Dataset Information

Age of onset of cannabis use and decision making under uncertainty.

ABSTRACT:

Objective

Cannabis, like other substances, negatively affects health, inducing respiratory problems and mental and cognitive alterations. Memory and learning disorders, as well as executive dysfunctions, are also neuropsychological disorders associated to cannabis use. Recent evidence reveals that cannabis use during adolescence may disrupt the normal development of the brain. This study is aimed to analyze possible differences between early-onset and late-onset cannabis consumers.

Method

We used a task based on a card game with four decks and different programs of gains/losses. A total of 72 subjects (19 women; 53 men) participated in the study; they were selected through a purposive sampling and divided into three groups: early-onset consumers, late-onset consumers, and control (non-consumers). The task used was the "Cartas" program (computerized version based on the Iowa Gambling Task (IGT)), with two versions: direct and inverse. The computational model "Prospect Valence Learning" (PVL) was applied in order to describe the decision according to four characteristics: utility, loss aversion, recency, and consistency.

Results

The results evidence worst performance in the IGT in the early-onset consumers as compared to late-onset consumers and control. Differences between groups were also found in the PVL computational model parameters, since the process of decision making of the early-onset consumers was more influenced by the magnitude of the gains-losses, and more determined by short-term results without loss aversion.

Conclusions

Early onset cannabis use may involve decision-making problems, and therefore intervention programs are necessary in order to reduce the prevalence and delay the onset of cannabis use among teenagers.

SUBMITTER: Alameda-Bailen JR

PROVIDER: S-EPMC6034599 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Age of onset of cannabis use and decision making under uncertainty.

Alameda-Bailén Jose Ramón JR Salguero-Alcañiz Pilar P Merchán-Clavellino Ana A Paíno-Quesada Susana S

PeerJ 20180703

<h4>Objective</h4>Cannabis, like other substances, negatively affects health, inducing respiratory problems and mental and cognitive alterations. Memory and learning disorders, as well as executive dysfunctions, are also neuropsychological disorders associated to cannabis use. Recent evidence reveals that cannabis use during adolescence may disrupt the normal development of the brain. This study is aimed to analyze possible differences between early-onset and late-onset cannabis consumers.<h4>Me ...[more]

PMID: 30002988

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Project description:BackgroundStatistical inference based on small datasets, commonly found in precision oncology, is subject to low power and high uncertainty. In these settings, drawing strong conclusions about future research utility is difficult when using standard inferential measures. It is therefore important to better quantify the uncertainty associated with both significant and non-significant results based on small sample sizes.MethodsWe developed a new method, Bayesian Additional Evidence (BAE), that determines (1) how much additional supportive evidence is needed for a non-significant result to reach Bayesian posterior credibility, or (2) how much additional opposing evidence is needed to render a significant result non-credible. Although based in Bayesian analysis, a prior distribution is not needed; instead, the tipping point output is compared to reasonable effect ranges to draw conclusions. We demonstrate our approach in a comparative effectiveness analysis comparing two treatments in a real world biomarker-defined cohort, and provide guidelines for how to apply BAE in practice.ResultsOur initial comparative effectiveness analysis results in a hazard ratio of 0.31 with 95% confidence interval (0.09, 1.1). Applying BAE to this result yields a tipping point of 0.54; thus, an observed hazard ratio of 0.54 or smaller in a replication study would result in posterior credibility for the treatment association. Given that effect sizes in this range are not extreme, and that supportive evidence exists from a similar published study, we conclude that this problem is worthy of further research.ConclusionsOur proposed method provides a useful framework for interpreting analytic results from small datasets. This can assist researchers in deciding how to interpret and continue their investigations based on an initial analysis that has high uncertainty. Although we illustrated its use in estimating parameters based on time-to-event outcomes, BAE easily applies to any normally-distributed estimator, such as those used for analyzing binary or continuous outcomes.

Dataset Information

Age of onset of cannabis use and decision making under uncertainty.

Objective

Method

Results

Conclusions

Publications

Age of onset of cannabis use and decision making under uncertainty.

Similar Datasets

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