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Breast-cancer screening A study published in 2010 in the New England Journal of Medicine discusses a breastcancer screening program that began in Norway in 1996 and was expanded geographically through \(2005 .\) Women in the study were offered mammography screening every two years. The goal of the study was to compare incidencebased rates of death from breast cancer across four groups: 1\. Women who from 1996 through 2005 were living in countries with screening. 2\. Women who from 1996 through 2005 were living in countries without screening. 3\. A historical-comparison group who lived in screening countries from 1986 through \(1995 .\) 4\. A historical-comparison group who lived in nonscreening countries from 1986 through 1995 . Data were analyzed for 40,075 women. Rates of death were reduced in the screening group as compared to the historical screening group, and also in the nonscreening group as compared to the historical nonscreening group. a. Is this an observational or experimental study? b. Identify the explanatory and response variable(s). c. Does the study prove that being offered mammography screening causes a reduction in death rates associated with breast cancer? Why or why not?

Step by step solution

01

Identifying the Type of Study

This study is an observational study. In an observational study, researchers observe the effect of a risk factor, diagnostic test, treatment, or other intervention without trying to change who is or isn't exposed to it. In this case, researchers observed and compared the death rates of women in different countries with varying access to mammography screening, without controlling who received the screenings.

02

Determining Variables

The explanatory variable (also called the independent variable) is whether or not the women were offered mammography screening. The response variable (also known as the dependent variable) is the incidence-based rate of death from breast cancer. The study considers these variables over different times and countries, comparing them with and without the screening program.

03

Evaluating Causation

The study does not prove causation, even though there is an association between mammography screening and reduced death rates. Observational studies can show associations but cannot prove causation because they do not account for all possible confounding variables. Many factors could contribute to the reduction in death rates, such as improved treatments or overall health care advancements, that are not controlled in observational studies.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Explanatory Variable

In the context of the breast-cancer screening study, understanding the explanatory variable is key. The explanatory variable explains changes in another variable within a study. Think of it like the "cause" variable that potentially influences the outcome. In this study, the explanatory variable is whether or not the women were offered mammography screening. This is the factor that potentially impacts the outcome, or what researchers are observing to understand its effects on breast cancer death rates.

Explanatory variables are sometimes referred to as independent variables because their values are not influenced by other variables in the context of the experiment. In simpler terms, the offer of mammography is independent of how the outcome unfolds. However, remember that just because it's the main input or the condition from which changes are expected, it doesn't mean it is the cause of anything observed in an observational study. Observational study limitations mean there might be other underlying variables influencing the results, which researchers observe but do not control.

Response Variable

The response variable in a study is the outcome that is measured and believed to be influenced by the explanatory variable. For the breast-cancer screening study, the response variable is the incidence-based rate of death from breast cancer. This is what researchers measured to see if there was a difference between women who were offered screenings and those who weren't.

The response variable, also known as the dependent variable, depends on the explanatory variable's presence or absence. Essentially, it is the result that the study seeks to measure or explain, often the focal point of the study's findings. In observational studies like this one, outcomes are observed as naturally occurring. Researchers do not manipulate the response variable but rather document it as it naturally unfolds, keeping in mind potential influences beyond the primary explanatory variable.

Causation in Studies

Causation implies a cause-and-effect relationship, where one event results from another. In studies, proving causation means showing that one factor directly results in a specific outcome. In the context of the breast-cancer screening study, we encounter a crucial limitation of observational studies—lack of proven causation.

While the study observed a reduction in death rates among women who were offered mammography screenings, it does not definitively prove that the screening caused this reduction. There could be numerous external factors contributing to the lowered death rates, such as advancements in medical treatments, improvements in healthcare systems, or healthier lifestyle adaptations.

Observational studies can identify associations or correlations, but they cannot confirm that one variable directly causes changes in another. To establish causation, a controlled experimental study is often necessary, where researchers can control and isolate variables to eliminate potential confounding factors.