Interlaboratory studies on in vitro test methods for estimating in vivo resorption of calcium phosphate ceramics

A potential standard method for measuring the relative dissolution rate to estimate the resorbability of calcium-phosphate-based ceramics is proposed. Tricalcium phosphate (TCP), magnesium-substituted TCP (MgTCP) and zinc-substituted TCP (ZnTCP) were dissolved in a buffer solution free of calcium and phosphate ions at pH 4.0, 5.5 or 7.3 at nine research centers. Relative values of the initial dissolution rate (relative dissolution rates) were in good agreement among the centers. The relative dissolution rate coincided with the relative volume of resorption pits of ZnTCP in vitro. The relative dissolution rate coincided with the relative resorbed volume in vivo in the case of comparison between microporous MgTCPs with different Mg contents and similar porosity. However, the relative dissolution rate was in poor agreement with the relative resorbed volume in vivo in the case of comparison between microporous TCP and MgTCP due to the superimposition of the Mg-mediated decrease in TCP solubility on the Mg-mediated increase in the amount of resorption. An unambiguous conclusion could not be made as to whether the relative dissolution rate is predictive of the relative resorbed volume in vivo in the case of comparison between TCPs with different porosity. The relative dissolution rate may be useful for predicting the relative amount of resorption for calcium-phosphate-based ceramics having different solubility under the condition that the differences in the materials compared have little impact on the resorption process such as the number and activity of resorbing cells.

A Conditional Approach for Modelling Patient Re-admissions in Hospital using a mixture of Coxian phase-type distributions incorporating Bayes Theorem

The number of elderly patients requiring hospitalisation in Europe is rising. With a greater proportion of elderly people in the population comes a greater demand for health services and, in particular, hospital care. Thus, with a growing number of elderly patients requiring hospitalisation competing with non-elderly patients for a fixed (and in some cases, decreasing) number of hospital beds, this results in much longer waiting times for patients, often with a less satisfactory hospital experience. However, if a better understanding of the recurring nature of elderly patient movements between the community and hospital can be developed, then it may be possible for alternative provisions of care in the community to be put in place and thus prevent readmission to hospital. The research in this paper aims to model the multiple patient transitions between hospital and community by utilising a mixture of conditional Coxian phase-type distributions that incorporates Bayes' theorem. For the purpose of demonstration, the results of a simulation study are presented and the model is applied to hospital readmission data from the Lombardy region of Italy.

Discrete Conditional Phase-type Model Utilising a Multiclass Support Vector Machine for the Prediction of Retinopathy of Prematurity

Retinopathy of prematurity (ROP) is a rare disease in which retinal blood vessels of premature infants fail to develop normally, and is one of the major causes of childhood blindness throughout the world. The Discrete Conditional Phase-type (DC-Ph) model consists of two components, the conditional component measuring the inter-relationships between covariates and the survival component which models the survival distribution using a Coxian phase-type distribution. This paper expands the DC-Ph models by introducing a support vector machine (SVM), in the role of the conditional component. The SVM is capable of classifying multiple outcomes and is used to identify the infant's risk of developing ROP. Class imbalance makes predicting rare events difficult. A new class decomposition technique, which deals with the problem of multiclass imbalance, is introduced. Based on the SVM classification, the length of stay in the neonatal ward is modelled using a 5, 8 or 9 phase Coxian distribution.

On the Assumption of Bivariate Normality in Selection Models: A Copula Approach Applied to Estimating HIV Prevalence

Background: Heckman-type selection models have been used to control HIV prevalence estimates for selection bias when participation in HIV testing and HIV status are associated after controlling for observed variables. These models typically rely on the strong assumption that the error terms in the participation and the outcome equations that comprise the model are distributed as bivariate normal.
Methods: We introduce a novel approach for relaxing the bivariate normality assumption in selection models using copula functions. We apply this method to estimating HIV prevalence and new confidence intervals (CI) in the 2007 Zambia Demographic and Health Survey (DHS) by using interviewer identity as the selection variable that predicts participation (consent to test) but not the outcome (HIV status).
Results: We show in a simulation study that selection models can generate biased results when the bivariate normality assumption is violated. In the 2007 Zambia DHS, HIV prevalence estimates are similar irrespective of the structure of the association assumed between participation and outcome. For men, we estimate a population HIV prevalence of 21% (95% CI = 16%–25%) compared with 12% (11%–13%) among those who consented to be tested; for women, the corresponding figures are 19% (13%–24%) and 16% (15%–17%).
Conclusions: Copula approaches to Heckman-type selection models are a useful addition to the methodological toolkit of HIV epidemiology and of epidemiology in general. We develop the use of this approach to systematically evaluate the robustness of HIV prevalence estimates based on selection models, both empirically and in a simulation study.

Estimating biomass, fishing mortality, and "total allowable discards" for surveyed non-target fish

Demersal fisheries targeting a few high-value species often catch and discard other "non-target" species. It is difficult to quantify the impact of this incidental mortality when population biomass of a non-target species is unknown. We calculate biomass for 14 demersal fish species in ICES Area VIIg (Celtic Sea) by applying species-and length-based catchability corrections to catch records from the Irish Groundfish Survey (IGFS). We then combine these biomass estimates with records of commercial discards (and landings for marketable non-target species) to calculate annual harvesting rates (HR) for each study species. Uncertainty is incorporated into estimates of both biomass andHR. Our survey-based HR estimates for cod and whiting compared well with HR-converted fishing mortality (F) estimates from analytical assessments for these two stocks. Of the non-target species tested, red gurnard (Chelidonichthys cuculus) recorded some annual HRs greater than those for cod or whiting; challenging "Pope's postulate" that F on non-target stocks in an assemblage will not exceed that on target stocks. We relate HR for each species to two corresponding maximum sustainable yield (MSY) reference levels; six non-target species (including three ray species) show annual HRs >= HRMSY. This result suggests that it may not be possible to conserve vulnerable non-target species when F is coupled to that of target species. Based on biomass, HR, and HRMSY, we estimate "total allowable catch" for each non-target species.

Inference in Polytrees with Sets of Probabilities

Inferences in directed acyclic graphs associated with probability intervals and sets of probabilities are NP-hard, even for polytrees. We propose: 1) an improvement on Tessem’s A/R algorithm for inferences on polytrees associated with probability intervals; 2) a new algorithm for approximate inferences based on local search; 3) branch-and-bound algorithms that combine the previous techniques. The first two algorithms produce complementary approximate solutions, while branch-and-bound procedures can generate either exact or approximate solutions. We report improvements on existing techniques for inference with probability sets and intervals, in some cases reducing computational effort by several orders of magnitude.

Methodological variations in estimating apparent progressive visual field loss in clinical trials of glaucoma treatment.

OBJECTIVES:

To compare methods to estimate the incidence of visual field progression used by 3 large randomized trials of glaucoma treatment by applying these methods to a common data set of annually obtained visual field measurements of patients with glaucoma followed up for an average of 6 years.

METHODS:

The methods used by the Advanced Glaucoma Intervention Study (AGIS), the Collaborative Initial Glaucoma Treatment Study (CIGTS), and the Early Manifest Glaucoma Treatment study (EMGT) were applied to 67 eyes of 56 patients with glaucoma enrolled in a 10-year natural history study of glaucoma using Program 30-2 of the Humphrey Field Analyzer (Humphrey Instruments, San Leandro, Calif). The incidence of apparent visual field progression was estimated for each method. Extent of agreement between the methods was calculated, and time to apparent progression was compared.

RESULTS:

The proportion of patients progressing was 11%, 22%, and 23% with AGIS, CIGTS, and EMGT methods, respectively. Clinical assessment identified 23% of patients who progressed, but only half of these were also identified by CIGTS or EMGT methods. The CIGTS and the EMGT had comparable incidence rates, but only half of those identified by 1 method were also identified by the other.

CONCLUSIONS:

The EMGT and CIGTS methods produced rates of apparent progression that were twice those of the AGIS method. Although EMGT, CIGTS, and clinical assessment rates were comparable, they did not identify the same patients as having had field progression.

Multilinear and Integer Programming for Markov Decision Processes with Imprecise Probabilities

Markov Decision Processes (MDPs) are extensively used to encode sequences of decisions with probabilistic effects. Markov Decision Processes with Imprecise Probabilities (MDPIPs) encode sequences of decisions whose effects are modeled using sets of probability distributions. In this paper we examine the computation of Γ-maximin policies for MDPIPs using multilinear and integer programming. We discuss the application of our algorithms to “factored” models and to a recent proposal, Markov Decision Processes with Set-valued Transitions (MDPSTs), that unifies the fields of probabilistic and “nondeterministic” planning in artificial intelligence research. 

Partially Ordered Preferences in Decision Trees: Computing Strategies with Imprecision in Probabilities

Partially ordered preferences generally lead to choices that do not abide by standard expected utility guidelines; often such preferences are revealed by imprecision in probability values. We investigate five criteria for strategy selection in decision trees with imprecision in probabilities: “extensive” Γ-maximin and Γ-maximax, interval dominance, maximality and E-admissibility. We present algorithms that generate strategies for all these criteria; our main contribution is an algorithm for Eadmissibility that runs over admissible strategies rather than over sets of probability distributions.