Association and Bias in Observational Studies Discussion Nursing Assignment Help

This week you will review different types of bias, present an example of a study, and discuss whether bias was a factor in the study outcome. You will also discuss how the study design could have been altered to minimize or eliminate the risk of invalidating the results.

To prepare for this Discussion:

Review the types of bias listed below.

Non-differential recall bias

Differential recall bias

  • Publication bias

Loss to follow-up

  • Refusal to participate
  • Interviewer bias
  • Confounding
  • Select one type of bias from the list above, and consider the ways that bias could impact a study.
  • To complete this Discussion, post a real or hypothetical example of one study. You can use one of the studies you designed during Weeks 3 or 4 or search for a different study.
  • How could bias be introduced in the study?
  • Would the bias be considered a selection bias or an information bias? Why?

Name two or three variables that might be possible confounders in your study.  

Describe at least one method of controlling those confounding variables.

Expert Solution Preview

Introduction:
In this assignment, we will be discussing different types of bias and their impact on the outcome of a study. We will also explore how the study design can be altered to minimize or eliminate the risk of bias and invalidating the results.

Answer:
One study that can be used as an example is a research investigating the relationship between exposure to air pollution and the development of respiratory diseases in a specific population. The study aims to determine whether there is a significant association between air pollution levels and the incidence of respiratory diseases.

Bias can be introduced in this study in several ways. One potential source of bias is non-differential recall bias. This could occur if participants have difficulty accurately remembering their exposure to air pollution. Differences in recall accuracy or recall bias between individuals can lead to an inaccurate assessment of the true relationship between air pollution and respiratory diseases.

This bias would be considered an information bias because it affects the accuracy of the information collected from the participants regarding their exposure to air pollution. It distorts the measurement of exposure and can lead to errors in the estimation of the association between air pollution and respiratory diseases.

Some possible confounding variables in this study could be age, smoking status, and socioeconomic status. Age is a known risk factor for respiratory diseases, and it can confound the relationship between air pollution and respiratory diseases if not properly controlled. Similarly, smoking status and socioeconomic status can also impact the development of respiratory diseases and may confound the association with air pollution.

To control for these confounding variables, the study design can incorporate various strategies. One method is stratification, where participants can be divided into different age groups, smoking status categories, and socioeconomic strata. This allows for the examination of the association between air pollution and respiratory diseases within each stratum, ensuring that potential confounding factors are balanced across the exposure levels.

Another method is multivariable regression analysis, where the confounding variables are included as covariates in the statistical model. By adjusting for these variables, the potential confounding effects can be minimized, and a more accurate estimate of the association between air pollution and respiratory diseases can be obtained.

In conclusion, bias can significantly impact the outcome of a study. In the example study of air pollution and respiratory diseases, non-differential recall bias can introduce information bias, affecting the measurement of exposure. Age, smoking status, and socioeconomic status can serve as confounding variables, and their effects can be controlled through stratification or multivariable regression analysis. These strategies play a crucial role in minimizing bias and obtaining valid research results.

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