Dataset Information

Country and Sex Differences in Decision Making Under Uncertainty and Risk.

ABSTRACT: Whether males and females differ in decision-making remains highly debatable. However, a male advantage in decision making is observed in animal as well as human models of the iowa gambling task (IGT), and, in case of the latter, the difference is observed across a wide range of age groups. It is unclear if these sex differences on the IGT are malleable to environmental influences such as sociocultural factors. We tested sex differences during the uncertainty and risk phases of the IGT in data pooled from three countries that reflected high, moderate, to low gender-equity (Germany, United States, and India: N = 531, female = 269). Comparing the net scores in uncertainty vs. risk blocks (first two vs. last two blocks) confirmed the male-advantage on the IGT across the three countries, specifically in the risk blocks, with the highest male-advantage observed for Germany. Results are discussed in terms of sex differences in reaction to uncertainty vs. risk, and the counter-intuitive effect of gender-equitable environment suggesting that national/environmental factors might influence advantageous decision making, but in ways that accentuate rather than abate sex differences.

SUBMITTER: Singh V

PROVIDER: S-EPMC7101158 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Country and Sex Differences in Decision Making Under Uncertainty and Risk.

Singh Varsha V Schiebener Johannes J Müller Silke M SM Liebherr Magnus M Brand Matthias M Buelow Melissa T MT

Frontiers in psychology 20200320

Whether males and females differ in decision-making remains highly debatable. However, a male advantage in decision making is observed in animal as well as human models of the iowa gambling task (IGT), and, in case of the latter, the difference is observed across a wide range of age groups. It is unclear if these sex differences on the IGT are malleable to environmental influences such as sociocultural factors. We tested sex differences during the uncertainty and risk phases of the IGT in data p ...[more]

PMID: 32265793

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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

Country and Sex Differences in Decision Making Under Uncertainty and Risk.

Publications

Country and Sex Differences in Decision Making Under Uncertainty and Risk.

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