Estimating parameters in Markov models for longitudinal studies with missing data or surrogate outcomes

The discrete-time Markov chain is commonly used in describing changes of health states for chronic diseases in a longitudinal study. Statistical inferences on comparing treatment effects or on finding determinants of disease progression usually require estimation of transition probabilities. In many situations when the outcome data have some missing observations or the variable of interest (called a latent variable) can not be measured directly, the estimation of transition probabilities becomes more complicated. In the latter case, a surrogate variable that is easier to access and can gauge the characteristics of the latent one is usually used for data analysis. ^ This dissertation research proposes methods to analyze longitudinal data (1) that have categorical outcome with missing observations or (2) that use complete or incomplete surrogate observations to analyze the categorical latent outcome. For (1), different missing mechanisms were considered for empirical studies using methods that include EM algorithm, Monte Carlo EM and a procedure that is not a data augmentation method. For (2), the hidden Markov model with the forward-backward procedure was applied for parameter estimation. This method was also extended to cover the computation of standard errors. The proposed methods were demonstrated by the Schizophrenia example. The relevance of public health, the strength and limitations, and possible future research were also discussed. ^

Characterization of a genomic signature of pregnancy identified in the breast.

The objective of this study was to comprehensively compare the genomic profiles in the breast of parous and nulliparous postmenopausal women to identify genes that permanently change their expression following pregnancy. The study was designed as a two-phase approach. In the discovery phase, we compared breast genomic profiles of 37 parous with 18 nulliparous postmenopausal women. In the validation phase, confirmation of the genomic patterns observed in the discovery phase was sought in an independent set of 30 parous and 22 nulliparous postmenopausal women. RNA was hybridized to Affymetrix HG_U133 Plus 2.0 oligonucleotide arrays containing probes to 54,675 transcripts, scanned and the images analyzed using Affymetrix GCOS software. Surrogate variable analysis, logistic regression, and significance analysis of microarrays were used to identify statistically significant differences in expression of genes. The false discovery rate (FDR) approach was used to control for multiple comparisons. We found that 208 genes (305 probe sets) were differentially expressed between parous and nulliparous women in both discovery and validation phases of the study at an FDR of 10% and with at least a 1.25-fold change. These genes are involved in regulation of transcription, centrosome organization, RNA splicing, cell-cycle control, adhesion, and differentiation. The results provide initial evidence that full-term pregnancy induces long-term genomic changes in the breast. The genomic signature of pregnancy could be used as an intermediate marker to assess potential chemopreventive interventions with hormones mimicking the effects of pregnancy for prevention of breast cancer.

Economic design of two-stage (X)over-bar charts: the Markov chain approach

When the (X) over bar chart is in use, samples are regularly taken from the process, and their means are plotted on the chart. In some cases, it is too expensive to obtain the X values, but not the values of a correlated variable Y. This paper presents a model for the economic design of a two-stage control chart, that is. a control chart based on both performance (X) and surrogate (Y) variables. The process is monitored by the surrogate variable until it signals an out-of-control behavior, and then a switch is made to the (X) over bar chart. The (X) over bar chart is built with central, warning. and action regions. If an X sample mean falls in the central region, the process surveillance returns to the (Y) over bar chart. Otherwise. The process remains under the (X) over bar chart's surveillance until an (X) over bar sample mean falls outside the control limits. The search for an assignable cause is undertaken when the performance variable signals an out-of-control behavior. In this way, the two variables, are used in an alternating fashion. The assumption of an exponential distribution to describe the length of time the process remains in control allows the application of the Markov chain approach for developing the cost function. A study is performed to examine the economic advantages of using performance and surrogate variables. (C) 2003 Elsevier B.V. All rights reserved.

Poincaré plot indexes of heart rate variability: Relationships with other nonlinear variables

The Poincaré plot for heart rate variability analysis is a technique considered geometrical and non-linear, that can be used to assess the dynamics of heart rate variability by a representation of the values of each pair of R-R intervals into a simplified phase space that describes the system's evolution. The aim of the present study was to verify if there is some correlation between SD1, SD2 and SD1/SD2 ratio and heart rate variability nonlinear indexes either in disease or healthy conditions. 114 patients with arterial coronary disease and 65 healthy subjects underwent 30. minute heart rate registration, in supine position and the analyzed indexes were as follows: SD1, SD2, SD1/SD2, Sample Entropy, Lyapunov Exponent, Hurst Exponent, Correlation Dimension, Detrended Fluctuation Analysis, SDNN, RMSSD, LF, HF and LF/HF ratio. Correlation coefficients between SD1, SD2 and SD1/SD2 indexes and the other variables were tested by the Spearman rank correlation test and a regression analysis. We verified high correlation between SD1/SD2 index and HE and DFA (α1) in both groups, suggesting that this ratio can be used as a surrogate variable. © 2013 Elsevier B.V.

Slope estimation in a parametric measurement error model: A simulation study

In regression analysis, covariate measurement error occurs in many applications. The error-prone covariates are often referred to as latent variables. In this proposed study, we extended the study of Chan et al. (2008) on recovering latent slope in a simple regression model to that in a multiple regression model. We presented an approach that applied the Monte Carlo method in the Bayesian framework to the parametric regression model with the measurement error in an explanatory variable. The proposed estimator applied the conditional expectation of latent slope given the observed outcome and surrogate variables in the multiple regression models. A simulation study was presented showing that the method produces estimator that is efficient in the multiple regression model, especially when the measurement error variance of surrogate variable is large.^

Evaluation of Variable Advisory Speed Limits in Work Zones, TPF-5(081), 2013

Variable advisory speed limit (VASL) systems could be effective at both urban and rural work zones, at both uncongested and congested sites. At uncongested urban work zones, the average speeds with VASL were lower than without VASL. But the standard deviation of speeds with VASL was higher. The increase in standard deviation may be due to the advisory nature of VASL. The speed limit compliance with VASL was about eight times greater than without VASL. At the congested sites, the VASL were effective in making drivers slow down gradually as they approached the work zone, reducing any sudden changes in speeds. Mobility-wise the use of VASL resulted in a decrease in average queue length, throughput, number of stops, and an increase in travel time. Several surrogate safety measures also demonstrated the benefits of VASL in congested work zones. VASL deployments in rural work zones resulted in reductions in mean speed, speed variance, and 85th percentile speeds downstream of the VASL sign. The study makes the following recommendations based on the case studies investigated: 1. The use of VASL is recommended for uncongested work zones to achieve better speed compliance and lower speeds. Greater enforcement of regulatory speed limits could help to decrease the standard deviation in speeds; 2. The use of VASL to complement the static speed limits in rural work zones is beneficial even if the VASL is only used to display the static speed limits. It leads to safer traffic conditions by encouraging traffic to slow down gradually and by reminding traffic of the reduced speed limit. A well-designed VASL algorithm, like the P5 algorithm developed in this study, can significantly improve the mobility and safety conditions in congested work zones. The use of simulation is recommended for optimizing the VASL algorithms before field deployment.

Variable ECG signs of ischemia during controlled occlusion of the left and right coronary artery in humans

Infarct size (IS) increases with vascular occlusion time, area at risk for infarction, lack of collateral supply, absence of preconditioning, and myocardial demand for O2 supply. ECG S-T segment elevation is used as a measure of severity of ischemia and a surrogate for IS. This study in 50 patients with coronary artery disease undergoing a first 120-s balloon occlusion of a stenosis sought to determine whether S-T segment elevation, corrected for the above-mentioned variables, in the left coronary artery (LCA group, n = 36) is different from that in the right coronary artery (RCA group, n = 14) territory. After consideration of all known determinants of IS, particularly mass at risk and collateral supply, the LCA territory is more sensitive than the RCA region to a 2-min period of myocardial ischemia.

A Mechanistic Latent Variable Model for Estimating Drug Concentrations in the Male Genital Tract

The purpose of this study is to develop statistical methodology to facilitate indirect estimation of the concentration of antiretroviral drugs and viral loads in the prostate gland and the seminal vesicle. The differences in antiretroviral drug concentrations in these organs may lead to suboptimal concentrations in one gland compared to the other. Suboptimal levels of the antiretroviral drugs will not be able to fully suppress the virus in that gland, lead to a source of sexually transmissible virus and increase the chance of selecting for drug resistant virus. This information may be useful selecting antiretroviral drug regimen that will achieve optimal concentrations in most of male genital tract glands. Using fractionally collected semen ejaculates, Lundquist (1949) measured levels of surrogate markers in each fraction that are uniquely produced by specific male accessory glands. To determine the original glandular concentrations of the surrogate markers, Lundquist solved a simultaneous series of linear equations. This method has several limitations. In particular, it does not yield a unique solution, it does not address measurement error, and it disregards inter-subject variability in the parameters. To cope with these limitations, we developed a mechanistic latent variable model based on the physiology of the male genital tract and surrogate markers. We employ a Bayesian approach and perform a sensitivity analysis with regard to the distributional assumptions on the random effects and priors. The model and Bayesian approach is validated on experimental data where the concentration of a drug should be (biologically) differentially distributed between the two glands. In this example, the Bayesian model-based conclusions are found to be robust to model specification and this hierarchical approach leads to more scientifically valid conclusions than the original methodology. In particular, unlike existing methods, the proposed model based approach was not affected by a common form of outliers.

Chloride as a signature indicator of soil textural and hydrologic stratigraphies in variable charge deep profiles

Soil properties that influence water movement through profiles are important for determining flow paths, reactions between soil and solute, and the ultimate destination of solutes. This is particularly important in high rainfall environments. For highly weathered deep profiles, we hypothesize that abrupt changes in the distribution of the quotient [QT = (silt + sand)/clay] reflect the boundaries between textural units or textural (TS) and hydrologic (HS) stratigraphies. As a result, QT can be used as a parameter to characterize TS and as a surrogate for HS. Secondly, we propose that if chloride distributions were correlated with QT, under non-limiting anion exchange, then chloride distributions can be used as a signature indicator of TS and HS. Soil cores to a depth of 12.5 in were taken from 16 locations in the wet tropical Johnstone River catchment of northeast Queensland, Australia. The cores belong to nine variable charge soil types and were under sugarcane (Saccharun officinarum-S) production, which included the use of potassium chloride, for several decades. The cores were segmented at I m depth increments and subsamples were analysed for chloride, pH, soil water content (theta), clay, silt and sand contents. Selected bores were capped to serve as piezometers to monitor groundwater dynamics. Depth incremented QT, theta and chloride correlated, each individually, significantly with the corresponding profile depth increments, indicating the presence of textural, hydrologic and chloride gradients in profiles. However, rapid increases in QT down the profile indicated abrupt changes in TS, suggesting that QT can be used as a parameter to characterize TS and as a surrogate for HS. Abrupt changes in chloride distributions were similar to QT, suggesting that chloride distributions can be used as a signature indicator of QT (TS) and HS. Groundwater data indicated that chloride distributions depended, at least partially, on groundwater dynamics, providing further support to our hypothesis that chloride distribution can be used as a signature indicator of HS. Copyright (c) 2005 John Wiley & Sons, Ltd.

Ihwg-μnir: A Miniaturised Near-infrared Gas Sensor Based On Substrate-integrated Hollow Waveguides Coupled To A Micro-nir-spectrophotometer.

A miniaturised gas analyser is described and evaluated based on the use of a substrate-integrated hollow waveguide (iHWG) coupled to a microsized near-infrared spectrophotometer comprising a linear variable filter and an array of InGaAs detectors. This gas sensing system was applied to analyse surrogate samples of natural fuel gas containing methane, ethane, propane and butane, quantified by using multivariate regression models based on partial least square (PLS) algorithms and Savitzky-Golay 1(st) derivative data preprocessing. The external validation of the obtained models reveals root mean square errors of prediction of 0.37, 0.36, 0.67 and 0.37% (v/v), for methane, ethane, propane and butane, respectively. The developed sensing system provides particularly rapid response times upon composition changes of the gaseous sample (approximately 2 s) due the minute volume of the iHWG-based measurement cell. The sensing system developed in this study is fully portable with a hand-held sized analyser footprint, and thus ideally suited for field analysis. Last but not least, the obtained results corroborate the potential of NIR-iHWG analysers for monitoring the quality of natural gas and petrochemical gaseous products.

Petroleomics By Electrospray Ionization Ft-icr Mass Spectrometry Coupled To Partial Least Squares With Variable Selection Methods: Prediction Of The Total Acid Number Of Crude Oils.

Negative-ion mode electrospray ionization, ESI(-), with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was coupled to a Partial Least Squares (PLS) regression and variable selection methods to estimate the total acid number (TAN) of Brazilian crude oil samples. Generally, ESI(-)-FT-ICR mass spectra present a power of resolution of ca. 500,000 and a mass accuracy less than 1 ppm, producing a data matrix containing over 5700 variables per sample. These variables correspond to heteroatom-containing species detected as deprotonated molecules, [M - H](-) ions, which are identified primarily as naphthenic acids, phenols and carbazole analog species. The TAN values for all samples ranged from 0.06 to 3.61 mg of KOH g(-1). To facilitate the spectral interpretation, three methods of variable selection were studied: variable importance in the projection (VIP), interval partial least squares (iPLS) and elimination of uninformative variables (UVE). The UVE method seems to be more appropriate for selecting important variables, reducing the dimension of the variables to 183 and producing a root mean square error of prediction of 0.32 mg of KOH g(-1). By reducing the size of the data, it was possible to relate the selected variables with their corresponding molecular formulas, thus identifying the main chemical species responsible for the TAN values.

Skewed Distribution of Circulating Activated Natural Killer T (NKT) Cells in Patients with Common Variable Immunodeficiency Disorders (CVID)

Common variable immunodeficiency disorder (CVID) is the commonest cause of primary antibody failure in adults and children, and characterized clinically by recurrent bacterial infections and autoimmune manifestations. Several innate immune defects have been described in CVID, but no study has yet investigated the frequency, phenotype or function of the key regulatory cell population, natural killer T (NKT) cells. We measured the frequencies and subsets of NKT cells in patients with CVID and compared these to healthy controls. Our results show a skewing of NKT cell subsets, with CD4+ NKT cells at higher frequencies, and CD8+ NKT cells at lower frequencies. However, these cells were highly activated and expression CD161. The NKT cells had a higher expression of CCR5 and concomitantly expression of CCR5+CD69+CXCR6 suggesting a compensation of the remaining population of NKT cells for rapid effector action.

BONA FIDE, STRONG-VARIABLE GALACTIC LUMINOUS BLUE VARIABLE STARS ARE FAST ROTATORS: DETECTION OF A HIGH ROTATIONAL VELOCITY IN HR CARINAE

We report optical observations of the luminous blue variable (LBV) HR Carinae which show that the star has reached a visual minimum phase in 2009. More importantly, we detected absorptions due to Si lambda lambda 4088-4116. To match their observed line profiles from 2009 May, a high rotational velocity of nu(rot) similar or equal to 150 +/- 20 km s(-1) is needed (assuming an inclination angle of 30 degrees), implying that HR Car rotates at similar or equal to 0.88 +/- 0.2 of its critical velocity for breakup (nu(crit)). Our results suggest that fast rotation is typical in all strong-variable, bona fide galactic LBVs, which present S-Dor-type variability. Strong-variable LBVs are located in a well-defined region of the HR diagram during visual minimum (the ""LBV minimum instability strip""). We suggest this region corresponds to where nu(crit) is reached. To the left of this strip, a forbidden zone with nu(rot)/nu(crit) > 1 is present, explaining why no LBVs are detected in this zone. Since dormant/ex LBVs like P Cygni and HD 168625 have low nu(rot), we propose that LBVs can be separated into two groups: fast-rotating, strong-variable stars showing S-Dor cycles (such as AG Car and HR Car) and slow-rotating stars with much less variability (such as P Cygni and HD 168625). We speculate that supernova (SN) progenitors which had S-Dor cycles before exploding (such as in SN 2001ig, SN 2003bg, and SN 2005gj) could have been fast rotators. We suggest that the potential difficulty of fast-rotating Galactic LBVs to lose angular momentum is additional evidence that such stars could explode during the LBV phase.

Automated supervised classification of variable stars in the CoRoT programme Method and application to the first four exoplanet fields

Aims. In this work, we describe the pipeline for the fast supervised classification of light curves observed by the CoRoT exoplanet CCDs. We present the classification results obtained for the first four measured fields, which represent a one-year in-orbit operation. Methods. The basis of the adopted supervised classification methodology has been described in detail in a previous paper, as is its application to the OGLE database. Here, we present the modifications of the algorithms and of the training set to optimize the performance when applied to the CoRoT data. Results. Classification results are presented for the observed fields IRa01, SRc01, LRc01, and LRa01 of the CoRoT mission. Statistics on the number of variables and the number of objects per class are given and typical light curves of high-probability candidates are shown. We also report on new stellar variability types discovered in the CoRoT data. The full classification results are publicly available.

Plasma Superoxide Dismutase-1 as a Surrogate Marker of Vivax Malaria Severity

Background: Severe outcomes have been described for both Plasmodium falciparum and P. vivax infections. The identification of sensitive and reliable markers of disease severity is fundamental to improving patient care. An intense pro-inflammatory response with oxidative stress and production of reactive oxygen species is present in malaria. Inflammatory cytokines such as tumor necrosis factor-alpha (TNF-alpha) and antioxidant agents such as superoxide dismutase-1 (SOD-1) are likely candidate biomarkers for disease severity. Here we tested whether plasma levels of SOD-1 could serve as a biomarker of severe vivax malaria. Methodology/Principal Findings: Plasma samples were obtained from residents of the Brazilian Amazon with a high risk for P. vivax transmission. Malaria diagnosis was made by both microscopy and nested PCR. A total of 219 individuals were enrolled: non-infected volunteers (n = 90) and individuals with vivax malaria: asymptomatic (n = 60), mild (n = 50) and severe infection (n = 19). SOD-1 was directly associated with parasitaemia, plasma creatinine and alanine amino-transaminase levels, while TNF-alpha correlated only with the later enzyme. The predictive power of SOD-1 and TNF-alpha levels was compared. SOD-1 protein levels were more effective at predicting vivax malaria severity than TNF-alpha. For discrimination of mild infection, elevated SOD-1 levels showed greater sensitivity than TNF-alpha (76% vs. 30% respectively; p < 0.0001), with higher specificity (100% vs. 97%; p < 0.0001). In predicting severe vivax malaria, SOD-1 levels exhibited higher sensitivity than TNF-alpha (80% vs. 56%, respectively; p < 0.0001; likelihood ratio: 7.45 vs. 3.14; p, 0.0001). Neither SOD-1 nor TNF-alpha could discriminate P. vivax infections from those caused by P. falciparum. Conclusion: SOD-1 is a powerful predictor of disease severity in individuals with different clinical presentations of vivax malaria.