Considering the XY Problem in Biostatistics: Are we Answering the Right Questions?
The XY problem, a common pitfall in problem-solving and communication, also frequently surfaces in the field of biostatistics. It occurs when someone asks for help with an attempted solution rather than their actual underlying problem. This can lead to misdirected efforts, incorrect analyses, and ultimately, flawed conclusions.
Generic example scenario:
You want to do X.
You don’t know how to do X, but you think you can find a solution if you do Y.
But you actually don’t know how to do Y either. So, you ask for help with Y.
Others try to help figure out Y, but get confused. As a consequence, secondary problems and questions may arise.
The path of inquiry becomes unproductive with unnecessary interactions and wasted time.
Eventually, it becomes clear to others that you really wanted help with X (the underlying question), and that Y was not even an appropriate method or solution for figuring out X.
In summary, this problem occurs because people can easily get fixated on what they believe is a possible solution, rather than stepping back and communicating the underlying issue.
Biostatistics example scenario:
In biostatistics, the XY problem often manifests when researchers or clinicians approach a biostatistician with a request for a very specific statistical test or method (Y) without first clearly articulating their core research question or the true clinical/biological problem they are trying to address (X).
Imagine a clinician (let's call her Dr. A) wants to understand if a new drug prolongs survival in patients with a certain condition. Instead of posing the question directly or providing details about the different subjects and research conditions, Dr. A might approach a biostatistician saying, "I need to run a Kaplan-Meier analysis and a log-rank test on my patient data."
While Kaplan-Meier and log-rank tests are appropriate for survival analysis, Dr. A might have overlooked other critical aspects or nuances of their data and research question that would be otherwise important for a thorough statistical analysis. Perhaps their study design has specific complexities (i.e., competing risks, time-dependent covariates) that would necessitate a different, more robust analytical approach, such as a Cox proportional hazards model or even a multi-state model. By immediately jumping to the solution (Y, i.e., Kaplan-Meier/log-rank test), Dr. A bypasses the crucial step of defining the problem (X, i.e., investigating the drug's effect on survival considering all relevant factors).
The consequences of this XY dynamic in biostatistics can be significant and include:
Wasted time and resources: Biostatisticians might spend time implementing a method that isn't truly optimal for the underlying question.
Incorrect or suboptimal analyses: The chosen statistical method (Y) might not fully address the research question (X), leading to biased or incomplete results.
Flawed conclusions: Decisions made based on an analysis of Y when X was the real concern can have serious implications for biomedical product development or patient care.
Frustration and miscommunication: Both parties can become frustrated when the proposed solution doesn't seem to fit the unstated problem.
How can we, as biostatisticians and researchers, mitigate the XY problem?
For biostatisticians at Hensley Biostats: We prioritize asking clarifying questions. Instead of immediately diving into the requested method, we take a step back and probe: "What is the core question you're trying to answer with this analysis?", "What are your objectives?", "What clinical or biological insight are you hoping to gain?"
For researchers: Before approaching a biostatistician, take the time to clearly articulate your research question. Think deeply about the "why" behind your data and the "what" you truly want to learn. Be open to discussing the underlying problem, not just your preconceived solution. Consider whether you have a clear understanding of your study design, the nature of your variables, and potential confounding factors.
In general, foster open communication. Create an environment where it's safe to explore the "X" before settling on a "Y." The biostatistician's role is not just to run analyses, but to partner in defining the most appropriate scientific and statistical approaches to answer important questions for your research.
By consciously addressing the XY problem, we ensure that our biostatistical collaborations are more efficient, our analyses more accurate, and our scientific conclusions more robust—ultimately leading to stronger research for our clients and improved health outcomes for future patients.
