How do you handle missing data in your analyses?

Your Answer

How To Answer This Question?

Handling missing data is a critical skill in biostatistics, especially in the context of clinical trials and pharmaceutical research where data integrity is paramount. When answering this question, you should demonstrate your understanding of different types of missing data (MCAR, MAR, MNAR) and the appropriate methods to handle them. Here’s a structured way to answer:

1. Identify the Type of Missing Data: Explain how you determine whether data is Missing Completely at Random (MCAR), Missing at Random (MAR), or Missing Not at Random (MNAR).

2. Choose an Appropriate Method: Discuss the methods you use to handle missing data, such as:

3. Deletion Methods: Listwise or pairwise deletion, and when they are appropriate.
4. Imputation Methods: Mean/mode/median imputation, regression imputation, or more advanced methods like Multiple Imputation by Chained Equations (MICE) or Maximum Likelihood Estimation (MLE).

5. Model-Based Methods: Mixed models, Bayesian methods, etc.

6. Justify Your Choice: Provide reasoning for your chosen method based on the context of the data and the analysis requirements.

7. Practical Example: Share a specific example from your past experience where you successfully handled missing data, explaining the steps you took and the outcome.

Example Answer:

"In my previous role, I encountered a dataset from a clinical trial with a significant amount of missing data. First, I assessed the pattern and mechanism of the missing data and determined it was MAR. I chose to use Multiple Imputation by Chained Equations (MICE) because it allows for the uncertainty of the missing data to be properly accounted for. After performing the imputation, I validated the results by comparing the imputed values with known data points and found the imputation to be reliable. This approach helped maintain the integrity of the dataset and provided robust results for the analysis."