登录查看更多内容

What are common observational study biases and how can you prevent them?

由人工智能和领英社区提供技术支持

Observational studies are a type of research that involves collecting and analyzing data from existing or natural situations, without manipulating any variables or assigning any treatments. They can provide valuable insights into causal relationships, trends, patterns, and associations, but they also have some limitations and challenges. One of the main challenges is dealing with potential biases that can affect the validity and reliability of the results. In this article, you will learn about some common observational study biases and how you can prevent them.

此文章中的业界达人

由社区从 2 条内容中精选。了解更多

Nithya ( Nithhyaa) Ramamoorthy

Analytics Leader | Digital Health | Mayo Clinic

1 Selection bias

Selection bias occurs when the sample of participants or units that are observed is not representative of the population of interest. This can happen due to non-random sampling methods, self-selection, attrition, or exclusion criteria. For example, if you want to study the effect of smoking on lung cancer, but you only recruit smokers from a health clinic, you may end up with a sample that is more likely to have other health problems or seek medical care than the general population of smokers. To prevent selection bias, you should use random or stratified sampling methods, minimize dropouts, and include a control group if possible.

添加您的观点

Avi Corcos
举报内容
In my experience, the most common biases observed are often of 2 types: The first is linked to past investments and their performance. In my opinion, this bias can be corrected by an investment team with diverse backgrounds. The second is linked to mimicry which leads financial players to favor this or that sector to the detriment of another or this or that investment "standard" because the market is always "faithful". According to me, this bias is the most potentially damaging because it can lead to partially excluding the investor's rationality.

已翻译

赞

2 Measurement bias

Measurement bias occurs when the data that are collected or recorded are inaccurate, inconsistent, or incomplete. This can happen due to errors in instruments, methods, or procedures, or due to subjective or biased judgments by the observers or participants. For example, if you want to study the effect of exercise on mood, but you rely on self-reported measures of physical activity and mood, you may end up with data that are influenced by memory, social desirability, or mood swings. To prevent measurement bias, you should use reliable and valid instruments, standardize and calibrate the methods and procedures, and train and monitor the observers or participants.

添加您的观点

3 Confounding bias

Confounding bias occurs when the observed relationship between the exposure and the outcome is distorted by the presence of a third variable that is associated with both. This can happen due to omitted variables, reverse causation, or common causes. For example, if you want to study the effect of coffee consumption on blood pressure, but you do not account for the effect of stress, which may affect both coffee intake and blood pressure, you may end up with a spurious or misleading correlation. To prevent confounding bias, you should identify and measure the potential confounders, use statistical methods to adjust or control for them, or use matching or stratification techniques to balance them.

添加您的观点

4 Reporting bias

Reporting bias occurs when the dissemination or publication of the results is influenced by the direction, magnitude, or significance of the findings. This can happen due to selective reporting, suppression, or exaggeration of the results by the researchers, reviewers, editors, or sponsors. For example, if you want to study the effect of a new drug on a disease, but you only report the positive outcomes and ignore the negative or null outcomes, you may end up with a distorted or inflated estimate of the effect. To prevent reporting bias, you should follow the principles of transparency, completeness, and accountability, and adhere to the relevant reporting standards and guidelines.

添加您的观点

Nithya ( Nithhyaa) Ramamoorthy

Analytics Leader | Digital Health | Mayo Clinic
(已编辑)
举报内容
There are simple actions you can take to prevent reporting bias while presenting your analysis findings. - Always include the measurement timeframe and sample size while presenting each of the findings. - Ensure that all segments are uniformly represented in your analysis. If the data is inherently biased, denote the share of each segment in the overall population while presenting conclusions from the data. - If the analysis findings don’t align with strategic priorities, be transparent about it and clearly describe why that’s the case and provide recommendations and next steps to mitigate this.

已翻译

赞

5 Ecological fallacy

Ecological fallacy occurs when the inferences or conclusions that are drawn from the analysis of aggregated or group-level data are applied to individual or lower-level units. This can happen due to ignoring the heterogeneity or diversity within the groups, or assuming that the relationships or patterns that hold at one level of analysis also hold at another level. For example, if you want to study the effect of income on health, but you use the average income and health indicators of different countries, you may end up with a misleading or oversimplified generalization that does not account for the variation or confounding factors within each country. To prevent ecological fallacy, you should use individual or disaggregated data whenever possible, or use appropriate methods to account for the differences or interactions between the groups.

添加您的观点

6 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

Analytical Skills

+ 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

What are common observational study biases and how can you prevent them?

1

2

3

4

5

6

1 Selection bias

2 Measurement bias

3 Confounding bias

4 Reporting bias

5 Ecological fallacy

6 Here’s what else to consider

Analytical Skills

给文章评分

感谢您的反馈

更多Analytical Skills相关文章

更多相关阅读内容

What are common observational study biases and how can you prevent them?

1

2

3

4

5

6

1 Selection bias

2 Measurement bias

3 Confounding bias

4 Reporting bias

5 Ecological fallacy

6 Here’s what else to consider

Analytical Skills

给文章评分

感谢您的反馈

查看其他技能