Shear bond strength of microwaveable acrylic resin for denture repair.

Microwaveable acrylic denture resins are believed to provide an effective means of repairing fractured dentures. This in vitro investigation compared the bond strength of a microwaveable acrylic resin as a denture repair material to two established auto-polymerized resins. Fifty-one specimens were made using Lucitone 199 as a simulated denture base, and were then divided into three groups of 17 samples each. Each test group was bonded with the following acrylic resins: Acron Mc, Rapid Repair and Palapress. A shear bond strength test was carried out 24 h after the samples were bonded. Fracture analysis showed that bond failure was adhesive for all groups. Shear bond values showed a statistically significant difference at P < 0.05 level between Acron Mc and Rapid Repair; Palapress and Rapid Repair, and indicated that Acron Mc and Palapress were superior to Rapid Repair as a repair material. However, there was no statistical difference found between Acron Mc and Palapress. Microwaveable acrylic resins produce repaired junctions of adequate strength.

Estimation of operating characteristics for dependent diagnostic tests based on latent Markov models.

We describe a method for making inferences about the joint operating characteristics of multiple diagnostic tests applied longitudinally and in the absence of a definitive reference test. Log-linear models are adopted for the classification distributions conditional on the latent state, where inclusion of appropriate interaction terms accommodates conditional dependencies among the tests. A marginal likelihood is constructed by marginalizing over a latent two-state Markov process. Specific latent processes we consider include a first-order Markov model, a second-order Markov model, and a time-nonhomogeneous Markov model, although the method is described in full generality. Adaptations to handle missing data are described. Model diagnostics are considered based on the bootstrap distribution of conditional residuals. The methods are illustrated by application to a study of diffuse bilateral infiltrates among patients in intensive care wards in which the objective was to assess aspects of validity and clinical agreement.

Two-state mixed renewal processes for chronic disease.

We develop a model based on a two-state mixed renewal process and propose its use for modelling disease activity in studies of chronic conditions. We specify Weibull forms for the conditional transition intensities to allow for time trends, and bivariate frailties to accommodate subject-to-subject variability in the disease process. Extensions of this model are considered for stratified analyses in which strata are defined by the number of past exacerbations. Data from a motivating study of chronic bronchitis are analysed to illustrate the methodology.

A logistic-bivariate normal model for overdispersed two-state Markov processes.

We describe a logistic-bivariate normal mixture model for a two-state Markov chain in which each individual makes transitions between states according to a subject-specific transition probability matrix. The use of the bivariate normal mixing distribution facilitates inferences regarding the correlation of the random effects and hence provides insight as to the nature of the subject-to-subject variability in the transition probabilities. Tests regarding the correlation can be based on likelihood ratio, score, or Wald statistics. Estimates of the transition intensities of a latent continuous time conditionally Markov process may also be computed. We illustrate this methodology by application to a parasitic infection field study and contrast our findings with those previously published on this data set.

Modeling two-state disease processes with random effects.

Many chronic medical conditions are manifested by alternating sojourns in symptom-free and symptomatic states. In many cases, in addition to their relapsing and remitting nature, these conditions lead to worsening disease patterns over time and may exhibit seasonal trends. We develop a mixed-effect two-state model for such disease processes in which covariate effects are modeled multiplicatively on transition intensities. The transition intensities, in turn, are functions of three time scales: the semi-Markov scale involving the backward recurrence time for the cyclical component, the Markov scale for the time trend component, and a seasonal time scale. Multiplicative bivariate log-normal random effects are introduced to accommodate heterogeneity in disease activity between subjects and to admit a possible negative correlation between the transition intensities. Maximum likelihood estimation is carried out using Gauss-Hermite integration and a standard Newton-Raphson procedure. Tests of homogeneity are presented based on score statistics. An application of the methodology to data from a multi-center clinical trial of chronic bronchitis is provided for illustrative purposes.