Stan

Gelman, Andrew; Lee, Daniel; Guo, Jiqiang

doi:10.3102/1076998615606113

Cited by 444 publications

(139 citation statements)

References 12 publications

Supporting

Mentioning

138

Contrasting

Order By: Relevance

“…Finally, recent research (Alvarez et al, 2014) has revealed that the scaled inverse Wishart distribution (O'Malley & Zaslavsky, 2008) and hierarchical half-t prior distribution proposed by Huang and Wand (2013) are less informative than the inverse Wishart, although they experience similar issues, albeit at a lesser scale (Alvarez et al, 2014). We do not believe we have completely resolved this issue, and recent advances in Bayesian methods (Gelman, Lee, & Guo, 2015) may further our understanding. Finally, our simulations indicate that specifying hyperpriors for the components of a covariance matrix is significantly more effective than using the inverse Wishart distribution, or other recently developed approaches (Huang & Wand, 2013;O'Malley & Zaslavsky, 2008) for capture-mark-recapture and other demographic analyses, as the hyperprior approach leads to accurate estimates of process correlations and variances given sufficient sample sizes.…”

Section: Implications For Future Researchmentioning

confidence: 81%

Estimating correlations among demographic parameters in population models

Riecke

Sedinger

Williams

et al. 2019

Ecology and Evolution

View full text Add to dashboard Cite

Estimating correlations among demographic parameters is critical to understanding population dynamics and life‐history evolution, where correlations among parameters can inform our understanding of life‐history trade‐offs, result in effective applied conservation actions, and shed light on evolutionary ecology. The most common approaches rely on the multivariate normal distribution, and its conjugate inverse Wishart prior distribution. However, the inverse Wishart prior for the covariance matrix of multivariate normal distributions has a strong influence on posterior distributions. As an alternative to the inverse Wishart distribution, we individually parameterize the covariance matrix of a multivariate normal distribution to accurately estimate variances (σ 2) of, and process correlations (ρ) between, demographic parameters. We evaluate this approach using simulated capture–mark–recapture data. We then use this method to examine process correlations between adult and juvenile survival of black brent geese marked on the Yukon–Kuskokwim River Delta, Alaska (1988–2014). Our parameterization consistently outperformed the conjugate inverse Wishart prior for simulated data, where the means of posterior distributions estimated using an inverse Wishart prior were substantially different from the values used to simulate the data. Brent adult and juvenile annual apparent survival rates were strongly positively correlated (ρ = 0.563, 95% CRI 0.181–0.823), suggesting that habitat conditions have significant effects on both adult and juvenile survival. We provide robust simulation tools, and our methods can readily be expanded for use in other capture–recapture or capture‐recovery frameworks. Further, our work reveals limits on the utility of these approaches when study duration or sample sizes are small.

show abstract

Section: Implications For Future Researchmentioning

confidence: 81%

Estimating correlations among demographic parameters in population models

Riecke

Sedinger

Williams

et al. 2019

Ecology and Evolution

View full text Add to dashboard Cite

show abstract

“…We fit this model using Stan [25] with a uniform (improper) prior on the entries of B and T. The treatmentassociated mutational signature N was calculated from a point estimate of T as:…”

Section: Mutational Signaturesmentioning

confidence: 99%

Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer

O’Donnell

Christie

Buros

et al. 2016

Preprint

View full text Add to dashboard Cite

Background: Patients with highly mutated tumors, such as melanoma or smoking-related lung cancer, have higher rates of response to immune checkpoint blockade therapy, perhaps due to increased neoantigen expression. Many chemotherapies including platinum compounds are known to be mutagenic, but the impact of standard treatment protocols on mutational burden and resulting neoantigen expression in most human cancers is unknown. Methods: We sought to quantify the effect of chemotherapy treatment on computationally predicted neoantigen expression for high grade serous ovarian carcinoma patients enrolled in the Australian Ovarian Cancer Study. In this series, 35 of 114 samples were collected after exposure to chemotherapy; 14 are matched with an untreated sample from the same patient. Our approach integrates whole genome and RNA sequencing of bulk tumor samples with class I MHC binding prediction and mutational signatures extracted from studies of chemotherapy-exposed Caenorhabditis elegans and Gallus gallus cells. We additionally investigated the relationship between neoantigens, tumor infiltrating immune cells estimated from RNA-seq with CIBERSORT, and patient survival. Results: Greater neoantigen burden and CD8+ T cell infiltration in primary, pre-treatment samples were independently associated with improved survival. Relapse samples collected after chemotherapy harbored a median of 78% more expressed neoantigens than untreated primary samples, a figure that combines the effects of chemotherapy and other processes operative during relapse. The contribution from chemotherapy-associated signatures was small, accounting for a mean of 5% (range 0-16) of the expressed neoantigen burden in relapse samples. In both treated and untreated samples, most neoantigens were attributed to COSMIC Signature (3), associated with BRCA disruption, Signature (1), associated with a slow mutagenic process active in healthy tissue, and Signature (8), of unknown etiology. Conclusion: Relapsed ovarian cancers harbor more predicted neoantigens than primary tumors, but the increase is due to pre-existing mutational processes, not mutagenesis from chemotherapy.

show abstract

“…We chose β 1 ∼ N (0, 1) for the (standardized) predictors. This prior is listed as a recommended 'generic weakly informative prior' in the Stan manual (Betancourt, Vehtari, & Gelman, 2015), and has been used in this context before (e.g., Gelman, Lee, & Guo, 2015).…”

Section: Statistical Modelsmentioning

confidence: 99%

Religious Belief and Cognitive Conflict Sensitivity: A Preregistered fMRI Study

Hoogeveen

Snoek

Elk

2020

Preprint

View full text Add to dashboard Cite

AbstractIn the current preregistered fMRI study, we investigated the relationship between religiosity and behavioral and neural mechanisms of conflict processing, as a conceptual replication of the study by Inzlicht et al. (2009). Participants (N = 193) performed a gender-Stroop task and afterwards completed standardized measures to assess their religiosity. As expected, the task induced cognitive conflict at the behavioral level and at a neural level this was reflected in increased activity in the anterior cingulate cortex (ACC). However, individual differences in religiosity were not related to performance on the Stroop task as measured in accuracy and interference effects, nor to neural markers of response conflict (correct responses vs. errors) or informational conflict (congruent vs. incongruent stimuli). Overall, we obtained moderate to strong evidence in favor of the null hypotheses that religiosity is unrelated to cognitive conflict sensitivity. We discuss the implications for the neuroscience of religion and emphasize the importance of designing studies that more directly implicate religious concepts and behaviors in an ecologically valid manner.

show abstract

Stan

Cited by 444 publications

References 12 publications

Estimating correlations among demographic parameters in population models

Estimating correlations among demographic parameters in population models

Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer

Religious Belief and Cognitive Conflict Sensitivity: A Preregistered fMRI Study

Contact Info

Product

Resources

About