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What's the Risk? A simple approach for estimating adjusted risk measures from nonlinear models including logistic regression.

ABSTRACT: OBJECTIVE: 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. STUDY DESIGN: 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 COLLECTION: Data sets produced using Monte Carlo simulations. PRINCIPAL FINDINGS: 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. CONCLUSIONS: 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.

SUBMITTER: Kleinman LC

PROVIDER: S-EPMC2669627 | biostudies-literature | 2009 Feb

REPOSITORIES: biostudies-literature

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What's the Risk? A simple approach for estimating adjusted risk measures from nonlinear models including logistic regression.

Kleinman Lawrence C LC Norton Edward C EC

Health services research 20080911 1

<h4>Objective</h4>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.<h4>Study design</h4>Regression risk analysis estimates were compared with internal standards as well as wit ...[more]

PMID: 18793213

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Project description:BACKGROUND:Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex disease, whose exact cause remains unclear. A wide range of risk factors has been proposed that helps understanding potential disease pathogenesis. However, there is little consistency for many risk factor associations, thus we undertook an exploratory study of risk factors using data from the UK ME/CFS Biobank participants. We report on risk factor associations in ME/CFS compared with multiple sclerosis participants and healthy controls. METHODS:This was a cross-sectional study of 269 people with ME/CFS, including 214 with mild/moderate and 55 with severe symptoms, 74 people with multiple sclerosis (MS), and 134 healthy controls, who were recruited from primary and secondary health services. Data were collected from participants using a standardised written questionnaire. Data analyses consisted of univariate and multivariable regression analysis (by levels of proximity to disease onset). RESULTS:A history of frequent colds (OR = 8.26, P <= 0.001) and infections (OR = 25.5, P = 0.015) before onset were the strongest factors associated with a higher risk of ME/CFS compared to healthy controls. Being single (OR = 4.41, P <= 0.001), having lower income (OR = 3.71, P <= 0.001), and a family history of anxiety is associated with a higher risk of ME/CFS compared to healthy controls only (OR = 3.77, P < 0.001). History of frequent colds (OR = 6.31, P < 0.001) and infections before disease onset (OR = 5.12, P = 0.005), being single (OR = 3.66, P = 0.003) and having lower income (OR = 3.48, P = 0.001), are associated with a higher risk of ME/CFS than MS. Severe ME/CFS cases were associated with lower age of ME/CFS onset (OR = 0.63, P = 0.022) and a family history of neurological illness (OR = 6.1, P = 0.001). CONCLUSIONS:Notable differences in risk profiles were found between ME/CFS and healthy controls, ME/CFS and MS, and mild-moderate and severe ME/CFS. However, we found some commensurate overlap in risk associations between all cohorts. The most notable difference between ME/CFS and MS in our study is a history of recent infection prior to disease onset. Even recognising that our results are limited by the choice of factors we selected to investigate, our findings are consistent with the increasing body of evidence that has been published about the potential role of infections in the pathogenesis of ME/CFS, including common colds/flu.

Dataset Information

What's the Risk? A simple approach for estimating adjusted risk measures from nonlinear models including logistic regression.

Publications

What's the Risk? A simple approach for estimating adjusted risk measures from nonlinear models including logistic regression.

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