What type of bias is introduced when 19% of subjects in a clinical trial for a new prostate cancer medication fail to complete a survey on perceived side effects?

Attrition Bias is the Most Likely Type of Bias in This Clinical Trial

The most likely type of bias introduced when 19% of subjects fail to complete a survey on perceived side effects is attrition bias. 1

Understanding Attrition Bias in Clinical Trials

Attrition bias occurs when there are systematic differences between study groups in the number and characteristics of participants who drop out or fail to complete study assessments. In your prostate cancer drug trial, the 19% of subjects who failed to complete the side effect survey represent a significant attrition rate that could substantially impact your results.

Why This Is Attrition Bias Rather Than Other Types:

1. Attrition vs. Confounding:

   • Attrition bias results from systematic differences in dropouts/non-completion 1
   • Confounding bias occurs when an external variable affects both the exposure and outcome, which is not what's happening with survey non-completion 1

2. Attrition vs. Information Bias:

   • Information bias relates to systematic errors in measurement or data collection
   • Your scenario describes missing data rather than inaccurate data collection 1

3. Attrition vs. Publication Bias:

   • Publication bias refers to selective publication of studies based on results
   • Your scenario involves data missing within a single study 1

4. Attrition vs. Recall Bias:

   • Recall bias occurs when participants have differential recall of past events
   • Your scenario involves non-completion of surveys, not inaccurate recall 1

Impact of Attrition Bias on Your Trial Results

The 19% non-completion rate is particularly concerning because:

• It exceeds the 10-20% attrition rate often seen in placebo-controlled trials 1
• Missing data from attrition was identified as the most common domain of high-risk bias (found in 47-53% of RCTs) 1
• Participants who drop out or fail to complete assessments are unlikely to be representative of all study participants 1

Why Attrition Bias Matters in Your Prostate Cancer Drug Trial

The non-completion of side effect surveys may be directly related to the treatment itself:

• Patients experiencing severe side effects might be more likely to discontinue participation
• Alternatively, patients experiencing no benefit might be less motivated to complete surveys
• Either scenario creates a biased picture of the drug's side effect profile 1

Methods to Address Attrition Bias in Your Analysis

1. Intention-to-Treat (ITT) Analysis:

   • Include all randomized participants in the analysis regardless of protocol adherence
   • Many studies (42%) fail to use ITT analyses, increasing risk of bias 1

2. Multiple Imputation:

   • Impute missing values based on available data
   • May not fully correct bias if data is Missing Not At Random (MNAR) 2, 3

3. Pattern Mixture Modeling:

   • Can potentially reduce bias in studies with suspected informative attrition 3

4. Sensitivity Analysis:

   • Compare results using different methods for handling missing data
   • Helps determine robustness of findings 1

Prevention Strategies for Future Trials

To minimize attrition bias in future clinical trials:

• Over-recruit participants to account for expected dropout rates 4
• Implement targeted retention strategies for high-risk groups 5
• Collect auxiliary variables that might predict dropout 3
• Use sampling weights to compensate for effects of attrition 4
• Ensure follow-up protocols are not overly burdensome for participants

Clinical Implications

The presence of attrition bias in your prostate cancer drug trial has significant implications for clinical practice:

• The true side effect profile may be underestimated if patients with severe side effects failed to complete surveys
• Treatment decisions based on biased data could lead to unexpected adverse outcomes in real-world settings
• Clinicians should interpret the reported side effect profile with caution, recognizing potential underreporting

Understanding this bias is critical for accurately assessing the risk-benefit profile of this new prostate cancer medication and making informed treatment decisions that prioritize patient morbidity, mortality, and quality of life.