A mixed-effects two-part model for twin-data and an application on identifying important factors associated with extremely preterm children's health disorders.

Our recent studies identifying factors significantly associated with the positive child health index (PCHI) in a mixed cohort of preterm-born singletons, twins, and triplets posed some analytic and modeling challenges. The PCHI transforms the total number of health disorders experienced (of the eleven ascertained) to a scale from 0 to 100%. While some of the children had none of the eleven health disorders (i.e., PCHI = 1), others experienced a subset or all (i.e., 0 ≤PCHI< 1). This indicates the existence of two distinct data processes-one for the healthy children, and another for those with at least one health disorder, necessitating a two-part model to accommodate both. Further, the scores for twins and triplets are potentially correlated since these children share similar genetics and early environments. The existing approach for analyzing PCHI data dichotomizes the data (i.e., number of health disorders) and uses a mixed-effects logistic or multiple logistic regression to model the binary feature of the PCHI (1 vs. < 1). To provide an alternate analytic framework, in this study we jointly model the two data processes under a mixed-effects two-part model framework that accounts for the sample correlations between and within the two data processes. The proposed method increases power to detect factors associated with disorders. Extensive numerical studies demonstrate that the proposed joint-test procedure consistently outperforms the existing method when the type I error is controlled at the same level. Our numerical studies also show that the proposed method is robust to model misspecifications and it is applicable to a set of correlated semi-continuous data.

Efficacy of antimicrobial polymer coatings in an animal model of bacterial infection associated with foreign body implants.

OBJECTIVES: To assess support discs, comprising polyethylene terephthalate (PET), coated with different polymer/levofloxacin combinations for antimicrobial activity in an animal model of infection, in order to explore the use of specific polymer coatings incorporating levofloxacin as a means of reducing device-related infections. METHODS: Aliphatic polyester-polyurethanes containing different ratios of poly(lactic acid) diol and poly(caprolactone) diol were prepared, blended with levofloxacin and then used to coat support discs. The in vitro levofloxacin release profiles from these discs were measured in aqueous solution. Mice were surgically implanted with the coated discs placed subcutaneously and infection was initiated by injection of 10(6) cfu of Staphylococcus aureus into the subcutaneous pocket containing the implant. After 5, 10, 20 and 30 days, the discs were removed, and the number of bacteria adhering to the implant and the residual antimicrobial activity of the discs were determined. RESULTS: In vitro, the release of levofloxacin from the coated discs occurred at a constant rate and then reached a plateau at different timepoints, depending on the polymer preparation used. In vivo, none of the discs coated with polymer blends containing levofloxacin was colonized by S. aureus, whereas 94% of the discs coated with polymer alone were infected. All discs coated with levofloxacin-blended polymers displayed residual antimicrobial activity for at least 20 days post-implantation. CONCLUSIONS: Bioerodable polyester-polyurethane polymer coatings containing levofloxacin can prevent bacterial colonization of implants in an intra-operative model of device-related infections.