Question

. Researchers who examined health records of thousands of males found that men who died of myocardial infarction (heart attack) tended to be shorter than men who did not. a) Is this an experiment? If not, what kind of study is it? b) Is it correct to conclude that shorter men are at higher risk for heart attack? Explain.

Short answer

a) No, it's an observational study. b) No causation; only an association was observed.

Step-by-step solution

Identify the Type of Study

To determine if the described research is an experiment, we need to check if there is a manipulation of variables. An experiment requires the researcher to manipulate one variable and control/randomize the rest to observe the effect. Since this study merely examined health records without any manipulation, it is not an experiment. Thus, this is an observational study, specifically a retrospective cohort study, where researchers look back at data collected in the past.

Analyze the Cause-and-Effect Relationship

Observational studies can show associations but not causation due to potential confounding variables. An association between men's height and heart attack risk was found, but this does not prove that being shorter causes heart attacks. There could be other factors, like genetics or lifestyle, that affect both height and heart attack risk.

Conclusion on Risk Interpretation

Based on the study, concluding that shorter men are at higher risk for heart attacks without further rigorous experimental or longitudinal studies would be inaccurate. The association noted in the study could result from unmeasured confounding variables that influence both height and heart disease.

Key concepts

Retrospective Cohort Study

A retrospective cohort study is a type of observational study. In these studies, researchers look back into past data to understand the relationships between variables. This is different from experiments where researchers actively intervene or manipulate the environment. Instead, in retrospective cohort studies, scientists assess existing records, such as health records, to draw conclusions. Since they rely on historical data, these studies are quicker and less costly compared to prospective studies, which need to gather data over a prolonged period. However, they may have limitations, such as missing data or inaccurate records, that can affect the study's conclusions. Retrospective cohort studies are valuable for exploring associations and generating hypotheses for further research.

Confounding Variables

Confounding variables are factors that might interfere with the apparent relationship between the study variables. In the context of the original exercise, while an association between shorter stature and heart attack risk was identified, confounding variables can cloud the true nature of this relationship. Various factors, such as genetics, diet, or levels of physical activity, could act as confounders. These confounding variables could affect both men's height and their risk of having heart attacks. Therefore, it's crucial not to jump to causal conclusions based solely on observational studies as they might not account for all potential confounders. Researchers must use strategies, such as statistical controls, to address these confounding variables in their analysis.

Causation versus Correlation

The phrases 'causation' and 'correlation' are often confused, but they have distinct meanings. Correlation means that two variables are related to each other. In our study's case, the correlation is between men's height and their likelihood of having a heart attack. However, this does not mean that one causes the other. Causation would imply that changes in one variable directly result in changes in another. A common saying, "Correlation does not imply causation," emphasizes that just because two things correlate doesn't mean one causes the other. To determine causation, controlled experiments are usually necessary, as they minimize the influence of confounding variables and allow researchers to see the effect of one variable on another directly. Without such evidence, it's prudent to view conclusions drawn from observational studies with a healthy amount of skepticism.