Researchers Katherine Tucker and associates wanted to determine whether consumption of cola is associated with lower bone mineral density. They looked at 1125 men and 1413 women in the Framingham Osteoporosis Study, which is a cohort that began in 1971. The first examination in this study began between 1971 and 1975, with participants returning for an examination every 4 years. Based on results of questionnaires, the researchers were able to determine cola consumption on a weekly basis. Analysis of the results indicated that women who consumed at least one cola per day (on average) had a bone mineral density that was significantly lower at the femoral neck than those who consumed less than one cola per day. The researchers did not find this relation in men.


e. Can you think of any lurking variables that should be accounted for?

Verified step by step guidance

1. Understand that a lurking variable is an unobserved variable that influences both the explanatory variable (cola consumption) and the response variable (bone mineral density), potentially confounding the observed association.
2. Consider lifestyle factors that might be related to both cola consumption and bone health, such as physical activity level, calcium and vitamin D intake, smoking status, and alcohol consumption.
3. Think about demographic variables like age, socioeconomic status, or hormonal factors (especially in women) that could affect bone mineral density independently of cola consumption.
4. Recognize medical conditions or medications that influence bone density, such as osteoporosis, use of corticosteroids, or other chronic illnesses, which might also correlate with cola consumption habits.
5. Conclude that to properly assess the relationship between cola consumption and bone mineral density, the analysis should control for these potential lurking variables using methods like multivariable regression or stratification.

Key Concepts

Lurking Variables

Lurking variables are hidden factors not included in the study that can influence both the independent and dependent variables, potentially confounding the observed relationship. Identifying these variables is crucial to avoid misleading conclusions about causality.

Confounding Variables

Confounding variables are extraneous factors that correlate with both the exposure and the outcome, making it difficult to isolate the effect of the primary variable of interest. Properly accounting for confounders helps clarify true associations in observational studies.

Cohort Study Design

A cohort study follows a group of people over time to observe how exposures affect outcomes. Understanding this design helps in recognizing strengths like temporal sequence and limitations such as potential confounding and selection bias.