Newest Articles

A time-varying effect model for intensive longitudinal data

Tan,X.

Understanding temporal change in human behavior and psychological processes is a central issue in the behavioral sciences. With technological advances, intensive longitudinal data (ILD) are increasingly generated by studies of human behavior that repeatedly administer assessments over time. ILD offer unique opportunities to describe temporal behavioral changes in detail and identify related environmental and psychosocial antecedents and consequences. Traditional analytical approaches impose strong parametric assumptions about the nature of change in the relationship between time-varying covariates and outcomes of interest. This article introduces time-varying effect models (TVEMs) that explicitly model changes in the association between ILD covariates and ILD outcomes over time in a flexible manner. In this article, we describe unique research questions that the TVEM addresses, outline the model-estimation procedure, share a SAS macro for implementing the model, demonstrate model utility with a simulated example, and illustrate model applications in ILD collected as part of a smoking-cessation study to explore the relationship between smoking urges and self-efficacy during the course of the pre- and postcessation period. (PsycINFO Database Record (c) 2011 APA, all rights reserved).

Semiparametric quantile regression with high dimensional covariates

Zhu,L.

How to cluster gene expression dynamics in response to environmental signals

Wang,Y.

Organisms usually cope with change in the environment by altering the dynamic trajectory of gene expression to adjust the complement of active proteins. The identification of particular sets of genes whose expression is adaptive in response to environmental changes helps to understand the mechanistic base of gene-environment interactions essential for organismic development. We describe a computational framework for clustering the dynamics of gene expression in distinct environments through Gaussian mixture fitting to the expression data measured at a set of discrete time points. We outline a number of quantitative testable hypotheses about the patterns of dynamic gene expression in changing environments and gene-environment interactions causing developmental differentiation. The future directions of gene clustering in terms of incorporations of the latest biological discoveries and statistical innovations are discussed. We provide a set of computational tools that are applicable to modeling and analysis of dynamic gene expression data measured in multiple environments.

Investigating preferences for mosquito-control technologies in Mozambique with latent class analysis

Smith,R. A.

BACKGROUND: It is common practice to seek the opinions of future end-users during the development of innovations. Thus, the aim of this study is to investigate latent classes of users in Mozambique based on their preferences for mosquito-control technology attributes and covariates of these classes, as well as to explore which current technologies meet these preferences. METHODS: Surveys were administered in five rural villages in Mozambique. The data were analysed with latent class analysis. RESULTS: This study showed that users' preferences for malaria technologies varied, and people could be categorized into four latent classes based on shared preferences. The largest class, constituting almost half of the respondents, would not avoid a mosquito-control technology because of its cost, heat, odour, potential to make other health issues worse, ease of keeping clean, or inadequate mosquito control. The other three groups are characterized by the attributes which would make them avoid a technology; these groups are labelled as the bites class, by-products class, and multiple-concerns class. Statistically significant covariates included literacy, self-efficacy, willingness to try new technologies, and perceived seriousness of malaria for the household. CONCLUSIONS: To become widely diffused, best practices suggest that end-users should be included in product development to ensure that preferred attributes or traits are considered. This study demonstrates that end-user preferences can be very different and that one malaria control technology will not satisfy everyone.

Differential effects for sexual risk behavior: An application of finite mixture regression

Lanza,S. T.

New variable selection methods for zero-inflated count data with applications to the substance abuse field

Buu,A.

Zero-inflated count data are very common in health surveys. This study develops new variable selection methods for the zero-inflated Poisson regression model. Our simulations demonstrate the negative consequences which arise from the ignorance of zero-inflation. Among the competing methods, the one-step SCAD method is recommended because it has the highest specificity, sensitivity, exact fit, and lowest estimation error. The design of the simulations is based on the special features of two large national databases commonly used in the alcoholism and substance abuse field so that our findings can be easily generalized to the real settings. Applications of the methodology are demonstrated by empirical analyses on the data from a well-known alcohol study.

Transitions in first-year college student drinking behaviors: Does pre-college drinking moderate the effects of parent- and peer-based intervention components?

Cleveland,M. J.

This study used Latent Transition Analysis (LTA) to examine a stage-sequential model of alcohol use among a sample of high-risk matriculating college students (N = 1,275). Measures of alcohol use were collected via web-administered surveys during the summer before entering college and followed-up during the fall semester of college. Seven indicators of alcohol use were used in the LTA models, including temporal measures of typical drinking throughout the week. The results indicated that four latent statuses characterized patterns of drinking at both times, though the proportion of students in each status changed during the transition to college: (a) nondrinkers; (b) weekend nonbingers; (c) weekend bingers; and (d) heavy drinkers. Heavy drinkers were distinguished by heavy episodic drinking (HED), and increased likelihood of drinking throughout the week, especially on Thursdays. Covariates were added to the LTA model to examine the main and interaction effects of parent- and peer-based intervention components. Results indicated that participants in the parent and peer conditions were least likely to transition to the heavy drinkers status. Results also indicated that the parent condition was most effective at preventing baseline nondrinkers from transitioning to heavy drinkers whereas the peer condition was most effective at preventing escalation of use among weekend nonbingers. The results underscore the value of considering multiple dimensions of alcohol use within a person-centered approach. Differential treatment effects were found across baseline drinking class, suggesting the benefit for tailored intervention programs to reduce high-risk drinking among college students. (PsycINFO Database Record (c) 2011 APA, all rights reserved).

Parental work stress and latent profiles of father-infant parenting quality

Goodman,W. B.

Latent class analysis: An alternative perspective on subgroup analysis in prevention and treatment

Lanza,S. T.

The overall goal of this study is to introduce latent class analysis (LCA) as an alternative approach to latent subgroup analysis. Traditionally, subgroup analysis aims to determine whether individuals respond differently to a treatment based on one or more measured characteristics. LCA provides a way to identify a small set of underlying subgroups characterized by multiple dimensions which could, in turn, be used to examine differential treatment effects. This approach can help to address methodological challenges that arise in subgroup analysis, including a high Type I error rate, low statistical power, and limitations in examining higher-order interactions. An empirical example draws on N = 1,900 adolescents from the National Longitudinal Survey of Adolescent Health. Six characteristics (household poverty, single-parent status, peer cigarette use, peer alcohol use, neighborhood unemployment, and neighborhood poverty) are used to identify five latent subgroups: Low Risk, Peer Risk, Economic Risk, Household & Peer Risk, and Multi-Contextual Risk. Two approaches for examining differential treatment effects are demonstrated using a simulated outcome: 1) a classify-analyze approach and, 2) a model-based approach based on a reparameterization of the LCA with covariates model. Such approaches can facilitate targeting future intervention resources to subgroups that promise to show the maximum treatment response.

Modeling multiple risks during infancy to predict quality of the caregiving environment: Contributions of a person-centered approach

Lanza,S. T.

The primary goal of this study was to compare several variable-centered and person-centered methods for modeling multiple risk factors during infancy to predict the quality of caregiving environments at six months of age. Nine risk factors related to family demographics and maternal psychosocial risk, assessed when children were two months old, were explored in the understudied population of children born in low-income, non-urban communities in Pennsylvania and North Carolina (N=1047). These risk factors were (1) single (unpartnered) parent status, (2) marital status, (3) mother's age at first child birth, (4) maternal education, (5) maternal reading ability, (6) poverty status, (7) residential crowding, (8) prenatal smoking exposure, and (9) maternal depression. We compared conclusions drawn using a bivariate approach, multiple regression analysis, the cumulative risk index, and latent class analysis (LCA). The risk classes derived using LCA provided a more intuitive summary of how multiple risks were organized within individuals as compared to the other methods. The five risk classes were: married low-risk; married low-income; cohabiting multiproblem; single low-income; and single low-income/education. The LCA findings illustrated how the association between particular family configurations and the infants' caregiving environment quality varied across race and site. Discussion focuses on the value of person-centered models of analysis to understand complexities of prediction of multiple risk factors.

Recent history functional linear models for sparse longitudinal data

Kim,K.

We consider the recent history functional linear models, relating a longitudinal response to a longitudinal predictor where the predictor process only in a sliding window into the recent past has an effect on the response value at the current time. We propose an estimation procedure for recent history functional linear models that is geared towards sparse longitudinal data, where the observation times across subjects are irregular and total number of measurements per subject is small. The proposed estimation procedure builds upon recent developments in literature for estimation of functional linear models with sparse data and utilizes connections between the recent history functional linear models and varying coefficient models. We establish uniform consistency of the proposed estimators, propose prediction of the response trajectories and derive their asymptotic distribution leading to asymptotic point-wise confidence bands. We include a real data application and simulation studies to demonstrate the efficacy of the proposed methodology.

Active learning for personalizing treatment

Deng,K.

Active learning for developing personalized treatment

Deng,K.

A risk-based model predictive control approach to adaptive interventions in behavioral health

Zafra-Cabeza,A.

This paper examines how control engineering and risk management techniques can be applied in the field of behavioral health through their use in the design and implementation of adaptive behavioral interventions. Adaptive interventions are gaining increasing acceptance as a means to improve prevention and treatment of chronic, relapsing disorders, such as abuse of alcohol, tobacco, and other drugs, mental illness, and obesity. A risk-based Model Predictive Control (MPC) algorithm is developed for a hypothetical intervention inspired by Fast Track, a real-life program whose long-term goal is the prevention of conduct disorders in at-risk children. The MPC-based algorithm decides on the appropriate frequency of counselor home visits, mentoring sessions, and the availability of after-school recreation activities by relying on a model that includes identifiable risks, their costs, and the cost/benefit assessment of mitigating actions. MPC is particularly suited for the problem because of its constraint-handling capabilities, and its ability to scale to interventions involving multiple tailoring variables. By systematically accounting for risks and adapting treatment components over time, an MPC approach as described in this paper can increase intervention effectiveness and adherence while reducing waste, resulting in advantages over conventional fixed treatment. A series of simulations are conducted under varying conditions to demonstrate the effectiveness of the algorithm.

Social support and employee well-being: The conditioning effect of perceived patterns of supportive exchange

Nahum-Shani,I.

Seeking to explain divergent empirical findings regarding the direct effect of social support on well-being, the authors posit that the pattern of supportive exchange (i.e., reciprocal, under-, or over-reciprocating) determines the impact of receiving support on well-being. Findings generated on the basis of longitudinal data collected from a sample of older blue-collar workers support the authors' predictions, indicating that receiving emotional support is associated with enhanced well-being when the pattern of supportive exchange is perceived by an individual as being reciprocal (support received equals support given), with this association being weaker when the exchange of support is perceived as being under-reciprocating (support given exceeds support received). Moreover, receiving support was found to adversely affect well-being when the pattern of exchange was perceived as being over-reciprocating (support received exceeds support given). Theoretical and practical implications of these findings are discussed.

Replication in Prevention Science

Valentine,J. C.

Replication research is essential for the advancement of any scientific field. In this paper, we argue that prevention science will be better positioned to help improve public health if (a) more replications are conducted; (b) those replications are systematic, thoughtful, and conducted with full knowledge of the trials that have preceded them; and (c) state-of-the art techniques are used to summarize the body of evidence on the effects of the interventions. Under real-world demands it is often not feasible to wait for multiple replications to accumulate before making decisions about intervention adoption. To help individuals and agencies make better decisions about intervention utility, we outline strategies that can be used to help understand the likely direction, size, and range of intervention effects as suggested by the current knowledge base. We also suggest structural changes that could increase the amount and quality of replication research, such as the provision of incentives and a more vigorous pursuit of prospective research registers. Finally, we discuss methods for integrating replications into the roll-out of a program and suggest that strong partnerships with local decision makers are a key component of success in replication research. Our hope is that this paper can highlight the importance of replication and stimulate more discussion of the important elements of the replication process. We are confident that, armed with more and better replications and state-of-the-art review methods, prevention science will be in a better position to positively impact public health.

A dynamical model for describing behavioural interventions for weight loss and body composition change

Navarro-Barrientos,J. E.

We present a dynamical model incorporating both physiological and psychological factors that predicts changes in body mass and composition during the course of a behavioral intervention for weight loss. The model consists of a three-compartment energy balance integrated with a mechanistic psychological model inspired by the Theory of Planned Behavior (TPB). The latter describes how important variables in a behavioural intervention can influence healthy eating habits and increased physical activity over time. The novelty of the approach lies in representing the behavioural intervention as a dynamical system, and the integration of the psychological and energy balance models. Two simulation scenarios are presented that illustrate how the model can improve the understanding of how changes in intervention components and participant differences affect outcomes. Consequently, the model can be used to inform behavioural scientists in the design of optimised interventions for weight loss and body composition change.

Performance guarantees for individualized treatment rules

Qian,M.

Because many illnesses show heterogeneous response to treatment, there is increasing interest in individualizing treatment to patients [11]. An individualized treatment rule is a decision rule that recommends treatment according to patient characteristics. We consider the use of clinical trial data in the construction of an individualized treatment rule leading to highest mean response. This is a difficult computational problem because the objective function is the expectation of a weighted indicator function that is non-concave in the parameters. Furthermore there are frequently many pretreatment variables that may or may not be useful in constructing an optimal individualized treatment rule yet cost and interpretability considerations imply that only a few variables should be used by the individualized treatment rule. To address these challenges we consider estimation based on l(1) penalized least squares. This approach is justified via a finite sample upper bound on the difference between the mean response due to the estimated individualized treatment rule and the mean response due to the optimal individualized treatment rule.

FADTTS: Functional analysis of diffusion tensor tract statistics

Zhu,H.

The aim of this paper is to present a functional analysis of a diffusion tensor tract statistics (FADTTS) pipeline for delineating the association between multiple diffusion properties along major white matter fiber bundles with a set of covariates of interest, such as age, diagnostic status and gender, and the structure of the variability of these white matter tract properties in various diffusion tensor imaging studies. The FADTTS integrates five statistical tools: (i) a multivariate varying coefficient model for allowing the varying coefficient functions in terms of arc length to characterize the varying associations between fiber bundle diffusion properties and a set of covariates, (ii) a weighted least squares estimation of the varying coefficient functions, (iii) a functional principal component analysis to delineate the structure of the variability in fiber bundle diffusion properties, (iv) a global test statistic to test hypotheses of interest, and (v) a simultaneous confidence band to quantify the uncertainty in the estimated coefficient functions. Simulated data are used to evaluate the finite sample performance of FADTTS. We apply FADTTS to investigate the development of white matter diffusivities along the splenium of the corpus callosum tract and the right internal capsule tract in a clinical study of neurodevelopment. FADTTS can be used to facilitate the understanding of normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles. The advantages of FADTTS compared with the other existing approaches are that they are capable of modeling the structured inter-subject variability, testing the joint effects, and constructing their simultaneous confidence bands. However, FADTTS is not crucial for estimation and reduces to the functional analysis method for the single measure.

The Bayesian lasso for genome-wide association studies

Li,J.

MOTIVATION: Despite their success in identifying genes that affect complex disease or traits, current genome-wide association studies (GWASs) based on a single SNP analysis are too simple to elucidate a comprehensive picture of the genetic architecture of phenotypes. A simultaneous analysis of a large number of SNPs, although statistically challenging, especially with a small number of samples, is crucial for genetic modeling. METHOD: We propose a two-stage procedure for multi-SNP modeling and analysis in GWASs, by first producing a 'preconditioned' response variable using a supervised principle component analysis and then formulating Bayesian lasso to select a subset of significant SNPs. The Bayesian lasso is implemented with a hierarchical model, in which scale mixtures of normal are used as prior distributions for the genetic effects and exponential priors are considered for their variances, and then solved by using the Markov chain Monte Carlo (MCMC) algorithm. Our approach obviates the choice of the lasso parameter by imposing a diffuse hyperprior on it and estimating it along with other parameters and is particularly powerful for selecting the most relevant SNPs for GWASs, where the number of predictors exceeds the number of observations. RESULTS: The new approach was examined through a simulation study. By using the approach to analyze a real dataset from the Framingham Heart Study, we detected several significant genes that are associated with body mass index (BMI). Our findings support the previous results about BMI-related SNPs and, meanwhile, gain new insights into the genetic control of this trait. AVAILABILITY: The computer code for the approach developed is available at Penn State Center for Statistical Genetics web site, http://statgen.psu.edu.

Propensity score modelling in observational studies using dimension reduction methods

Ghosh,D.

Conditional independence assumptions are very important in causal inference modelling as well as in dimension reduction methodologies. These are two very strikingly different statistical literatures, and we study links between the two in this article. The concept of covariate sufficiency plays an important role, and we provide theoretical justification when dimension reduction and partial least squares methods will allow for valid causal inference to be performed. The methods are illustrated with application to a medical study and to simulated data.