Causal Structural Learning on MPHIA Individual Dataset.

The Population-based HIV Impact Assessment (PHIA) is an ongoing project that conducts nationally representative HIV-focused surveys for measuring national and regional progress toward UNAIDS' 90-90-90 targets, the primary strategy to end the HIV epidemic. We believe the PHIA survey offers a unique opportunity to better understand the key factors that drive the HIV epidemics in the most affected countries in sub-Saharan Africa. In this article, we propose a novel causal structural learning algorithm to discover important covariates and potential causal pathways for 90-90-90 targets. Existing constrained-based causal structural learning algorithms are quite aggressive in edge removal. The proposed algorithm preserves more information about important features and potential causal pathways. It is applied to the Malawi PHIA (MPHIA) data set and leads to interesting results. For example, it discovers age and condom usage to be important for female HIV awareness; the number of sexual partners to be important for male HIV awareness; and knowing the travel time to HIV care facilities leads to a higher chance of being treated for both females and males. We further compare and validate the proposed algorithm using BIC and using Monte Carlo simulations, and show that the proposed algorithm achieves improvement in true positive rates in important feature discovery over existing algorithms.

Feature Screening in Ultrahigh Dimensional Generalized Varying-coefficient Models.

Generalized varying coefficient models are particularly useful for examining dynamic effects of covariates on a continuous, binary or count response. This paper is concerned with feature screening for generalized varying coefficient models with ultrahigh dimensional covariates. The proposed screening procedure is based on joint quasi-likelihood of all predictors, and therefore is distinguished from marginal screening procedures proposed in the literature. In particular, the proposed procedure can effectively identify active predictors that are jointly dependent but marginally independent of the response. In order to carry out the proposed procedure, we propose an effective algorithm and establish the ascent property of the proposed algorithm. We further prove that the proposed procedure possesses the sure screening property. That is, with probability tending to one, the selected variable set includes the actual active predictors. We examine the finite sample performance of the proposed procedure and compare it with existing ones via Monte Carlo simulations, and illustrate the proposed procedure by a real data example.

Prioritizing genetic variants in GWAS with lasso using permutation-assisted tuning.

MOTIVATION: Large scale genome-wide association studies (GWAS) have resulted in the identification of a wide range of genetic variants related to a host of complex traits and disorders. Despite their success, the individual single-nucleotide polymorphism (SNP) analysis approach adopted in most current GWAS can be limited in that it is usually biologically simple to elucidate a comprehensive genetic architecture of phenotypes and statistically underpowered due to heavy multiple-testing correction burden. On the other hand, multiple-SNP analyses (e.g. gene-based or region-based SNP-set analysis) are usually more powerful to examine the joint effects of a set of SNPs on the phenotype of interest. However, current multiple-SNP approaches can only draw an overall conclusion at the SNP-set level and does not directly inform which SNPs in the SNP-set are driving the overall genotype-phenotype association. RESULTS: In this article, we propose a new permutation-assisted tuning procedure in lasso (plasso) to identify phenotype-associated SNPs in a joint multiple-SNP regression model in GWAS. The tuning parameter of lasso determines the amount of shrinkage and is essential to the performance of variable selection. In the proposed plasso procedure, we first generate permutations as pseudo-SNPs that are not associated with the phenotype. Then, the lasso tuning parameter is delicately chosen to separate true signal SNPs and non-informative pseudo-SNPs. We illustrate plasso using simulations to demonstrate its superior performance over existing methods, and application of plasso to a real GWAS dataset gains new additional insights into the genetic control of complex traits. AVAILABILITY AND IMPLEMENTATION: R codes to implement the proposed methodology is available at https://github.com/xyz5074/plasso. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Is monetary reward processing altered in drug-naïve youth with a behavioral addiction? Findings from internet gaming disorder.

Current models of addiction biology highlight altered neural responses to non-drug rewards as a central feature of addiction. However, given that drugs of abuse can directly impact reward-related dopamine circuitry, it is difficult to determine the extent to which reward processing alterations are a trait feature of individuals with addictions, or primarily a consequence of exogenous drug exposure. Examining individuals with behavioral addictions is one promising approach for disentangling neural features of addiction from the direct effects of substance exposure. The current fMRI study compared neural responses during monetary reward processing between drug naïve young adults with a behavioral addiction, internet gaming disorder (IGD; n = 22), and healthy controls (n = 27) using a monetary incentive delay task. Relative to controls, individuals with IGD exhibited blunted caudate activity associated with loss magnitude at the outcome stage, but did not differ from controls in neural activity at other stages. These findings suggest that decreased loss sensitivity might be a critical feature of IGD, whereas alterations in gain processing may be less characteristic of individuals with IGD, relative to those with substance use disorders. Therefore, classic theories of altered reward processing in substance use disorders should be translated to behavioral addictions with caution.

Examining measurement reactivity in daily diary data on substance use: Results from a randomized experiment.

The debate about whether measurement reactivity exists in daily diary research on substance use is still unsettled due to the issues of study design and statistical methodology. This study proposes a time-varying effect model (TVEM) that characterizes the trajectory of substance use behaviors with nonparametric functions determined by the data rather than imposes presumed parametric functions. It also allows researchers to investigate the effect of measurement reactivity on not only the likelihood of using substances but also the amount of substance use. The TVEM was applied to analyze diary data on alcohol and marijuana use collected from an experiment, which randomized 307 participants in Michigan into daily and weekly assessment schedules during 2014-2016. This study found short-term measurement reactivity on alcohol use, but did not find a significant reactivity effect on marijuana use. The daily group had smaller odds of abstinence from drinking but lower expected drinking quantity in the first week of assessment, which dissipated by the second week. The results indicate that although daily self-monitoring could have short-term reactivity on substance use behaviors that tend to fluctuate across days, such as alcohol use, it does not affect substance use behaviors that are quite consistent, such as marijuana use. Our findings imply that although daily monitoring of drinking may motivate people to reduce the quantity consumed once they start to drink, it may also arouse their desire to start drinking. Yet, both effects tend to last only one week, as participants accommodate to the monitoring by the second week.

Time-varying Effects of GABRG1 and Maladaptive Peer Behavior on Externalizing Behavior from Childhood to Adulthood: Testing Gene × Environment × Development Effects.

Engagement in externalizing behavior is problematic. Deviant peer affiliation increases risk for externalizing behavior. Yet, peer effects vary across individuals and may differ across genes. This study determines gene × environment × development interactions as they apply to externalizing behavior from childhood to adulthood. A sample (n = 687; 68% male, 90% White) of youth from the Michigan Longitudinal Study was assessed from ages 10 to 25. Interactions between γ-amino butyric acid type A receptor γ1 subunit (GABRG1; rs7683876, rs13120165) and maladaptive peer behavior on externalizing behavior were examined using time-varying effect modeling. The findings indicate a sequential risk gradient in the influence of maladaptive peer behavior on externalizing behavior depending on the number of G alleles during childhood through adulthood. Individuals with the GG genotype are most vulnerable to maladaptive peer influences, which results in greater externalizing behavior during late childhood through early adulthood.

A time-varying effect model for studying gender differences in health behavior.

This study proposes a time-varying effect model that can be used to characterize gender-specific trajectories of health behaviors and conduct hypothesis testing for gender differences. The motivating examples demonstrate that the proposed model is applicable to not only multi-wave longitudinal studies but also short-term studies that involve intensive data collection. The simulation study shows that the accuracy of estimation of trajectory functions improves as the sample size and the number of time points increase. In terms of the performance of the hypothesis testing, the type I error rates are close to their corresponding significance levels under all combinations of sample size and number of time points. Furthermore, the power increases as the alternative hypothesis deviates more from the null hypothesis, and the rate of this increasing trend is higher when the sample size and the number of time points are larger.

A time-varying effect model for examining group differences in trajectories of zero-inflated count outcomes with applications in substance abuse research.

This study proposes a time-varying effect model for examining group differences in trajectories of zero-inflated count outcomes. The motivating example demonstrates that this zero-inflated Poisson model allows investigators to study group differences in different aspects of substance use (e.g., the probability of abstinence and the quantity of alcohol use) simultaneously. The simulation study shows that the accuracy of estimation of trajectory functions improves as the sample size increases; the accuracy under equal group sizes is only higher when the sample size is small (100). In terms of the performance of the hypothesis testing, the type I error rates are close to their corresponding significance levels under all settings. Furthermore, the power increases as the alternative hypothesis deviates more from the null hypothesis, and the rate of this increasing trend is higher when the sample size is larger. Moreover, the hypothesis test for the group difference in the zero component tends to be less powerful than the test for the group difference in the Poisson component. Copyright © 2016 John Wiley & Sons, Ltd.