correlations between a pair of repeated measures taken very closely together in time rarely approaches one b/c of measurement error….even if you took a measure simultaneously – like in a blood sample – correlation would not be one

Hypotheses concerning response profiles Given a sequence of n repeated measures on a number of distinct groups of individuals, three main questions: Are the mean response profiles similar in the groups, in the sense that the mean response profiles are parallel? No group x time interaction

Hypotheses concerning response profiles cont. 2. Assuming mean response profiles are parallel, are the means constant over time, in the sense that the mean response profiles are flat? No change over time 3. Assuming that the population mean response profiles are parallel, are they also at the same level in the sense that the mean response profiles for the groups coincide? Absence of a group effect