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A pressing challenge in medical research is to identify optimal treatments for individual patients. This is particularly challenging in mental health settings where mean responses are often similar across multiple treatments. For example, the mean longitudinal trajectory for patients treated with an active drug versus placebo may be similar trajectories, but different treatments may exhibit trajectory shapes that are distinct from trajectories in other treatment groups. Most precision medicine approaches using longitudinal data often ignore information from the longitudinal data structure. This paper investigates a powerful precision medicine approach by examining the impact of baseline covariates on longitudinal outcome trajectories to guide treatment decisions instead of traditional scalar outcome measures derived from longitudinal data, such as a change score. We introduce a method of estimating “biosignatures” defined as linear combinations of baseline characteristics (i.e., a single index) that optimally separate longitudinal trajectories among different treatment groups. The criterion used is to maximize the Kullback–Leibler Divergences between different treatment outcome distributions. The approach is illustrated via simulation studies and a depression clinical trial. The approach is also contrasted with more traditional methods and compares performance in the presence of missing data. [ABSTRACT FROM AUTHOR]

Individualized treatment rules (ITRs) recommend treatments that are tailored specifically according to each patient's own characteristics. It can be challenging to estimate optimal ITRs when there are many features, especially when these features have arisen from multiple data domains (e.g., demographics, clinical measurements, neuroimaging modalities). Considering data from complementary domains and using multiple similarity measures to capture the potential complex relationship between features and treatment can potentially improve the accuracy of assigning treatments. Outcome weighted learning (OWL) methods that are based on support vector machines using a predetermined single kernel function have previously been developed to estimate optimal ITRs. In this article, we propose an approach to estimate optimal ITRs by exploiting multiple kernel functions to describe the similarity of features between subjects both within and across data domains within the OWL framework, as opposed to preselecting a single kernel function to be used for all features for all domains. Our method takes into account the heterogeneity of each data domain and combines multiple data domains optimally. Our learning process estimates optimal ITRs and also identifies the data domains that are most important for determining ITRs. This approach can thus be used to prioritize the collection of data from multiple domains, potentially reducing cost without sacrificing accuracy. The comparative advantage of our method is demonstrated by simulation studies and by an application to a randomized clinical trial for major depressive disorder that collected features from multiple data domains. for this article are available online. [ABSTRACT FROM AUTHOR]

This paper discusses methods for clustering a continuous covariate in a survival analysis model. The advantages of using a categorical covariate defined from discretizing a continuous covariate (via clustering) is (i) enhanced interpretability of the covariate's impact on survival and (ii) relaxing model assumptions that are usually required for survival models, such as the proportional hazards model. Simulations and an example are provided to illustrate the methods. [ABSTRACT FROM AUTHOR]

Statistical learning-extracting regularities in the environment-may underlie complex social behavior. 124 children, 56 with autism and 68 typically developing, ages 2-8 years, completed a novel visual statistical learning task on an iPad. Averaged together, children with autism demonstrated less learning on the task compared to typically developing children. However, multivariate classification analyses characterized individual behavior patterns, and demonstrated a subset of children with autism had similar learning patterns to typically developing children and that subset of children had less severe autism symptoms. Therefore, statistically averaging data resulted in missing critical heterogeneity. Variability in statistical learning may help to understand differences in autism symptoms across individuals and could be used to tailor and inform treatment decisions. [ABSTRACT FROM AUTHOR]

A common descriptive statistic in cluster analysis is the R2 that measures the overall proportion of variance explained by the cluster means. This note highlights properties of the R2 for clustering. In particular, we show that generally the R2 can be artificially inflated by linearly transforming the data by "stretching" and by projecting. Also, the R2 for clustering will often be a poor measure of clustering quality in high-dimensional settings. We also investigate the R2 for clustering for misspecified models. Several simulation illustrations are provided highlighting weaknesses in the clustering R2, especially in high-dimensional settings. A functional data example is given showing how that R2 for clustering can vary dramatically depending on how the curves are estimated. [ABSTRACT FROM AUTHOR]

In a randomized clinical trial (RCT), it is often of interest not only to estimate the effect of various treatments on the outcome, but also to determine whether any patient characteristic has a different relationship with the outcome, depending on treatment. In regression models for the outcome, if there is a non-zero interaction between treatment and a predictor, that predictor is called an "effect modifier". Identification of such effect modifiers is crucial as we move towards precision medicine, that is, optimizing individual treatment assignment based on patient measurements assessed when presenting for treatment. In most settings, there will be several baseline predictor variables that could potentially modify the treatment effects. This article proposes optimal methods of constructing a composite variable (defined as a linear combination of pre-treatment patient characteristics) in order to generate an effect modifier in an RCT setting. Several criteria are considered for generating effect modifiers and their performance is studied via simulations. An example from a RCT is provided for illustration. [ABSTRACT FROM AUTHOR]

We illustrate practical uses of logistic regression and Markov chains by applying these concepts to the problem of developing optimal strategy in the popular basketball game of H-O-R-S-E. Based on data collected by the authors, we estimate model parameters for each author, describe strategies of optimizing each author’s probability of winning, and calculate the stationary distribution of a Markov chain that arises from the game. [ABSTRACT FROM AUTHOR]

Principal subspace theorems deal with the problem of finding subspaces supporting optimal approximations of multivariate distributions. The optimality criterion considered in this paper is the minimization of the mean squared distance between the given distribution and an approximating distribution, subject to some constraints. Statistical applications include, but are not limited to, cluster analysis, principal components analysis and projection pursuit. Most principal subspace theorems deal with elliptical distributions or with mixtures of spherical distributions. We generalize these results using the notion of self-consistency. We also show their connections with the skew-normal distribution and projection pursuit techniques. We also discuss their implications, with special focus on principal points and self-consistent points. Finally, we access the practical relevance of the theoretical results by means of several simulation studies. [ABSTRACT FROM AUTHOR]

This article presents a counterintuitive result regarding the estimation of a regression slope coefficient. Paradoxically, the precision of the slope estimator can deteriorate when additional information is used to estimate its value. In a randomized experiment, the distribution of baseline variables should be identical across treatments due to randomization. The motivation for this article came from noting that the precision of slope estimators deteriorated when pooling baseline predictors across treatment groups. [ABSTRACT FROM AUTHOR]

A novel functional additive model is proposed, which is uniquely modified and constrained to model nonlinear interactions between a treatment indicator and a potentially large number of functional and/or scalar pretreatment covariates. The primary motivation for this approach is to optimize individualized treatment rules based on data from a randomized clinical trial. We generalize functional additive regression models by incorporating treatment‐specific components into additive effect components. A structural constraint is imposed on the treatment‐specific components in order to provide a class of additive models with main effects and interaction effects that are orthogonal to each other. If primary interest is in the interaction between treatment and the covariates, as is generally the case when optimizing individualized treatment rules, we can thereby circumvent the need to estimate the main effects of the covariates, obviating the need to specify their form and thus avoiding the issue of model misspecification. The methods are illustrated with data from a depression clinical trial with electroencephalogram functional data as patients' pretreatment covariates. [ABSTRACT FROM AUTHOR]

A recent meta-regression of antidepressant efficacy on baseline depression severity has caused considerable controversy in the popular media. A central source of the controversy is a lack of clarity about the relation of meta-regression parameters to corresponding parameters in models for subject-level data. This paper focuses on a linear regression with continuous outcome and predictor, a case that is often considered less problematic. We frame meta-regression in a general mixture setting that encompasses both finite and infinite mixture models. In many applications of meta-analysis, the goal is to evaluate the efficacy of a treatment from several studies, and authors use meta-regression on grouped data to explain variations in the treatment efficacy by study features. When the study feature is a characteristic that has been averaged over subjects, it is difficult not to interpret the meta-regression results on a subject level, a practice that is still widespread in medical research. Although much of the attention in the literature is on methods of estimating meta-regression model parameters, our results illustrate that estimation methods cannot protect against erroneous interpretations of meta-regression on grouped data. We derive relations between meta-regression parameters and within-study model parameters and show that the conditions under which slopes from these models are equal cannot be verified on the basis of group-level information only. The effects of these model violations cannot be known without subject-level data. We conclude that interpretations of meta-regression results are highly problematic when the predictor is a subject-level characteristic that has been averaged over study subjects. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A longstanding problem in clinical research is distinguishing drug-treated subjects that respond due to specific effects of the drug from those that respond to nonspecific (or placebo) effects of the treatment. Linear mixed effect models are commonly used to model longitudinal clinical trial data. In this paper we present a solution to the problem of identifying placebo responders using an optimal partitioning methodology for linear mixed effects models. Since individual outcomes in a longitudinal study correspond to curves, the optimal partitioning methodology produces a set of prototypical outcome profiles. The optimal partitioning methodology can accommodate both continuous and discrete covariates. The proposed partitioning strategy is compared and contrasted with the growth mixture modeling approach. The methodology is applied to a two-phase depression clinical trial where subjects in a first phase were treated openly for 12 weeks with fluoxetine followed by a double blind discontinuation phase where responders to treatment in the first phase were randomized to either stay on fluoxetine or switched to a placebo. The optimal partitioning methodology is applied to the first phase to identify prototypical outcome profiles. Using time to relapse in the second phase of the study, a survival analysis is performed on the partitioned data. The optimal partitioning results identify prototypical profiles that distinguish whether subjects relapse depending on whether or not they stay on the drug or are randomized to a placebo. [ABSTRACT FROM AUTHOR]

Finite mixture models have come to play a very prominent role in modelling data. The finite mixture model is predicated on the assumption that distinct latent groups exist in the population. The finite mixture model therefore is based on a categorical latent variable that distinguishes the different groups. Often in practice, distinct sub-populations do not actually exist. For example, disease severity (e.g., depression) may vary continuously and therefore, a distinction of diseased and non-diseased may not be based on the existence of distinct sub-populations. Thus, what is needed is a generalization of the finite mixture's discrete latent predictor to a continuous latent predictor. We cast the finite mixture model as a regression model with a latent Bernoulli predictor. A latent regression model is proposed by replacing the discrete Bernoulli predictor by a continuous latent predictor with a beta distribution. Motivation for the latent regression model arises from applications where distinct latent classes do not exist, but instead individuals vary according to a continuous latent variable. The shapes of the beta density are very flexible and can approximate the discrete Bernoulli distribution. Examples and a simulation are provided to illustrate the latent regression model. In particular, the latent regression model is used to model placebo effect among drug-treated subjects in a depression study. [ABSTRACT FROM AUTHOR]

Abstract: Principal points are cluster means for theoretical distributions. A discriminant methodology based on principal points is introduced. The principal point classification method is useful in clinical trials where the goal is to distinguish and differentiate between different treatment effects. Particularly, in psychiatric studies where placebo response rates can be very high, the principal point classification is illustrated to distinguish specific drug responders from non-specific placebo responders. [Copyright &y& Elsevier]

A common problem in statistical modelling is to distinguish between finite mixture distribution and a homogeneous non-mixture distribution. Finite mixture models are widely used in practice and often mixtures of normal densities are indistinguishable from homogenous non-normal densities. This paper illustrates what happens when the EM algorithm for normal mixtures is applied to a distribution that is a homogeneous non-mixture distribution. In particular, a population-based EM algorithm for finite mixtures is introduced and applied directly to density functions instead of sample data. This algorithm is used to find finite mixture approximations to common homogeneous distributions. An example regarding the nature of a placebo response in drug treated depressed subjects is used to illustrate ideas. [ABSTRACT FROM AUTHOR]

Functional data can be clustered by plugging estimated regression coefficients from individual curves into the κ-means algorithm. Clustering results can differ depending on how the curves are fit to the data. Estimating curves using different sets of basis functions corresponds to different linear transformations of the data. κ-means clustering is not invariant to linear transformations of the data. The optimal linear transformation for clustering will stretch the distribution so that the primary direction of variability aligns with actual differences in the clusters. It is shown that clustering the raw data will often give results similar to clustering regression coefficients obtained using an orthogonal design matrix. Clustering functional data using an L2 metric on function space can be achieved by clustering a suitable linear transformation of the regression coefficients. An example where depressed individuals are treated with an antidepressant is used for illustration. [ABSTRACT FROM AUTHOR]

Many statistical methods for continuous distributions assume a linear conditional expectation. Components of multivariate distributions are often measured on a discrete ordinal scale based on a discretization of an underlying continuous latent variable. The results in this paper show that common examples of discretized bivariate and trivariate distributions will have a linear conditional expectation. Examples and simulations are provided to illustrate the results. [ABSTRACT FROM AUTHOR]

The problem of clustering functional data is addressed. Results on principal points (cluster means for probability distributions) are given for functional Gaussian distributions. Examples and simulations are provided to illustrate results. [ABSTRACT FROM AUTHOR]

Identification of placebo responders among subjects treated with active drug has significant clinical and research implications. In clinical practice, when a patient treated with medication improves, this improvement may be attributed to the chemical component of the drug itself, a "placebo effect," or some combinations of these. Determining the proper subsequent treatment and maintenance of the patient may be greatly aided by understanding the mechanism of patient improvement. In a research context, classification of patient response has bearing on how efficacy and effectiveness clinical trials are designed and conducted. This article presents a framework for studying placebo response in diverse areas of medicine. To identify placebo responders among drug-treated patients, a profile of the clinical status over time (outcome profile) is estimated for each subject. Self-consistent partitioning techniques are used to group subjects based on the amount of curvature in the profile as well as the overall trend in the profile. The resulting partitions determine representative profiles for subjects in the drug group involving placebo and the active drug phenelzine. Data from the placebo arm of the study is used to help validate the procedure, because the drug-treated and placebo-treated subjects should share common profiles.. [ABSTRACT FROM AUTHOR]

The slope is usually the parameter of primary importance in a simple linear regression. If the straight line model gives a poor fit to the data, one can consider the average slope of the non-linear response. In this paper, we show that if the response is quadratic, then the average slope can be obtained by simply using the slope from a straight line fit. In fact, if the slope of the best fitting line to a smooth non-linear function equals the average slope of the function over an arbitrary interval, then the function must be quadratic. This paper illustrates the case where intentionally fitting a wrong model (in this case, a straight line) gives the correct result (the average slope). The example which motivated this study is used to illustrate the results. [ABSTRACT FROM AUTHOR]

The Problem of clustering functional data is addressed. Results on principal points (cluster means for probability distributions) are given for functional Gaussian distributions. Examples and simulations are provided to illustrate results. [ABSTRACT FROM AUTHOR]

The relationship between the volume of a liquid in a bottle as a function of the height of the liquid is used to illustrate polynomial regression and the fitting of spline models. A polynomial regression exercise is provided where students attempt to determine the shape of a bottle using only the height and volume data from the bottle. The height and volume data for bottles are easy to obtain and provide an interesting data-collecting activity for introducing spline models in a regression course. [ABSTRACT FROM AUTHOR]

Background: There is a need to understand the relationship between COVID-19 Convalescent Plasma (CCP) anti-SARS-CoV-2 IgG levels and clinical outcomes to optimize CCP use. This study aims to evaluate the relationship between recipient baseline clinical status, clinical outcomes, and CCP antibody levels. Methods: The study analyzed data from the COMPILE study, a meta-analysis of pooled individual patient data from 8 randomized clinical trials (RCTs) assessing the efficacy of CCP vs. control, in adults hospitalized for COVID-19 who were not receiving mechanical ventilation at randomization. SARS-CoV-2 IgG levels, referred to as 'dose' of CCP treatment, were retrospectively measured in donor sera or the administered CCP, semi-quantitatively using the VITROS Anti-SARS-CoV-2 IgG chemiluminescent immunoassay (Ortho-Clinical Diagnostics) with a signal-to-cutoff ratio (S/Co). The association between CCP dose and outcomes was investigated, treating dose as either continuous or categorized (higher vs. lower vs. control), stratified by recipient oxygen supplementation status at presentation. Results: A total of 1714 participants were included in the study, 1138 control- and 576 CCP-treated patients for whom donor CCP anti-SARS-CoV2 antibody levels were available from the COMPILE study. For participants not receiving oxygen supplementation at baseline, higher-dose CCP (/control) was associated with a reduced risk of ventilation or death at day 14 (OR = 0.19, 95% CrI: [0.02, 1.70], posterior probability Pr(OR < 1) = 0.93) and day 28 mortality (OR = 0.27 [0.02, 2.53], Pr(OR < 1) = 0.87), compared to lower-dose CCP (/control) (ventilation or death at day 14 OR = 0.79 [0.07, 6.87], Pr(OR < 1) = 0.58; and day 28 mortality OR = 1.11 [0.10, 10.49], Pr(OR < 1) = 0.46), exhibiting a consistently positive CCP dose effect on clinical outcomes. For participants receiving oxygen at baseline, the dose-outcome relationship was less clear, although a potential benefit for day 28 mortality was observed with higher-dose CCP (/control) (OR = 0.66 [0.36, 1.13], Pr(OR < 1) = 0.93) compared to lower-dose CCP (/control) (OR = 1.14 [0.73, 1.78], Pr(OR < 1) = 0.28). Conclusion: Higher-dose CCP is associated with its effectiveness in patients not initially receiving oxygen supplementation, however, further research is needed to understand the interplay between CCP anti-SARS-CoV-2 IgG levels and clinical outcome in COVID-19 patients initially receiving oxygen supplementation. [ABSTRACT FROM AUTHOR]

The k-means algorithm and the principal curve algorithm are special cases of a self-consistency algorithm. A general self-consistency algorithm is described and results are provided describing the behavior of the algorithm for theoretical distributions, in particular elliptical distributions. The results are used to contrast the behavior of the algorithms when applied to a theoretical model and when applied to finite datasets from the model. The algorithm is also used to determine principal loops for the bivariate normal distribution. [ABSTRACT FROM AUTHOR]

Focuses on a study that examined the self-consistency of a principal component axis that is when a distribution is centered about a principal component axis. Factors that can cause a principal component axis to fail in being self-consistent; Characterization of the class of elliptical distribution by self-consistency of principal axes; Details of the relationship between self-consistent points and self-consistent principal component subspaces.

Abstract: The set of k points that optimally represent a distribution in terms of mean squared error have been called principal points (Flury 1990). Principal points are a special case of self-consistent points. Any given set of k distinct points in R[sup p] induce a partition of R[sup p] into Voronoi regions or domains of attraction according to minimal distance. A set of k points are called self-consistent for a distribution if each point equals the conditional mean of the distribution over its respective Voronoi region. For symmetric multivariate distributions, sets of self-consistent points typically form symmetric patterns. This paper investigates the optimality of different symmetric patterns of self-consistent points for symmetric multivariate distributions and in particular for the bivariate normal distribution. These results are applied to the problem of estimating principal points. [ABSTRACT FROM AUTHOR]

SUMMARY The term 'principal points' originated in a problem of determining 'typical' heads for the design of protection masks, as described by Flury. Two principal points in the mask example correspond to a small and a large size. Principal points are cluster means for theoretical distributions, and sample cluster means from a k -means algorithm are non-parametric estimators of principal points. This paper demonstrates that maximum likelihood estimators and semi-parametric estimators based on symmetry constraints typically perform much better than the k -means estimators. Asymptotic results on the efficiency of these estimators of two principal points for four symmetric univariate distributions are given. Simulation results are provided to examine the performance of the estimators for finite sample sizes. Finally, the different estimators of two principal points are compared using the head dimension data for the design of protection masks. [ABSTRACT FROM AUTHOR]

The purpose of this study was to test the feasibility and safety of High-Level Mobility (HLM) training on adults with Acquired Brain Injury (ABI). Our hypotheses were that HLM training would be feasible and safe. This study was a pilot randomized control trial with a Simple Skill Group (SSG) and a Complex Skill Group (CSG). Both groups received 12 sessions over 8 weeks and completed 4 testing sessions over 16 weeks. The SSG focused on locomotion, while CSG focused on the acquisition of running. Feasibility was assessed in terms of process, resources, management, and scientific metrics, including safety. Among the 41 participants meeting inclusion criteria, 28 consented (CSG, n = 13, SSG, n = 15), 20 completed the assigned protocol and 8 withdrew (CSG n = 4, SSG n = 4). Adherence rate to assigned protocol was 100%. There were two Adverse Events (AEs), 1 over 142 SSG sessions and 1 over 120 CSG sessions. The AE Odd Ratio (OR) (CSG:SSG) was 1.18 (95% CI: 0.07, 19.15). The data support our hypotheses that HLM training is feasible and safe on ambulatory adults with ABI. [ABSTRACT FROM AUTHOR]

Sparse additive modeling is a class of effective methods for performing high-dimensional nonparametric regression. This article develops a sparse additive model focused on estimation of treatment effect modification with simultaneous treatment effect-modifier selection. We propose a version of the sparse additive model uniquely constrained to estimate the interaction effects between treatment and pretreatment covariates, while leaving the main effects of the pretreatment covariates unspecified. The proposed regression model can effectively identify treatment effect-modifiers that exhibit possibly nonlinear interactions with the treatment variable that are relevant for making optimal treatment decisions. A set of simulation experiments and an application to a dataset from a randomized clinical trial are presented to demonstrate the method. [ABSTRACT FROM AUTHOR]

This article focuses on the problem of modeling and estimating interaction effects between covariates and a continuous treatment variable on an outcome, using a single-index regression. The primary motivation is to estimate an optimal individualized dose rule and individualized treatment effects. To model possibly nonlinear interaction effects between the patients' covariates and a continuous treatment variable, we employ a two-dimensional penalized spline regression on an index-treatment domain, where the index is defined as a linear projection of the covariates. The method is illustrated using two applications as well as simulation experiments. A unique contribution of this work is in the parsimonious (single-index) parameterization specifically defined for the interaction effect term, that can be used to assess the treatment benefit. Supplemental materials for this article are available online. [ABSTRACT FROM AUTHOR]

There is a well-known simple formula for computing prediction sum of squares (PRESS) residuals in a regression problem without having to refit the curve for each observation. This note shows that the same basic result holds for fitting a regression function when the regression coefficients are subject to linear constraints. [ABSTRACT FROM AUTHOR]

As the world faced the devastation of the COVID‐19 pandemic in late 2019 and early 2020, numerous clinical trials were initiated in many locations in an effort to establish the efficacy (or lack thereof) of potential treatments. As the pandemic has been shifting locations rapidly, individual studies have been at risk of failing to meet recruitment targets because of declining numbers of eligible patients with COVID‐19 encountered at participating sites. It has become clear that it might take several more COVID‐19 surges at the same location to achieve full enrollment and to find answers about what treatments are effective for this disease. This paper proposes an innovative approach for pooling patient‐level data from multiple ongoing randomized clinical trials (RCTs) that have not been configured as a network of sites. We present the statistical analysis plan of a prospective individual patient data (IPD) meta‐analysis (MA) from ongoing RCTs of convalescent plasma (CP). We employ an adaptive Bayesian approach for continuously monitoring the accumulating pooled data via posterior probabilities for safety, efficacy, and harm. Although we focus on RCTs for CP and address specific challenges related to CP treatment for COVID‐19, the proposed framework is generally applicable to pooling data from RCTs for other therapies and disease settings in order to find answers in weeks or months, rather than years. [ABSTRACT FROM AUTHOR]

Proposes a method of selecting a few representative curves from a large collection of curves based on estimation of principal points. Cluster analysis; Elliptical distributions; Principal components.

We consider a single‐index regression model, uniquely constrained to estimate interactions between a set of pretreatment covariates and a treatment variable on their effects on a response variable, in the context of analyzing data from randomized clinical trials. We represent interaction effect terms of the model through a set of treatment‐specific flexible link functions on a linear combination of the covariates (a single index), subject to the constraint that the expected value given the covariates equals 0, while leaving the main effects of the covariates unspecified. We show that the proposed semiparametric estimator is consistent for the interaction term of the model, and that the efficiency of the estimator can be improved with an augmentation procedure. The proposed single‐index regression provides a flexible and interpretable modeling approach to optimizing individualized treatment rules based on patients' data measured at baseline, as illustrated by simulation examples and an application to data from a depression clinical trial. [ABSTRACT FROM AUTHOR]

A response from the authors of the article "A Paradoxical Result in Estimating Regression Coefficients" in the 2014 issue is presented.

In this paper, we propose a unified Bayesian joint modeling framework for studying association between a binary treatment outcome and a baseline matrix‐valued predictor. Specifically, a joint modeling approach relating an outcome to a matrix‐valued predictor through a probabilistic formulation of multilinear principal component analysis is developed. This framework establishes a theoretical relationship between the outcome and the matrix‐valued predictor, although the predictor is not explicitly expressed in the model. Simulation studies are provided showing that the proposed method is superior or competitive to other methods, such as a two‐stage approach and a classical principal component regression in terms of both prediction accuracy and estimation of association; its advantage is most notable when the sample size is small and the dimensionality in the imaging covariate is large. Finally, our proposed joint modeling approach is shown to be a very promising tool in an application exploring the association between baseline electroencephalography data and a favorable response to treatment in a depression treatment study by achieving a substantial improvement in prediction accuracy in comparison to competing methods. [ABSTRACT FROM AUTHOR]

In a regression model for treatment outcome in a randomized clinical trial, a treatment effect modifier is a covariate that has an interaction with the treatment variable, implying that the treatment efficacies vary across values of such a covariate. In this paper, we present a method for determining a composite variable from a set of baseline covariates, that can have a nonlinear association with the treatment outcome, and acts as a composite treatment effect modifier. We introduce a parsimonious generalization of the single-index models that targets the effect of the interaction between the treatment conditions and the vector of covariates on the outcome, a single-index model with multiple-links (SIMML) that estimates a single linear combination of the covariates(i.e., a single-index), with treatment-specific nonparametric link functions. The approach emphasizes a focus on the treatment-by-covariates interaction effects on the treatment outcome that are relevant for making optimal treatment decisions. Asymptotic results for estimator are obtained under possible model misspecification. A treatment decision rule based on the derived single-index is defined, and it is compared to other methods for estimating optimal treatment decision rules. An application to a clinical trial for the treatment of depression is presented. [ABSTRACT FROM AUTHOR]

A major focus of personalized medicine is on the development of individualized treatment rules that depend upon baseline measures. Good decision rules have the potential to significantly advance patient care and reduce the burden of a host of diseases. Statistical methods for developing such rules are progressing rapidly, but few methods have considered the use of pretreatment functional data to guide decision-making. Furthermore, those methods that do allow for the incorporation of functional pretreatment covariates typically make strong assumptions about the relationships between the functional covariates and the response of interest. We propose two approaches for using functional data to select an optimal treatment that address some of the shortcomings of previously developed methods. Specifically, we combine the flexibility of functional additive regression models with Q-learning or A-learning in order to obtain treatment decision rules. Properties of the corresponding estimators are discussed. Our approaches are evaluated in several realistic settings using synthetic data and are applied to data arising from a clinical trial comparing two treatments for major depressive disorder in which baseline imaging data are available for subjects who are subsequently treated. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

k -Principal points of a random variable are k points that minimize the mean squared distance (MSD) between the random variable and the nearest of the k points. This paper focuses on finding optimal estimators of principal points in terms of the expected mean squared distance (EMSD) between the random variable and the nearest principal point estimator. These estimators are compared with nonparametric and maximum likelihood estimators. It turns out that a minimum EMSD estimator of k -principal points of univariate normal distributions is determined by the k -principal points of the t -distribution with n + 1 degrees of freedom, where n is the sample size. Extensions of the results to location-scale families, multivariate distributions, and principal surfaces are also discussed. [ABSTRACT FROM AUTHOR]

Many techniques of functional data analysis require choosing a measure of distance between functions, with the most common choice being [ABSTRACT FROM AUTHOR]

The "mother knows best" hypothesis states that adults should choose hosts for oviposition on which their offspring will best perform, maximizing their own fitness. It has been hypothesized that this preference-performance relationship for wood-boring insects is especially important because larvae are not able to switch hosts, although no study has examined oviposition choices for these insects. We examined oviposition preferences of the emerald ash borer, Agrito planipennis Fairmaire (Coleoptera: Buprestidae), in two common gardens, one on the campus of Wright State University in Dayton, OH, and the other at the Michigan State University Tollgate Research Farm in Novi, MI, by wrapping cheesecloth around ash trunks to assess passive oviposition patterns. We found that in both gardens, ash species native to North America, which are highly susceptible to the emerald ash borer, consistently received more ova than Manchurian ash, which is indigenous to Asia and more resistant to the emerald ash borer. Susceptible trees in the Novi garden received 93 times the number of ova and susceptible trees at the Wright State garden received up to 25 times the number of ova that were received by Manchurian ash in each of their respective gardens. Neither tree size nor vigor affected oviposition choice. There were also higher numbers of adult exit holes on North American than Manchurian ash in both common gardens. The observed oviposition preferences in this study align with patterns of adult feeding preference, ash host mortality, and exit hole numbers from other studies. These observations also suggest that oviposition preferences may contribute to interspecific patterns of host resistance and mortality. Collectively, our results demonstrate that the emerald ash borer prefers to oviposit on species on which its offspring will best perform, suggesting that there is strong selection for the ability to recognize host cues that predict better larval survival and performance. [ABSTRACT FROM AUTHOR]

Differences in motivation during adolescence relative to childhood and adulthood in autism was tested in a cross-sectional study. 156 Typically developing individuals and 79 individuals with autism ages 10–30 years of age completed a go/nogo task with social and non-social cues. To assess age effects, linear and quadratic models were used. Consistent with prior studies, typically developing adolescents and young adults demonstrated more false alarms for positive relative to neutral social cues. In autism, there were no changes in attention across age for social or non-social cues. Findings suggest reduced orienting to motivating cues during late adolescence and early adulthood in autism. The findings provide a unique perspective to explain the challenges for adolescents with autism transitioning to adulthood. [ABSTRACT FROM AUTHOR]

Cognitive activity and awareness during cardiac arrest (CA) are reported but ill understood. This first of a kind study examined consciousness and its underlying electrocortical biomarkers during cardiopulmonary resuscitation (CPR). In a prospective 25-site in-hospital study, we incorporated a) independent audiovisual testing of awareness, including explicit and implicit learning using a computer and headphones, with b) continuous real-time electroencephalography(EEG) and cerebral oxygenation(rSO 2) monitoring into CPR during in-hospital CA (IHCA). Survivors underwent interviews to examine for recall of awareness and cognitive experiences. A complementary cross-sectional community CA study provided added insights regarding survivors' experiences. Of 567 IHCA, 53(9.3%) survived, 28 of these (52.8%) completed interviews, and 11(39.3%) reported CA memories/perceptions suggestive of consciousness. Four categories of experiences emerged: 1) emergence from coma during CPR (CPR-induced consciousness [CPRIC]) 2/28(7.1%), or 2) in the post-resuscitation period 2/28(7.1%), 3) dream-like experiences 3/28(10.7%), 4) transcendent recalled experience of death (RED) 6/28(21.4%). In the cross-sectional arm, 126 community CA survivors' experiences reinforced these categories and identified another: delusions (misattribution of medical events). Low survival limited the ability to examine for implicit learning. Nobody identified the visual image, 1/28(3.5%) identified the auditory stimulus. Despite marked cerebral ischemia (Mean rSO 2 = 43%) normal EEG activity (delta, theta and alpha) consistent with consciousness emerged as long as 35–60 minutes into CPR. Consciousness. awareness and cognitive processes may occur during CA. The emergence of normal EEG may reflect a resumption of a network-level of cognitive activity, and a biomarker of consciousness, lucidity and RED (authentic "near-death" experiences). [ABSTRACT FROM AUTHOR]

Chronic mental and physical fatigue and post-exertional malaise are the more debilitating symptoms of long COVID-19. The study objective was to explore factors contributing to exercise intolerance in long COVID-19 to guide development of new therapies. Exercise capacity data of patients referred for a cardiopulmonary exercise test (CPET) and included in a COVID-19 Survivorship Registry at one urban health center were retrospectively analyzed. Results : Most subjects did not meet normative criteria for a maximal test, consistent with suboptimal effort and early exercise termination. Mean O 2 pulse peak % predicted (of 79 ± 12.9) was reduced, supporting impaired energy metabolism as a mechanism of exercise intolerance in long COVID, n = 59. We further identified blunted rise in heart rate peak during maximal CPET. Our preliminary analyses support therapies that optimize bioenergetics and improve oxygen utilization for treating long COVID-19. • Reduced O 2 pulse peak % predicted (impaired oxygen extraction) may be a mechanism of chronic fatigue and exercise intolerance in long COVID. • Our study identified chronotropic intolerance as a potential biomarker in long COVID. • Impaired O 2 extraction may have contributed to both mental and peripheral muscle fatigue and early exercise termination in long COVID. • We recommend low-intensity, sub-symptom aerobic exercise to treat long COVID. [ABSTRACT FROM AUTHOR]

The amount and complexity of patient-level data being collected in randomized-controlled trials offer both opportunities and challenges for developing personalized rules for assigning treatment for a given disease or ailment. For example, trials examining treatments for major depressive disorder are not only collecting typical baseline data such as age, gender, or scores on various tests, but also data that measure the structure and function of the brain such as images from magnetic resonance imaging (MRI), functional MRI (fMRI), or electroencephalography (EEG). These latter types of data have an inherent structure and may be considered as functional data. We propose an approach that uses baseline covariates, both scalars and functions, to aid in the selection of an optimal treatment. In addition to providing information on which treatment should be selected for a new patient, the estimated regime has the potential to provide insight into the relationship between treatment response and the set of baseline covariates. Our approach can be viewed as an extension of "advantage learning" to include both scalar and functional covariates. We describe our method and how to implement it using existing software. Empirical performance of our method is evaluated with simulated data in a variety of settings and also applied to data arising from a study of patients with major depressive disorder from whom baseline scalar covariates as well as functional data from EEG are available. [ABSTRACT FROM AUTHOR]

A penalized approach is proposed for performing large numbers of parallel nonparametric analyses of either of two types: restricted likelihood ratio tests of a parametric regression model versus a general smooth alternative, and nonparametric regression. Compared with naïvely performing each analysis in turn, our techniques reduce computation time dramatically. Viewing the large collection of scatterplot smooths produced by our methods as functional data, we develop a clustering approach to summarize and visualize these results. Our approach is applicable to ultra-high-dimensional data, particularly data acquired by neuroimaging; we illustrate it with an analysis of developmental trajectories of functional connectivity at each of approximately 70,000 brain locations. Supplementary materials, including an appendix and an R package, are available online. [ABSTRACT FROM AUTHOR]

B CLINICAL IMPLICATIONS: b Results from further studies like this will help personalize CPR to an individual's specific resuscitative needs rather than standardizing CPR to all patients. Future studies examining hemodynamic-directed CPR vs current CPR standards are needed to enhance the effective use of physiologic measures to improve resuscitation outcomes. [Extracted from the article]