Mon, Jan 23, 2017 at noon:
Decline of cash assistance and child well-being, Luke Shaefer
Elliott, Michael R. 2010. "Matched Cohort Analysis in Traffic Injury Epidemiology: Including Adults when Estimating Exposure Risks for Children." Injury Prevention, 16: 367-371.
Background: Matched cohort analyses and their extension to conditional logistic and Poisson regression are powerful tools for assessing risk and protective factors in automobile crashes. However, these analyses rely on assumptions about latent (unobserved) 'crash severity' risk measures for subjects in the vehicle being common for all such subjects if confounding with crash severity is present. The assumptions may be questionable if adults are being matched to children.
Methods: Simulations were conducted to evaluate conditional Poisson regression in settings where different types of subjects may have different underlying baseline summary risk measures in a given crash—for example, adults and children. Situations were considered where baseline summary risk measures and protective factors are confounded with each other and where the adult and child baseline risk measures are either highly correlated or weakly correlated.
Results: When the risk or protective factor is confounded with crash severity, baseline summary risk measures due to crash severity must be perfectly correlated for all subjects in the crash for the factor to be consistently estimated. Relative bias estimates ranged from 5% to 50% depending on the degree of correlation of the risk measure and the number of matched subjects in a crash.
Conclusions: Matched cohort analysis and its regression extensions are useful tools in the injury epidemiology toolkit, but require assumptions about high correlation of baseline summary risk measures among adults and children to accurately account for confounding between risk or protective factors of interest by crash severity. Conservative coverage intervals can preserve correct nominal coverage as long as the adult–child risk factors are highly correlated.