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Learning individualized treatment rules is an important topic in precision medicine. Current literature mainly focuses on deriving individualized treatment rules from a single source population. We consider the observational data setting when the source population differs from a target population of interest. Compared with causal generalization for the average treatment effect that is a scalar quantity, individualized treatment rule generalization poses new challenges due to the need to model and generalize the rules based on a prespecified class of functions that may not contain the unrestricted true optimal individualized treatment rule. The aim of this paper is to develop a weighting framework to mitigate the impact of such misspecification, and thus facilitate the generalizability of optimal individualized treatment rules from a source population to a target population. Our method seeks covariate balance over a nonparametric function class characterized by a reproducing kernel Hilbert space and can improve many individualized treatment rule learning methods that rely on weights. We show that the proposed method encompasses importance weights and overlap weights as two extreme cases, allowing for a better bias-variance trade-off in between. Numerical examples demonstrate that the use of our weighting method can greatly improve individualized treatment rule estimation for the target population compared with other weighting methods. [ABSTRACT FROM AUTHOR]

Inverse‐probability‐weighted estimators are the oldest and potentially most commonly used class of procedures for the estimation of causal effects. By adjusting for selection biases via a weighting mechanism, these procedures estimate an effect of interest by constructing a pseudopopulation in which selection biases are eliminated. Despite their ease of use, these estimators require the correct specification of a model for the weighting mechanism, are known to be inefficient, and suffer from the curse of dimensionality. We propose a class of nonparametric inverse‐probability‐weighted estimators in which the weighting mechanism is estimated via undersmoothing of the highly adaptive lasso, a nonparametric regression function proven to converge at nearly n−1/3$ n^{-1/3}$‐rate to the true weighting mechanism. We demonstrate that our estimators are asymptotically linear with variance converging to the nonparametric efficiency bound. Unlike doubly robust estimators, our procedures require neither derivation of the efficient influence function nor specification of the conditional outcome model. Our theoretical developments have broad implications for the construction of efficient inverse‐probability‐weighted estimators in large statistical models and a variety of problem settings. We assess the practical performance of our estimators in simulation studies and demonstrate use of our proposed methodology with data from a large‐scale epidemiologic study. [ABSTRACT FROM AUTHOR]

Complementary features of randomized controlled trials (RCTs) and observational studies (OSs) can be used jointly to estimate the average treatment effect of a target population. We propose a calibration weighting estimator that enforces the covariate balance between the RCT and OS, therefore improving the trial‐based estimator's generalizability. Exploiting semiparametric efficiency theory, we propose a doubly robust augmented calibration weighting estimator that achieves the efficiency bound derived under the identification assumptions. A nonparametric sieve method is provided as an alternative to the parametric approach, which enables the robust approximation of the nuisance functions and data‐adaptive selection of outcome predictors for calibration. We establish asymptotic results and confirm the finite sample performances of the proposed estimators by simulation experiments and an application on the estimation of the treatment effect of adjuvant chemotherapy for early‐stage non‐small‐cell lung patients after surgery. [ABSTRACT FROM AUTHOR]

Background: Even though the population-attributable fraction (PAF) is a well-established metric, it is often incorrectly estimated or interpreted not only in clinical application, but also in statistical research articles. The risk of bias is especially high in more complex time-to-event data settings.Methods: We explain how the PAF can be defined, identified and estimated in time-to-event settings with competing risks and time-dependent exposures. By using multi-state methodology and inverse probability weighting, we demonstrate how to reduce or completely avoid severe types of biases including competing risks bias, immortal time bias and confounding due to both baseline and time-varying patient characteristics.Results: The method is exemplarily applied to a real data set. Moreover, we estimate the number of deaths that were attributable to ventilator-associated pneumonia in France in the year 2016. The example demonstrates how, under certain simplifying assumptions, PAF estimates can be extrapolated to a target population of interest.Conclusions: Defining and estimating the PAF in advanced time-to-event settings within a framework that unifies causal and multi-state modelling enables to tackle common sources of bias and allows straightforward implementation with standard software packages. [ABSTRACT FROM AUTHOR]

Motivation The amount of genomic data is increasing exponentially. Using many genotyped and phenotyped individuals for genomic prediction is appealing yet challenging. Results We present SLEMM (short for Stochastic-Lanczos-Expedited Mixed Models), a new software tool, to address the computational challenge. SLEMM builds on an efficient implementation of the stochastic Lanczos algorithm for REML in a framework of mixed models. We further implement SNP weighting in SLEMM to improve its predictions. Extensive analyses on seven public datasets, covering 19 polygenic traits in three plant and three livestock species, showed that SLEMM with SNP weighting had overall the best predictive ability among a variety of genomic prediction methods including GCTA's empirical BLUP, BayesR, KAML, and LDAK's BOLT and BayesR models. We also compared the methods using nine dairy traits of ∼300k genotyped cows. All had overall similar prediction accuracies, except that KAML failed to process the data. Additional simulation analyses on up to 3 million individuals and 1 million SNPs showed that SLEMM was advantageous over counterparts as for computational performance. Overall, SLEMM can do million-scale genomic predictions with an accuracy comparable to BayesR. Availability and implementation The software is available at https://github.com/jiang18/slemm. [ABSTRACT FROM AUTHOR]

Inverse probability weighting (IPW) is widely used in many areas when data are subject to unrepresentativeness, missingness, or selection bias. An inevitable challenge with the use of IPW is that the IPW estimator can be remarkably unstable if some probabilities are very close to zero. To overcome this problem, at least three remedies have been developed in the literature: stabilizing, thresholding, and trimming. However, the final estimators are still IPW-type estimators, and inevitably inherit certain weaknesses of the naive IPW estimator: they may still be unstable or biased. We propose a biased-sample empirical likelihood weighting (ELW) method to serve the same general purpose as IPW, while completely overcoming the instability of IPW-type estimators by circumventing the use of inverse probabilities. The ELW weights are always well defined and easy to implement. We show theoretically that the ELW estimator is asymptotically normal and more efficient than the IPW estimator and its stabilized version for missing data problems. Our simulation results and a real data analysis indicate that the ELW estimator is shift-equivariant, nearly unbiased, and usually outperforms the IPW-type estimators in terms of mean square error. [ABSTRACT FROM AUTHOR]

There is often delayed entry into observational studies, which results in left truncation. In the estimation of the distribution of time‐to‐event from left‐truncated data, standard survival analysis methods require quasi‐independence between the truncation time and event time. Incorrectly assuming quasi‐independence may lead to biased estimation. We address the problem of estimation of the survival distribution when dependence between the event time and its left truncation time is induced by shared covariates. We introduce propensity scores for truncated data and propose two inverse probability weighting methods that adjust for both truncation and dependence, if all of the shared covariates are measured. The proposed methods additionally allow for right censoring. We evaluate the proposed methods in simulations, conduct sensitivity analyses, and provide guidelines for use in practice. We illustrate our approach in application to data from a central nervous system lymphoma study. The proposed methods are implemented in the R package, depLT. [ABSTRACT FROM AUTHOR]

Multimode data collection has emerged as a common approach for conducting household surveys in the United States. A number of different data collection schemes have been investigated, with an emphasis on collecting as many respondents by the Web prior to going to paper data collection to reduce costs. Despite this, little research has been conducted on the approaches to weighting data from multimode surveys. The typical approach assumes that all respondents should be treated the same regardless of mode even though it is well known that the response patterns by mode vary substantially. We examine an adaptive mode adjustment to address these differences and propose an imbalance measure to help determine the adjustment factor using ideas from responsive design. We then compare the effects of the alternative weighting method in two recent sequential mixed-mode surveys and show it appears to reduce bias while only slightly increasing variances of the estimates. [ABSTRACT FROM AUTHOR]

We introduce causal interaction tree (CIT) algorithms for finding subgroups of individuals with heterogeneous treatment effects in observational data. The CIT algorithms are extensions of the classification and regression tree algorithm that use splitting criteria based on subgroup‐specific treatment effect estimators appropriate for observational data. We describe inverse probability weighting, g‐formula, and doubly robust estimators of subgroup‐specific treatment effects, derive their asymptotic properties, and use them to construct splitting criteria for the CIT algorithms. We study the performance of the algorithms in simulations and implement them to analyze data from an observational study that evaluated the effectiveness of right heart catheterization for critically ill patients. [ABSTRACT FROM AUTHOR]

Probability samples are the preferred method for providing inferences that are generalizable to a larger population. However, in many cases, this approach is unlikely to yield a sample size large enough to produce precise inferences. Our goal here is to improve the efficiency of inferences from a probability sample by combining (or blending) it with a nonprobability sample, which is (by itself) potentially fraught with selection biases that would compromise the generalizability of results. We develop novel methods of statistical weighting that may be used for this purpose. Specifically, we make a distinction between weights that can be used to make the two samples representative of the population individually (disjoint blending) and those that make only the combined sample representative (simultaneous blending). Our focus is on weights constructed using propensity scores, but consideration is also given to calibration weighting. We include simulation studies that, among other illustrations, show the gain in precision provided by the convenience sample is lower in circumstances where the outcome is strongly related to the auxiliary variables used to align the samples. Motivating the exposition is a survey of military caregivers; our interest is focused on unpaid caregivers of wounded, ill, or injured US servicemembers and veterans who served following September 11, 2001. Our work serves not only to illustrate the proper execution of blending but also to caution the reader with respect to its dangers, as invoking a nonprobability sample may not yield substantial improvements in precision when assumptions are valid and may induce biases in the event that they are not. [ABSTRACT FROM AUTHOR]

An underground nuclear explosion (UNE) couples mechanical energy into crustal rock, which propagates as seismic and acoustic waves. These different physical phenomena transport, by different pathways, to standoff detectors at varying distances. The transport pathways attenuate the original signal but in different ways. Enabled by correct statistical weighting, signal attenuation models can be used to combine these disparate sensor data to estimate the yield of an UNE. Contemporaneous statistical models, used in yield estimation, can be improved with an advanced partition of error for these physical signal propagation models. We present an advanced multivariate approach to error modelling of multiphenomenology physical signatures. In addition to measurement error, our error model represents physical model biases as random with a physics-based covariance structure. To illustrate this proposed framework, we demonstrate the estimation of explosion yield using openly available seismic and acoustic data from chemical single-point explosions. [ABSTRACT FROM AUTHOR]

The largest earthquake recorded in South Korea occurred in Gyeongju on 2016 September 12. Since then, understanding the geological structures near the Gyeongju earthquake's epicentre has been demanded. As a preliminary step to understand the geological structures around the epicentre, a 3-D audio-magnetotelluric (AMT) survey was conducted. These 3-D AMT data were imaged through data processing and inversion. However, because the AMT data acquired in Gyeongju were very noisy, conventional imaging procedures did not yield reliable results. To obtain a reliable model inverted from such noisy data, we propose various imaging strategies: an additional data processing technique using the Nyquist diagram after conventional data processing and several inversion strategies related to the selection of data, the weighting of the data, the constraints on the model parameter vector and the Lagrange multiplier used for the regularization. By applying the additional data processing step and several inversion strategies, we were able to successfully invert the noisy field data. The inversion results verify that the data-screening procedure applying the Nyquist diagram remarkably improves the results compared with those obtained using only conventional data processing. The imaging strategies proposed in this case study can be used to image noisy MT/AMT field data for other regions. [ABSTRACT FROM AUTHOR]

The effect of treatment on binary disease outcome can differ across subgroups characterised by other covariates. Testing for the existence of subgroups that are associated with heterogeneous treatment effects can provide valuable insight regarding the optimal treatment recommendation in practice. Our research in this paper is motivated by the question of whether host genetics could modify a vaccine's effect on HIV acquisition risk. To answer this question, we used data from an HIV vaccine trial with a two‐phase sampling design and developed a general threshold‐based model framework to test for the existence of subgroups associated with the heterogeneity in disease risks, allowing for subgroups based on multivariate covariates. We developed a testing procedure based on maximum of likelihood ratio statistics over change‐planes and demonstrated its advantage over alternative methods. We further developed the testing procedure to account for bias sampling of expensive (i.e. resource‐intensive to measure) covariates through the incorporation of inverse probability weighting techniques. We used the proposed method to analyse the motivating HIV vaccine trial data. Our proposed testing procedure also has broad applications in epidemiological studies for assessing heterogeneity in disease risk with respect to univariate or multivariate predictors. [ABSTRACT FROM AUTHOR]

In non‐experimental research, a sensitivity analysis helps determine whether a causal conclusion could be easily reversed in the presence of hidden bias. A new approach to sensitivity analysis on the basis of weighting extends and supplements propensity score weighting methods for identifying the average treatment effect for the treated (ATT). In its essence, the discrepancy between a new weight that adjusts for the omitted confounders and an initial weight that omits them captures the role of the confounders. This strategy is appealing for a number of reasons including that, regardless of how complex the data generation functions are, the number of sensitivity parameters remains small and their forms never change. A graphical display of the sensitivity parameter values facilitates a holistic assessment of the dominant potential bias. An application to the well‐known LaLonde data lays out the implementation procedure and illustrates its broad utility. The data offer a prototypical example of non‐experimental evaluations of the average impact of job training programmes for the participant population. [ABSTRACT FROM AUTHOR]

We explore an alternative method to the usual shear correlation function approach for the estimation of aperture mass statistics in weak lensing survey data. Our approach builds on the direct estimator method. In this paper, to test and validate the methodology, we focus on the aperture mass dispersion. After computing the signal and noise for a weighted set of measured ellipticites we show how the direct estimator can be made into a linear order algorithm that enables a fast and efficient computation. We then investigate the applicability of the direct estimator approach in the presence of a real survey mask with holes and chip gaps. For this we use a large ensemble of full ray-tracing mock simulations. By using various weighting schemes for combining information from different apertures we find that inverse variance weighting the individual aperture estimates with an aperture completeness greater than 70 per cent yields an answer that is in close agreement with the correlation function approach. We then apply this approach to the CFHTLenS as a pilot scheme and find that our method recovers to high accuracy the official result for the variance of both the E- and B-mode signal. We then explore the cosmological information content of the direct estimator using the Fisher information approach. We show that there is a only modest loss in cosmological information from the rejection of apertures that are of low completeness. This method unlocks the door to efficient methods for recovering higher order aperture mass statistics in linear order operations. [ABSTRACT FROM AUTHOR]

A common phenomenon in cancer syndromes is for an individual to have multiple primary cancers (MPC) at different sites during his/her lifetime. Patients with Li-Fraumeni syndrome (LFS), a rare pediatric cancer syndrome mainly caused by germline TP53 mutations, are known to have a higher probability of developing a second primary cancer than those with other cancer syndromes. In this context, it is desirable to model the development of MPC to enable better clinical management of LFS. Here, we propose a Bayesian recurrent event model based on a non-homogeneous Poisson process in order to obtain penetrance estimates for MPC related to LFS. We employed a familywise likelihood that facilitates using genetic information inherited through the family pedigree and properly adjusted for the ascertainment bias that was inevitable in studies of rare diseases by using an inverse probability weighting scheme. We applied the proposed method to data on LFS, using a family cohort collected through pediatric sarcoma patients at MD Anderson Cancer Center from 1944 to 1982. Both internal and external validation studies showed that the proposed model provides reliable penetrance estimates for MPC in LFS, which, to the best of our knowledge, have not been reported in the LFS literature. [ABSTRACT FROM AUTHOR]

Summary: For various reasons, cohort studies generally forgo probability sampling required to obtain population representative samples. However, such cohorts lack population representativeness, which invalidates estimates of population prevalences for novel health factors that are only available in cohorts. To improve external validity of estimates from cohorts, we propose a kernel weighting (KW) approach that uses survey data as a reference to create pseudoweights for cohorts. A jackknife variance is proposed for the KW estimates. In simulations, the KW method outperformed two existing propensity‐score‐based weighting methods in mean‐squared error while maintaining confidence interval coverage. We applied all methods to estimating US population mortality and prevalences of various diseases from the non‐representative US National Institutes of Health–American Association of Retired Persons cohort, using the sample from the US‐representative National Health Interview Survey as the reference. Assuming that the survey estimates are correct, the KW approach yielded generally less biased estimates compared with the existing propensity‐score‐based weighting methods. [ABSTRACT FROM AUTHOR]

Quantifying and predicting the user attention on web image elements finds applications in synthesis and rendering of elements on webpages. However, the majority of the existing approaches either overlook the visual characteristics of these elements or do not incorporate the users' visual attention. Especially, obtaining a representative quantified attention (for images) from the attention allocation of multiple users is a challenging task. Toward overcoming the challenge for free-viewing attention, this paper introduces four weighted voting strategies to assign effective visual attention (fixation index (FI)) for web image elements. Subsequently, the prominent image visual features in explaining the assigned attention are identified. Further, the association between image visual features and the assigned attention is modeled as a multi-class prediction problem, which is solved through support vector machine-based classification. The analysis of the proposed approach on real-world webpages reveals the following: (i) image element's position, size and mid-level color histograms are highly informative for the four weighting schemes; (ii) the presented computational approach outperforms the baseline for four weighted voting schemes with an average accuracy of 85% and micro F1-score of 60%; and (iii) uniform weighting (same weight for all FIs) is adequate for estimating the user's initial attention while the proportional weighting (weight the FI in proportion to its likelihood of occurrence) extends to the latter attention prediction. [ABSTRACT FROM AUTHOR]

Building on past research, this article illustrates when a price-quality relationship holds in the presence of multiple extrinsic cues. When intrinsic information is scarce, the relationship is more pronounced when a positive price cue is paired with a positive second cue (e.g., strong warranty, positive country of origin, or strong brand). When the two cues are inconsistent, consumers find the negative cue more salient and overweight it in their evaluations. This interaction is moderated by the presence of abundant levels of intrinsic attribute information. Our predictions are replicated across five studies, and the underlying process is supported. [ABSTRACT FROM AUTHOR]

We developed a multi-objective optimization method for inverting marine controlled source electromagnetic data using a fast-non-dominated sorting genetic algorithm. Deterministic methods for inverting electromagnetic data rely on selecting weighting parameters to balance the data misfit with the model roughness and result in a single solution which do not provide means to assess the non-uniqueness associated with the inversion. Here, we propose a robust stochastic global search method that considers the objective as a two-component vector and simultaneously minimizes both components: data misfit and model roughness. By providing an estimate of the entire set of the Pareto-optimal solutions, the method allows a better assessment of non-uniqueness than deterministic methods. Since the computational expense of the method increases as the number of objectives and model parameters increase, we parallelized our algorithm to speed up the forward modelling calculations. Applying our inversion to noisy synthetic data sets generated from horizontally stratified earth models for both isotropic and anisotropic assumptions and for different measurement configurations, we demonstrate the accuracy of our method. By comparing the results of our inversion with the regularized genetic algorithm, we also demonstrate the necessity of casting this problem as a multi-objective optimization for a better assessment of uncertainty as compared to a scalar objective optimization method. [ABSTRACT FROM AUTHOR]

Survey researchers are actively seeking powerful auxiliary data sources capable of correcting for possible nonresponse bias in survey estimates of the general population. While several auxiliary data options exist, concerns about their usefulness for addressing nonresponse bias remain. One underutilized—but potentially rich—source of auxiliary data for nonresponse bias adjustment is federal administrative records. While federal records are routinely used to study nonresponse in countries where it is possible to directly link them (via a unique identifier) to population-based samples, such records are not widely used for this purpose in countries which lack a unique identifier to facilitate direct linkage. In this article, we examine the utility of indirectly linked administrative data from a federal employment database for nonresponse bias adjustment in a general population survey in Germany. In short, we find that the linked administrative variables have stronger correlations with the substantive survey variables than do standard paradata variables and that incorporating linked administrative data in nonresponse weighting adjustments reduces relative nonresponse bias to a greater extent than paradata-only weighting adjustments. However, for the majority of weighted survey estimates, including the administrative variables in the weighting adjustment procedure has minimal impact on the point estimates and their variances. We conclude with a general discussion of these findings and comment on the logistical issues associated with this type of linkage relevant to survey practice. [ABSTRACT FROM AUTHOR]

Background: Household surveys are an essential tool for vaccine coverage monitoring in developing countries, and the World Health Organization (WHO) Expanded Program on Immunization (EPI) cluster survey design has been a default choice for decades. In response to methodological limitations of the traditional EPI sampling, alternative methods have been proposed, based on modern statistical and geographical techniques. This study compared the coverage estimates and the time efficiency of the EPI sampling design and two alternative methods: the compact segment sampling and innovative grid-based geographical information system (GIS) sampling.Methods: We conducted a series of equal-sized concurrent prospective vaccine coverage surveys in Karachi, Pakistan, from January to December 2016, using traditional EPI, compact segment and grid-based GIS sampling methods.Results: No differences in vaccine coverage estimates were identified across sampling methods in the peri-urban setting; however, due to stronger clustering effects and correct incorporation of sampling weights, the compact segment [design effect (DEFF) = 2.03] and the grid-based GIS surveys (DEFF = 1.72) had higher design effects and, therefore, appeared to have lower statistical precision than the traditional EPI surveys (DEFF = 1.57). To achieve the same level of apparent precision, data collection activities in the compact segment surveys would require more than twice the implementation time needed compared with the traditional EPI surveys.Conclusions: The precision of the EPI surveys appeared higher than that of the alternative methods because, under a questionable self-weighting assumption, the estimated design effect did not account for variable sampling weights. The compact segment and grid-based GIS methods were designed to improve randomness and representativeness of sampling households. Although these alternative methods did not result in coverage estimates that differed from the EPI survey results in the peri-urban setting, they have a lower risk of selection bias and therefore may be preferred. [ABSTRACT FROM AUTHOR]

Summary: Quantile estimation has attracted significant research interest in recent years. However, there has been only a limited literature on quantile estimation in the presence of incomplete data. We propose a general framework to address this problem. Our framework combines the two widely adopted approaches for missing data analysis, the imputation approach and the inverse probability weighting approach, via the empirical likelihood method. The method proposed is capable of dealing with many different missingness settings. We mainly study three of them: estimating the marginal quantile of a response that is subject to missingness while there are fully observed covariates; estimating the conditional quantile of a fully observed response while the covariates are partially available; estimating the conditional quantile of a response that is subject to missingness with fully observed covariates and extra auxiliary variables. The method proposed allows multiple models for both the missingness probability and the data distribution. The resulting estimators are multiply robust in the sense that they are consistent if any one of these models is correctly specified. The asymptotic distributions are established by using empirical process theory. [ABSTRACT FROM AUTHOR]

Marketers often base decisions about marketing strategies on the results of research designed to elicit information about consumers' preferences. A large body of research indicates, however, that preferences often are labile. That is, preferences can be reversed depending on factors such as how the preference is elicited. In three studies, we examine the effect of familiarity in two preference-elicitation tasks, choice and matching judgments. We provide evidence of an interaction between familiarity and response mode (choice or matching) in each study. In study 3, we test the explanation that preference reversals may occur when the interaction of response mode with product-category familiarity leads to systematic changes in attribute weighting. [ABSTRACT FROM AUTHOR]

A central issue in research on multiattribute decision structures is the relationship between attribute weights and variation in brand scores. Conventional wisdom postulates that, as the variation or range of levels on an attribute increases, so will the relative weight the attribute receives in decisions. The results presented in this research do not confirm the conventional wisdom. In particular, subjects were capable of cognitively standardizing scores on each attribute so that judged utilities reflected an attribute's psychological importance, not the statistical variation in its scores. This result and those reported for several other tests appear to be promising for efforts that emphasize structural applications of multiattribute models for marketing communications strategy. [ABSTRACT FROM AUTHOR]

An attribution model of information processing is proposed and experimentally tested to explain the alleged disproportionate weighting of unfavorable product information. The findings of the experiment generally support hypotheses proposing that unfavorable ratings, as compared to favorable product ratings on the same attributes, prompt significantly stronger attributions to product performance, belief strength, and affect toward products. [ABSTRACT FROM AUTHOR]

Weighting methods offer an approach to estimating causal treatment effects in observational studies. However, if weights are estimated by maximum likelihood, misspecification of the treatment assignment model can lead to weighted estimators with substantial bias and variance. In this paper, we propose a unified framework for constructing weights such that a set of measured pretreatment covariates is unassociated with treatment assignment after weighting. We derive conditions for weight estimation by eliminating the associations between these covariates and treatment assignment characterized in a chosen treatment assignment model after weighting. The moment conditions in covariate balancing weight methods for binary, categorical and continuous treatments in cross-sectional settings are special cases of the conditions in our framework, which extends to longitudinal settings. Simulation shows that our method gives treatment effect estimates with smaller biases and variances than the maximum likelihood approach under treatment assignment model misspecification. We illustrate our method with an application to systemic lupus erythematosus data. [ABSTRACT FROM AUTHOR]

Summary: The timing of antiretroviral therapy (ART) initiation for HIV and tuberculosis (TB) co‐infected patients needs to be considered carefully. CD4 cell count can be used to guide decision making about when to initiate ART. Evidence from recent randomized trials and observational studies generally supports early initiation but does not provide information about effects of initiation time on a continuous scale. In this article, we develop and apply a highly flexible structural proportional hazards model for characterizing the effect of treatment initiation time on a survival distribution. The model can be fitted using a weighted partial likelihood score function. Construction of both the score function and the weights must accommodate censoring of the treatment initiation time, the outcome, or both. The methods are applied to data on 4903 individuals with HIV/TB co‐infection, derived from electronic health records in a large HIV care program in Kenya. We use a model formulation that flexibly captures the joint effects of ART initiation time and ART duration using natural cubic splines. The model is used to generate survival curves corresponding to specific treatment initiation times; and to identify optimal times for ART initiation for subgroups defined by CD4 count at time of TB diagnosis. Our findings potentially provide ‘higher resolution’ information about the relationship between ART timing and mortality, and about the differential effect of ART timing within CD4 subgroups. [ABSTRACT FROM AUTHOR]

Summary: Doubly truncated data arise when event times are observed only if they fall within subject‐specific, possibly random, intervals. While non‐parametric methods for survivor function estimation using doubly truncated data have been intensively studied, only a few methods for fitting regression models have been suggested, and only for a limited number of covariates. In this article, we present a method to fit the Cox regression model to doubly truncated data with multiple discrete and continuous covariates, and describe how to implement it using existing software. The approach is used to study the association between candidate single nucleotide polymorphisms and age of onset of Parkinson's disease. [ABSTRACT FROM AUTHOR]

Summary: Structural nested failure time models (SNFTMs) are models for the effect of a time‐dependent exposure on a survival outcome. They have been introduced along with so‐called G‐estimation methods to provide valid adjustment for time‐dependent confounding induced by time‐varying variables. Adjustment for informative censoring in SNFTMs is possible via inverse probability of censoring weighting (IPCW). In the presence of considerable dropout, this can imply substantial information loss and consequently imprecise effect estimates. In this article, we aim to increase the efficiency of IPCW G‐estimators under a SNFTM by deriving an augmented estimator that uses both censored and uncensored observations, and offers robustness against misspecification of the model for the censoring process, provided that a model for a specific functional of the survival time and time‐dependent covariates is correctly specified. The empirical properties of the proposed estimators are studied in a simulation experiment, and the estimators are used in the analysis of surveillance data from the field of hospital epidemiology. [ABSTRACT FROM AUTHOR]

We consider a general statistical model defined by moment restrictions when data are missing at random. Using inverse probability weighting, we show that such a model is equivalent to a model for the observed variables only, augmented by a moment condition defined by the missingness mechanism. Our framework covers parametric and semiparametric mean regressions and quantile regressions. We allow for missing responses, missing covariates and any combination of them. The equivalence result sheds new light on various aspects of missing data, and provides guidelines for building efficient estimators. [ABSTRACT FROM AUTHOR]

Next-generation radio interferometers, like the Square Kilometre Array, will acquire large amounts of data with the goal of improving the size and sensitivity of the reconstructed images by orders of magnitude. The efficient processing of large-scale data sets is of great importance. We propose an acceleration strategy for a recently proposed primal-dual distributed algorithm. A preconditioning approach can incorporate into the algorithmic structure both the sampling density of the measured visibilities and the noise statistics. Using the sampling density information greatly accelerates the convergence speed, especially for highly non-uniform sampling patterns, while relying on the correct noise statistics optimizes the sensitivity of the reconstruction. In connection to CLEAN, our approach can be seen as including in the same algorithmic structure both natural and uniform weighting, thereby simultaneously optimizing both the resolution and the sensitivity. The method relies on a new non-Euclidean proximity operator for the data fidelity term, that generalizes the projection on to the ࡁ2 ball where the noise lives for naturally weighted data, to the projection on to a generalized ellipsoid incorporating sampling density information through uniform weighting. Importantly, this non-Euclidean modification is only an acceleration strategy to solve the convex imaging problem with data fidelity dictated only by noise statistics. We show through simulations with realistic sampling patterns the acceleration obtained using the preconditioning. We also investigate the algorithm performance for the reconstruction of the 3C129 radio galaxy from real visibilities and compare with multiscale CLEAN, showing better sensitivity and resolution. Our MATLAB code is available online on GitHub. [ABSTRACT FROM AUTHOR]

The goal of this article is to introduce the revised Conceptual Understanding Weighting System (CUWS)Greene & Shorter (2012,Trans. Dial.), a tool for categorizing and weighting a mathematical task in order to identify the level of conceptual understanding being assessed by the task. In addition, there is discussion of how this weighting system relates to other frameworks that are currently used to classify mathematical tasks and student reasoning. Examples are provided to demonstrate how the revised CUWS can be used to calculate a conceptual understanding score for a particular exam, to create mathematical tasks that assess understanding at different levels and to analyse student understanding within those different levels. This in turn allows instructors to identify concepts with which students are struggling and quickly adapt classroom activities to improve students' understanding. The ability to use the CUWS to create mathematical tasks (not just categorize them) is one of the important goals of the CUWS and one that is not regularly emphasized whenworking with other frameworks to categorizemathematical tasks. [ABSTRACT FROM AUTHOR]

Realized kinship is a key statistic in analyses of genetic data involving relatedness of individuals or structure of populations. There are several estimators of kinship that make use of dense SNP genotypes. We introduce a class of estimators, of which some existing estimators are special cases. Within this class, we derive properties of the estimators and determine an optimal estimator. Additionally, we introduce an alternative marker weighting that takes allelic associations [linkage disequilibrium (LD)] into account, and apply this weighting to several estimators. In a simulation study, we show that improved estimators are obtained (1) by optimal weighting of markers, (2) by taking physical contiguity of genome into account, and (3) by weighting on the basis of LD. [ABSTRACT FROM AUTHOR]

Cancer population studies based on cancer registry databases are widely conducted to address various research questions. In general, cancer registry databases do not collect information on cause of death. The net survival rate is defined as the survival rate if a subject would not die for any causes other than cancer. This counterfactual concept is widely used for the analyses of cancer registry data. Perme, Stare, and Estève (2012) proposed a nonparametric estimator of the net survival rate under the assumption that the censoring time is independent of the survival time and covariates. Kodre and Perme (2013) proposed an inverse weighting estimator for the net survival rate under the covariate-dependent censoring. An alternative approach to estimating the net survival rate under covariate-dependent censoring is to apply a regression model for the conditional net survival rate given covariates. In this article, we propose a new estimator for the net survival rate. The proposed estimator is shown to be doubly robust in the sense that it is consistent at least one of the regression models for survival time and for censoring time. We examine the theoretical and empirical properties of our proposed estimator by asymptotic theory and simulation studies. We also apply the proposed method to cancer registry data for gastric cancer patients in Osaka, Japan. [ABSTRACT FROM AUTHOR]

The article examines the types of interference and weighting strategies in nonprobability surveys as criteria for correspondence. A background of nonprobability survey is provided which is used by most Internet surveys that offers numerous advantages in data collection. Also discussed are nonprobability data collection methods, types of interference, and relations between variables.

We develop a new method for large-scale frequentist multiple testing with Bayesian prior information. We find optimal p-value weights that maximize the average power of the weighted Bonferroni method. Due to the nonconvexity of the optimization problem, previous methods that account for uncertain prior information are suitable for only a small number of tests. For a Gaussian prior on the effect sizes, we give an efficient algorithm that is guaranteed to find the optimal weights nearly exactly. Our method can discover new loci in genome-wide association studies and compares favourably to competitors. An open-source implementation is available. [ABSTRACT FROM AUTHOR]

Outlier detection is an integral component of statistical modelling and estimation. For high-dimensional data, classical methods based on the Mahalanobis distance are usually not applicable. We propose an outlier detection procedure that replaces the classical minimum covariance determinant estimator with a high-breakdown minimum diagonal product estimator. The cut-off value is obtained from the asymptotic distribution of the distance, which enables us to control the Type I error and deliver robust outlier detection. Simulation studies show that the proposed method behaves well for high-dimensional data. [ABSTRACT FROM AUTHOR]

Dual-frame telephone surveys that sample from both landline and cell-phone frames have become common because the US population is increasingly abandoning landline phones. Designing a dual-frame telephone survey requires a decision on the sample allocation, and ultimately the allocation of respondents, from each of the two frames. The allocation of the sample to the two frames influences the unequal weighting effect (UWE), and consequently, the precision of estimates calculated from the survey data. The sample allocation also influences cost, since obtaining cell-phone respondents is often more expensive than obtaining landline respondents. In this paper, we present results that designers of dual-frame studies can use to determine an allocation of responders that optimizes the precision of the survey estimates for a given cost. [ABSTRACT FROM AUTHOR]

Given the theoretical promise of these auxiliary data for overcoming the challenge of nonresponse, survey researchers have shown an increased interest in collecting paradata, data from screening interviews, and other contextual information. However, very few studies have systematically assessed the use of these data for post-survey nonresponse adjustments. Those studies that do exist generally do not identify auxiliary variables that correlate with response propensity and key survey variables, a necessary condition for auxiliary data to be effective tools for reducing nonresponse bias. Using the National Survey of Family Growth (NSFG), this paper leverages a large set of auxiliary variables available for the full NSFG sample to assess their potential as independent tools for post-survey nonresponse adjustments. We begin by using this auxiliary information to predict response propensity (RP) for each person in the full sample. We then display descriptive estimates for a variety of attitudes and behaviors measured in the NSFG, using post-stratification weighting adjustments as well as RP adjustments followed by post-stratification. The results show that accounting for RP in the weighting adjustment often produces noteworthy differences in the estimates, thus supporting the collection of these types of auxiliary variables in practice. These results also suggest that standard post-stratification adjustments may not be entirely effective at removing nonresponse bias from all survey estimates, and that some subgroup analyses may be especially subject to bias when adjusting survey estimates using post-stratification alone. [ABSTRACT FROM AUTHOR]

Propensity score weighting adjustment is commonly used to handle unit nonresponse. When the response mechanism is nonignorable in the sense that the response probability depends directly on the study variable, a follow-up sample is commonly used to obtain an unbiased estimator using the framework of two-phase sampling, where the follow-up sample is assumed to respond completely. In practice, the follow-up sample is also subject to missingness. We consider propensity score weighting adjustment for nonignorable nonresponse when there are several follow-ups and the final follow-up sample is also subject to missingness. We propose a method-of-moments estimator for estimating parameters in the response probability. The proposed method can be implemented using the generalized method of moments and a consistent variance estimate can be obtained relatively easily. A limited simulation study shows the robustness of the proposed method. The proposed methods are applied to a Korean household survey of employment. [ABSTRACT FROM PUBLISHER]

This article studies model averaging for linear mixed-effects models. We establish an unbiased estimator of the squared risk for the model averaging, and use the estimator as a criterion for choosing weights. The resulting model average estimator is proved to be asymptotically optimal under some regularity conditions. Simulation experiments show it is superior or comparable to estimators based on the final models selected by the commonly-used methods and some existing averaging procedures. The proposed procedure is applied to data from an AIDS clinic trial. [ABSTRACT FROM AUTHOR]

In visual search, the detection of pop-out targets is facilitated when the target-defining dimension remains the same compared with when it changes across trials. We tested the brain regions necessary for these dimensional carry-over effects using a voxel-based morphometry study with brain-lesioned patients. Participants had to search for targets defined by either their colour (red or blue) or orientation (right- or left-tilted), and the target dimension either stayed the same or changed on consecutive trials. Twenty-five patients were categorized according to whether they showed an effect of dimensional change on search or not. The two groups did not differ with regard to their performance on several working memory tasks, and the dimensional carry-over effects were not correlated with working memory performance. With spatial, sustained attention and working memory deficits as well as lesion volume controlled, damage within the right inferior parietal lobule (the angular and supramarginal gyri) extending into the intraparietal sulcus was associated with an absence of dimensional carry-over (P < 0.001, cluster-level corrected for multiple comparisons). The data suggest that these regions of parietal cortex are necessary to implement attention shifting in the context of visual dimensional change. [ABSTRACT FROM AUTHOR]

Linear mixed models cover a wide range of statistical methods, which have found many uses in the estimation for complex surveys. The purpose of this work is to consider methods by which linear mixed models may be used at the design stage of a survey to incorporate available auxiliary information. This paper reviews the ideas of balanced sampling and the cube algorithm, and proposes an implementation of the latter by which penalized balanced samples can be selected. Such samples can reduce or eliminate the need for linear mixed model weight adjustments, a result demonstrated theoretically and via simulation. Horvitz–Thompson estimators for such samples will be highly efficient for any responses well approximated by a linear mixed model in the auxiliary information. In Monte Carlo experiments using nonparametric and temporal linear mixed models, the strategy of penalized balanced sampling with Horvitz–Thompson estimation dominates a variety of standard strategies. [ABSTRACT FROM PUBLISHER]

Many proper scoring rules such as the Brier and log scoring rules implicitly reward a probability forecaster relative to a uniform baseline distribution. Recent work has motivated weighted proper scoring rules, which have an additional baseline parameter. To date two families of weighted proper scoring rules have been introduced, the weighted power and pseudospherical scoring families. These families are compatible with the log scoring rule: when the baseline maximizes the log scoring rule over some set of distributions, the baseline also maximizes the weighted power and pseudospherical scoring rules over the same set. We characterize all weighted proper scoring families and prove a general property: every proper scoring rule is compatible with some weighted scoring family, and every weighted scoring family is compatible with some proper scoring rule. [ABSTRACT FROM PUBLISHER]

We introduce a novel method for weak-lensing measurements, which is based on a mathematically exact deconvolution of the moments of the apparent brightness distribution of galaxies from the telescope's point spread function (PSF). No assumptions on the shape of the galaxy or the PSF are made. The (de)convolution equations are exact for unweighted moments only, while in practice a compact weight function needs to be applied to the noisy images to ensure that the moment measurement yields significant results. We employ a Gaussian weight function, whose centroid and ellipticity are iteratively adjusted to match the corresponding quantities of the source. The change of the moments caused by the application of the weight function can then be corrected by considering higher order weighted moments of the same source. Because of the form of the deconvolution equations, even an incomplete weighting correction leads to an excellent shear estimation if galaxies and PSF are measured with a weight function of identical size. We demonstrate the accuracy and capabilities of this new method in the context of weak gravitational lensing measurements with a set of specialized tests and show its competitive performance on the GREAT08 Challenge data. A complete implementation of the method can be requested from the authors. [ABSTRACT FROM AUTHOR]

Consider estimating the mean of an outcome in the presence of missing data or estimating population average treatment effects in causal inference. A doubly robust estimator remains consistent if an outcome regression model or a propensity score model is correctly specified. We build on a previous nonparametric likelihood approach and propose new doubly robust estimators, which have desirable properties in efficiency if the propensity score model is correctly specified, and in boundedness even if the inverse probability weights are highly variable. We compare the new and existing estimators in a simulation study and find that the robustified likelihood estimators yield overall the smallest mean squared errors. [ABSTRACT FROM PUBLISHER]

A joint analysis of full-waveform information content in ground penetrating radar (GPR) and electromagnetic induction (EMI) synthetic data was investigated to reconstruct the electrical properties of multilayered media. The GPR and EMI systems operate in zero-offset, off-ground mode and are designed using vector network analyser technology. The inverse problem is formulated in the least-squares sense. We compared four approaches for GPR and EMI data fusion. The two first techniques consisted of defining a single objective function, applying different weighting methods. As a first approach, we weighted the EMI and GPR data using the inverse of the data variance. The ideal point method was also employed as a second weighting scenario. The third approach is the naive Bayesian method and the fourth technique corresponds to GPR–EMI and EMI–GPR sequential inversions. Synthetic GPR and EMI data were generated for the particular case of a two-layered medium. Analysis of the objective function response surfaces from the two first approaches demonstrated the benefit of combining the two sources of information. However, due to the variations of the GPR and EMI model sensitivities with respect to the medium electrical properties, the formulation of an optimal objective function based on the weighting methods is not straightforward. While the Bayesian method relies on assumptions with respect to the statistical distribution of the parameters, it may constitute a relevant alternative for GPR and EMI data fusion. Sequential inversions of different configurations for a two layered medium show that in the case of high conductivity or permittivity for the first layer, the inversion scheme can not fully retrieve the soil hydrogeophysical parameters. But in the case of low permittivity and conductivity for the first layer, GPR–EMI inversion provides proper estimation of values compared to the EMI–GPR inversion. [ABSTRACT FROM AUTHOR]

We have developed a new array method combining conventional migration with a slowness-backazimuth deviation weighting scheme. All seismic traces are shifted based on the theoretical traveltime of the scattered wave from specific gridpoints in a 3-D volume. Observed slowness and backazimuth are calculated for each raypath and compared with theoretical values in order to estimate slowness and backazimuth deviations. Subsequently, stacked energy calculated by a conventional migration method is weighted by the slowness and backazimuth deviations to suppress any arrival energy whose slowness and backazimuth are inconsistent with the expected theoretical values. This new method was applied to two P-wave data sets which comprise (1) underside reflections at the 410 and 660 km mantle discontinuities and (2) D″ reflections as well as their corresponding synthetic data sets. The results show that the weighting scheme dramatically increases the resolution of the migrated images and enables us to obtain well-constrained, focused images, making upper-mantle discontinuities and D″ reflections more distinct by reducing their surrounding energy. [ABSTRACT FROM AUTHOR]

The paper proposes an estimation approach for panel models with mixed continuous and ordered categorical outcomes based on generalized estimating equations for the mean and pseudoscore equations for the covariance parameters. A numerical study suggests that efficiency can be gained in the mean parameter estimators by using individual covariance matrices in the estimating equations for the mean parameters. The approach is applied to estimate the returns to occupational qualification in terms of income and perceived job security in a 9-year period based on the German Socio-Economic Panel. To compensate for missing data, a combined multiple imputation–weighting approach is adopted. [ABSTRACT FROM AUTHOR]