A study is performed that looks at individuals who suffer from opioid use disorder and the effect the disorder has on their daily life and functionality. Individuals are surveyed using a validated method and followed up in 6-month intervals for 5 years. At the end of the study period, 40% of individuals have replied to the survey throughout the entire period. Analyzing only those subjects who responded throughout the entire period, it is determined that opioid use disorder only minimally impairs individuals in their daily functioning. Which of the following most likely occurred in this study?
Confounding variable
Recall bias
Volunteer bias
Attrition bias
Attrition bias
This study following individuals who abuse opioids with a 40% follow-up response rate suggests that opioid use disorder (known for impairing daily functioning) has only a minor effect on patient functionality. This is most likely secondary to attrition bias, or a loss to follow-up of participants in a non-random fashion whereby more severe cases stopped responding and were thus left out.

Bias in studies distorts outcomes and can make interpreting results challenging. For this reason, it is important to be meticulous when designing a study with every effort to minimize bias. The attrition bias can occur when individuals are lost to follow-up in a non-random fashion. For example, consider the assessment of daily functionality via a validated method in individuals with coronary artery disease. Those who are severely impaired from disease progression may not follow up in this study, thus altering results and removing more severe cases from the analysis. Having a higher response rate can help mitigate this bias, but is more costly as it may be difficult to ensure that every participant follows up.

Nunan et al. discuss the attrition bias. They note how it can affect studies and the data collected. They recommend mitigating the attrition bias for more accurate results and clinical application.

Incorrect Answers:
Answer B: Confounding variables are variables that are correlated with the studied variable that better explain the difference between 2 groups. For example, it may be found that alcohol is associated with lung cancer when in reality, the confounding variable of smoking (more common in individuals who drink alcohol) explains this difference.

Answer C: Late-look bias occurs when patients with severe disease are not studied because they are dead, critically ill, or unavailable to be studied, making the disease overall appear less severe. For example, patients with HIV may not have increased morbidity and mortality in a study. However, this may be a result of all the critically ill cases being too sick or dead and thus unable to participate thus distorting the outcome.

Answer D: Recall bias occurs when individuals with disease or an adverse outcome are more likely to recall an exposure given the occurred outcome, skewing their memory and thus the results. For example, pregnant mothers who have a newborn with a defect or condition may be more likely to recall certain exposures (such as exposure to pollution).

Answer E: Volunteer bias occurs when people who volunteer for a study are fundamentally different from the general population or those who do not volunteer. An example may be individuals volunteering in a study involving exercise as those who are interested in participating in an exercise study may be more motivated or healthier at baseline.

Bullet Summary:
Attrition bias occurs when individuals are lost to follow-up in a non-random fashion leading to skewed results.