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Polygenic risk scores (PRS) are rapidly emerging as aggregated measures of disease-risk associated with many genetic variants. Understanding the interplay of PRS with environmental factors is critical for interpreting and applying PRS in a wide variety of settings. We develop an efficient method for simultaneously modeling gene-environment correlations and interactions using PRS in case-control studies. We use a logistic-normal regression modeling framework to specify the disease risk and PRS distribution in the underlying population and propose joint inference across the two models using the retrospective likelihood of the case-control data. Extensive simulation studies demonstrate the flexibility of the method in trading-off bias and efficiency for the estimation of various model parameters compared to the standard logistic regression or a case-only analysis for gene-environment interactions, or a control-only analysis for gene-environment correlations. Finally, using simulated case-control datasets within the UK Biobank study, we demonstrate the power of the proposed method for its ability to recover results from the full prospective cohort for the detection of an interaction between long-term oral contraceptive use and PRS on the risk of breast cancer. This method is computationally efficient and implemented in a user-friendly R package.

We develop a generalized partially additive model to build a single semiparametric risk scoring system for physical activity across multiple populations. A score comprised of distinct and objective physical activity measures is a new concept that offers challenges due to the nonlinear relationship between physical behaviors and various health outcomes. We overcome these challenges by modelling each score component as a smooth term, an extension of generalized partially linear single-index models. We use penalized splines and propose two inferential methods, one using profile likelihood and a nonparametric bootstrap, the other using a full Bayesian model, to solve additional computational problems. Both methods exhibit similar and accurate performance in simulations. These models are applied to the National Health and Nutrition Examination Survey (NHANES) and quantify nonlinear and interpretable shapes of score components for all-cause mortality.

Dietary supplement (DS) use is widespread in the U.S. and contributes large amounts of micronutrients to users. Most studies have relied on data from one assessment method to characterize the prevalence of DS use. Combining multiple methods enhances the ability to capture nutrient exposures from DS and examine trends over time.The objective of this study was to characterize DS use and examine trends in any DS as well as micronutrient-containing (MN) DS use among a nationally representative sample of the U.S. population (≥1y) from the 2007-2018 NHANES using a combined approach (i.e., DSMQ and/or ≥1 24HR).NHANES obtains an in-home inventory with a frequency-based DS and Prescription Medicine Questionnaire (DSMQ), and two 24-hr recalls (24HR). The objective of this study was to characterize DS use and examine trends in any and micronutrient-containing (MN) DS use among a nationally representative sample of the U.S. population (≥1y) from the 2007-2018 NHANES, using a combined approach (i.e., DSMQ and/or ≥1 24HR). Trends in the prevalence of use and selected types of products used were estimated for the population and by sex, age, race/Hispanic origin, family income (poverty-to-income ratio (PIR)), and household food security (food-secure vs. food-insecure). Linear trends were tested using orthogonal polynomials (significance set at p0.05).DS use increased from 50% in 2007 to 56% in 2018 (p = 0.001); use of MN products increased from 46% to 49% (p = 0.03), and single-nutrient DS (e.g., magnesium, vitamins B12 and D) use also increased (all p0.001). In contrast, multivitamin-mineral use decreased (70% to 56%; p0.001). Among adults (≥19y), any (54% to 61%) and MN (49% to 54%) DS use increased, especially among men, non-Hispanic Blacks and Hispanics, and low-income adults (PIR≤130%). Among children (1-18y), any DS use remained stable (∼38%), as did MN use, except for food-insecure children, whose use increased from 24% to 31% over the decade (p = 0.03).The prevalence of any and MN DS use increased over time in the U.S. This may be partially attributed to increased use of single-nutrient products. Population subgroups differed in their DS use.

Microarray gene expression data contains missing values (MVs). However, some methods for downstream analyses, including some predic- tion tools, require a complete expression data matrix. Current methods for estimating the MVs include sample mean and K-nearest neighbors (KNN). Whether the accuracy of estimation (imputation) methods depends on the actual gene expression has not been thoroughly investigated. Under this set- ting, we examine how the accuracy depends on the actual expression level and propose new methods that provide improvements in accuracy relative to the current methods in certain ranges of gene expression. In particular, we propose regression methods, namely multiple imputation via ordinary least squares (OLS) and missing value prediction using partial least squares (PLS). Mean estimation of MVs ignores the observed correlation structure of the genes and is highly inaccurate. Estimating MVs using KNN, a method which incorporates pairwise gene expression information, provides substan- tial improvement in accuracy on average. However, the accuracy of KNN across the wide range of observed gene expression is unlikely to be uniform and this is revealed by evaluating accuracy as a function of the expression level.

Fatal car crashes are the leading cause of death among teenagers in the USA. The Graduated Driver Licensing (GDL) programme is one effective policy for reducing the number of teen fatal car crashes. Our study focuses on the number of fatal car crashes in Michigan during 1990–2004 excluding 1997, when the GDL started. We use Poisson regression with spatially dependent random effects to model the county level teen car crash counts. We develop a measurement error model to account for the fact that the total teenage population in the county level is used as a proxy for the teenage driver population. To the best of our knowledge, there is no existing literature that considers adjustment for measurement error in an offset variable. Furthermore, limited work has addressed the measurement errors in the context of spatial data. In our modelling, a Berkson measurement error model with spatial random effects is applied to adjust for the error-prone offset variable in a Bayesian paradigm. The Bayesian Markov chain Monte Carlo (MCMC) sampling is implemented in rstan. To assess the consequence of adjusting for measurement error, we compared two models with and without adjustment for measurement error. We found the effect of a time indicator becomes less significant with the measurement-error adjustment. It leads to our conclusion that the reduced number of teen drivers can help explain, to some extent, the effectiveness of GDL.

The effective prompt response function full width at half maximum, PRF FWHM of 637 ps (obtained from the prompt gamma pairs of 477 keV and 700 keV associated with the yrast 2+ state in 206Po), and 1007 ps (obtained from the Compton gamma pairs of 189 keV and 237 keV associated with the 192Os(18O,16O)194Os 2 neutron transfer reaction) were used in fitting the time difference spectra obtained from the gamma coincident pairsof 206 keV and 374 keV in a symmetrised LaBr3(Ce) associated with the gamma transitions in 192Os, using the Half-life program. The values of half-life measured by fitting these PRF FWHM of 637 ps and 1007 ps separately show an excellent agreement of 282(16) ps and 272(21) ps, respectively, which correspond to the global half-life value of 282(4) ps for the 192Os. The mean value of 277(12) ps from these two measurements was used in calculating the B(E2; IL ->IL-2) of 4233(114) e2fm4, which is equivalent to be 81(19) W.u.

Dietary data collected from 24-hour dietary recalls are observed with significant measurement errors. In the nonparametric curve estimation literature, much of the effort has been devoted to designing methods that are consistent under contamination by noise, and which have been traditionally applied for analyzing those data. However, some foods such as alcohol or fruits are consumed only episodically, and may not be consumed during the day when the 24-hour recall is administered. These so-called excess zeros make existing nonparametric estimators break down, and new techniques need to be developed for such data. We develop two new consistent semiparametric estimators of the distribution of such episodically consumed food data, making parametric assumptions only on some less important parts of the model. We establish its theoretical properties and illustrate the good performance of our fully data-driven method in simulated and real data. Supplementary materials for this article are available online.

Canonical correlation analysis investigates linear relationships between two sets of variables, but often works poorly on modern data sets due to high-dimensionality and mixed data types such as continuous, binary and zero-inflated. To overcome these challenges, we propose a semiparametric approach for sparse canonical correlation analysis based on Gaussian copula. Our main contribution is a truncated latent Gaussian copula model for data with excess zeros, which allows us to derive a rank-based estimator of the latent correlation matrix for mixed variable types without the estimation of marginal transformation functions. The resulting canonical correlation analysis method works well in high-dimensional settings as demonstrated via numerical studies, as well as in application to the analysis of association between gene expression and micro RNA data of breast cancer patients., Accepted to Biometrika. Main text: 19 pages and 3 figures. Supplementary material: 28 pages and 9 figures

We continue our review of issues related to measurement error and misclassification in epidemiology. We further describe methods of adjusting for biased estimation caused by measurement error in continuous covariates, covering likelihood methods, Bayesian methods, moment reconstruction, moment-adjusted imputation, and multiple imputation. We then describe which methods can also be used with misclassification of categorical covariates. Methods of adjusting estimation of distributions of continuous variables for measurement error are then reviewed. Illustrative examples are provided throughout these sections. We provide lists of available software for implementing these methods and also provide the code for implementing our examples in the Supporting Information. Next, we present several advanced topics, including data subject to both classical and Berkson error, modeling continuous exposures with measurement error, and categorical exposures with misclassification in the same model, variable selection when some of the variables are measured with error, adjusting analyses or design for error in an outcome variable, and categorizing continuous variables measured with error. Finally, we provide some advice for the often met situations where variables are known to be measured with substantial error, but there is only an external reference standard or partial (or no) information about the type or magnitude of the error.

Measurement error and misclassification of variables frequently occur in epidemiology and involve variables important to public health. Their presence can impact strongly on results of statistical analyses involving such variables. However, investigators commonly fail to pay attention to biases resulting from such mismeasurement. We provide, in two parts, an overview of the types of error that occur, their impacts on analytic results, and statistical methods to mitigate the biases that they cause. In this first part, we review different types of measurement error and misclassification, emphasizing the classical, linear and Berkson models, and on the concepts of non-differential and differential error. We describe the impacts of these types of error in covariates and in outcome variables on various analyses, including estimation and testing in regression models and estimating distributions. We outline types of ancillary studies required to provide information about such errors and discuss the implications of covariate measurement error for study design. Methods for ascertaining sample size requirements are outlined, both for ancillary sub-studies designed to provide information about measurement error and for main studies where the exposure of interest is measured with error. We describe two of the simpler methods, regression calibration and simulation extrapolation (SIMEX), that adjust for bias in regression coefficients caused by measurement error in continuous covariates, and illustrate their use through examples drawn from the Observing Protein and Energy (OPEN) dietary validation study. Finally, we review software available for implementing these methods. The second part of the paper deals with more advanced topics.

BACKGROUND: Most dietary indices reflect foods and beverages and do not include exposures from dietary supplements (DS) that provide substantial amounts of micronutrients. A nutrient-based approach that captures total intake inclusive of DS can strengthen exposure assessment. OBJECTIVE: To examine the construct and criterion validity of the Total Nutrient Index (TNI) among U.S. adults (≥19y; non-pregnant or lactating). METHODS: The TNI includes eight under-consumed micronutrients identified by the Dietary Guidelines for Americans: calcium; magnesium; potassium; choline; and vitamins A, C, D, and E. The TNI is expressed as a percentage of the Recommended Dietary Allowance or Adequate Intake to compute micronutrient component scores; the mean of the component scores yields the TNI score, ranging from 0-100. Data from exemplary menus and the 2003-2006 (≥19y; n = 8,861) and 2011-2014 NHANES (≥19y; n = 9,954) were employed. Exemplary menus were used to determine if the TNI yielded high scores from dietary sources (women 31-50y; men ≥70y). TNI scores were correlated with Healthy Eating Index (HEI)-2015 overall and component scores for dairy, fruits, and vegetables; TNI component scores for vitamins A, C, D, and E were correlated with respective biomarker data. TNI scores were compared between groups with known differences in nutrient intake based on the literature. RESULTS: The TNI yielded high scores on exemplary menus (84.8-93.3/100) and was moderately correlated (r = 0.48) with the HEI-2015. Mean TNI scores were significantly different for DS users (83.5) vs. non-users (67.1), non-smokers (76.8) vs. smokers (70.3), and those living with food security (76.6) vs. food insecurity (69.1). Correlations of TNI vitamin component scores with available biomarkers ranged from r = 0.12 (α-tocopherol) to r = 0.36 (serum 25(OH)D), and were significantly higher than correlations obtained from the diet alone. CONCLUSION: The evaluation of validity supports that the TNI is a useful construct to assess total micronutrient exposures of under-consumed micronutrients among U.S. adults.

In nutrition, epidemiology, and other public health fields, composite scores are a common tool used to assess a health behaviour. These composite scores compare an individual's health behaviour to an idealized standard and provide a number, often between 0 and 100, to indicate their compliance to a health behaviour. Crucially, this measure of health behaviour is applied across populations (gender, smoking status, etc.) and health outcomes (colon cancer, breast cancer, etc.) to create a single interpretable score. One such composite score is the 2005 Healthy Eating Index that breaks diet into 12 components and evaluates nutritional intake by adherence to these components. We provide a general method that can be used to reassess the importance of these 12 components using flexible non-linear models, across populations and diseases, based on an asymptotic least squares approximation. We establish oracle properties of this variable selection technique in our context, which is different from the usual one population and one disease context. Although our methods are motivated by the Healthy Eating Index, they are broad enough to be applied to any composite score and a broad range of non-linear models.

The structure of Po-208 populated through the EC/beta(+) decay of At-208 is investigated using gamma-ray spectroscopy at the ISOLDE Decay Station. The presented level scheme contains 27 new excited states and 43 new transitions, as well as a further 50 previously observed. rays which have been (re)assigned a position. The level scheme is compared to shell model calculations. Through this analysis approximately half of the beta-decay strength of At-208 is found to proceed via allowed decay and half via first-forbidden decay. The first-forbidden transitions predominantly populate core excited states at high excitation energies, which is qualitatively understood using shell model considerations. This mass region provides an excellent testing ground for the competition between allowed and first-forbidden beta-decay calculations, important for the detailed understanding of the nucleosynthesis of heavy elements.

Case-controls studies are popular epidemiological designs for detecting gene–environment interactions in the etiology of complex diseases, where the genetic susceptibility and environmental exposures may often be reasonably assumed independent in the source population. Various papers have presented analytical methods exploiting gene–environment independence to achieve better efficiency, all of which require either a rare disease assumption or a distributional assumption on the genetic variables. We relax both assumptions. We construct a semiparametric estimator in case-control studies exploiting gene–environment independence, while the distributions of genetic susceptibility and environmental exposures are both unspecified and the disease rate is assumed unknown and is not required to be close to zero. The resulting estimator is semiparametric efficient and its superiority over prospective logistic regression, the usual analysis in case-control studies, is demonstrated in various numerical illustrations.

Studies for the associations between physical activity and disease risk have been supported by newly developed wearable accelerometer-based devices. These devices record raw activity/movement information in real time on a second-by-second basis and the data can be converted to a variety of summary metrics, such as energy expenditure, sedentary time and moderate-vigorous intensity physical activity. Here we review some of the methods used to analyze the accelerometer data and the R packages that can generate activity related variables from raw data. We also discuss longitudinal data and functional data approaches to perform analyses for various research purposes.

Numerous statistical methods have been developed for analyzing high-dimensional data. These methods often focus on variable selection approaches but are limited for the purpose of testing with high-dimensional data. They are often required to have explicit-likelihood functions. In this article, we propose a "hybrid omnibus test" for high-dicmensional data testing purpose with much weaker requirements. Our hybrid omnibus test is developed under a semiparametric framework where a likelihood function is no longer necessary. Our test is a version of a frequentist-Bayesian hybrid score-type test for a generalized partially linear single-index model, which has a link function being a function of a set of variables through a generalized partially linear single index. We propose an efficient score based on estimating equations, define local tests, and then construct our hybrid omnibus test using local tests. We compare our approach with an empirical-likelihood ratio test and Bayesian inference based on Bayes factors, using simulation studies. Our simulation results suggest that our approach outperforms the others, in terms of type I error, power, and computational cost in both the low- and high-dimensional cases. The advantage of our approach is demonstrated by applying it to genetic pathway data for type II diabetes mellitus.

Model averaging generally provides better predictions than model selection, but the existing model averaging methods cannot lead to parsimonious models. Parsimony is an especially important property when the number of parameters is large. To achieve a parsimonious model averaging coefficient estimator, we suggest a novel criterion for choosing weights. Asymptotic properties are derived in two practical scenarios: (i) one or more correct models exist in the candidate model set and (ii) all candidate models are misspecified. Under the former scenario, it is proved that our method can put the weight one to the smallest correct model and the resulting model averaging estimators of coefficients have many zeros and thus lead to a parsimonious model. The asymptotic distribution of the estimators is also provided. Under the latter scenario, prediction is mainly focused on and we prove that the proposed procedure is asymptotically optimal in the sense that its squared prediction loss and risk are asymptotically identical to those of the best—but infeasible—model averaging estimator. Numerical analysis shows the promise of the proposed procedure over existing model averaging and selection methods.

5 pags., 3 figs., 1 tab. -- Open Access funded by Creative Commons Atribution Licence 4.0, Gamow-Teller β decay is forbidden if the number of nodes in the radial wave functions of the initial and final states is different. This Δn=0 requirement plays a major role in the β decay of heavy neutron-rich nuclei, affecting the nucleosynthesis through the increased half-lives of nuclei on the astrophysical r-process pathway below both Z=50 (for N>82) and Z=82 (for N>126). The level of forbiddenness of the Δn=1ν1g →π0g transition has been investigated from the β decay of the ground state of Hg into the single-proton-hole nucleus Tl in an experiment at the ISOLDE Decay Station. From statistical observational limits on possible γ-ray transitions depopulating the π0g state in Tl, an upper limit of 3.9×10 % was obtained for the probability of this decay, corresponding to log⁡ft>8.8 within a 95% confidence limit. This is the most stringent test of the Δn=0 selection rule to date., Support from the European Union seventh framework through ENSAR contract no. 262010, the Science and Technology Facilities Council through grants ST/P005314/1, ST/L005743/1 and ST/J000051/1 (UK), the MINECO projects FPA2015-64969-P, FPA2015-65035-P and FPA2017-87568-P (Spain), FWO-Vlaanderen (Belgium), GOA/2015/010 (BOF KU Leuven), the Excellence of Science programme (EOS-FWO), and the Interuniversity Attraction Poles Programme initiated by the Belgian Science Policy Office (BriX network P7/12) is acknowledged. ZsP acknowledges support by the ExtreMe Matter Institute EMMI at the GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany. PHR and SMJ ac-knowledge support from the UK Department for Business, Energy and Industrial Strategy via the National Measurement Office.

© 2019 American Society for Nutrition. All rights reserved. The use of dietary supplements (DS) is pervasive and can provide substantial amounts of micronutrients to those who use them. Therefore when characterizing dietary intakes, describing the prevalence of inadequacy or excess, or assessing relations between nutrients and health outcomes, it is critical to incorporate DS intakes to improve exposure estimates. Unfortunately, little is known about the best methods to assess DS, and the structure of measurement error in DS reporting. Several characteristics of nutrients from DS are salient to understand when comparing to those in foods. First, DS can be consumed daily or episodically, in bolus form and can deliver discrete and often very high doses of nutrients that are not limited by energy intakes. These characteristics contribute to bimodal distributions and distributions severely skewed to the right. Labels on DS often provide nutrient forms that differ from those found in conventional foods, and underestimate analytically derived values. Finally, the bioavailability of many nutrient-containing DS is not known and it may not be the same as the nutrients in a food matrix. Current methods to estimate usual intakes are not designed specifically to handle DS. Two temporal procedures are described to refer to the order that nutrient intakes are combined relative to usual intake procedures, referred to as a shrinking the distribution to remove random error. The shrink then add approach is preferable to the add then shrink approach when users and nonusers are combined for most research questions. Stratifying by DS before usual intake methods is another defensible option. This review describes how to incorporate nutrient intakes from DS to usual intakes from foods, and describes the available methods and fit-for-purpose of different analytical strategies to address research questions where total usual intakes are of interest at the group level for use in nutrition research and to inform policy decisions. Clinical Trial Registry: NCT03400436. J Nutr 2019;149:181-197.

© 2019 Frontiers in Bioscience. All rights reserved. Amino acid nutrition studies often involve repeated measures data. An example is that the concentrations of plasma citrulline in steers are repeatedly measured from the same animals. The standard repeated measures ANOVA method does not detect significant time changes in the concentrations of plasma citrulline within 6 hours after steers consumed rumen-protected citrulline, while a graphical analysis indicates that there exists a time effect. Here we describe three mixed model analyses that capture the time effect in a statistically significant way, while accounting for the correlations of measurements over time from the same steers. First, we allow flexible variance-covariance structures on our model. Second, we use baseline measurements as a covariate in our model. Third, we use percent-change from baseline as a data normalization method. In our data analysis, all these three approaches can lead to meaningful statistical results that oral administration of rumen-protected citrulline enhances the concentrations of plasma citrulline over time in ruminants. This supports the notion that rumen-protected citrulline can bypass the rumen to effectively enter the blood circulation.

Summary Quantile regression is a semiparametric method for modeling associations between variables. It is most helpful when the covariates have complex relationships with the location, scale, and shape of the outcome distribution. Despite the method’s robustness to distributional assumptions and outliers in the outcome, regression quantiles may be biased in the presence of measurement error in the covariates. The impact of function-valued covariates contaminated with heteroscedastic error has not yet been examined previously; although, studies have investigated the case of scalar-valued covariates. We present a two-stage strategy to consistently fit linear quantile regression models with a function-valued covariate that may be measured with error. In the first stage, an instrumental variable is used to estimate the covariance matrix associated with the measurement error. In the second stage, simulation extrapolation (SIMEX) is used to correct for measurement error in the function-valued covariate. Point-wise standard errors are estimated by means of nonparametric bootstrap. We present simulation studies to assess the robustness of the measurement error corrected for functional quantile regression. Our methods are applied to National Health and Examination Survey data to assess the relationship between physical activity and body mass index among adults in the United States.

5 pags., 3 figs., 2 tabs., Neutron-rich radioactive isotope (RI) beams in the vicinity of Zr110 were produced using the in-flight fission of a 345-MeV/nucleon U238 beam at the RIKEN Radioactive Isotope Beam Factory. The RI beams were separated in flight by the large-acceptance two-stage fragment separator BigRIPS. Isotopes were clearly identified in a low-background particle-identification plot, which was obtained from the measurements of time of flight, magnetic rigidity, and energy loss using beam-line detectors placed in the BigRIPS separator and the ZeroDegree spectrometer. Nine new isotopes, Br101, Kr102, Rb105,106, Sr108, Y110,111, Zr114, and Nb117, are reported for the first time, with their measured production cross sections compared with the extrapolations of lighter isotopes down to the femtobarn regime., This experiment was carried out at the RIBF operated by the RIKEN Nishina Center, and the Center for Nuclear Study, the University of Tokyo. The authors acknowledge the RIBF accelerator staffs for their efforts in delivering an intense 238U beam. This work was supported by JSPS KAKENHI Grants No. 2301752, No. 24740188, No. 25247045, No. 26800117, and No. 16K17680, NRF Grant No. 2016K1A3A7A09005575, STFC Grants No. ST/J000132/1, No. ST/J000051/1, No. ST/K502431/1, No. ST/L005743/1, No. ST/P003885/1, No. ST/P003982/1, and No. ST/P005314/1, DOE Grant No. DE-FG02-91ER 40609, BMBF Grant No. 05P19RDFN1, and Spanish Ministerio de Ciencia e Innovación under Contracts No. FPA2009- 13377-C02 and No. FPA2011-29854-C04. P.H.R. and G.L. acknowledge additional support from the UK Department for Business, Energy and Industrial Strategy (BEIS) via funding for the UK National Measurement System. P.-A.S. was financed by the RIKEN Foreign Postdoctoral Researcher Program

A priori dietary indices provide a standardized, reproducible way to evaluate adherence to dietary recommendations across different populations. Existing nutrient-based indices were developed to reflect food/beverage intake; however, given the high prevalence of dietary supplement (DS) use and its potentially large contribution to nutrient intakes for those that use them, exposure classification without accounting for DS is incomplete. The purpose of this article is to review existing nutrient-based indices and describe the development of the Total Nutrient Index (TNI), an index developed to capture usual intakes from all sources of under-consumed micronutrients among the U.S. population. The TNI assesses U.S. adults' total nutrient intakes relative to recommended nutrient standards for eight under-consumed micronutrients identified by the Dietary Guidelines for Americans: calcium, magnesium, potassium, choline, and vitamins A, C, D, E. The TNI is scored from 0 to 100 (truncated at 100). The mean TNI score of U.S. adults (≥19 y; n = 9,954) based on dietary data from NHANES 2011-2014, was 75.4; the mean score for the index ignoring DS contributions was only 69.0 (t-test; p < 0.001). The TNI extends existing measures of diet quality by including nutrient intakes from all sources and was developed for research, monitoring, and policy purposes.

Summary We consider the problem where the data consist of a survival time and a binary outcome measurement for each individual, as well as corresponding predictors. The goal is to select the common set of predictors which affect both the responses, and not just one of them. In addition, we develop a survival prediction model based on data integration. The paper is motivated by the Cancer Genomic Atlas databank, which is currently the largest genomics and transcriptomics database. The data contain cancer survival information along with cancer stages for each patient. Furthermore, it contains reverse phase protein array measurements for each individual, which are the predictors associated with these responses. The biological motivation is to identify the major actionable proteins associated with both survival outcomes and cancer stages. We develop a Bayesian hierarchical model to model jointly the survival time and the classification of the cancer stages. Moreover, to deal with the high dimensionality of the reverse phase protein array measurements, we use a shrinkage prior to identify significant proteins. Simulations and Cancer Genomic Atlas data analysis show that the joint integrated modelling approach improves survival prediction.

The β decay of ^{208}Hg into the one-proton hole, one neutron-particle _{81}^{208}Tl_{127} nucleus was investigated at CERN-ISOLDE. Shell-model calculations describe well the level scheme deduced, validating the proton-neutron interactions used, with implications for the whole of the N>126, Z

The identification of valid surrogate markers of disease or disease progression has the potential to decrease the length and costs of future studies. Most available methods that assess the value of a surrogate marker ignore the fact that surrogates are often measured with error. Failing to adjust for measurement error can erroneously identify a useful surrogate marker as not useful or vice versa. We investigate and propose robust methods to correct for the effect of measurement error when evaluating a surrogate marker using multiple estimators developed for parametric and nonparametric estimates of the proportion of treatment effect explained by the surrogate marker. In addition, we quantify the attenuation bias induced by measurement error and develop inference procedures to allow for variance and confidence interval estimation. Through a simulation study, we show that our proposed estimators correct for measurement error in the surrogate marker and that our inference procedures perform well in finite samples. We illustrate these methods by examining a potential surrogate marker which is measured with error, hemoglobin A1c, using data from the Diabetes Prevention Program clinical trial.

Summary We develop model averaging estimation in the linear regression model where some covariates are subject to measurement error. The absence of the true covariates in this framework makes the calculation of the standard residual-based loss function impossible. We take advantage of the explicit form of the parameter estimators and construct a weight choice criterion. It is asymptotically equivalent to the unknown model average estimator minimizing the loss function. When the true model is not included in the set of candidate models, the method achieves optimality in terms of minimizing the relative loss, whereas, when the true model is included, the method estimates the model parameter with root n rate. Simulation results in comparison with existing Bayesian information criterion and Akaike information criterion model selection and model averaging methods strongly favour our model averaging method. The method is applied to a study on health.

Twenty-one two-proton knockout (p; 3p) cross sections from neutron-rich nuclei at ~ 250 MeV/nucleon were measured. For the first time, the angular distribution of the three emitted protons were measured in coincidence with the tracker and hydrogen target MINOS, demonstrating that the (p; 3p) kinematics is consistent with two sequential proton-proton collisions within the projectile nucleus. Ratios of (p; 3p) over (p; 2p) inclusive cross sections follow the trend of other many-proton (neutron) removal reactions, further reinforcing the sequential nature of (p; 3p) in neutron-rich nuclei.

The β decay of 207Hg into the single-proton-hole nucleus 207Tl has been studied through γ-ray spectroscopy at the ISOLDE Decay Station (IDS) with the aim of identifying states resulting from coupling of the πs−11/2, πd−13/2, and πh−111/2 shell model orbitals to the collective octupole vibration. Twenty-two states were observed lying between 2.6 and 4.0 MeV, eleven of which were observed for the first time, and 78 new transitions were placed. Two octupole states (s1/2-coupled) are identified and three more states (d3/2-coupled) are tentatively assigned using spin-parity inferences, while further h11/2-coupled states may also have been observed for the first time. Comparisons are made with state-of-the-art large-scale shell model calculations and previous observations made in this region, and systematic underestimation of the energy of the octupole vibrational states is noted. We suggest that in order to resolve the difference in predicted energies for collective and noncollective t=1 states (t is the number of nucleons breaking the 208Pb core), the effect of t=2 mixing may be reduced for octupole-coupled states. The inclusion of mixing with t=0,2,3 excitations is necessary to replicate all t=1 state energies accurately.

Background: Neutron-rich nuclei with mass number between 100 and 110 attract much attention, since several kinds of shapes, such as spherical, prolate, oblate, and triaxial shapes, are predicted. In particular, for neutron-rich Mo isotopes, different models predict different magnitudes and rigidity of triaxial deformation. Previous interpretations of experimental results based solely on low-lying 2+2 states are insufficient to distinguish between the rigid triaxial shape, γ vibration, or γ -soft rotor. Purpose: The shape evolution of 106Mo, 108Mo, and 110Mo is investigated through their 2+1-state lifetimes, decay-branching ratios of the 2+2 state, and energies of the low-lying collective excited states with Kπ = 0+, 2+, and 4+. Method: β -delayed γ -ray spectroscopy was employed for neutron-rich Nb and Zr isotopes produced at the RIKEN RI Beam Factory to populate excited states in 106Mo , 108Mo , and 110Mo . The EUroball-RIKEN Cluster Array was used for high-resolution γ -ray detection and lifetimes of the 2+1 states were determined using the UK fast-timing array of LaBr3(Ce) detectors. Results: New γ -ray transitions and levels are reported, including newly assigned 0+2 states in 108,110Mo . Quadrupole deformations were obtained for 106,108,110Mo from their 2+1 energies and lifetimes. The β -delayed neutron-emission probabilities of 108 Nb and 110 Nb were determined by examining the γ rays of their respective daughter decays. Conclusions: The even-odd energy staggering in the 2+2 band was compared with typical patterns of the γ -vibrational band, rigid triaxial rotor, and γ -soft rotor. The very small even-odd staggering of 106 Mo, 108Mo, and 110Mo favors a γ -vibrational band assignment. The kinematic moment of inertia for the 2+2 band showed a trend similar to the ground-state band, which is as expected for the γ -vibrational band. Beyond-mean-field calculations employing the constrained Hartree-Fock-Bogoliubov and local quasiparticle-random-phase approximation method using the SLy 5 + T interaction reproduced the ground and 2+2 bands in 106Mo and 108Mo . The collective wave functions are consistent with the interpretation of the 2+2 band as the γ -vibrational band of the prolate shape. However, the staggering pattern observed in 110Mo differs from the one suggested in the calculations which predict a γ -soft rotor. There was no experimental indication of the oblate shape or the γ -soft rotor predicted in these Mo isotopes.

Summary Huntington disease is an autosomal dominant, neurodegenerative disease without clearly identified biomarkers for when motor-onset occurs. Current standards to determine motor-onset rely on a clinician’s subjective judgment that a patient’s extrapyramidal signs are unequivocally associated with Huntington disease. This subjectivity can lead to error which could be overcome using an objective, data-driven metric that determines motor-onset. Recent studies of motor-sign decline—the longitudinal degeneration of motor-ability in patients—have revealed that motor-onset is closely related to an inflection point in its longitudinal trajectory. We propose a nonlinear location-shift marker model that captures this motor-sign decline and assesses how its inflection point is linked to other markers of Huntington disease progression. We propose two estimating procedures to estimate this model and its inflection point: one is a parametric method using nonlinear mixed effects model and the other one is a multi-stage nonparametric approach, which we developed. In an empirical study, the parametric approach was sensitive to correct specification of the mean structure of the longitudinal data. In contrast, our multi-stage nonparametric procedure consistently produced unbiased estimates regardless of the true mean structure. Applying our multi-stage nonparametric estimator to Neurobiological Predictors of Huntington Disease, a large observational study of Huntington disease, leads to earlier prediction of motor-onset compared to the clinician’s subjective judgment.

We propose a data-driven Neural Network (NN) optimization framework to determine the optimal multi-period dynamic asset allocation strategy for outperforming a general stochastic target. We formulate the problem as an optimal stochastic control with an asymmetric, distribution shaping, objective function. The proposed framework is illustrated with the asset allocation problem in the accumulation phase of a defined contribution pension plan, with the goal of achieving a higher terminal wealth than a stochastic benchmark. We demonstrate that the data-driven approach is capable of learning an adaptive asset allocation strategy directly from historical market returns, without assuming any parametric model of the financial market dynamics. Following the optimal adaptive strategy, investors can make allocation decisions simply depending on the current state of the portfolio. The optimal adaptive strategy outperforms the benchmark constant proportion strategy, achieving a higher terminal wealth with a 90% probability, a 46% higher median terminal wealth, and a significantly more right-skewed terminal wealth distribution. We further demonstrate the robustness of the optimal adaptive strategy by testing the performance of the strategy on bootstrap resampled market data, which has different distributions compared to the training data., Comment: 33 pages

The extremely neutron-deficient isotopes 177,179Au were studied by means of in-beam ?-ray spectroscopy. Specific tagging techniques, ?-decay tagging in 177Au and isomer tagging in 179Au, were used for these studies. Feeding of positive-parity, nearly spherical states, which are associated with 2d3/2 and 3s1/2 proton-hole configurations, from the 1i13/2 proton-intruder configuration was observed in 177Au. Such a decay path has no precedent in odd-Au isotopes and it is explained by the effect of mixing of wave functions of the initial state. © 2020, 262010 2/0129/17 Science and Technology Facilities Council, STFC European Research Council, ERC: 203481 Academy of Finland: 131665 Agentúra na Podporu V?skumu a V?voja, APVV: APVV-15-0225, The authors express their gratitude to the staff of the Accelerator Laboratory at the University of Jyväskylä for their excellent technical support. This work has been supported by the EU-FP7-IA project ENSAR (No. 262010 ), the Academy of Finland (CoE in Nuclear and Accelerator Based Physics, grant to T.G., Contract No. 131665 ), the European Research Council through the project SHESTRUCT (Grant Agreement No. 203481 ), the UK Science and Technology Facilities Council , the Slovak Research and Development Agency under Contract No. APVV-15-0225 , and the Slovak Grant Agency VEGA (Contract No. 2/0129/17 ).

In biomedical studies, parametric mixture models are widely used for modeling data collected from a non-homogenous population. For instance, in genetic linkage analysis, parametric admixture models have been considered to account for the fact that linkage may exist only in a proportion of families (Liang and Rathouz, 1999; Fu et al., 2006; Di and Liang, 2011). In microarray data analysis, mixture models have been applied to test for partially differentially expressed genes (Ghosh and Chinnaiyan, 2009; Van Wieringen and Van de Viel, 2009). Generally, testing for homogeneity in mixture models is a nonregular problem because the mixture proportion parameter lies on the boundary of its parameter space and some nuisance parameters are absent under the null hypothesis of homogeneity. Therefore, theoretical development for the asymptotic distributions of tests is more difficult than that in regular statistical models. Much work has been done for testing homogeneity in parametric mixture models. For example, a score test is proposed by Liang and Rathouz (1999), and (modified) likelihood ratio tests are derived by Lemdani and Pons (1999), Chen et al. (2001) and Di and Liang (2011), among many others. For more comprehensive overview of parametric mixture models, see McLachlan and Basford (1988) and Lindsay (1995). However, in many modern applications such as the DNA methylation data analysis, the distributions of methylation values are often highly heterogeneous across different CpG sites (Wang, 2011), such that one single parametric mixture model cannot provide sufficient goodness of fit for the methylation data measured at different CpG sites. To relax the parametric assumption, Qin and Liang (2011) proposed a two-sample semiparametric exponential tilt mixture model (ETMM), where the distribution of the reference group is completely unspecified and the distribution of the other group is a mixture of two unknown distributions linked by an exponential tilt with a single parameter. Such an ETMM contains the mixture of natural exponential family (NEF) models (Morris, 2006). However, this ETMM still does not provide sufficient degrees of freedom in modeling, because it only allows one single free parameter which reduces to the non-homogeneity of means in normal models. In applications such as DNA methylation data, the two experimental groups can be different in variations but not in means, or different in both means and variations, and such difference in variation may suggest important associations between the CpG sites and diseases (Teschendorff et al., 2014; Sun et al., 2017). In such circumstances, existing methods are not sufficient to capture these key features of data and can lead to a substantial loss of power. Hong et al. (2017) proposed a pseudolikelihood EM based test (PLEMT) using a multi-parameter generalized exponential tilt mixture model (GETMM). The GETMM generalizes the ETMM by incorporating pre-specified multidimensional basis functions, which offers more flexible characterization of the non-homogeneity between two groups. Unlike the empirical likelihood method in Qin and Liang (2011), a pairwise conditioning procedure was used to eliminate the unspecified distribution function, and a penalized pseudolikelihood was constructed to handle the identifiability problem. Despite the superior power of the PLEMT over existing tests in capturing the signals in mean and variations, there are several limitations. First, it involves choosing multiple tuning parameters including the penalty parameter and number of steps for the EM algorithm. There is no universally accepted values for the tuning parameters. Second, this method is computationally expensive since EM algorithm is used to obtain the test statistic. In this paper, we propose a novel score test for homogeneity based on GETMM. We adopt the same procedure as in Hong et al. (2017) in elimination of the unspecified distribution function of the reference group. Instead of using penalization, we work on the pseudolikelihood directly and show that the parameters of interest are still identifiable. To address the challenge that some nuisance parameters disappear under the null hypothesis, we modify the score function by a novel procedure and derive the asymptotic null distribution of the proposed score test. Unlike the standard asymptotic results in Davies (1977, 1987), we unveil new Wilks phenomenon of the proposed score test, i.e., asymptotic null distributions are independent of nuisance parameters. The simple asymptotic distribution controls the Type I error well, and avoids the need of permutation based procedures. In addition, we show that, with fixed nuisance parameters, the proposed score test is locally most powerful among a class of tests. This justifies the optimality of our method in finite samples. To obtain further insight about the power of the proposed test, we derive the asymptotic power functions under local alternative hypotheses. Two types of local alternatives are studied, with respect to two different geometric paths for approximating the null hypothesis. This result is particularly useful in performing sample size calculation and experimental design. The rest of this article is organized as follows. In Section 2, we introduce the GETMM and construct the pseudolikelihood. In Section 3, we derive the score test based on the constructed pseudolikelihood and derive the asymptotic null distribution. In Section 4, we derive the local asymptotic power of the proposed tests and provide insights on experimental designs. In Section 5, we conduct simulation studies to compare the proposed tests with the existing ones. In Section 6, we apply these tests to the methylation data from an ovarian cancer study. Concluding remarks are given in Section 7.

Infection by a single virus can evoke diverse immune responses, resulting in different neurological outcomes, depending on the host's genetic background. To study heterogenous viral response, we use Theiler's Murine Encephalomyelitis Virus (TMEV) to model virally induced neurological phenotypes and immune responses in Collaborative Cross (CC) mice. The CC resource consists of genetically distinct and reproducible mouse lines, thus providing a population model with genetic heterogeneity similar to humans. We examined different CC strains for the effect of chronic stage TMEV-induced immune responses on neurological outcomes throughout 90 days post infection (dpi), with a particular focus on limb paralysis, by measuring serum levels of 23 different cytokines and chemokines. Each CC strain demonstrated a unique set of immune responses, regardless of presence or absence of TMEV RNA. Using stepwise regression, significant associations were identified between IL-1α, RANTES, and paralysis frequency scores. To better understand these interactions, we evaluated multiple aspects of the different CC genetic backgrounds, including haplotypes of genomic regions previously linked with TMEV pathogenesis and viral clearance or persistence, individual cytokine levels, and TMEV-relevant gene expression. These results demonstrate how loci previously associated with TMEV outcomes provide incomplete information regarding TMEV-induced paralysis in the CC strains. Overall, these findings provide insight into the complex roles of immune response in the pathogenesis of virus-associated neurological diseases influenced by host genetic background., Highlights • Paralysis resulting from long-term TMEV infection varies by host genetic background. • For CC mice, no single immune environment causes paralysis following TMEV infection. • IL-1A and RANTES significantly influence the progression of paralysis over time. • H2 haplotype alone did not predict viral presence or TMEV-induced paralysis. • Paralysis severity is influenced by multiple TMEV- and immune-related loci.

Complex interplay between genetic and environmental factors characterizes the etiology of many diseases. Modeling gene-environment (GxE) interactions is often challenged by the unknown functional form of the environment term in the true data-generating mechanism. We study the impact of misspecification of the environmental exposure effect on inference for the GxE interaction term in linear and logistic regression models. We first examine the asymptotic bias of the GxE interaction regression coefficient, allowing for confounders as well as arbitrary misspecification of the exposure and confounder effects. For linear regression, we show that under gene-environment independence and some confounder-dependent conditions, when the environment effect is misspecified, the regression coefficient of the GxE interaction can be unbiased. However, inference on the GxE interaction is still often incorrect. In logistic regression, we show that the regression coefficient is generally biased if the genetic factor is associated with the outcome directly or indirectly. Further, we show that the standard robust sandwich variance estimator for the GxE interaction does not perform well in practical GxE studies, and we provide an alternative testing procedure that has better finite sample properties.

In-flight fission of a 345 MeV per nucleon 238U primary beam on a 2 mm thick 9Be target has been used to produce and study the decays of a range of neutron-rich nuclei centred around the doubly mid-shell nucleus 170Dy at the RIBF Facility, RIKEN, Japan. The produced secondary fragments of interest were identified eventby- event using the BigRIPS separator. The fragments were implanted into the WAS3ABI position sensitive silicon active stopper which allowed pixelated correlations between implants and their subsequent β-decay. Discrete γ-ray transitions emitted following decays from either metastable states or excited states populated following beta decay were identified using the 84 coaxial high-purity germanium (HPGe) detectors of the EURICA spectrometer, which was complemented by 18 additional cerium-doped lanthanum bromide (LaBr3)

We take a functional data approach to longitudinal studies with complex bivariate outcomes. This work is motivated by data from a physical activity study that measured two responses over time in five-minute intervals. One response is the proportion of time active in each interval, a continuous proportions with excess zeros and ones. The other response, energy expenditure rate in the interval, is a continuous variable with excess zeros and skewness. This outcome is complex because there are three possible activity patterns in each interval (inactive, partially active and completely active), and those patterns, which are observed, induce both non-random and random associations between the responses. More specifically, the inactive pattern requires a zero value in both the proportion for active behavior and the energy expenditure rate; a partially active pattern means that the proportion of activity is strictly between zero and one and that the energy expenditure rate is greater than zero and likely to be moderate, and the completely active pattern means that the proportion active is exactly one, and the energy expenditure rate is greater than zero and likely to be higher. To address these challenges, we propose a three-part functional data joint modeling approach. The first part is a continuation-ratio model to reorder the ordinal valued three activity patterns. The second part models the proportions when they are in interval (0, 1). The last component specifies the skewed continuous energy expenditure rate with Box-Cox transformations when they are greater than zero. In this three-part model, the regression structures are specified as smooth curves measured at various time-points with random effects that have a correlation structure. The smoothed random curves for each variable are summarized using a few important principal components, and the association of the three longitudinal components is modeled through the association of the principal component scores. The difficulties in handling the ordinal and proportional variables are addressed using a quasilikelihood type approximation. We develop an efficient algorithm to fit the model which also involves the selection of the number of principal components. The method is applied to physical activity data and is evaluated empirically by a simulation study.

C.M. Shand et al. -- 6 pags., 3 figs., tab. --Open Access funded by Creative Commons Atribution Licence 4.0, We report on the measurement of new low-lying states in the neutron-rich Zn nuclei via in-beam γ-ray spectroscopy. These include the 4 →2 transition in Zn, the 2 →0 and 4 →2 transitions in Zn, and low-lying states in Zn were observed for the first time. The reduced E(2 ) energies and increased E(4 )/E(2 ) ratios at N=52, 54 compared to those in Zn attest that the magicity is confined to the neutron number N=50 only. The deduced level schemes are compared to three state-of-the-art shell model calculations and a good agreement is observed with all three calculations. The newly observed 2 and 4 levels in Zn suggest the onset of deformation towards heavier Zn isotopes, which has been incorporated by taking into account the upper sdg orbitals in the Ni78-II and the PFSDG-U models., All UK authors are supported by the Science and Technology Facilities Council (STFC). The development of MINOS and the core MINOS team have been supported by the European Research Council through the ERC Grant No. MINOS-258567. A. Jungclaus was supported by the Spanish Ministerio de Economía y Competitividad under contract FPA2014-57196-C5-4-P. Authors from Cologne were supported by German BMBF Grant No. 05P15PKFNA. Authors from TU Darmstadt were supported by German BMBF Grant No. 05P12RDFN8 and 05P15RDFN1. L.X. Chung and B.D. Linh would like to acknowledge Vietnam Ministry of Science and Technology for the support, and Radioactive Isotope Physics Laboratory of the RIKEN Nishina Center for supporting their stay during the experiments.

Many methods have recently been proposed for efficient analysis of case-control studies of gene-environment interactions using a retrospective likelihood framework that exploits the natural assumption of gene-environment independence in the underlying population. However, for polygenic modelling of gene-environment interactions, which is a topic of increasing scientific interest, applications of retrospective methods have been limited due to a requirement in the literature for parametric modelling of the distribution of the genetic factors. We propose a general, computationally simple, semiparametric method for analysis of case-control studies that allows exploitation of the assumption of gene-environment independence without any further parametric modelling assumptions about the marginal distributions of any of the two sets of factors. The method relies on the key observation that an underlying efficient profile likelihood depends on the distribution of genetic factors only through certain expectation terms that can be evaluated empirically. We develop asymptotic inferential theory for the estimator and evaluate its numerical performance via simulation studies. An application of the method is presented.

© 2017 American Statistical Association. We consider a problem motivated by issues in nutritional epidemiology, across diseases and populations. In this area, it is becoming increasingly common for diseases to be modeled by a single diet score, such as the Healthy Eating Index, the Mediterranean Diet Score, etc. For each disease and for each population, a partially linear single-index model is fit. The partially linear aspect of the problem is allowed to differ in each population and disease. However, and crucially, the single-index itself, having to do with the diet score, is common to all diseases and populations, and the nonparametrically estimated functions of the single-index are the same up to a scale parameter. Using B-splines with an increasing number of knots, we develop a method to solve the problem, and display its asymptotic theory. An application to the NIH-AARP Study of Diet and Health is described, where we show the advantages of using multiple diseases and populations simultaneously rather than one at a time in understanding the effect of increased Milk consumption. Simulations illustrate the properties of the methods. Supplementary materials for this article are available online.

© 2017 American Statistical Association. Prediction precision is arguably the most relevant criterion of a model in practice and is often a sought after property. A common difficulty with covariates measured with errors is the impossibility of performing prediction evaluation on the data even if a model is completely given without any unknown parameters. We bypass this inherent difficulty by using special properties on moment relations in linear regression models with measurement errors. The end product is a model selection procedure that achieves the same optimality properties that are achieved in classical linear regression models without covariate measurement error. Asymptotically, the procedure selects the model with the minimum prediction error in general, and selects the smallest correct model if the regression relation is indeed linear. Our model selection procedure is useful in prediction when future covariates without measurement error become available, for example, due to improved technology or better management and design of data collection procedures. Supplementary materials for this article are available online.

We consider a problem of data integration. Consider determining which genes affect a disease. The genes, which we call predictor objects, can be measured in different experiments on the same individual. We address the question of finding which genes are predictors of disease by any of the experiments. Our formulation is more general. In a given data set, there are a fixed number of responses for each individual, which may include a mix of discrete, binary and continuous variables. There is also a class of predictor objects, which may differ within a subject depending on how the predictor object is measured, i.e., depend on the experiment. The goal is to select which predictor objects affect any of the responses, where the number of such informative predictor objects or features tends to infinity as sample size increases. There are marginal likelihoods for each way the predictor object is measured, i.e., for each experiment. We specify a pseudolikelihood combining the marginal likelihoods, and propose a pseudolikelihood information criterion. Under regularity conditions, we establish selection consistency for the pseudolikelihood information criterion with unbounded true model size, which includes a Bayesian information criterion with appropriate penalty term as a special case. Simulations indicate that data integration improves upon, sometimes dramatically, using only one of the data sources.

Objective measures of oxygen consumption and carbon dioxide production by mammals are used to predict their energy expenditure. Since energy expenditure is not directly observable, it can be viewed as a latent construct with multiple physical indirect measures such as respiratory quotient, volumetric oxygen consumption, and volumetric carbon dioxide production. Metabolic rate is defined as the rate at which metabolism occurs in the body. Metabolic rate is also not directly observable. However, heat is produced as a result of metabolic processes within the body. Therefore, metabolic rate can be approximated by heat production plus some errors. While energy expenditure and metabolic rates are correlated, they are not equivalent. Energy expenditure results from physical function, while metabolism can occur within the body without the occurrence of physical activities. In this manuscript, we present a novel approach for studying the relationship between metabolic rate and indicators of energy expenditure. We do so by extending our previous work on MIMIC ME models to allow responses that are sparsely observed functional data, defining the sparse functional multiple indicators, multiple cause measurement error (FMIMIC ME) models. The mean curves in our proposed methodology are modeled using basis splines. A novel approach for estimating the variance of the classical measurement error based on functional principal components is presented. The model parameters are estimated using the EM algorithm and a discussion of the model's identifiability is provided. We show that the defined model is not a trivial extension of longitudinal or functional data methods, due to the presence of the latent construct. Results from its application to data collected on Zucker diabetic fatty rats are provided. Simulation results investigating the properties of our approach are also presented.

© The Author(s) 2017. Media-based event data-i.e., data comprised from reporting by media outlets-are widely used in political science research. However, events of interest (e.g., strikes, protests, conflict) are often underreportedby these primary and secondary sources, producing incomplete data that risks inconsistency and bias in subsequent analysis. While general strategies exist to help ameliorate this bias, these methods do not make full use of the information often available to researchers. Specifically, much of the event data used in the social sciences is drawn from multiple, overlapping news sources (e.g., Agence France-Presse, Reuters). Therefore, we propose a novel maximum likelihood estimator that corrects for misclassification in data arising from multiple sources. In the most general formulation of our estimator, researchers can specify separate sets of predictors for the true-event model and each of the misclassification models characterizing whether a source fails to report on an event. As such, researchers are able to accurately test theories on both the causes of and reporting on an event of interest. Simulations evidence that our technique regularly outperforms current strategies that either neglect misclassification, the unique features of the data-generating process, or both. We also illustrate the utility of this method with a model of repression using the Social Conflict in Africa Database.