A Bayesian nonparametric meta-analysis model for estimating the reference interval

A reference interval represents the normative range for measurements from a healthy population. It plays an important role in laboratory testing, as well as in differentiating healthy from diseased patients. The reference interval based on a single study might not be applicable to a broader population. Meta-analysis can provide a more generalizable reference interval based on the combined population by synthesizing results from multiple studies. However, the assumptions of normally distributed underlying study-specific means and equal within-study variances, which are commonly used in existing methods, are strong and may not hold in practice. We propose a Bayesian nonparametric model with more flexible assumptions to extend random effects meta-analysis for estimating reference intervals. We illustrate through simulation studies and two real data examples the performance of our proposed approach when the assumptions of normally distributed study means and equal within-study variances do not hold.

Multimodality During Fixation - Part II: Evidence for Multimodality in Spatial Precision-Related Distributions and Impact on Precision Estimates

This paper is a follow-on to our earlier paper (7), which focused on the multimodality of angular offsets. This paper applies the same analysis to the measurement of spatial precision. Following the literature, we refer these measurements as estimates of device precision, but, in fact, subject characteristics clearly affect the measurements. One typical measure of the spatial precision of an eye-tracking device is the standard deviation (SD) of the position signals (horizontal and vertical) during a fixation. The SD is a highly interpretable measure of spread if the underlying error distribution is unimodal and normal. However, in the context of an underlying multimodal distribution, the SD is less interpretable. We will present evidence that the majority of such distributions are multimodal (68-70% strongly multimodal). Only 21-23% of position distributions were unimodal. We present an alternative method for measuring precision that is appropriate for both unimodal and multimodal distributions. This alternative method produces precision estimates that are substantially smaller than classic measures. We present illustrations of both unimodality and multimodality with either drift or a microsaccade present during fixation. At present, these observations apply only to the EyeLink 1000, and the subjects evaluated herein.

Angular Offset Distributions During Fixation Are, More Often Than Not, Multimodal

Typically, the position error of an eye-tracking device is measured as the distance of the eye-position from the target position in two-dimensional space (angular offset). Accuracy is the mean angular offset. The mean is a highly interpretable measure of central tendency if the underlying error distribution is unimodal and normal. However, in the context of an underlying multimodal distribution, the mean is less interpretable. We will present evidence that the majority of such distributions are multimodal. Only 14.7% of fixation angular offset distributions were unimodal, and of these, only 11.5% were normally distributed. (Of the entire dataset, 1.7% were unimodal and normal.) This multimodality is true even if there is only a single, continuous tracking fixation segment per trial. We present several approaches to measure accuracy in the face of multimodality. We also address the role of fixation drift in partially explaining multimodality.

A Comparison of 6 Diagnostic Tests for Carpal Tunnel Syndrome Using Latent Class Analysis

Background: Nerve conduction studies (NCS), ultrasonography (US), Carpal Tunnel Syndrome 6 (CTS-6), Wainner, Lo, and Kamath are clinical diagnostic tools that can be used to diagnose carpal tunnel syndrome (CTS). Latent class analysis (LCA) is a proven statistical technique that can be used to evaluate diagnostic tests in a lack of a reference standard. Given that there is no accepted reference standard, we elected to perform an LCA to evaluate the 6 clinical diagnostic tests. Methods: One hundred eighty-seven wrists were prospectively evaluated by a board-certified hand surgeon using US, the CTS-6, Wainner, Lo, and Kamath. The NCS were performed by an electrophysiologist according to the standards of the American Association of Neuromuscular & Electrodiagnostic Medicine. The LCA was performed to evaluate individual performance and pairwise combinations of the tests. Results: The NCS demonstrated the highest estimated sensitivity of 97%, and the Wainner had the highest estimated specificity of 97%. Alternatively, the Lo had the lowest estimated sensitivity (36%), and NCS had the lowest estimated specificity (40%). When evaluating pairwise combinations, positive US and NCS demonstrated the highest overall sensitivity at 86%, and negative US and NCS had a specificity of 83%. Conclusions: There is no perfect clinical diagnostic test, with the 6 clinical diagnostic tests having differing sensitivities and specificities. Pairwise combinations of the test can be used to complement one another.

Bayes factors for choosing among six common survival models

A super model that includes proportional hazards, proportional odds, accelerated failure time, accelerated hazards, and extended hazards models, as well as the model proposed in Diao et al. (Biometrics 69(4):840-849, 2013) accounting for crossed survival as special cases is proposed for the purpose of testing and choosing among these popular semiparametric models. Efficient methods for fitting and computing fast, approximate Bayes factors are developed using a nonparametric baseline survival function based on a transformed Bernstein polynomial. All manner of censoring is accommodated including right, left, and interval censoring, as well as data that are observed exactly and mixtures of all of these; current status data are included as a special case. The method is tested on simulated data and two real data examples. The approach is easily carried out via a new function in the spBayesSurv R package.

Generalized accelerated failure time spatial frailty model for arbitrarily censored data

Flexible incorporation of both geographical patterning and risk effects in cancer survival models is becoming increasingly important, due in part to the recent availability of large cancer registries. Most spatial survival models stochastically order survival curves from different subpopulations. However, it is common for survival curves from two subpopulations to cross in epidemiological cancer studies and thus interpretable standard survival models can not be used without some modification. Common fixes are the inclusion of time-varying regression effects in the proportional hazards model or fully nonparametric modeling, either of which destroys any easy interpretability from the fitted model. To address this issue, we develop a generalized accelerated failure time model which allows stratification on continuous or categorical covariates, as well as providing per-variable tests for whether stratification is necessary via novel approximate Bayes factors. The model is interpretable in terms of how median survival changes and is able to capture crossing survival curves in the presence of spatial correlation. A detailed Markov chain Monte Carlo algorithm is presented for posterior inference and a freely available function frailtyGAFT is provided to fit the model in the R package spBayesSurv. We apply our approach to a subset of the prostate cancer data gathered for Louisiana by the surveillance, epidemiology, and end results program of the National Cancer Institute.

Flexible parametrization of variance functions for quantal response data derived from counts

Although the Poisson model has been widely used to fit count data, a well-known drawback is that the Poisson mean equals its variance. Many alternative models for counts that are overdispersed relative to Poisson have been developed to solve this issue, including the negative binomial model. In this article, the negative binomial model with a four-parameter logistic mean is proposed to handle these types of counts, with variance that flexibly depends on the mean. Various parameterizations for the variance are considered, including extra-Poisson variability modeled as an exponentiated B-spline. Thus, the proposed model ably captures the leveling off of the mean, i.e., the "lazy-S" shape often encountered for overdispersed dose-response counts, simultaneously taking into account both overdispersion and natural mortality. Two real datasets illustrate the merits of the proposed approach: media colony counts after tuberculosis decontamination, and the number of monkeys killed by Ache hunters over several hunting trips in the Paraguayan tropical forest.

Association of green tea consumption with mortality from all-cause, cardiovascular disease and cancer in a Chinese cohort of 165,000 adult men

Tea is the most ancient and popular beverage in the world, and its beneficial health effects has attracted tremendous attention worldwide. However, the prospective evidence relating green tea consumption to total and cause-specific mortality is still limited and inconclusive. We recruited 164,681 male participants free of pre-existing disease during 1990-1991, with green tea consumption and other covariates assessed by the standardized questionnaire and mortality follow up continued until 2006 (mean 11 years; total person-years: 1,961,791). Cox regression analyses were used to quantify the associations of green tea consumption with all-cause (n = 32,700), CVD (n = 11,839) and cancer (n = 7002) mortality, adjusting simultaneously for potential confounders. At baseline, 18 % reported regular consumption of green tea. Compared with non-green tea drinkers, regular drinkers had significantly lower all-cause mortality, with adjusted hazard ratios (HRs) being 0.94 (95 % CI 0.89, 0.99) for ≤5 g/day, 0.95 (0.91, 0.99) for 5-10 g/day and 0.89 (0.85, 0.93) for >10 g/day. For CVD mortality, the corresponding HRs were 0.93 (0.85, 1.01) 0.91 (0.85, 0.98) and 0.86 (0.79, 0.93), respectively, while for cancer they were 0.86 (0.78, 0.98), 0.92 (0.83, 1.00) and 0.79 (0.71, 0.88), respectively. The patterns of these associations varied by smoking, alcohol drinking and locality. This large prospective study shows that regular green tea consumption is associated with significantly reduced risk of death from all-cause, CVD and cancer among Chinese adults.

Assessment of DPOAE test-retest difference curves via hierarchical Gaussian processes

Distortion product otoacoustic emissions (DPOAE) testing is a promising alternative to behavioral hearing tests and auditory brainstem response testing of pediatric cancer patients. The central goal of this study is to assess whether significant changes in the DPOAE frequency/emissions curve (DP-gram) occur in pediatric patients in a test-retest scenario. This is accomplished through the construction of normal reference charts, or credible regions, that DP-gram differences lie in, as well as contour probabilities that measure how abnormal (or in a certain sense rare) a test-retest difference is. A challenge is that the data were collected over varying frequencies, at different time points from baseline, and on possibly one or both ears. A hierarchical structural equation Gaussian process model is proposed to handle the different sources of correlation in the emissions measurements, wherein both subject-specific random effects and variance components governing the smoothness and variability of each child's Gaussian process are coupled together.

A Comparison of Three Diagnostic Tests for Carpal Tunnel Syndrome Using Latent Class Analysis

BACKGROUND

The current reference standard for carpal tunnel syndrome is under debate. Recent studies have demonstrated similar diagnostic accuracy between ultrasound and nerve conduction studies. The purpose of the present study was to determine the sensitivity and specificity of ultrasound, nerve conduction studies, and Carpal Tunnel Syndrome 6 (CTS-6) for the diagnosis of carpal tunnel syndrome using latent class analysis.

METHODS

Latent class analysis is a statistical technique that can be used to estimate the accuracy of diagnosis when there is no universally accepted reference standard. This type of analysis is useful in the setting of carpal tunnel syndrome as there remains substantial controversy with respect to the necessity of nerve conduction studies and other confirmatory testing. CTS-6 is a validated clinical diagnostic tool for the diagnosis of carpal tunnel syndrome that has been shown to have a high sensitivity and specificity. Data from a database on the cases of eighty-five consecutive patients who had had nerve conduction studies, CTS-6, and ultrasound were analyzed using classical latent class analysis, assuming that the three tests were imperfect and conditionally independent.

RESULTS

The sensitivities of ultrasound, CTS-6, and nerve conduction studies were 91% (95% confidence interval [CI], 81% to 98%), 95% (95% CI, 86% to 99%), and 91% (95% CI, 81% to 97%), respectively. The specificities of ultrasound, CTS-6, and nerve conduction studies were 94% (95% CI, 80% to 100%), 91% (95% CI, 74% to 99%), and 83% (95% CI, 66% to 95%), respectively.

CONCLUSIONS

Ultrasound, nerve conduction studies, and CTS-6 have similar sensitivity and specificity for the diagnosis of carpal tunnel syndrome. The currently accepted reference standard (nerve conduction studies) had the lowest sensitivity and specificity of the three tests. These findings support previous studies that have suggested that CTS-6 and ultrasound are highly accurate in the diagnosis of carpal tunnel syndrome and that nerve conduction studies are not necessary in most cases.

Marginal Bayesian nonparametric model for time to disease arrival of threatened amphibian populations

The global emergence of Batrachochytrium dendrobatidis (Bd) has caused the extinction of hundreds of amphibian species worldwide. It has become increasingly important to be able to precisely predict time to Bd arrival in a population. The data analyzed herein present a unique challenge in terms of modeling because there is a strong spatial component to Bd arrival time and the traditional proportional hazards assumption is grossly violated. To address these concerns, we develop a novel marginal Bayesian nonparametric survival model for spatially correlated right-censored data. This class of models assumes that the logarithm of survival times marginally follow a mixture of normal densities with a linear-dependent Dirichlet process prior as the random mixing measure, and their joint distribution is induced by a Gaussian copula model with a spatial correlation structure. To invert high-dimensional spatial correlation matrices, we adopt a full-scale approximation that can capture both large- and small-scale spatial dependence. An efficient Markov chain Monte Carlo algorithm with delayed rejection is proposed for posterior computation, and an R package spBayesSurv is provided to fit the model. This approach is first evaluated through simulations, then applied to threatened frog populations in Sequoia-Kings Canyon National Park.

MODELLING COUNTY LEVEL BREAST CANCER SURVIVAL DATA USING A COVARIATE-ADJUSTED FRAILTY PROPORTIONAL HAZARDS MODEL

Understanding the factors that explain differences in survival times is an important issue for establishing policies to improve national health systems. Motivated by breast cancer data arising from the Surveillance Epidemiology and End Results program, we propose a covariate-adjusted proportional hazards frailty model for the analysis of clustered right-censored data. Rather than incorporating exchangeable frailties in the linear predictor of commonly-used survival models, we allow the frailty distribution to flexibly change with both continuous and categorical cluster-level covariates and model them using a dependent Bayesian nonparametric model. The resulting process is flexible and easy to fit using an existing R package. The application of the model to our motivating example showed that, contrary to intuition, those diagnosed during a period of time in the 1990s in more rural and less affluent Iowan counties survived breast cancer better. Additional analyses showed the opposite trend for earlier time windows. We conjecture that this anomaly has to be due to increased hormone replacement therapy treatments prescribed to more urban and affluent subpopulations.

Spatial extended hazard model with application to prostate cancer survival

This article develops a Bayesian semiparametric approach to the extended hazard model, with generalization to high-dimensional spatially grouped data. County-level spatial correlation is accommodated marginally through the normal transformation model of Li and Lin (2006, Journal of the American Statistical Association 101, 591-603), using a correlation structure implied by an intrinsic conditionally autoregressive prior. Efficient Markov chain Monte Carlo algorithms are developed, especially applicable to fitting very large, highly censored areal survival data sets. Per-variable tests for proportional hazards, accelerated failure time, and accelerated hazards are efficiently carried out with and without spatial correlation through Bayes factors. The resulting reduced, interpretable spatial models can fit significantly better than a standard additive Cox model with spatial frailties.

Accelerated hazards model based on parametric families generalized with Bernstein polynomials

A transformed Bernstein polynomial that is centered at standard parametric families, such as Weibull or log-logistic, is proposed for use in the accelerated hazards model. This class provides a convenient way towards creating a Bayesian nonparametric prior for smooth densities, blending the merits of parametric and nonparametric methods, that is amenable to standard estimation approaches. For example optimization methods in SAS or R can yield the posterior mode and asymptotic covariance matrix. This novel nonparametric prior is employed in the accelerated hazards model, which is further generalized to time-dependent covariates. The proposed approach fares considerably better than previous approaches in simulations; data on the effectiveness of biodegradable carmustine polymers on recurrent brain malignant gliomas is investigated.

Glioma cell density in a rat gene therapy model gauged by water relaxation rate along a fictitious magnetic field

Longitudinal and transverse rotating-frame relaxation time constants, T(1) (ρ) and T(2) (ρ) , have previously been successfully applied to detect gene therapy responses and acute stroke in animal models. Those experiments were performed with continuous-wave irradiation or with frequency-modulated pulses operating in an adiabatic regime. The technique called Relaxation Along a Fictitious Field (RAFF) is a recent extension of frequency-modulated rotating-frame relaxation methods. In RAFF, spin locking takes place along a fictitious magnetic field, and the decay rate is a function of both T(1ρ) and T(2ρ) processes. In this work, the time constant characterizing water relaxation with RAFF (T(RAFF) ) was evaluated for its utility as a marker of response to gene therapy in a rat glioma model. To investigate the sensitivity to early treatment response, we measured several rotating-frame and free-precession relaxation time constants and the water apparent diffusion coefficients, and these were compared with histological cell counts in 8 days of treated and control groups of animals. T(RAFF) was the only parameter exhibiting significant association with cell density in three different tumor regions (border, intermediate, and core tissues). These results indicate that T(RAFF) may provide a marker to identify tumors responding to treatment.

Radiofrequency heating in porcine models with a "large" 32 cm internal diameter, 7 T (296 MHz) head coil

Temperatures were measured in vivo in four pigs (mean animal weight = 110.75 kg and standard deviation = 6.13 kg) due to a continuous wave radiofrequency (RF) power irradiation with a 31.75 cm internal diameter and a 15.24 cm long, 7 T (296 MHz), eight channel, transverse electromagnetic head coil. The temperatures were measured in the subcutaneous layer of the scalp, 5, 10, 15, and 20 mm deep in the brain, and rectum using fluoroptic temperature probes. The RF power was delivered to the pig's head for ∼3 h (mean deposition time = 3.14 h and standard deviation = 0.06 h) at the whole head average specific absorption rate of ∼3 W kg(-1) (mean average specific absorption rate = 3.08 W kg(-1) and standard deviation = 0.09 W kg(-1)). Next, simple bioheat transfer models were used to simulate the RF power induced temperature changes. Results show that the RF power produced uniform temperature changes in the pigs' heads (mean temperature change = 1.68°C and standard deviation = 0.13°C) with no plateau achieved during the heating. No thermoregulatory alterations were detected due to the heating because the temperature responses of the pre-RF and post-RF epochs were not statistically significantly different. Simple, validated bioheat models may provide accurate temperature changes.

Effect of the extracranial deep brain stimulation lead on radiofrequency heating at 9.4 Tesla (400.2 MHz)

PURPOSE

To study the effect of the extracranial portion of a deep brain stimulation (DBS) lead on radiofrequency (RF) heating with a transmit and receive 9.4 Tesla head coil.

MATERIALS AND METHODS

The RF heating was studied in four excised porcine heads (mean animal head weight = 5.46 +/- 0.14 kg) for each of the following two extracranial DBS lead orientations: one, parallel to the coil axial direction; two, perpendicular to the coil axial direction (i.e., azimuthal). Temperatures were measured using fluoroptic probes at four locations: one, scalp; two, near the second DBS lead electrode-brain contact; three, near the distal tip of the DBS lead; and four, air surrounding the head. A continuous wave RF power was delivered to each head for 15 min using the coil. Net, delivered RF power was measured at the coil (mean whole head average specific absorption rate = 2.94 +/- 0.08 W/kg).

RESULTS

RF heating was significantly reduced when the extracranial DBS lead was placed in the axial direction (temperature change = 0-5 degrees C) compared with the azimuthal direction (temperature change = 1-27 degrees C).

CONCLUSION

Development of protocols seems feasible to keep RF heating near DBS electrodes clinically safe during ultra-high field head imaging.

A Semiparametric Bayesian Approach to Multivariate Longitudinal Data

We extend the standard multivariate mixed model by incorporating a smooth time effect and relaxing distributional assumptions. We propose a semiparametric Bayesian approach to multivariate longitudinal data using a mixture of Polya trees prior distribution. Usually, the distribution of random effects in a longitudinal data model is assumed to be Gaussian. However, the normality assumption may be suspect, particularly if the estimated longitudinal trajectory parameters exhibit multimodality and skewness. In this paper we propose a mixture of Polya trees prior density to address the limitations of the parametric random effects distribution. We illustrate the methodology by analyzing data from a recent HIV-AIDS study.

Relationship of cranial robusticity to cranial form, geography and climate in Homo sapiens

Variation in cranial robusticity among modern human populations is widely acknowledged but not well-understood. While the use of "robust" cranial traits in hominin systematics and phylogeny suggests that these characters are strongly heritable, this hypothesis has not been tested. Alternatively, cranial robusticity may be a response to differences in diet/mastication or it may be an adaptation to cold, harsh environments. This study quantifies the distribution of cranial robusticity in 14 geographically widespread human populations, and correlates this variation with climatic variables, neutral genetic distances, cranial size, and cranial shape. With the exception of the occipital torus region, all traits were positively correlated with each other, suggesting that they should not be treated as individual characters. While males are more robust than females within each of the populations, among the independent variables (cranial shape, size, climate, and neutral genetic distances), only shape is significantly correlated with inter-population differences in robusticity. Two-block partial least-squares analysis was used to explore the relationship between cranial shape (captured by three-dimensional landmark data) and robusticity across individuals. Weak support was found for the hypothesis that robusticity was related to mastication as the shape associated with greater robusticity was similar to that described for groups that ate harder-to-process diets. Specifically, crania with more prognathic faces, expanded glabellar and occipital regions, and (slightly) longer skulls were more robust than those with rounder vaults and more orthognathic faces. However, groups with more mechanically demanding diets (hunter-gatherers) were not always more robust than groups practicing some form of agriculture.

T1rho and T2rho MRI in the evaluation of Parkinson's disease

Prior work has shown that adiabatic T(1rho) and T(2rho) relaxation time constants may have sensitivity to cellular changes and the presence of iron, respectively, in Parkinson's disease (PD). Further understanding of these magnetic resonance imaging (MRI) methods and how they relate to measures of disease severity and progression in PD is needed. Using T(1rho) and T(2rho) on a 4T MRI scanner, we assessed the substantia nigra (SN) of nine non-demented moderately affected PD and ten gender- and age-matched control participants. When compared to controls, the SN of PD subjects had increased T(1rho) and reduced T(2rho). We also found a significant correlation between asymmetric motor features and asymmetry based on T(1rho). This study provides additional validation of T(1rho) and T(2rho) as a means to separate PD from control subjects, and T(1rho) may be a useful marker of asymmetry in PD.

Radio frequency heating at 9.4T (400.2 MHz): in vivo thermoregulatory temperature response in swine

In vivo thermoregulatory temperature response to radio frequency (RF) heating at 9.4T was studied by measuring temperatures in nine anesthetized swine. Temperatures were measured in the scalp, brain, and rectum. The RF energy was deposited using a four-loop head coil tuned to 400.2 MHz. Sham RF was delivered to three swine to understand the thermal effects of anesthesia (animal weight = 54.16 kg, SD = 3.08 kg). Continuous wave (CW) RF energy was delivered to the other six animals for 2.5-3.4 h (animal weight = 74.01 +/- 26.0 kg, heating duration = 3.05 +/- 0.29 h). The whole-head specific absorption rate (SAR) varied between 2.71 W/kg and 3.20 W/kg (SAR = 2.93 +/- 0.18 W/kg). Anesthesia caused the brain and rectal temperatures to drop linearly. Altered thermoregulatory response was detected by comparing the difference in the temperature slopes before and after the RF delivery from zero. RF heating statistically significantly altered the rate of cooling down of the animal. The temperature slope changes correlated well with the RF energy per unit head weight and heating duration, and the maximum rectal temperature change during heating in heated animals. The temperature slope changes did not correlate well to the whole-head average SARs.

Radiofrequency heating at 9.4T: in vivo temperature measurement results in swine

In vivo temperatures were correlated to the whole head average specific absorption rate (SAR(avg)) at 9.4T using 12 anesthetized swine (mean animal weight = 52 kg, standard deviation = 6.7 kg). Correlating the temperatures and SAR(avg) is necessary to ensure safe levels of human heating during ultra-high field MR exams. The temperatures were measured at three depths inside the brain, in the rectum, and at the head-skin of swine. A 400 MHz, continuous wave RF power was deposited to the head using a volume coil. The SAR(avg) values were varied between 2.7-5.8 W/kg. The RF power exposure durations were varied between 1.4-3.7 hr. To differentiate the temperature response caused by the RF from that of the anesthesia, the temperatures were recorded in four unheated swine. To study the effect of the spatial distribution of the RF and tissue properties, the temperature probes were placed at two brain locations (n = 4 swine for each location). Results showed that the in vivo brain temperatures correlated to the SAR(avg) in a geometry-dependent manner. Additionally, 1) the skin temperature change was not the maximum temperature change; 2) the RF heating caused an inhomogeneous brain temperature distribution; and 3) the maximum temperature occurred inside the brain.

Bayesian Semiparametric Proportional Odds Models

Methodology for implementing the proportional odds regression model for survival data assuming a mixture of finite Polya trees (MPT) prior on baseline survival is presented. Extensions to frailties and generalized odds rates are discussed. Although all manner of censoring and truncation can be accommodated, we discuss model implementation, regression diagnostics, and model comparison for right-censored data. An advantage of the MPT model is the relative ease with which predictive densities, survival, and hazard curves are generated. Much discussion is devoted to practical implementation of the proposed models, and a novel MCMC algorithm based on an approximating parametric normal model is developed. A modest simulation study comparing the small sample behavior of the MPT model to a rank-based estimator and a real data example is presented.

A mixture model for bovine abortion and foetal survival

The effect of spontaneous abortion on the dairy industry is substantial, costing the industry on the order of US dollars 200 million per year in California alone. We analyse data from a cohort study of nine dairy herds in Central California. A key feature of the analysis is the observation that only a relatively small proportion of cows will abort (around 10;15 per cent), so that it is inappropriate to analyse the time-to-abortion (TTA) data as if it were standard censored survival data, with cows that fail to abort by the end of the study treated as censored observations. We thus broaden the scope to consider the analysis of foetal lifetime distribution (FLD) data for the cows, with the dual goals of characterizing the effects of various risk factors on (i). the likelihood of abortion and, conditional on abortion status, on (ii). the risk of early versus late abortion. A single model is developed to accomplish both goals with two sets of specific herd effects modelled as random effects. Because multimodal foetal hazard functions are expected for the TTA data, both a parametric mixture model and a non-parametric model are developed. Furthermore, the two sets of analyses are linked because of anticipated dependence between the random herd effects. All modelling and inferences are accomplished using modern Bayesian methods.

Interference in spectrophotometric analysis of cerebrospinal fluid by haemolysis induced by transport through a pneumatic tube system

The hypothesis that sending blood-stained cerebrospinal fluid (CSF) through a pneumatic tube causes in vitro haemolysis has been tested. Spectrophotometric scanning of CSF supernatants demonstrated a significantly greater absorbance at 415 nm in those CSF samples that had been sent through the tube system compared to those that had not (P=0.0034). It is concluded that passage of blood-stained CSF down a pneumatic tube system causes in vitro haemolysis, accompanied by the release of oxyhaemoglobin from the lysed cells into the surrounding CSF. In view of this observation, it is recommended that CSF samples requiring spectrophotometric analysis, as part of the investigation of subarachnoid haemorrhage, should not be transported via a pneumatic tube system.

Comparison of neutrophil and monocyte function by microbicidal cell-kill assay in patients with cancer receiving granulocyte colony-stimulating factor, granulocyte-macrophage colony-stimulating factor, or no cytokine after cytotoxic chemotherapy: a phase II trial

Functional effects of recombinant human granulocyte colony-stimulating factor (rhG-CSF) and recombinant human granulocyte-macrophage colony-stimulating factor (rhGM-CSF) were prospectively measured by harvesting blood samples from 51 oncology patients (21 who were receiving no cytokines, 14 receiving rhGM-CSF, and 16 who were receiving rhG-CSF) just before cytotoxic chemotherapy (baseline) immediately before the last cytokine dose (pre), 2 hours after the last cytokine dose (post), and 48 hours after the pre period (follow-up). Neutrophils and monocytes were separated and functional effects were measured by comparing cell-kill percentages, as determined by a microbial cell-kill assay against Staphylococcus aureus and Candida albicans. Optimal cell concentrations (2 x 10(6) monocytes/ml; 4 x 10(6) neutrophils/ml) and effector-to-cell ratios (1:50) were initially determined with blood samples harvested from 23 healthy volunteers. Results in oncology patients indicated that rhGM-CSF improved monocyte-killing activity against S. aureus at follow-up, compared with controls (p = 0.0094) and compared with monocytes from rhG-CSF-treated patients at the post period (p = 0.014). Cell-killing percentage of the rhGM-CSF-treated patients was also enhanced against C. albicans during the post period, compared with controls (p = 0.011) and rhG-CSF-treated patients (p = 0.067). Neutrophil activity was not altered by either cytokine. In conclusion, monocyte-induced microbial killing was enhanced in oncology patients receiving rhGM-CSF after cytotoxic chemotherapy, compared with patients receiving rhG-CSF or no cytokines. No differences in neutrophil activity were observed between patients receiving either cytokine.

Anti-CD40 antibody binding modulates human multiple myeloma clonogenicity in vitro

Ligand binding of the B-cell lineage antigen CD40 enhances growth and interleukin-6 (IL-6) secretion in human B cells (the CD40/IL-6 loop). IL-6 has an autocrine and paracrine role in human multiple myeloma (MM) cell growth. With the use of the CD40 monoclonal antibody (MoAb) G28-5, we examined CD40 expression and the effect of CD40 binding on MM clonogenic colony (MCC) formation to characterize the IL-6/CD40 loop activity in MM. CD40 was expressed on plasmacytoid cells in 21 of 28 plasma cell dyscrasia (PCD) bone marrow (BM) biopsies tested (10 of 14 MM, 2 of 2 Waldenstrom's macroglobulinemia [WM], 2 of 2 plasma cell leukemia [PCL], 6 of 8 monoclonal gammopathy of undetermined significance [MGUS], and 1 of 2 primary amyloidosis [AL]). G28-5 binding increased MCCs by 35% to 150% in 11 of 17 CD40+ PCD BM cultures, but did not affect MCC formation in CD40- specimens or normal BM colony forming units (CFU-GEMM, CFU-GM, BFU-E). Responsive cultures originated from BM of patients with MM (2 of 5 cases tested), WM (2 of 2), PCL (2 of 2), and MGUS (5 of 6). CD40-responsiveness was not significantly inhibited by the presence of an anti-IL-6 MoAb (2 of 2 MGUS cultures tested), and did not correlate with the capacity to respond to IL-6 stimulation (n = 17, P > .05) or a detectable level of endogenous IL-6 (n = 15, P > .05). Additional studies were performed with PCD cell lines to characterize the interrelationship of CD40 activation and IL-6 production. Fifty percent to greater than 95% of cells from the RPMI 8226 and ARH77 lines expressed CD40, whereas 6% of U266 cells were CD40+. For RPMI 8226, ARH-77, and U266 cells, the increased MCC formation after anti-CD40 stimulation was not affected by the presence of an anti-IL-6 neutralizing MoAb and was not accompanied by detectable IL-6 secretion. There was no apparent increase in IL-6 mRNA transcription following G28-5 treatment of U266 or RPMI 8226 cells. Our observations indicate that CD40 is expressed in a subset of human myeloma cells present in various PCDs. Cell-line studies suggest that the CD40+ myeloma cell may regulate MM clonogenic colony formation without activating the IL-6 pathway.

Strain redistribution in the canine femur resulting from hip implants of different stiffnesses

Bone remodeling adjacent to orthopedic implants has been attributed to bone strain changes. Although many animal studies have assessed bone remodeling near implants, the altered bone strains and even the strains in the intact bone prior to implantation have not been mapped extensively. Instead, bone changes are often correlated with implant stiffnesses. In this study, a benchtop loading system was developed using measurements from in vivo strain analysis to simulate physiologic loading of a canine femur. The effect on bone strains of three different stiffness canine hip implants with the same anatomic shape were compared by taking measurements from the proximal greyhound femur during loading. Peak compressive and tensile strains of the order of 200 to 400 microstrain were measured in the intact and implanted femora. The measurements indicate that during simulated in vivo loading, none of the implants substantially alter the normal strain state of the bone. If initial axial strains significantly affect the remodeling response of bone, the similarity of measurements with the different implants in place suggests that the same remodeling response would be expected to both the stiffest and least stiff implant, as has been noted in animal studies adjacent to the intermediate stiffness implant. It also suggests that this implant shape and initial bone implant interface condition can compensate for strain reductions expected near stiff straight-stemmed implants.

Successful childbirth by a patient with congenital factor XI deficiency and an acquired inhibitor

Acquired inhibitors in factor XI deficiency (FXI) are rare. The presence of an inhibitor during pregnancy poses a potential haemorrhagic risk to the fetus. We report an uncomplicated pregnancy and successful childbirth by a woman with congenital FXI deficiency and an acquired inhibitor, and discuss the persistence of residual FXI activity in the presence of an inhibitor.