Making Decisions with Trees: Examining Marijuana Outcomes among College Students using Recursive Partitioning.
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ABSTRACT:
SUBMITTER: Ad W
PROVIDER: S-EPMC6516071 | biostudies-literature | 2018 Sep
REPOSITORIES: biostudies-literature
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Project description:Marijuana (MJ) is the most widely used illicit substance among adolescents and young adults. Frequent MJ use has been associated with impairments in cognitive flexibility and inhibition, both of which play important roles in decision-making. However, the impact of frequent MJ use on decision-making performance is mixed and not well understood. The current study examined the influence of frequent MJ use on risky decision-making in college students, 18-22 years old. Methods: From 2017 to 2019, data was collected from young adult college students (n = 65) consisting of 32 healthy controls (HC) and 33 frequent marijuana users (MJ+). Participants completed the Iowa Gambling Task (IGT), a measure of risky decision-making, and net IGT scores (advantageous-disadvantageous decisions) were used as a measure of optimal decision-making. Results: The main finding indicated there was a significant effect of group on net IGT scores (p = 0.018), which remained significant when sex was included in the model (p = 0.006), such that MJ+ had lower net IGT scores than HC. Conclusions: These findings highlight potential differences in risky decision-making between MJ+ and HC, but it is uncertain whether these differences are pre-existing and increase vulnerability for frequent MJ use or if they are related to the effects of frequent MJ use on decision-making.
| S-EPMC7244905 | biostudies-literature
Project description:The HapMap (haplotype map) projects have produced valuable genetic resources in life science research communities, allowing researchers to investigate sequence variations and conduct genome-wide association study (GWAS) analyses. A typical HapMap project may require sequencing hundreds, even thousands, of individual lines or accessions within a species. Due to limitations in current sequencing technology, the genotype values for some accessions cannot be clearly called. Additionally, allelic heterozygosity can be very high in some lines, causing genetic and sometimes phenotypic segregation in their descendants. Genetic and phenotypic segregation degrades the original accession's specificity and makes it difficult to distinguish one accession from another. Therefore, it is vitally important to determine and validate HapMap accessions before one conducts a GWAS analysis. However, to the best of our knowledge, there are no prior methodologies or tools that can readily distinguish or validate multiple accessions in a HapMap population. We devised a bioinformatics approach to distinguish multiple HapMap accessions using only a minimum number of genetic markers. First, we assign each candidate marker with a distinguishing score (DS), which measures its capability in distinguishing accessions. The DS score prioritizes those markers with higher percentages of homozygous genotypes (allele combinations), as they can be stably passed on to offspring. Next, we apply the "set-partitioning" concept to select optimal markers by recursively partitioning accession sets. Subsequently, we build a hierarchical decision tree in which a specific path represents the selected markers and the homogenous genotypes that can be used to distinguish one accession from others in the HapMap population. Based on these algorithms, we developed a web tool named MAD-HiDTree (Multiple Accession Distinguishment-Hierarchical Decision Tree), designed to analyze a user-input genotype matrix and construct a hierarchical decision tree. Using genetic marker data extracted from the Medicago truncatula HapMap population, we successfully constructed hierarchical decision trees by which the original 262 M. truncatula accessions could be efficiently distinguished. PCR experiments verified our proposed method, confirming that MAD-HiDTree can be used for the identification of a specific accession. MAD-HiDTree was developed in C/C++ in Linux. Both the source code and test data are publicly available at https://bioinfo.noble.org/MAD-HiDTree/.
| S-EPMC7886675 | biostudies-literature
Project description:BACKGROUND AND AIMS:The 21-item Brief Marijuana Consequences Questionnaire (B-MACQ) has been shown to be a valid measure to assess negative marijuana-related consequences among US college students. The present study aimed to: (a) examine measurement invariance of the B-MACQ among college student marijuana users in five countries, (b) evaluate latent mean differences on the B-MACQ as a function of sex and country if invariance is met and (c) compare criterion-related validity across different countries and sex. DESIGN:Instrumental study. SETTING:Argentina, the Netherlands, Spain, Uruguay and United States. PARTICIPANTS:A subsample of last-month marijuana users who completed the B-MACQ (n = 1145; 62.9% female). MEASUREMENTS:The B-MACQ, several dimensions of marijuana use and perceptions of marijuana use. FINDINGS:Results supported configural and scalar invariance (all ?CFI/TLI ? 0.01; ?RMSEA ? 0.015) of a 20-item B-MACQ across sex and four countries (the Netherlands being the exception). In examining latent mean differences, Spanish students reported a higher number of consequences than US (P < 0.001) and Argentinian students (P = 0.003). In examining criterion-related validity, marijuana use indicators (0.01 < rs < 0.64), descriptive norms (0.04 < rs < 0.49) and injunctive norms for best friend (0.06 < rs < 0.28) largely had small-to-moderate positive correlations with negative marijuana-related consequences. CONCLUSIONS:The 20-item B-MACQ accurately assesses marijuana-related negative consequences among male and female college student marijuana users across the United States, Argentina, Spain and Uruguay. The B-MACQ could be used effectively to identify marijuana-related consequences in college students from different countries or cultures.
| S-EPMC6732001 | biostudies-literature
Project description:First-generation (FG) college students (students for whom neither parent earned a bachelor’s degree) are typically less likely to interact with their instructors and communicate with them by email or in person, compared to continuing-generation (CG) students. Qualitative research suggests FG students are less likely to seek help when they need it, and when they do seek help they are more likely to engage in passive help-seeking (e.g., waiting quietly for assistance) as opposed to active help-seeking (e.g., promptly requesting assistance through multiple methods), compared to CG students. The current laboratory study provided students with an opportunity to seek academic and non-academic help and measured whether students engaged in active help-seeking behavior. We also tested whether having a shared identity with a help-provider could increase active help-seeking behavior among FG students. Results showed that FG students were less likely to seek academic help. Among FG and CG students who sought academic help, the intervention had no significant impact on active help-seeking. However, among students seeking non-academic help, active help-seeking behaviors were significantly higher for FG college students assigned a help-provider who signaled a FG identity. In other words, having a shared identity with a help-provider led to more active help-seeking among FG college students seeking non-academic assistance. FG faculty, staff, and student workers who provide non-academic assistance may want to consider self-identifying as FG to increase help-seeking behaviors among FG students struggling to navigate the college environment. Supplementary Information The online version contains supplementary material available at 10.1007/s11218-023-09794-y.
| S-EPMC10218772 | biostudies-literature
Project description:Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and bioinformatics within the past few years. High-dimensional problems are common not only in genetics, but also in some areas of psychological research, where only a few subjects can be measured because of time or cost constraints, yet a large amount of data is generated for each subject. Random forests have been shown to achieve a high prediction accuracy in such applications and to provide descriptive variable importance measures reflecting the impact of each variable in both main effects and interactions. The aim of this work is to introduce the principles of the standard recursive partitioning methods as well as recent methodological improvements, to illustrate their usage for low and high-dimensional data exploration, but also to point out limitations of the methods and potential pitfalls in their practical application. Application of the methods is illustrated with freely available implementations in the R system for statistical computing.
| S-EPMC2927982 | biostudies-literature
Project description:BackgroundAlcohol and marijuana are the two most abused substances in US colleges. However, research on the combined influence (cross sectional or longitudinal) of these substances on academic performance is currently scant.MethodsData were derived from the longitudinal 2-year Brain and Alcohol Research in College Students (BARCS) study including 1142 freshman students who completed monthly marijuana use and alcohol consumption surveys. Subjects were classified into data-driven groups based on their alcohol and marijuana consumption. A linear mixed-model (LMM) was employed using this grouping factor to predict grade point average (GPA), adjusted for a variety of socio-demographic and clinical factors.ResultsThree data-driven clusters emerged: 1) No/low users of both, 2) medium-high alcohol/no-low marijuana, and 3) medium-high users of both substances. Individual cluster derivations between consecutive semesters remained stable. No significant interaction between clusters and semester (time) was noted. Post-hoc analysis suggest that at the outset, compared to sober peers, students using moderate to high levels of alcohol and low marijuana demonstrate lower GPAs, but this difference becomes non-significant over time. In contrast, students consuming both substances at moderate-to-high levels score significantly lower at both the outset and across the 2-year investigation period. Our follow-up analysis also indicate that when students curtailed their substance use over time they had significantly higher academic GPA compared to those who remained stable in their substance use patterns over the two year period.ConclusionsOverall, our study validates and extends the current literature by providing important implications of concurrent alcohol and marijuana use on academic achievement in college.
| S-EPMC5342177 | biostudies-literature
Project description:In this paper we propose methods for estimating heterogeneity in causal effects in experimental and observational studies and for conducting hypothesis tests about the magnitude of differences in treatment effects across subsets of the population. We provide a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects. The approach enables the construction of valid confidence intervals for treatment effects, even with many covariates relative to the sample size, and without "sparsity" assumptions. We propose an "honest" approach to estimation, whereby one sample is used to construct the partition and another to estimate treatment effects for each subpopulation. Our approach builds on regression tree methods, modified to optimize for goodness of fit in treatment effects and to account for honest estimation. Our model selection criterion anticipates that bias will be eliminated by honest estimation and also accounts for the effect of making additional splits on the variance of treatment effect estimates within each subpopulation. We address the challenge that the "ground truth" for a causal effect is not observed for any individual unit, so that standard approaches to cross-validation must be modified. Through a simulation study, we show that for our preferred method honest estimation results in nominal coverage for 90% confidence intervals, whereas coverage ranges between 74% and 84% for nonhonest approaches. Honest estimation requires estimating the model with a smaller sample size; the cost in terms of mean squared error of treatment effects for our preferred method ranges between 7-22%.
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Project description:ObjectivePrevious research has suggested a link between vegetarianism, broadly defined, and symptoms of eating disorders (ED). However, the literature supporting this link is mixed and limited by possible measurement artifacts. Using data from a national sample of college students, the present study examines ED symptomatology among three groups: (a) vegetarians whose meat avoidance is motivated by weight concerns; (b) non-weight motivated vegetarians; and (c) nonvegetarians.MethodParticipants include 9,910 students from 12 colleges and universities across the United States who participated in the web-based Healthy Bodies Study. ED symptomatology was measured using the Short-Eating Disorder Examination-Questionnaire (S-EDE-Q). First, multi-group confirmatory factor analysis was conducted to test measurement invariance (MI) of the S-EDE-Q across weight-motivated vegetarians, non-weight-motivated vegetarians, and nonvegetarians. Gender- and BMI-adjusted ANCOVA was used to compare S-EDE-Q scores across groups.Results9.3% of participants were vegetarian. Cis-women and gender minority students were more likely to be vegetarian; those who became vegetarians after entering college were more likely to report weight-related motivations. Strict MI was supported for the S-EDE-Q global and subscale scores. Weight-motivated vegetarians reported higher levels of restraint, shape/weight overvaluation, body dissatisfaction, and global ED psychopathology relative to other participants.DiscussionTo our knowledge, this is the first to explicitly link weight motivations for vegetarianism to ED psychopathology in a large, representative sample of young adults. Results suggest that students presenting with ED symptoms should be assessed for their motivations for adopting a vegetarian diet, and this information should be considered in treatment decisions.
| S-EPMC7745711 | biostudies-literature
Project description:Cannabis exerts an indirect effect on dopamine (DA) output in the mesolimbic projection, a circuit implicated in reward processing and effort expenditure, and thus may be associated with aberrant effort-based decision making. The "amotivation syndrome" hypothesis suggests that regular cannabis use results in impaired capacity for goal-directed behavior. However, investigations of this hypothesis have used divergent methodology and have not controlled for key confounding variables. The present study extends these findings by examining the relation between cannabis use and effort-related decision making in a sample of college students. Cannabis using (n = 25; 68% meeting criteria for Cannabis Use Disorder) and noncannabis using (n = 22) students completed the Effort Expenditure for Rewards Task (EEfRT). In generalized estimating equation models, reward magnitude, reward probability, and expected value predicted greater likelihood of selecting a high-effort trial. Furthermore, past-month cannabis days and cannabis use disorder symptoms predicted the likelihood of selecting a high-effort trial, such that greater levels of both cannabis use days and symptoms were associated with an increased likelihood after controlling for Attention Deficit/Hyperactivity Disorder (ADHD) symptoms, distress tolerance, income, and delay discounting. The results provide preliminary evidence suggesting that college students who use cannabis are more likely to expend effort to obtain reward, even after controlling for the magnitude of the reward and the probability of reward receipt. Thus, these results do not support the amotivational syndrome hypothesis. Future research with a larger sample is required to evaluate possible associations between cannabis use and patterns of real-world effortful behavior over time. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
| S-EPMC10071406 | biostudies-literature
Project description:Approximately 4 μg of DNA was isolated from blood of each of 16 subjects using the Qiagen DNEasy Blood and Tissue Kit. Of the 16 samples, 8 were from group 1 (CESD-H/TST-H; 5 female, 3 male) and 8 were from group 2 (CESD-H/TST-L; 5 female, 3 male). All DNA samples were confirmed to be of high purity (A260/280: 1.7-2.0). Pooled samples were prepared for each group by combining approximately 75 ng of DNA from each of the eight subjects. These two samples were submitted to the Yale Genomics Core for epigenome-wide methylation profiling using the Illumina Infinium HumanMethylation450 BeadChip, which measures the level of methylation β (a value ranging from 0 to 1, where 0 represents a completely unmethylated site and 1 a completely methylated site) at each of the 485,577 CpG sites on the array. Illumina’s GenomeStudio software was used to calculate the degree of differential methylation by group for each CpG site on the array, and FDR-adjusted p-values (Q-values) were calculated for each site in order to adjust for multiple comparisons. CESD=depression score; TST=sleep score
2016-08-01 | GSE80559 | GEO