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Sensitivity of Predictors
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In a clinical study, a new predictive model is developed to identify patients at high risk for a specific disease. If the model has a sensitivity of 85% and a false positive rate of 10%, what can be inferred about the accuracy of the predictors when applied to a population with a prevalence of 20% for the disease?

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A

The accuracy will be higher than 90%

B

The accuracy will be lower than 50%

C

The accuracy will be around 78%

D

The accuracy cannot be determined without additional information

Understanding the Answer

Let's break down why this is correct

Sensitivity tells us how many sick people the model finds. Other options are incorrect because Many think a high sensitivity automatically means very high accuracy; Some assume that a low false positive rate guarantees accuracy above 50%.

Key Concepts

predictors
accuracy
false positive rate
Topic

Sensitivity of Predictors

Difficulty

hard level question

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understand

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Definition
Definition

The sensitivity of a predictor measures its responsiveness to changes in input features. Insensitive predictors exhibit stability in their predictions when inputs are close. Sensitivity is crucial for generalization and performance, especially with limited training data.

Topic Definition

The sensitivity of a predictor measures its responsiveness to changes in input features. Insensitive predictors exhibit stability in their predictions when inputs are close. Sensitivity is crucial for generalization and performance, especially with limited training data.

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