Putting It Together: Women, Aspirin, and Heart Attacks In a famous study by the Physicians Health Study Group from Harvard University from the late 1980s, 22,000 healthy male physicians were randomly divided into two groups; half the physicians took aspirin every other day, and the others were given a placebo. Of the physicians in the aspirin group, 104 heart attacks occurred; of the physicians in the placebo group, 189 heart attacks occurred. The results were statistically significant, which led to the advice that males should take an aspirin every other day in the interest of reducing the chance of having a heart attack. Does the same advice apply to women?
In a randomized, placebo-controlled study, 39,876 healthy women 45 years of age or older were randomly divided into two groups. The women in group 1 received 100 mg of aspirin every other day; the women in group 2 received a placebo every other day. The women were monitored for 10 years to determine if they experienced a cardiovascular event (such as heart attack or stroke). Of the 19,934 in the aspirin group, 477 experienced a heart attack. Of the 19,942 women in the placebo group, 522 experienced a heart attack. Source: Paul M. Ridker et al. “A Randomized Trial of Low-Dose Aspirin in the Primary Prevention of Cardiovascular Disease in Women.” New England Journal of Medicine 352:1293–1304.
e. How does randomization deal with the explanatory variables that were not controlled in the study?

Verified step by step guidance

1. Understand that randomization is a process used in experimental design to assign participants to different treatment groups purely by chance, which helps ensure that the groups are comparable.
2. Recognize that explanatory variables not controlled in the study (such as age variations, lifestyle factors, or genetic predispositions) can potentially influence the outcome, but randomization helps distribute these variables evenly across the treatment and control groups.
3. By evenly distributing uncontrolled explanatory variables, randomization reduces the risk of confounding, meaning that differences in outcomes can be more confidently attributed to the treatment effect rather than other factors.
4. Therefore, randomization helps to balance both known and unknown confounding variables between groups, making the comparison between aspirin and placebo groups fair and unbiased.
5. In summary, randomization deals with uncontrolled explanatory variables by ensuring they are similarly represented in each group, which supports the validity of causal conclusions drawn from the study.

Key Concepts

Randomization in Experimental Design

Randomization is the process of assigning participants to different groups purely by chance, which helps ensure that the groups are comparable. This minimizes the effects of confounding variables—factors other than the treatment that might influence the outcome. By balancing both known and unknown variables across groups, randomization supports the validity of causal conclusions.

Explanatory Variables and Confounding

Explanatory variables are factors that might influence the response variable in a study. Confounding occurs when an outside variable is related to both the explanatory variable and the outcome, potentially biasing results. Proper study design, including randomization, helps control for confounding by distributing these variables evenly across treatment groups.

Placebo-Controlled Study

A placebo-controlled study uses a control group receiving an inactive treatment to compare against the group receiving the active treatment. This design helps isolate the effect of the treatment by accounting for psychological or other effects unrelated to the treatment itself. It strengthens the study’s internal validity by reducing bias.