Bayesian Analysis for Errors in Variables with Changepoint Models

Changepoint regression models have originally been developed in connection with applications in quality control, where a change from the in-control to the out-of-control state has to be detected based on the avaliable random observations. Up to now various changepoint models have been suggested for differents applications like reliability, econometrics or medicine. In many practical situations the covariate cannot be measured precisely and an alternative model are the errors in variable regression models. In this paper we study the regression model with errors in variables with changepoint from a Bayesian approach. From the simulation study we found that the proposed procedure produces estimates suitable for the changepoint and all other model parameters.

Effects of neonatal castration and androgenization on sexual dimorphism in bone, leptin and corticosterone secretion

This study investigated the role of neonatal sex steroids in rats on sexual dimorphism in bone, as well as on leptin and corticosterone concentrations throughout the lifespan. Castration of males and androgenization of females were used as models to investigate the role of sex steroids shortly after birth. Newborn Wistar rats were divided into four groups, two male groups and two female groups. Male pups were cryoanesthetized and submitted to castration or sham-operation procedures within 24 h after birth. Female pups received a subcutaneous dose of testosterone propionate (100 mu g) or vehicle. Rats were euthanized at 20, 40, or 120 postnatal days. Body weight was also measured at 20, 40, and 120 days of age, and blood samples and femurs were collected. The length and thickness of the femurs were measured and the areal bone mineral density (areal BMD) was determined by dual-energy X-ray absorptiometry (DEXA). Biomechanical three-point bending testing was used to evaluate bone breaking strength, energy to fracture, and extrinsic stiffness. Blood samples were submitted to a biochemical assay to estimate calcium, phosphorus, alkaline phosphatase, leptin, and corticosterone levels. Weight gain, areal BMD and bone biomechanical properties increased rapidly with respect to age in all groups. In control animals, skeletal sexual dimorphism, leptin concentration, and dimorphic corticosterone concentration patterns were evident after puberty. However, androgen treatment induced changes in growth, areal BMD, and bone mass properties in neonatal animals. In addition, neonatally-castrated males had bone development and mechanical properties similar to those of control females. These results suggest that the exposure to neonatal androgens may represent at least one covariate that mediates dimorphic variation in leptin and corticosterone secretions. The study indicates that manipulation of the androgen environment during the critical period of sexual differentiation of the brain causes long-lasting changes in bone development, as well as serum leptin and corticosterone concentrations. In addition, this study provides useful models for the investigation of bone disorders induced by hypothalamic hypogonadism. (C) 2011 Elsevier Inc. All rights reserved.

Signal propagation in Bayesian networks and its relationship with intrinsically multivariate predictive variables

A set of predictor variables is said to be intrinsically multivariate predictive (IMP) for a target variable if all properly contained subsets of the predictor set are poor predictors of the. target but the full set predicts the target with great accuracy. In a previous article, the main properties of IMP Boolean variables have been analytically described, including the introduction of the IMP score, a metric based on the coefficient of determination (CoD) as a measure of predictiveness with respect to the target variable. It was shown that the IMP score depends on four main properties: logic of connection, predictive power, covariance between predictors and marginal predictor probabilities (biases). This paper extends that work to a broader context, in an attempt to characterize properties of discrete Bayesian networks that contribute to the presence of variables (network nodes) with high IMP scores. We have found that there is a relationship between the IMP score of a node and its territory size, i.e., its position along a pathway with one source: nodes far from the source display larger IMP scores than those closer to the source, and longer pathways display larger maximum IMP scores. This appears to be a consequence of the fact that nodes with small territory have larger probability of having highly covariate predictors, which leads to smaller IMP scores. In addition, a larger number of XOR and NXOR predictive logic relationships has positive influence over the maximum IMP score found in the pathway. This work presents analytical results based on a simple structure network and an analysis involving random networks constructed by computational simulations. Finally, results from a real Bayesian network application are provided. (C) 2012 Elsevier Inc. All rights reserved.

Effect of acute and chronic GVHD on relapse and survival after reduced-intensity conditioning allogeneic transplantation for myeloma

We evaluated the effect of acute and chronic GVHD on relapse and survival after allogeneic hematopoietic SCT (HSCT) for multiple myeloma using non-myeloablative conditioning (NMA) and reduced-intensity conditioning (RIC). The outcomes of 177 HLA-identical sibling HSCT recipients between 1997 and 2005, following NMA (n = 98) or RIC (n = 79) were analyzed. In 105 patients, autografting was followed by planned NMA/RIC allogeneic transplantation. The impact of GVHD was assessed as a time-dependent covariate using Cox models. The incidence of acute GVHD (aGVHD; grades I-IV) was 42% (95% confidence interval (CI), 35-49%) and of chronic GVHD (cGVHD) at 5 years was 59% (95% CI, 49-69%), with 70% developing extensive cGVHD. In multivariate analysis, aGVHD (>= grade I) was associated with an increased risk of TRM (relative risk (RR) = 2.42, P = 0.016), whereas limited cGVHD significantly decreased the risk of myeloma relapse (RR = 0.35, P = 0.035) and was associated with superior EFS (RR = 0.40, P = 0.027). aGVHD had a detrimental effect on survival, especially in those receiving autologous followed by allogeneic HSCT (RR = 3.52, P = 0.001). The reduction in relapse risk associated with cGVHD is consistent with a beneficial graft-vs-myeloma effect, but this did not translate into a survival advantage. Bone Marrow Transplantation (2012) 47, 831-837; doi:10.1038/bmt.2011.192; published online 26 September 2011

Random regression analyses using B-spline functions to model growth of Nellore cattle

The objective of this study was to estimate (co)variance components using random regression on B-spline functions to weight records obtained from birth to adulthood. A total of 82 064 weight records of 8145 females obtained from the data bank of the Nellore Breeding Program (PMGRN/Nellore Brazil) which started in 1987, were used. The models included direct additive and maternal genetic effects and animal and maternal permanent environmental effects as random. Contemporary group and dam age at calving (linear and quadratic effect) were included as fixed effects, and orthogonal Legendre polynomials of age (cubic regression) were considered as random covariate. The random effects were modeled using B-spline functions considering linear, quadratic and cubic polynomials for each individual segment. Residual variances were grouped in five age classes. Direct additive genetic and animal permanent environmental effects were modeled using up to seven knots (six segments). A single segment with two knots at the end points of the curve was used for the estimation of maternal genetic and maternal permanent environmental effects. A total of 15 models were studied, with the number of parameters ranging from 17 to 81. The models that used B-splines were compared with multi-trait analyses with nine weight traits and to a random regression model that used orthogonal Legendre polynomials. A model fitting quadratic B-splines, with four knots or three segments for direct additive genetic effect and animal permanent environmental effect and two knots for maternal additive genetic effect and maternal permanent environmental effect, was the most appropriate and parsimonious model to describe the covariance structure of the data. Selection for higher weight, such as at young ages, should be performed taking into account an increase in mature cow weight. Particularly, this is important in most of Nellore beef cattle production systems, where the cow herd is maintained on range conditions. There is limited modification of the growth curve of Nellore cattle with respect to the aim of selecting them for rapid growth at young ages while maintaining constant adult weight.

Multilevel Analysis in Household Survey: An Application to Health Condition Data

The aim of this thesis is to apply multilevel regression model in context of household surveys. Hierarchical structure in this type of data is characterized by many small groups. In last years comparative and multilevel analysis in the field of perceived health have grown in size. The purpose of this thesis is to develop a multilevel analysis with three level of hierarchy for Physical Component Summary outcome to: evaluate magnitude of within and between variance at each level (individual, household and municipality); explore which covariates affect on perceived physical health at each level; compare model-based and design-based approach in order to establish informativeness of sampling design; estimate a quantile regression for hierarchical data. The target population are the Italian residents aged 18 years and older. Our study shows a high degree of homogeneity within level 1 units belonging from the same group, with an intraclass correlation of 27% in a level-2 null model. Almost all variance is explained by level 1 covariates. In fact, in our model the explanatory variables having more impact on the outcome are disability, unable to work, age and chronic diseases (18 pathologies). An additional analysis are performed by using novel procedure of analysis :"Linear Quantile Mixed Model", named "Multilevel Linear Quantile Regression", estimate. This give us the possibility to describe more generally the conditional distribution of the response through the estimation of its quantiles, while accounting for the dependence among the observations. This has represented a great advantage of our models with respect to classic multilevel regression. The median regression with random effects reveals to be more efficient than the mean regression in representation of the outcome central tendency. A more detailed analysis of the conditional distribution of the response on other quantiles highlighted a differential effect of some covariate along the distribution.

A sequential Cox approach for estimating the causal effect of treatment in the presence of time-dependent confounding applied to data from the Swiss HIV Cohort Study

When estimating the effect of treatment on HIV using data from observational studies, standard methods may produce biased estimates due to the presence of time-dependent confounders. Such confounding can be present when a covariate, affected by past exposure, is both a predictor of the future exposure and the outcome. One example is the CD4 cell count, being a marker for disease progression for HIV patients, but also a marker for treatment initiation and influenced by treatment. Fitting a marginal structural model (MSM) using inverse probability weights is one way to give appropriate adjustment for this type of confounding. In this paper we study a simple and intuitive approach to estimate similar treatment effects, using observational data to mimic several randomized controlled trials. Each 'trial' is constructed based on individuals starting treatment in a certain time interval. An overall effect estimate for all such trials is found using composite likelihood inference. The method offers an alternative to the use of inverse probability of treatment weights, which is unstable in certain situations. The estimated parameter is not identical to the one of an MSM, it is conditioned on covariate values at the start of each mimicked trial. This allows the study of questions that are not that easily addressed fitting an MSM. The analysis can be performed as a stratified weighted Cox analysis on the joint data set of all the constructed trials, where each trial is one stratum. The model is applied to data from the Swiss HIV cohort study.

Impact of body mass index on the five-year outcome of patients having percutaneous coronary interventions with drug-eluting stents

The purpose of this study was to assess the impact of body mass index (BMI) on clinical outcome of patients treated by percutaneous coronary intervention (PCI) using drug-eluting stents. Patients were stratified according to BMI as normal (<25 kg/m(2)), overweight (25 to 30 kg/m(2)), or obese (>30 kg/m(2)). At 5-year follow-up all-cause death, myocardial infarction, clinically justified target vessel revascularization (TVR), and definite stent thrombosis were assessed. A complete dataset was available in 7,427 patients, of which 45%, 22%, and 33% were classified according to BMI as overweight, obese, and normal, respectively. Mean age of patients was significantly older in those with a normal BMI (p <0.05). Incidence of diabetes mellitus, hypertension, and dyslipidemia increased as BMI increased (p <0.05). Significantly higher rates of TVR (15.3% vs 12.8%, p = 0.02) and early stent thrombosis (1.5% vs 0.9%, p = 0.04) were observed in the obese compared to the normal BMI group. No significant difference among the 3 BMI groups was observed for the composite of death/myocardial infarction/TVR or for definite stent thrombosis at 5 years, whereas the normal BMI group was at higher risk for all-cause death at 5 years (obese vs normal BMI, hazard ratio 0.74, confidence interval 0.53 to 0.99, p = 0.05; overweight vs normal BMI, hazard ratio 0.73, confidence interval 0.59 to 0.94, p = 0.01) in the multivariate Cox proportional hazard model. Age resulted in a linearly dependent covariate with BMI in the all-cause 5-year mortality multivariate model (p = 0.001). In conclusion, the "obesity paradox" observed in 5-year all-cause mortality could be explained by the higher rate of elderly patients in the normal BMI group and the existence of colinearity between BMI and age. However, obese patients had a higher rate of TVR and early stent thrombosis and a higher rate of other risk factors such as diabetes mellitus, hypertension, and hypercholesterolemia.

An overview of the objectives of and the approaches to propensity score analyses

The assessment of treatment effects from observational studies may be biased with patients not randomly allocated to the experimental or control group. One way to overcome this conceptual shortcoming in the design of such studies is the use of propensity scores to adjust for differences of the characteristics between patients treated with experimental and control interventions. The propensity score is defined as the probability that a patient received the experimental intervention conditional on pre-treatment characteristics at baseline. Here, we review how propensity scores are estimated and how they can help in adjusting the treatment effect for baseline imbalances. We further discuss how to evaluate adequate overlap of baseline characteristics between patient groups, provide guidelines for variable selection and model building in modelling the propensity score, and review different methods of propensity score adjustments. We conclude that propensity analyses may help in evaluating the comparability of patients in observational studies, and may account for more potential confounding factors than conventional covariate adjustment approaches. However, bias due to unmeasured confounding cannot be corrected for.

Dorsal rather than ventral visual pathways discriminate freezing status in Parkinson's disease

BACKGROUND: Although visuospatial deficits have been linked with freezing of gait (FOG) in Parkinson's disease (PD), the specific effects of dorsal and ventral visual pathway dysfunction on FOG is not well understood. METHOD: We assessed visuospatial function in FOG using an angle discrimination test (dorsal visual pathway bias) and overlapping figure test (ventral visual pathway bias), and recorded overall response time, mean fixation duration and dwell time. Covariate analysis was conducted controlling for disease duration, motor severity, contrast sensitivity and attention with Bonferroni adjustments for multiple comparisons. RESULTS: Twenty seven people with FOG, 27 people without FOG and 24 controls were assessed. Average fixation duration during angle discrimination distinguished freezing status: [F (1, 43) = 4.77 p < 0.05] (1-way ANCOVA). CONCLUSION: Results indicate a preferential dysfunction of dorsal occipito-parietal pathways in FOG, independent of disease severity, attentional deficit, and contrast sensitivity.

An in vitro study on spontaneous myometrial contractility in the mare during estrus and diestrus

Uterine smooth muscle specimens were collected from euthanatized mares in estrus and diestrus. Longitudinal and circular specimens were mounted in organ baths and the signals transcribed to a Grass polygraph. After equilibration time and 2 g preload, their physiologic isometric contractility was recorded for a continuous 2.0 h. Area under the curve, frequency and time occupied by contractions were studied. Differences between cycle phases, between muscle layers, and over the recorded time periods were statistically evaluated using linear mixed-effect models. In the mare, physiologic contractility of the uterus decreased significantly over time for all variables evaluated (time as covariate on a continuous scale). For area under the curve, there was a significant effect of muscle layer (longitudinal > circular). For frequency, higher values were recorded in estrus for circular smooth muscle layer, whereas higher values were seen in longitudinal smooth muscle layers during diestrus. In longitudinal layer and in diestrus, more time was occupied by contractions than in circular layer, and in estrus. This study is describing physiologic myometrial motility in the organ bath depending on cycle phase.

THE ROLE OF AN EXPLICIT CAUSAL FRAMEWORK IN AFFECTED SIB PAIR DESIGNS WITH COVARIATES

The affected sib/relative pair (ASP/ARP) design is often used with covariates to find genes that can cause a disease in pathways other than through those covariates. However, such "covariates" can themselves have genetic determinants, and the validity of existing methods has so far only been argued under implicit assumptions. We propose an explicit causal formulation of the problem using potential outcomes and principal stratification. The general role of this formulation is to identify and separate the meaning of the different assumptions that can provide valid causal inference in linkage analysis. This separation helps to (a) develop better methods under explicit assumptions, and (b) show the different ways in which these assumptions can fail, which is necessary for developing further specific designs to test these assumptions and confirm or improve the inference. Using this formulation in the specific problem above, we show that, when the "covariate" (e.g., addiction to smoking) also has genetic determinants, then existing methods, including those previously thought as valid, can declare linkage between the disease and marker loci even when no such linkage exists. We also introduce design strategies to address the problem.

Concordance Probability and Discriminatory Power in Proportional Hazards Regression

The concordance probability is used to evaluate the discriminatory power and the predictive accuracy of nonlinear statistical models. We derive an analytic expression for the concordance probability in the Cox proportional hazards model. The proposed estimator is a function of the regression parameters and the covariate distribution only and does not use the observed event and censoring times. For this reason it is asymptotically unbiased, unlike Harrell's c-index based on informative pairs. The asymptotic distribution of the concordance probability estimate is derived using U-statistic theory and the methodology is applied to a predictive model in lung cancer.

Large Sample Theory for Semiparametric Regression Models with Two-Phase, Outcome Dependent Sampling

Outcome-dependent, two-phase sampling designs can dramatically reduce the costs of observational studies by judicious selection of the most informative subjects for purposes of detailed covariate measurement. Here we derive asymptotic information bounds and the form of the efficient score and influence functions for the semiparametric regression models studied by Lawless, Kalbfleisch, and Wild (1999) under two-phase sampling designs. We show that the maximum likelihood estimators for both the parametric and nonparametric parts of the model are asymptotically normal and efficient. The efficient influence function for the parametric part aggress with the more general information bound calculations of Robins, Hsieh, and Newey (1995). By verifying the conditions of Murphy and Van der Vaart (2000) for a least favorable parametric submodel, we provide asymptotic justification for statistical inference based on profile likelihood.

Estimation with Interval Censored Data in Longitudinal Studies

In biostatistical applications interest often focuses on the estimation of the distribution of a time-until-event variable T. If one observes whether or not T exceeds an observed monitoring time at a random number of monitoring times, then the data structure is called interval censored data. We extend this data structure by allowing the presence of a possibly time-dependent covariate process that is observed until end of follow up. If one only assumes that the censoring mechanism satisfies coarsening at random, then, by the curve of dimensionality, typically no regular estimators will exist. To fight the curse of dimensionality we follow the approach of Robins and Rotnitzky (1992) by modeling parameters of the censoring mechanism. We model the right-censoring mechanism by modeling the hazard of the follow up time, conditional on T and the covariate process. For the monitoring mechanism we avoid modeling the joint distribution of the monitoring times by only modeling a univariate hazard of the pooled monitoring times, conditional on the follow up time, T, and the covariates process, which can be estimated by treating the pooled sample of monitoring times as i.i.d. In particular, it is assumed that the monitoring times and the right-censoring times only depend on T through the observed covariate process. We introduce inverse probability of censoring weighted (IPCW) estimator of the distribution of T and of smooth functionals thereof which are guaranteed to be consistent and asymptotically normal if we have available correctly specified semiparametric models for the two hazards of the censoring process. Furthermore, given such correctly specified models for these hazards of the censoring process, we propose a one-step estimator which will improve on the IPCW estimator if we correctly specify a lower-dimensional working model for the conditional distribution of T, given the covariate process, that remains consistent and asymptotically normal if this latter working model is misspecified. It is shown that the one-step estimator is efficient if each subject is at most monitored once and the working model contains the truth. In general, it is shown that the one-step estimator optimally uses the surrogate information if the working model contains the truth. It is not optimal in using the interval information provided by the current status indicators at the monitoring times, but simulations in Peterson, van der Laan (1997) show that the efficiency loss is small.