Attrition Bias: Definition & Examples

What is Attrition Bias?

Attrition bias in research occurs when study participants who drop out have characteristics that differ significantly from those who remain. This selective dropout can lead to skewed results and misinterpretations if the researchers don’t adequately address it. This threat is higher for longitudinal studies and those with relatively high attrition rates.

For example, consider a long-term clinical trial testing a new drug for chronic pain management. If participants with less severe pain are more likely to drop out because they perceive less need for the treatment, the remaining sample may have disproportionately more severe pain cases than the target population. Consequently, the study might overestimate the drug’s effectiveness, as the data becomes skewed towards those with more severe symptoms.

Attrition bias is a form of selection bias. Learn more about Selection Bias: Definition & Examples.

Attribution Bias Problems

Attribution bias reduces a study’s ability to generalize its results to a broader population and detect causal relationships. In other words, it threatens a study’s external and internal validity. Let’s explore how that works.

Learn more about Internal and External Validity.

External Validity

Attrition bias can significantly alter your sample, resulting in a final group that markedly differs from the initial one. This shift occurs as specific segments of your original population become underrepresented in the sample. When dropouts consistently have different attributes, the remaining sample might no longer represent the original population. Consequently, the imbalanced final sample hinders your ability to generalize findings to the broader population you initially targeted, thus undermining the external validity of your study.

For example, a year-long study to evaluate a new fitness program begins with 500 participants of diverse ages and fitness levels. However, over time, older and less fit individuals drop out disproportionately. By the end, the remaining group skews younger and fitter, potentially overestimating the program’s effectiveness for the general population and undermining the study’s external validity due to the lost diversity from the original sample.

Learn more about the importance of a Representative Sample.

Internal Validity

Attrition bias poses a significant risk to the internal validity of experiments by potentially producing exaggerated or understated estimates of relationships and treatment effects. This problem occurs when the dropout characteristics relate to the study’s outcome.

Hence, attrition bias can impact the dynamic between independent and dependent variables in your research. It has the potential to falsely suggest correlations between variables or, conversely, obscure actual correlations that exist.

For example, in an educational experiment, if the more enthusiastic learners have more extracurricular events, they might be more likely to drop out of the study. Losing a lopsided number of devoted students can deceptively reduce the apparent effectiveness of an educational program.

Reasons for Attrition Bias in Research

When delving into the reasons behind attrition bias, it’s crucial to understand that this form of bias doesn’t occur randomly. Certain factors can systematically influence who stays and leaves a study, leading to this skewed phenomenon. Here are some key reasons why attrition bias happens:

• Duration of Study: Longer studies have a higher risk of participant dropout.
• Participant Burden: High testing frequency or intrusive methods can lead to dropout.
• Demographic Factors: Age, socioeconomic status, and health status can influence the likelihood of staying in a study.
• Lack of Engagement: A lack of perceived benefits or interest in the study can result in attrition.
• Adverse Events: In clinical trials, side effects or adverse events can cause participants to leave.

How to Reduce Attrition Bias

It’s essential to employ strategic measures to mitigate the effects of attrition bias and uphold the integrity of research findings. These strategies minimize dropout rates and ensure a more balanced representation of participants throughout the study. Let’s explore some effective tactics:

• Effective Communication: Regular, clear communication can keep participants engaged and informed about the study’s importance.
• Follow-Up Strategies: Implementing reminders, follow-up calls, or emails can encourage continued participation.
• Minimizing Burden: Reducing testing frequency and making participation as convenient as possible can decrease dropout rates.
• Incentives: Providing monetary or non-monetary incentives can motivate participants to stay.
• Keep Detailed Participant Information: Helps ensure ongoing communication with participants, even if they relocate.

Detecting Attrition Bias After a Study

Even with preventive measures in place, attrition bias can still occur in research. Detecting attrition bias is a crucial step in any longitudinal study. Recognizing its presence allows researchers to address it appropriately and ensure the credibility of their findings.

To detect attrition bias, compare the characteristics of participants who drop out with those who complete the study.

For both groups, examine participants across all variables, including demographics like gender, ethnicity, age, socioeconomic status, and other relevant variables. Significant differences in key variables might indicate the presence of attrition bias.

Analyze the timing and reasons for dropout. Consistent patterns, such as dropouts occurring after specific events or among certain demographic groups, can signal bias.

Employ statistical tests like logistic regression to examine whether dropout is related to treatment or outcome variables. Use dropout status as the binary dependent variable and the other variables as independent variables.

Understanding the characteristics of dropouts can help adjust analysis methods to account for potential bias.

Attrition causes missing data for the study. Consequently, understanding the missing data can help you evaluate attrition bias. Learn more about Missing Data Overview: Types, Implications & Handling.

Accounting for Attrition Bias in Research Results

After detection, the next challenge is to account for attrition bias in the study results. Properly addressing this bias is essential for presenting accurate and reliable statistical results.

Here are some approaches to account for this bias:

• Transparent Reporting: First and foremost, acknowledging the extent of attrition and its potential impacts in study reports enhances the credibility of the research.
• Sensitivity Analysis: This involves conducting additional analyses assuming different scenarios of dropout reasons. By comparing these scenarios, researchers can understand how different assumptions about dropouts affect the results.
• Weighting the Data: Apply weights to the remaining participants’ data to account for the underrepresented groups due to dropout. This method helps to approximate the characteristics of the original sample more closely.
• Multiple Imputation: This statistical technique involves imputing (or filling in) missing values based on the observed data. Multiple imputation provides a range of plausible values, which can give a more accurate picture of the potential outcomes had there been no dropouts.
• Use of Longitudinal Data Analysis Methods: Employing specialized analysis techniques for longitudinal data, such as mixed-effects models or time-to-event analyses, can help address the complexities introduced by attrition.

By incorporating these strategies, researchers can better understand and mitigate the effects of attrition bias, enhancing the robustness and reliability of their study’s conclusions.

In conclusion, while attrition bias is a common issue in longitudinal studies, understanding its causes and implementing strategies to minimize its impact are crucial for maintaining the integrity and validity of research findings. Researchers must proactively address this bias to ensure their results reflect the true effects in their study.

Reference

Bell M L, Kenward M G, Fairclough D L, Horton N J. Differential dropout and bias in randomised controlled trials: when it matters and when it may not BMJ 2013; 346 :e8668 doi:10.1136/bmj.e8668