Sample interview questions: How do you handle missing data in health outcomes research studies?
Sample answer:
- Assess the extent of missing data.
- Determine the proportion of missing data for each variable of interest.
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Consider the pattern of missing data (e.g., random, non-random).
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Identify potential biases.
- Missing data can lead to bias if the missing data are not missing at random (MNAR).
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For example, if patients with a certain condition are more likely to have missing data on a particular outcome, the results of the study may be biased towards those patients who do not have missing data.
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Choose an appropriate missing data handling method.
- There are a variety of missing data handling methods available, each with its own strengths and weaknesses.
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The choice of method depends on the type of data, the pattern of missing data, and the assumptions that can be made about the missing data.
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Implement the missing data handling method.
- Once a missing data handling method has been chosen, it must be implemented correctly.
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This may involve using statistical software or manually imputing missing data values.
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Evaluate the impact of missing data handling.
- After the missing data have been handled, it is important to evaluate the impact of the missing data handling method on the results of the study.
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Source: https://hireabo.com/job/2_3_21/Health%20Outcomes%20Researcher