A data scientist is analyzing a clinical trial dataset that includes the variables patient_age and systolic_blood_pressure (SBP). They observe that a significant number of SBP values are missing. Upon further investigation, the data scientist discovers that the probability of an SBP value being missing is correlated with patient_age, with younger patients being more likely to have a missing SBP value. However, within any specific age group, the reason for the missing SBP value is not related to the actual (unobserved) blood pressure level or any other unmeasured factor. Which type of missingness does this scenario describe?
The correct answer is Missing at Random (MAR). In this scenario, the missingness of the systolic_blood_pressure (SBP) is dependent on another observed variable, which is patient_age. This is the key characteristic of MAR: the probability of a value being missing is related to other observed information in the dataset but not to the unobserved value itself.
Missing Completely at Random (MCAR) is incorrect because the missingness is not completely random; it has a systematic relationship with the patient_age variable. If the data were MCAR, the probability of a missing SBP value would be the same for all patients, regardless of their age or any other characteristic.
Not Missing at Random (NMAR) is incorrect because the scenario explicitly states that the missingness is not related to the actual unobserved blood pressure level. NMAR would apply if, for example, patients with very high blood pressure were less likely to have their SBP recorded, meaning the missingness depends on the value of the missing variable itself.
Structural Missingness is incorrect. This term typically refers to data that is missing for a logical reason inherent in the study's design. For instance, a question about the number of pregnancies would be structurally missing for male participants. The scenario described does not fit this definition.
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What distinguishes Missing at Random (MAR) from Missing Completely at Random (MCAR)?
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How can a data scientist address Missing at Random (MAR) in a dataset?
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Why doesn't the scenario fall under Not Missing at Random (NMAR)?