UNASSIGNED: Improving the treatment of @entity130 , a leading cause of @entity204 worldwide, is of great clinical and public health interest. This analysis introduces flexible statistical methods for estimating center-level effects on individual outcomes in the context of highly variable @entity1 populations, such as those of the PRospective, Observational, Multi-center @entity130 Transfusion study. Ten US level I @entity130 centers enrolled a total of 1,245 @entity130 @entity1 who survived at least 30 minutes after admission and received at least one unit of red blood cells. Outcomes included @entity204 , @entity881 , @entity548 , and transfusion of blood products. The centers involved were classified as either large or small-volume based on the number of massive transfusion @entity1 enrolled during the study period. We focused on estimation of parameters inspired by causal inference, specifically estimated impacts on @entity1 outcomes related to the volume of the @entity130 hospital that treated them. We defined this association as the change in mean outcomes of interest that would be observed if, contrary to fact, subjects from large-volume sites were treated at small-volume sites (the effect of treatment among the treated). We estimated this parameter using three different methods, some of which use data-adaptive machine learning tools to derive the outcome models, minimizing residual confounding by reducing model misspecification. Differences between unadjusted and adjusted estimators sometimes differed dramatically, demonstrating the need to account for differences in @entity1 characteristics in clinic comparisons. In addition, the estimators based on robust adjustment methods showed potential impacts of hospital volume. For instance, we estimated a survival benefit for @entity1 who were treated at large-volume sites, which was not apparent in simpler, unadjusted comparisons. By removing arbitrary modeling decisions from the estimation process and concentrating on parameters that have more direct policy implications, these potentially automated approaches allow methodological standardization across similar comparativeness effectiveness studies.

Statistical Machines for @entity130 Hospital Outcomes Research: Application to the PRospective, Observational, Multi-Center XXXX Transfusion (PROMMTT) Study.

@entity1 :: ('9606', 'Species') :: ['patients', 'patient'] @entity548 :: ('MESH:D006470', 'Disease') :: ['substantial bleeding'] @entity881 :: ('MESH:D009102', 'Disease') :: ['multiple organ failure'] @entity204 :: ('MESH:D003643', 'Disease') :: ['death'] @entity130 :: ('MESH:D014947', 'Disease') :: ['Major Trauma', 'trauma']
@entity130 :: (MESH:D014947,Disease) :: ['Major Trauma']
