Methods Inf Med - The utility of imputed matched sets. Analyzing probabilistically linked databases in a low information setting.

Tópicos

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Resumo

JECTIVE: To compare results from high probability matched sets versus imputed matched sets across differing levels of linkage information.METHODS: A series of linkages with varying amounts of available information were performed on two simulated datasets derived from multiyear motor vehicle crash (MVC) and hospital databases, where true matches were known. Distributions of high probability and imputed matched sets were compared against the true match population for occupant age, MVC county, and MVC hour. Regression models were fit to simulated log hospital charges and hospitalization status.RESULTS: High probability and imputed matched sets were not significantly different from occupant age, MVC county, and MVC hour in high information settings (p >0.999). In low information settings, high probability matched sets were significantly different from occupant age and MVC county (p <0.002), but imputed matched sets were not (p >0.493). High information settings saw no significant differences in inference of simulated log hospital charges and hospitalization status between the two methods. High probability and imputed matched sets were significantly different from the outcomes in low information settings; however, imputed matched sets were more robust.CONCLUSIONS: The level of information available to a linkage is an important consideration. High probability matched sets are suitable for high to moderate information settings and for situations involving case-specific analysis. Conversely, imputed matched sets are preferable for low information settings when conducting population-based analyses.

Resumo Limpo

jectiv compar result high probabl match set versus imput match set across differ level linkag informationmethod seri linkag vari amount avail inform perform two simul dataset deriv multiyear motor vehicl crash mvc hospit databas true match known distribut high probabl imput match set compar true match popul occup age mvc counti mvc hour regress model fit simul log hospit charg hospit statusresult high probabl imput match set signific differ occup age mvc counti mvc hour high inform set p low inform set high probabl match set signific differ occup age mvc counti p imput match set p high inform set saw signific differ infer simul log hospit charg hospit status two method high probabl imput match set signific differ outcom low inform set howev imput match set robustconclus level inform avail linkag import consider high probabl match set suitabl high moder inform set situat involv casespecif analysi convers imput match set prefer low inform set conduct populationbas analys

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