登录查看更多内容

How can you avoid bias and confounding in biostatistical and epidemiological studies?

由人工智能和领英社区提供技术支持

Biostatistics and epidemiology are two branches of statistics that deal with the analysis of data related to health and disease. However, when conducting biostatistical and epidemiological studies, there are some potential pitfalls that can affect the validity and reliability of the results. Bias and confounding are two common sources of error that can distort the true relationship between an exposure and an outcome. In this article, you will learn what bias and confounding are, how they can affect your studies, and how you can avoid them or minimize their impact.

此文章中的业界达人

由社区从 4 条内容中精选。了解更多

Stefano Gaburro, PhD, CCC

Scientific Director @ Tecniplast | Neuropharmacology PhD | 1st Cialdini Certified Professional in Germany and Italy and…
Long Hoang

Lecturer / Epidemiologist at CHS VinUniversity

1 What is bias?

Bias is a systematic error that leads to an incorrect estimate of the effect of an exposure on an outcome. Bias can arise from different sources, such as the way the study is designed, the way the data is collected, the way the data is analyzed, or the way the results are interpreted. For example, if you select a sample of participants that is not representative of the population of interest, you may introduce selection bias. If you measure the exposure or the outcome inaccurately or inconsistently, you may introduce measurement bias. If you fail to account for other factors that may influence the outcome, you may introduce confounding bias.

添加您的观点

Stefano Gaburro, PhD, CCC

Scientific Director @ Tecniplast | Neuropharmacology PhD | 1st Cialdini Certified Professional in Germany and Italy and Founding Member| Keynote Speaker
举报内容
Selection bias: When study participants don't represent the wider population. Example: Using only healthy individuals in a drug study might not reflect its effects on those with existing conditions. Measurement bias: Systematic errors in how exposure or outcome is gauged. Faulty equipment or participant inaccuracies can introduce this bias. Confounding bias: The effect of an exposure on an outcome is confused with another related factor's effect. Not accounting for these can lead to false conclusions. Publication bias: Positive or significant results are more likely to be published than negative findings, tilting the evidence landscape. Observer bias: Researchers may unintentionally influence outcomes due to their expectations or beliefs.

已翻译

赞

2 How to avoid bias?

The best way to avoid bias is to design your study carefully and rigorously, following the principles of scientific research. You should define your research question clearly, identify your target population and your exposure and outcome variables, and choose an appropriate study design that can answer your question. You should also use valid and reliable methods to select your sample, measure your variables, and analyze your data. You should also be aware of the limitations and assumptions of your study, and report your results honestly and transparently.

添加您的观点

Stefano Gaburro, PhD, CCC

Scientific Director @ Tecniplast | Neuropharmacology PhD | 1st Cialdini Certified Professional in Germany and Italy and Founding Member| Keynote Speaker
举报内容
I would add what it possible to be done: Blinding: Whenever possible, blind researchers and participants to the treatment or intervention, eliminating any knowledge which might influence outcomes. Randomization: Use random selection and assignment, ensuring that each participant has an equal chance of being chosen or assigned to any group. Peer review: Submit your study for evaluation by experts in the field. Their feedback can uncover unnoticed biases or errors. Diverse perspectives: Engage a team with varied backgrounds and viewpoints to critically assess the study design and results, ensuring a holistic approach.

已翻译

赞

3 What is confounding?

Confounding is a situation where an observed association between an exposure and an outcome is influenced by a third variable that is related to both the exposure and the outcome. Confounding can make the association appear stronger, weaker, or even opposite to the true effect. For example, if you want to study the effect of smoking on lung cancer, you may find a positive association between the two variables. However, this association may be confounded by age, as older people are more likely to smoke and to develop lung cancer.

添加您的观点

4 How to identify confounding?

The first step to identify confounding is to have a good understanding of the causal mechanisms and pathways that link the exposure and the outcome, and to consider what other factors may be involved. You can use a causal diagram or a directed acyclic graph (DAG) to visualize these relationships and identify potential confounders. A confounder is a variable that meets three criteria: it is a cause or a proxy of the exposure, it is a cause or a proxy of the outcome, and it is not an intermediate or a collider on the causal path between the exposure and the outcome.

添加您的观点

5 How to control confounding?

There are two main strategies to control confounding: by design or by analysis. By design means that you try to prevent confounding from occurring in the first place, by choosing a study design that can eliminate or reduce the effect of confounders. For example, you can use a randomized controlled trial (RCT) to assign participants to different exposure groups, so that the confounders are balanced across the groups. You can also use a matching or a stratification technique to select or group participants based on their confounder values, so that the exposure groups are comparable. By analysis means that you try to adjust for confounding after it has occurred, by using statistical methods that can account for the effect of confounders. For example, you can use a regression model or a standardization technique to estimate the effect of the exposure on the outcome, while controlling for the confounder values.

添加您的观点

Long Hoang

Lecturer / Epidemiologist at CHS VinUniversity
举报内容
There are also modern causal inference methods that deal with confounding, such as propensity score methods, instrumental variables, and marginal structural models.

已翻译

赞

6 How to assess confounding?

The final step to deal with confounding is to assess whether your control strategy has been effective or not. You can do this by comparing the crude and the adjusted estimates of the effect of the exposure on the outcome, and see if there is a significant difference between them. A common rule of thumb is that if the adjusted estimate changes by more than 10% from the crude estimate, there is evidence of confounding. You can also use a sensitivity analysis to test how robust your results are to different assumptions or scenarios regarding the confounders.

添加您的观点

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Stefano Gaburro, PhD, CCC

Scientific Director @ Tecniplast | Neuropharmacology PhD | 1st Cialdini Certified Professional in Germany and Italy and Founding Member| Keynote Speaker
举报内容
There are several reproducibility centres across Europe that might help reduce such effects. It is worthwhile always to confront external groups because they might have another view.

已翻译

赞

Statistics

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

How can you avoid bias and confounding in biostatistical and epidemiological studies?

1

2

3

4

5

6

7

1 What is bias?

2 How to avoid bias?

3 What is confounding?

4 How to identify confounding?

5 How to control confounding?

6 How to assess confounding?

7 Here’s what else to consider

Statistics

给文章评分

感谢您的反馈

更多Statistics相关文章

更多相关阅读内容

How can you avoid bias and confounding in biostatistical and epidemiological studies?

1

2

3

4

5

6

7

1 What is bias?

2 How to avoid bias?

3 What is confounding?

4 How to identify confounding?

5 How to control confounding?

6 How to assess confounding?

7 Here’s what else to consider

Statistics

给文章评分

感谢您的反馈

查看其他技能