In 2002, ovarian cancer ranked as the fourth leading cause of cancer mortality a

Question

In 2002, 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 2002 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.

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

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

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

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

Explanation / Answer

Answer:

1. The types of bias that are particular concern in this case-control study are information bias.

2. 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, and make sure to clearly define the respondent requirements.

3. Confounding variable is an extraneous variable (usually unmonitored) that is allowed to change systematically along with the two variables being studied. In the context of an experiment, an extraneous variable that changes systematically along with the independent variable and has the potential to influence the dependent variable. A confounding variable is a threat to internal validity.

4. In this study, it is appropriate to match an age since age is associated with exposure of interest (oral contraceptive use) and is an independent risk factor for ovarian cancer.

Thus, age is a confounding factor. Failure to match, or otherwise control, for age would result in a biased assessment of the effect of oral contraceptive use.

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