Simulation study of joint transition and Weibull survival model with shared parameters

This study investigates a theoretical model where a longitudinal process, that is a stationary Markov-Chain, and a Weibull survival process share a bivariate random effect. Furthermore, a Quality-of-Life adjusted survival is calculated as the weighted sum of survival time. Theoretical values of population mean adjusted survival of the described model are computed numerically. The parameters of the bivariate random effect do significantly affect theoretical values of population mean. Maximum-Likelihood and Bayesian methods are applied on simulated data to estimate the model parameters. Based on the parameter estimates, predicated population mean adjusted survival can then be calculated numerically and compared with the theoretical values. Bayesian method and Maximum-Likelihood method provide parameter estimations and population mean prediction with comparable accuracy; however Bayesian method suffers from poor convergence due to autocorrelation and inter-variable correlation. ^

Anti-glomerular basement membrane glomerulonephritis: Characterization of an epitope, antibody response, and the potential role of IL-4Ralpha in disease progression

Anti-Glomerular Basement Membrane Glomerulonephritis (anti-GBM GM) is one of the earliest described autoimmune disorders. Patients present with proteinuria, anti-GBM antibodies, and renal failure. Studies have implicated a T Helper 1 (TH1) response in disease induction and a T Helper 2 (TH2) response for disease progression. A 13 amino acid long peptide sequence spanning residues 28 through 40 [pCol(28–40)] of the Collagen IV α3 non-collagen domain (Col IV α3 NCD) is immunogenic and induces anti-GBM GN. In order to fully understand disease initiation, this peptide was further characterized. Peptides were created containing one amino acid substitution for the entire length of pCol(28–40) and induction of anti-GBM GN was monitored. When residues 31, 33, or 34 contained the substitution, anti-GBM GN was unable to be induced. Thus, residues 31, 33, and 34 of pCol(28–40) are required for induction of anti-GBM. Glomerular injury is observed as early as 14 days post anti-GBM GN induction. However, the presence of anti-GBM antibodies is not observed until 20 days post immunization. An enlarged lymph node adjacent to the diseased kidney exhibits B cell activation after renal injury and produces antibodies toward GBM. Thus, anti-GBM antibodies are a consequence of the initial renal injury. Differences between disease susceptible and disease resistant rat strains exist in the expression of IL-4Rα, a major player in the TH2 response. IL-4Rα signaling is regulated by soluble IL-4Rα (sIL-4Rα). Low expression levels of sIL-4Rα result in the stabilization of IL-4 binding, while elevated expression sequesters IL-4. Quantitative PCR experiments noted low siL-4Rα expression levels in disease susceptible rats. Induction of an immune response toward sIL-4Rα in this strain was responsible for delayed disease progression in 15 out of the 17 experimental animals. Antibody transfer and in vivo biological activity experiments confirmed that delayed disease development was due to anti-sIL-4Rα antibodies. Together these experiments indicate that a T-cell epitope is required for activation of a TH1 autoimmune response and anti-GBM antibodies are a consequence of renal injury. More importantly, a role for IL-4Rα signaling is implicated in the progression of anti-GBM GN. ^

Understanding and Characterizing Shared Decision-Making and Behavioral Intent in Medical Uncertainty

Applying Theoretical Constructs to Address Medical Uncertainty Situations involving medical reasoning usually include some level of medical uncertainty. Despite the identification of shared decision-making (SDM) as an effective technique, it has been observed that the likelihood of physicians and patients engaging in shared decision making is lower in those situations where it is most needed; specifically in circumstances of medical uncertainty. Having identified shared decision making as an effective, yet often a neglected approach to resolving a lack of information exchange in situations involving medical uncertainty, the next step is to determine the way(s) in which SDM can be integrated and the supplemental processes that may facilitate its integration. SDM involves unique types of communication and relationships between patients and physicians. Therefore, it is necessary to further understand and incorporate human behavioral elements - in particular, behavioral intent - in order to successfully identify and realize the potential benefits of SDM. This paper discusses the background and potential interaction between the theories of shared decision-making, medical uncertainty, and behavioral intent. Identifying Shared Decision-Making Elements in Medical Encounters Dealing with Uncertainty A recent summary of the state of medical knowledge in the U.S. reported that nearly half (47%) of all treatments were of unknown effectiveness, and an additional 7% involved an uncertain tradeoff between benefits and harms. Shared decision-making (SDM) was identified as an effective technique for managing uncertainty when two or more parties were involved. In order to understand which of the elements of SDM are used most frequently and effectively, it is necessary to identify these key elements, and understand how these elements related to each other and the SDM process. The elements identified through the course of the present research were selected from basic principles of the SDM model and the “Data, Information, Knowledge, Wisdom” (DIKW) Hierarchy. The goal of this ethnographic research was to identify which common elements of shared decision-making patients are most often observed applying in the medical encounter. The results of the present study facilitated the understanding of which elements patients were more likely to exhibit during a primary care medical encounter, as well as determining variables of interest leading to more successful shared decision-making practices between patients and their physicians. Understanding Behavioral Intent to Participate in Shared Decision-Making in Medically Uncertain Situations Objective: This article describes the process undertaken to identify and validate behavioral and normative beliefs and behavioral intent of men between the ages of 45-70 with regard to participating in shared decision-making in medically uncertain situations. This article also discusses the preliminary results of the aforementioned processes and explores potential future uses of this information which may facilitate greater understanding, efficiency and effectiveness of doctor-patient consultations.Design: Qualitative Study using deductive content analysisSetting: Individual semi-structure patient interviews were conducted until data saturation was reached. Researchers read the transcripts and developed a list of codes.Subjects: 25 subjects drawn from the Philadelphia community.Measurements: Qualitative indicators were developed to measure respondents’ experiences and beliefs related to behavioral intent to participate in shared decision-making during medical uncertainty. Subjects were also asked to complete the Krantz Health Opinion Survey as a method of triangulation.Results: Several factors were repeatedly described by respondents as being essential to participate in shared decision-making in medical uncertainty. These factors included past experience with medical uncertainty, an individual’s personality, and the relationship between the patient and his physician.Conclusions: The findings of this study led to the development of a category framework that helped understand an individual’s needs and motivational factors in their intent to participate in shared decision-making. The three main categories include 1) an individual’s representation of medically uncertainty, 2) how the individual copes with medical uncertainty, and 3) the individual’s behavioral intent to seek information and participate in shared decision-making during times of medically uncertain situations.