clinical biostatistician Interview Questions and Answers

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100 Clinical Biostatistician Interview Questions and Answers

  1. What is your experience with statistical software packages like SAS, R, or Stata?

    • Answer: I have extensive experience with SAS, R, and Stata. In SAS, I'm proficient in PROC SQL, PROC MIXED, PROC GLM, and PROC FREQ for data manipulation, analysis, and reporting. My R skills encompass data wrangling with dplyr and tidyr, statistical modeling using lm, glm, and survival packages, and visualization with ggplot2. In Stata, I'm comfortable with regression analysis, survival analysis, and creating publication-ready tables and graphs. I can adapt quickly to new statistical software as needed.

  2. Explain the difference between a Type I and Type II error.

    • Answer: A Type I error (false positive) occurs when we reject the null hypothesis when it is actually true. A Type II error (false negative) occurs when we fail to reject the null hypothesis when it is actually false. The probability of a Type I error is denoted by α (alpha), and the probability of a Type II error is denoted by β (beta). The power of a test is 1 - β, representing the probability of correctly rejecting a false null hypothesis.

  3. Describe your experience with clinical trial design.

    • Answer: I have experience in designing various clinical trial types, including randomized controlled trials (RCTs), observational studies, and cohort studies. My experience encompasses sample size calculations using power analysis, determining appropriate randomization methods (e.g., stratified, blocked), and developing statistical analysis plans (SAPs) that outline the primary and secondary endpoints, statistical methods, and handling of missing data. I'm familiar with different trial phases and their specific design considerations.

  4. How do you handle missing data in a clinical trial?

    • Answer: Handling missing data depends on the mechanism of missingness (MCAR, MAR, MNAR). For Missing Completely at Random (MCAR) data, complete case analysis might be acceptable. However, for Missing at Random (MAR) or Missing Not at Random (MNAR) data, more sophisticated techniques are necessary. These include multiple imputation, inverse probability weighting, and maximum likelihood estimation. The choice of method depends on the nature of the data, the missingness mechanism, and the potential for bias. A sensitivity analysis is often conducted to assess the impact of different missing data handling approaches on the results.

  5. What are your experiences with different types of regression analysis?

    • Answer: I have extensive experience with linear, logistic, and Poisson regression. Linear regression models continuous outcomes, logistic regression models binary outcomes, and Poisson regression models count data. I understand the assumptions of each model and how to assess model fit and interpret results. I am also familiar with generalized linear models (GLMs) and mixed-effects models for handling more complex data structures.

  6. Explain the concept of confounding and how to address it.

    • Answer: Confounding occurs when the effect of an exposure on an outcome is distorted by the presence of a third variable (confounder) associated with both the exposure and the outcome. To address confounding, we can use stratification, regression adjustment (including propensity score matching), or randomization in the design stage of a study.

  7. What is your experience with survival analysis?

    • Answer: I have experience conducting survival analysis using Kaplan-Meier curves to estimate survival probabilities and Cox proportional hazards models to identify risk factors associated with time-to-event outcomes. I am familiar with handling censored data and assessing the proportional hazards assumption. I can also perform competing risks analysis when appropriate.

  8. How do you determine the appropriate sample size for a clinical trial?

    • Answer: Sample size calculation involves considering several factors, including the desired power, significance level (alpha), effect size, and variability of the outcome. I use statistical software and power analysis techniques to determine the required sample size to detect a clinically meaningful difference between treatment groups with sufficient power and precision.

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