Credible Ecological Inference for Personalized Medicine: Formalizing Clinical Judgment / Charles F. Manski.

Format:

Book

Author/Creator:

Manski, Charles F.

Contributor:

National Bureau of Economic Research.

Series:

Working Paper Series (National Bureau of Economic Research) no. w22643.

NBER working paper series no. w22643

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Other Title:

Credible Ecological Inference for Personalized Medicine

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2016.

Summary:

This paper studies the ecological inference problem that arises when clinicians seek to personalize patient care by making health risk assessments conditional on observed patient attributes. Let y be a patient outcome of interest and let (x = k, w = j) be patient attributes that a clinician observes. The clinician may want to choose a care option that maximizes the patient's expected utility conditional on the observed attributes. To accomplish this, the clinician needs to know the conditional probability distribution P(y|x = k, w = j). In practice, it is common to have a trustworthy evidence-based risk assessment that predicts y conditional on a subset of the observed attributes, say x, but not conditional on (x, w). Then the clinician knows P(y|x = k) but not P(y|x = k, w = j). Partial conclusions about P(y∣x = k, w = j) may be drawn if the clinician also knows P(w = j|x = k). Tighter conclusions may be possible if he combines knowledge of P(y|x) and P(w|x) with credible structural assumptions embodying some a priori knowledge of P(y|x, w). This is the ecological inference problem studied here. A substantial psychological literature comparing actuarial predictions and informal clinical judgments has concluded that clinicians should not attempt to subjectively predict patient outcomes conditional on attributes such as w that are not utilized in evidence-based risk assessments. The analysis in this paper suggests that formalizing clinical judgment through analysis of the inferential problem may enable clinicians to make more informative personalized risk assessments.

Notes:

Print version record

September 2016.