Question 1
Difficulty: medium
How do you approach designing the statistical analysis plan for a clinical study from the beginning?
Sample answer
I start by understanding the study objective, endpoint definitions, and operational realities of the protocol. Before writing any code, I make sure I know what decision the analysis needs to support, because that shapes everything from sample size assumptions to missing-data handling. I review the protocol, schedule of assessments, randomization scheme, and key covariates so I can identify potential sources of bias or ambiguity early. Then I define the primary and secondary analyses, the estimands, analysis populations, and any sensitivity analyses that will be needed. I also flag data risks such as expected dropouts, multiplicity, or uncommon endpoint timing. In practice, I like to document all of this clearly in the SAP so the clinical and programming teams can align on one version of the truth. My goal is to make the analysis reproducible, defensible, and aligned with the scientific question rather than just technically correct.
Question 2
Difficulty: easy
Tell me about a time you had to explain a complex statistical result to a non-statistical stakeholder.
Sample answer
In a previous role, I had to explain why a treatment showed a strong numerical improvement in the primary endpoint but did not meet statistical significance. The study team was understandably disappointed, and the discussion could have easily become confusing. I focused first on the practical meaning rather than the formula. I explained the estimated treatment effect, the confidence interval, and what the p-value was telling us about uncertainty. I also showed how the sample size and variability affected power, which helped frame the result as a study-design issue rather than a failure of the treatment itself. I avoided jargon and used a simple visual to compare groups over time. By the end, the team understood the result well enough to discuss next steps, including whether subgroup signals or future study design changes were worth exploring. That experience reinforced how important it is to communicate statistics in a way that supports decisions.
Question 3
Difficulty: hard
How would you handle missing data in a clinical trial analysis?
Sample answer
I treat missing data as both a statistical and scientific issue. First I try to understand why the data are missing, because the mechanism matters more than the percentage alone. If missingness is related to treatment response or adverse events, that can introduce bias that simple imputation will not fix. I usually begin with descriptive summaries and patterns over time, then evaluate whether the missingness is plausibly missing completely at random, missing at random, or not at random. From there, I choose methods that fit the endpoint and study design, such as mixed models for repeated measures, multiple imputation, or sensitivity analyses under different assumptions. I’m careful not to rely on one approach only. Regulators and stakeholders want to know whether the conclusion is robust, not just what one model says. So I would present the primary method, the assumptions behind it, and at least one or two sensitivity analyses to show how stable the result is.
Question 4
Difficulty: easy
Describe your experience with SAS, R, or other statistical software in a biostatistics setting.
Sample answer
I have used statistical software as part of the full analysis workflow, not just for generating output. In SAS, I’m comfortable with data cleaning, derivation of analysis datasets, PROC GLM, PROC MIXED, survival procedures, and producing tables and listings that are ready for review. In R, I use it for exploratory analysis, visualization, model development, and automation when flexibility is helpful. I value reproducible code, so I build scripts that can be rerun with minimal manual intervention and I comment them well enough for another statistician or programmer to follow later. I also like to validate results across tools when feasible, especially for key analyses. More than the software itself, I care about understanding the assumptions behind the method and whether the implementation matches the study objective. Good software skills matter, but in biostatistics they are most useful when paired with careful thinking and strong documentation.
Question 5
Difficulty: medium
How do you determine the appropriate sample size for a study?
Sample answer
Sample size planning starts with the scientific question and the endpoint, not with a generic formula. I identify the primary hypothesis, the expected effect size, the variability or event rate, the significance level, and the desired power. Then I think about practical issues such as dropout, noncompliance, stratification, and whether there will be interim looks that affect alpha spending. I also work closely with clinical colleagues to make sure the assumptions are realistic, because a mathematically correct sample size can still be useless if the inputs are not credible. If historical data are limited, I prefer to use a range of scenarios rather than a single optimistic estimate. That helps the team understand the tradeoffs between feasibility and statistical confidence. For me, sample size is not just a calculation; it is part of study strategy. A good calculation supports the trial objectives while acknowledging uncertainty and operational constraints.
Question 6
Difficulty: medium
Tell me about a situation where you found an issue in the data and how you handled it.
Sample answer
In one project, I noticed that a key baseline variable had unusually low variability compared with what we expected from prior studies. That raised a red flag because it affected both the analysis and the interpretation of covariate balance. I investigated by checking raw data, edit histories, and the derivation logic in the analysis dataset. It turned out that a programming rule had recoded several values into a narrower category than intended. I documented the issue, informed the data management and programming teams, and worked with them to correct the derivation. After that, I reran the affected analyses and compared the results to make sure the correction did not create new inconsistencies. I think the most important part was not just spotting the problem, but handling it in a way that was transparent, traceable, and calm. In regulated work, finding issues early is valuable, but resolving them clearly is what protects the integrity of the study.
Question 7
Difficulty: hard
How do you choose between different statistical methods when analyzing a study endpoint?
Sample answer
I start by matching the method to the endpoint type, study design, and question being asked. For example, continuous outcomes may call for ANCOVA or mixed models, binary outcomes may need logistic regression, and time-to-event outcomes may require survival methods such as Kaplan-Meier estimates and Cox models. But I do not stop at the endpoint label. I look at distributional assumptions, censoring patterns, repeated measurements, and whether treatment effects may vary over time. I also consider how interpretable the output will be for clinical teams and regulators. If several methods are reasonable, I usually prioritize the one that best reflects the study design and has the clearest assumption structure, then plan sensitivity analyses to check robustness. I like methods that are statistically sound and operationally practical. In biostatistics, the best method is rarely the most complex one; it is the one that answers the question faithfully and can stand up to scrutiny.
Question 8
Difficulty: easy
How do you handle tight deadlines when multiple study deliverables are due at the same time?
Sample answer
I handle deadline pressure by being very intentional about prioritization and communication. First I identify which deliverables are truly critical path items, such as outputs tied to a database lock, a CSR deadline, or an interim analysis. Then I break the work into smaller tasks so I can see what can be done in parallel and what depends on other teams. I communicate early if I see a conflict, because waiting until the last minute usually creates more problems than it solves. When necessary, I’ll also distinguish between items that need to be perfect and items that can be drafted for review with minor follow-up later. I’m comfortable asking for clarification or negotiating sequence when priorities compete. I think strong biostatisticians do more than analyze data; they help keep the project moving without compromising quality. That means staying organized, being transparent about risks, and protecting the work that has the biggest impact on study decisions.
Question 9
Difficulty: hard
How would you evaluate whether a treatment effect differs across subgroups?
Sample answer
I would approach subgroup analysis cautiously and systematically. First I would confirm that the subgroup definitions are pre-specified or clinically justified, because data-driven subgroup fishing can be misleading. Then I would test for interaction rather than just comparing p-values within each subgroup, since an apparent difference in significance does not necessarily mean there is a real treatment-by-subgroup effect. I would also look at sample sizes, balance across groups, and confidence intervals to understand how stable the estimates are. If the analysis is exploratory, I would say so clearly and avoid over-interpreting the results. I like to present subgroup findings in a way that highlights uncertainty, not just point estimates. In some studies, subgroup work can be very helpful for hypothesis generation or understanding heterogeneity of response, but it should never replace the primary analysis. My standard is to keep the analysis scientifically useful while avoiding conclusions the data cannot support.
Question 10
Difficulty: easy
Why do you want to work as a biostatistician, and what makes you effective in this role?
Sample answer
I like biostatistics because it sits at the intersection of science, evidence, and real-world decisions. The work matters: a well-designed analysis can influence whether a treatment moves forward, how safety is understood, and what questions get asked next. What I enjoy most is taking a messy scientific question and turning it into an analysis that is rigorous, transparent, and useful to a broader team. I think I’m effective in this role because I combine technical discipline with practical communication. I pay attention to details, but I also keep the bigger picture in mind so the analysis stays aligned with the study objective. I’m comfortable working with clinical, data management, and programming colleagues, and I try to be someone who solves problems rather than just identifies them. For me, being a strong biostatistician means producing accurate results and helping the team make confident decisions based on those results.
