SOHPIE: statistical approach via pseudo-value information and estimation for differential network analysis of microbiome data.

SUMMARY: The SOHPIE R package implements a novel functionality for "multivariable" differential co-abundance network (DN, hereafter) analyses of microbiome data. It incorporates a regression approach that adjusts for additional covariates for DN analyses. This distinguishes from previous prominent approaches in DN analyses such as MDiNE and NetCoMi which do not feature a covariate adjustment of finding taxa that are differentially connected (DC, hereafter) between individuals with different clinical and phenotypic characteristics. AVAILABILITY AND IMPLEMENTATION: SOHPIE with a vignette is available on CRAN repository https://CRAN.R-project.org/package=SOHPIE and published under General Public License (GPL) version 3 license.

Differential network connectivity analysis for microbiome data adjusted for clinical covariates using jackknife pseudo-values.

BACKGROUND: A recent breakthrough in differential network (DN) analysis of microbiome data has been realized with the advent of next-generation sequencing technologies. The DN analysis disentangles the microbial co-abundance among taxa by comparing the network properties between two or more graphs under different biological conditions. However, the existing methods to the DN analysis for microbiome data do not adjust for other clinical differences between subjects. RESULTS: We propose a Statistical Approach via Pseudo-value Information and Estimation for Differential Network Analysis (SOHPIE-DNA) that incorporates additional covariates such as continuous age and categorical BMI. SOHPIE-DNA is a regression technique adopting jackknife pseudo-values that can be implemented readily for the analysis. We demonstrate through simulations that SOHPIE-DNA consistently reaches higher recall and F1-score, while maintaining similar precision and accuracy to existing methods (NetCoMi and MDiNE). Lastly, we apply SOHPIE-DNA on two real datasets from the American Gut Project and the Diet Exchange Study to showcase the utility. The analysis of the Diet Exchange Study is to showcase that SOHPIE-DNA can also be used to incorporate the temporal change of connectivity of taxa with the inclusion of additional covariates. As a result, our method has found taxa that are related to the prevention of intestinal inflammation and severity of fatigue in advanced metastatic cancer patients. CONCLUSION: SOHPIE-DNA is the first attempt of introducing the regression framework for the DN analysis in microbiome data. This enables the prediction of characteristics of a connectivity of a network with the presence of additional covariate information in the regression. The R package with a vignette of our methodology is available through the CRAN repository ( https://CRAN.R-project.org/package=SOHPIE ), named SOHPIE (pronounced as Sofie). The source code and user manual can be found at https://github.com/sjahnn/SOHPIE-DNA .

A Behavioral Economic Examination of Sexual Behaviors in the Era of Pre-exposure Prophylaxis via Explanatory Sequential Mixed Methods.

Pre-exposure prophylaxis (PrEP) use may be associated with condom use decisions. The current investigation examined sexual decision-making in the context of PrEP among young adult men who have sex with men (MSM) between 18 and 30 years old, using an explanatory sequential mixed methods design. For the quantitative aim, 99 MSM currently taking PrEP (i.e., PrEP-experienced) and 140 MSM not currently taking PrEP (i.e., PrEP-naive) completed an online survey, including the Sexual Delay Discounting Task (SDDT), which captures likelihood of condom use. For the qualitative aim, 15 people from each group were interviewed about their (1) conceptualizations of risky sex and (2) ways they manage their sexual risk. Participants were, on average, 25.69 years old (SD = 3.07) and 64% White. Results from the quantitative aim revealed, controlling for covariates, PrEP-experienced participants exhibited significantly lower likelihood of (1) using an immediately available condom and (2) waiting for a delayed condom (i.e., sexual delay discounting) compared to PrEP-naive participants. Qualitative themes explaining what young adult MSM consider to be risky sex included: (1) any sex as risky sex, (2) risky sex as "sex without a conversation," and (3) risky sex as sex with risk for physical harm. Themes on ways young adult MSM manage sexual risk were classified as proactive, reactive, and passive. Results suggest that PrEP use is related to condom use decisions. Taken together, quantitative differences in sexual delay discounting, but qualitatively similar conceptualizations and management of risky sex, suggest that the SDDT may be a useful tool in sex research to capture processes (i.e., delay discounting) underlying sexual decision-making that may be missed by traditional self-reports. Implications of results, including potentially providing (good quality) condoms with every PrEP prescription, and future research topics are discussed.

Machine Learning Methods in Classification of Prolonged Radiation Therapy in Oropharyngeal Cancer: National Cancer Database.

OBJECTIVE: To investigate the accuracy of machine learning (ML) algorithms in stratifying risk of prolonged radiation treatment duration (RTD), defined as greater than 50 days, for patients with oropharyngeal squamous cell carcinoma (OPSCC). STUDY DESIGN: Retrospective cohort study. SETTING: National Cancer Database (NCDB). METHODS: The NCDB was queried between 2004 to 2016 for patients with OPSCC treated with radiation therapy (RT) or chemoradiation as primary treatment. To predict risk of prolonged RTD, 8 different ML algorithms were compared against traditional logistic regression using various performance metrics. Data was split into a distribution of 70% for training and 30% for testing. RESULTS: A total of 3152 patients were included (1928 prolonged RT, 1224 not prolonged RT). As a whole, based on performance metrics, random forest (RF) was found to most accurately predict prolonged RTD compared to both other ML methods and traditional logistic regression. CONCLUSION: Our assessment of various ML techniques showed that RF was superior to traditional logistic regression at classifying OPSCC patients at risk of prolonged RTD. Application of such algorithms may have potential to identify high risk patients and enable early interventions to improve survival.