In 2001, ovarian cancer ranked as the fourth leading cause of cancer mortality a

Question

In 2001, ovarian cancer ranked as the fourth leading cause of cancer mortality among women in the United States. An estimated 16,000 new cases and more than 9,000 attributable deaths occurred among American women that year.

Several studies had noted an increased risk of ovarian cancer among women of low parity, suggesting that pregnancy exerts a protective effect. By preventing pregnancy, oral contraceptives (OCs) might be expected to increase the risk of ovarian cancer. On the other hand, by simulating pregnancy through suppression of pituitary gonadotropin release and inhibition of ovulation, OCs might be expected to protect against the subsequent development of ovarian cancer. Because by 2001 OCs had been used by more than 70 million women in the United States, the public health impact of an association in either direction could be substantial.

To study the relationship between oral contraceptive use and ovarian cancer (as well as breast and endometrial cancer), CDC initiated a case-control study – the Cancer and Steroid Hormone (CASH) Study in 2001. Case-patients were enrolled through eight regional cancer registries participating in the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute.

1. What types of biases are of particular concern in this case control study?

2. What steps can you take to reduce or minimize these potential biases?

3. In many epidemiological studies you have confounding factors. What is confounding?

4. In this case study that you have been looking at what are some confounding factors?

Explanation / Answer

Bias: A systematic error in the design, recruitment, data collection or analysis that results in a mistaken estimation of the true effect of the exposure and the outcome.

Confounding: A situation in which the effect or association between an exposure and outcome is distorted by the presence of another variable. Positive confounding (when the observed association is biased away from the null) and negative confounding (when the observed association is biased toward the null) both occur. In this case study, the number of pills taken are the confounding factors.

For example, if you are researching whether lack of exercise leads to weight gain, lack of exercise is your independent variable and weight gain is your dependent variable. Confounding variables are any other variable that also has an effect on your dependent variable. They are like extra independent variables that are having a hidden effect on your dependent variables. Confounding variables can cause two major problems:

Increase variance

Introduce bias.

The types of bias that are particular concern in this case-control study are information bias. Steps to take to minimize these potential biases would be to ask the right questions, survey the right people, giving respondents an even chance, create data analysis plan, andmake sure to clearly define the respondent requirements.

Confounding is a mixing or blurring of effects: a researcher attempts to relate an exposure to an outcome but actually measures the effect of a third factor (the confounding variable). Confounding can be controlled in several ways: restriction, matching, stratification, and more sophisticated multivariate techniques.

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