A data science team is analyzing the results of a year-long randomized controlled trial (RCT) for a new hypertension medication. The initial randomization successfully created comparable treatment and control groups. However, the final analysis reveals that 30% of the participants in the treatment group dropped out of the study, citing adverse side effects, compared to only a 5% dropout rate in the control group. Despite this, a simple comparison of the endpoints between the remaining participants shows a statistically significant reduction in blood pressure for the treatment group. Which of the following is the most significant threat to the study's internal validity?
The presence of unknown confounding variables that were not accounted for during the initial randomization.
An increased risk of a Type I error due to the reduced sample size in the treatment group.
The study lacks external validity, as the results are not generalizable to a broader population.
Differential attrition between the groups compromises the initial randomization, potentially leading to selection bias.
The correct answer identifies differential attrition as the primary threat. In an RCT, randomization is used to create groups that are, on average, balanced in terms of both known and unknown confounding variables. This balance is what allows for causal inference. Differential attrition, which is the unequal loss of participants between groups, can break this balance. In this scenario, participants who experienced adverse side effects disproportionately left the treatment group. The remaining participants in the treatment group are therefore a self-selected subset who could tolerate the medication, and they are no longer comparable to the control group. This introduces selection bias, which is a major threat to the study's internal validity because the observed effect might be due to the differences in the remaining groups rather than the medication itself.
External validity refers to the generalizability of study results to a broader population, which is a different concept from internal validity. While it may be a concern, it is not the most direct threat described in the scenario. The initial randomization is designed to handle confounding variables; the problem here is that the integrity of the randomization was compromised after the study began. Finally, the issue is not primarily about a Type I error resulting from a reduced sample size; the core problem is the systematic bias introduced by the non-random dropout of participants.
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