Additive and multiplicative hazards modeling for recurrent event data analysis

<p>Abstract</p> <p>Background</p> <p>Sequentially ordered multivariate failure time or recurrent event duration data are commonly observed in biomedical longitudinal studies. In general, standard hazard regression methods cannot be applied because of correlation between recurrent failure times within a subject and induced dependent censoring. Multiplicative and additive hazards models provide the two principal frameworks for studying the association between risk factors and recurrent event durations for the analysis of multivariate failure time data.</p> <p>Methods</p> <p>Using emergency department visits data, we illustrated and compared the additive and multiplicative hazards models for analysis of recurrent event durations under (i) a varying baseline with a common coefficient effect and (ii) a varying baseline with an order-specific coefficient effect.</p> <p>Results</p> <p>The analysis showed that both additive and multiplicative hazards models, with varying baseline and common coefficient effects, gave similar results with regard to covariates selected to remain in the model of our real dataset. The confidence intervals of the multiplicative hazards model were wider than the additive hazards model for each of the recurrent events. In addition, in both models, the confidence interval gets wider as the revisit order increased because the risk set decreased as the order of visit increased.</p> <p>Conclusions</p> <p>Due to the frequency of multiple failure times or recurrent event duration data in clinical and epidemiologic studies, the multiplicative and additive hazards models are widely applicable and present different information. Hence, it seems desirable to use them, not as alternatives to each other, but together as complementary methods, to provide a more comprehensive understanding of data.</p

Solving the Organ Shortage Crisis: The 7th Annual American Society of Transplant Surgeons’ State-of-the-Art Winter Symposium

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73036/1/j.1600-6143.2007.02146.x.pd

Pretransplant Prediction of Posttransplant Survival for Liver Recipients with Benign End-Stage Liver Diseases: A Nonlinear Model

Background: The scarcity of grafts available necessitates a system that considers expected posttransplant survival, in addition to pretransplant mortality as estimated by the MELD. So far, however, conventional linear techniques have failed to achieve sufficient accuracy in posttransplant outcome prediction. In this study, we aim to develop a pretransplant predictive model for liver recipients ’ survival with benign end-stage liver diseases (BESLD) by a nonlinear method based on pretransplant characteristics, and compare its performance with a BESLD-specific prognostic model (MELD) and a generalillness severity model (the sequential organ failure assessment score, or SOFA score). Methodology/Principal Findings: With retrospectively collected data on 360 recipients receiving deceased-donor transplantation for BESLD between February 1999 and August 2009 in the west China hospital of Sichuan university, we developed a multi-layer perceptron (MLP) network to predict one-year and two-year survival probability after transplantation. The performances of the MLP, SOFA, and MELD were assessed by measuring both calibration ability and discriminative power, with Hosmer-Lemeshow test and receiver operating characteristic analysis, respectively. By the forward stepwise selection, donor age and BMI; serum concentration of HB, Crea, ALB, TB, ALT, INR, Na +; presence of pretransplant diabetes; dialysis prior to transplantation, and microbiologically proven sepsis were identified to be the optimal input features. The MLP, employing 18 input neurons and 12 hidden neurons, yielded high predictive accuracy, wit

Reimbursement and economic factors influencing dialysis modality choice around the world

The worldwide incidence of kidney failure is on the rise and treatment is costly; thus, the global burden of illness is growing. Kidney failure patients require either a kidney transplant or dialysis to maintain life. This review focuses on the economics of dialysis. Alternative dialysis modalities are haemodialysis (HD) and peritoneal dialysis (PD). Important economic factors influencing dialysis modality selection include financing, reimbursement and resource availability. In general, where there is little or no facility or physician reimbursement or payment for PD, the share of PD is very low. Regarding resource availability, when centre HD capacity is high, there is an incentive to use that capacity rather than place patients on home dialysis. In certain countries, there is interest in revising the reimbursement structure to favour home-based therapies, including PD and home HD. Modality selection is influenced by employment status, with an association between being employed and PD as the modality choice. Cost drivers differ for PD and HD. PD is driven mainly by variable costs such as solutions and tubing, while HD is driven mainly by fixed costs of facility space and staff. Many cost comparisons of dialysis modalities have been conducted. A key factor to consider in reviewing cost comparisons is the perspective of the analysis because different costs are relevant for different perspectives. In developed countries, HD is generally more expensive than PD to the payer. Additional research is needed in the developing world before conclusive statements may be made regarding the relative costs of HD and PD

Solid‐Organ Transplantation in Older Adults: Current Status and Future Research

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93680/1/j.1600-6143.2012.04245.x.pd