91Ó°ÊÓ

A study published in 2010 in the New England Journal of Medicine discusses a breast-cancer 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 incidence- based 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 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

Determine Study Type

This study observes outcomes for groups based on their exposure to breast cancer screenings, categorized into groups with and without mammography screening across different periods. Since the researchers did not assign women to screening or non-screening groups but rather observed existing conditions, this study is observational.

02

Identify Variables

The explanatory variable in this study is the presence or absence of mammography screening for the women in the different groups. The response variable is the incidence-based rate of death from breast cancer.

03

Analyze Causation Evidence

In observational studies, there can be many confounding factors that are not controlled by the researchers, affecting the outcome. This prevents the study from proving causation definitively. The study indicates an association between mammography screening and reduced death rates from breast cancer, but it cannot conclusively prove causation due to potential confounders or biases.

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Key Concepts

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

Observational Study

An observational study is a type of research in which the investigator observes outcomes without manipulating any variables. In the given study about breast cancer screening in Norway, women were not randomly assigned to attend or not attend mammography screening sessions. Rather, their experiences were merely observed and recorded as they naturally occurred. This differentiates observational studies from experimental ones, where the investigator controls the assignment to different groups.

Such studies are often used when ethical or practical issues make randomized controlled trials impossible. For instance, in this study, it would have been unethical to deny potentially life-saving screenings to a randomized group of women. However, it's essential to consider that observational studies can include many confounding variables that might influence outcomes. Therefore, while they are useful in identifying potential associations, they do not establish causation definitively.

Explanatory and Response Variables

In the realm of research, understanding the role of explanatory and response variables is pivotal. The explanatory variable is the one that tries to explain or causes change in another variable. Meanwhile, the response variable is the outcome or the effect being measured.

In the context of the breast cancer screening study, the explanatory variable is the presence or absence of mammography screening offered to women. It acts as the potential factor influencing another variable. The response variable here is the incidence-based rate of death from breast cancer. Researchers measured how this particular outcome varied with the presence or absence of screenings. Recognizing these variables helps to clarify the objectives of the study and how relationships between them can be interpreted.

Causation and Association

Understanding the difference between causation and association is crucial in research. **Association** implies a relationship between two variables, where changes in one variable are related to changes in another. However, it does not mean that the change in one variable causes the change in the other.

In this study, there was an observed association between mammography screenings and reduced death rates from breast cancer. This suggests that women who were offered screenings had lower rates of death compared to those who were not.

However, jumping to the conclusion of **causation**—where one variable directly affects the other—would be incorrect in this observational study. The presence of confounding variables, such as participants' health behaviors, access to medical care, or genetic factors, could influence outcomes. Hence, while there's an association, we cannot conclusively assert that mammography screenings caused the reduction in death rates. Experimentation, often through randomized controlled trials, is necessary to firmly establish causation.