Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

H. Luke Shaefer and colleagues argue for a universal child allowance

Hindustan Times points out high value of H-1B visas for US innovation, welfare, and tech firm profits

Novak, Geronimus, Martinez-Cardoso: Threat of deportation harmful to immigrants' health

More News

Highlights

Heather Ann Thompson wins Pulitzer Prize for book on Attica uprising

Lam explores dimensions of the projected 4 billion increase in world population before 2100

ISR's Nick Prieur wins UMOR award for exceptional contribution to U-M's research mission

How effectively can these nations handle outside investments in health R&D?

More Highlights

Next Brown Bag

Mon, April 10, 2017, noon:
Elizabeth Bruch

What's the Risk? A Simple Approach for Estimating Adjusted Risk Measures from Nonlinear Models Including Logistic Regression

Publication Abstract

Kleinman, L.C., and Edward Norton. 2009. "What's the Risk? A Simple Approach for Estimating Adjusted Risk Measures from Nonlinear Models Including Logistic Regression." Health Services Research, 44(1): 288-302.

To develop and validate a general method (called regression risk analysis) to estimate adjusted risk measures from logistic and other nonlinear multiple regression models. We show how to estimate standard errors for these estimates. These measures could supplant various approximations (e.g., adjusted odds ratio [AOR]) that may diverge, especially when outcomes are common. Regression risk analysis estimates were compared with internal standards as well as with Mantel-Haenszel estimates, Poisson and log-binomial regressions, and a widely used (but flawed) equation to calculate adjusted risk ratios (ARR) from AOR. Data sets produced using Monte Carlo simulations. Regression risk analysis accurately estimates ARR and differences directly from multiple regression models, even when confounders are continuous, distributions are skewed, outcomes are common, and effect size is large. It is statistically sound and intuitive, and has properties favoring it over other methods in many cases. Regression risk analysis should be the new standard for presenting findings from multiple regression analysis of dichotomous outcomes for cross-sectional, cohort, and population-based case-control studies, particularly when outcomes are common or effect size is large.

DOI:10.1111/j.1475-6773.2008.00900.x (Full Text)

Browse | Search : All Pubs | Next