@biostatistics

Official twitter account of the journal Biostatistics. Co-Editors: @drizopoulos and @RhubbBstat

biostatistics.oxfordjournals.org/
Joined February 2016
  • Tweets247
  • Following29
  • Followers5K
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Are you a @biostatistics author? ✍️ Reply with a 1-tweet summary of your paper, including a link. We’ll follow you & share it in our feed. Bonus points for graphics. 📊 New papers & old papers! 📄 We want to see them all, especially from students & early career researchers.
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Biostatistics retweeted
Rafael Irizarry@rafalab30 Jun 2022
Work with Izzy Grabski describing model-based approach to cell-type identification with scRNA-Seq data now published in @biostatistics. We show limitations with approaches that rely on clustering and marker genes and ones based on blackbox ML algorithms. academic.oup.com/biostatisti…

A probabilistic gene expression barcode for annotation of cell types from single-cell RNA-seq data

SUMMARY. Single-cell RNA sequencing (scRNA-seq) quantifies gene expression for individual cells in a sample, which allows distinct cell-type populations to

academic.oup.com
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New in @biostatistics: Reassessing pharmacogenomic cell sensitivity with multilevel statistical models Matt Ploenzke, Rafael Irizarry doi.org/10.1093/biostatistic…

Reassessing pharmacogenomic cell sensitivity with multilevel statistical models

Summary. Pharmacogenomic experiments allow for the systematic testing of drugs, at varying dosage concentrations, to study how genomic markers correlate wi

academic.oup.com
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New in @biostatistics: Distributed Cox proportional hazards regression using summary-level information Dongdong Li, Wenbin Lu, Di Shu, Sengwee Toh, Rui Wang doi.org/10.1093/biostatistic…

Distributed Cox proportional hazards regression using summary-level information

Summary. Individual-level data sharing across multiple sites can be infeasible due to privacy and logistical concerns. This article proposes a general dist

academic.oup.com
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New in @biostatistics: Semisupervised Calibration of Risk with Noisy Event Times (SCORNET) using electronic health record data Yuri Ahuja, Liang Liang, Doudou Zhou, Sicong Huang, Tianxi Cai doi.org/10.1093/biostatistic…
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New in @biostatistics: Nonparametric causal mediation analysis for stochastic interventional (in)direct effects Nima Hejazi, Kara Rudolph, Mark van der Laan, Iván Díaz doi.org/10.1093/biostatistic…

Nonparametric causal mediation analysis for stochastic interventional (in)direct effects

Summary. Causal mediation analysis has historically been limited in two important ways: (i) a focus has traditionally been placed on binary exposures and s

academic.oup.com
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Biostatistics retweeted
nima hejazi@nshejazi3 Feb 2022
our new mediation analysis paper is out in @biostatistics! flexible effects w/ stochastic interventions? ✅ intermediate confounders handled? ✅ efficient estimation w/ #machinelearning? ✅ fun work w/ @ildiazm @kara_rudolph, @mark_vdlaan #causalinference doi.org/10.1093/biostatistic…

Nonparametric causal mediation analysis for stochastic interventional (in)direct effects

Summary. Causal mediation analysis has historically been limited in two important ways: (i) a focus has traditionally been placed on binary exposures and s

academic.oup.com
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New in @biostatistics: Separating and reintegrating latent variables to improve classification of genomic data Nora Yujia Payne, Johann Gagnon-Bartsch doi.org/10.1093/biostatistic…
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New in @biostatistics: Estimation and false discovery control for the analysis of environmental mixtures Srijata Samanta, Joseph Antonelli doi.org/10.1093/biostatistic…

Estimation and false discovery control for the analysis of environmental mixtures

Summary. The analysis of environmental mixtures is of growing importance in environmental epidemiology, and one of the key goals in such analyses is to ide

academic.oup.com
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New in @biostatistics: A flexible Bayesian framework for individualized inference via adaptive borrowing Ziyu Ji, Julian Wolfson doi.org/10.1093/biostatistic…

A flexible Bayesian framework for individualized inference via adaptive borrowing

Summary. The explosion in high-resolution data capture technologies in health has increased interest in making inferences about individual-level parameters

academic.oup.com
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New in @biostatistics: Causal inference for semi-competing risks data Daniel Nevo, Malka Gorfine doi.org/10.1093/biostatistic…
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New in @biostatistics: Individual participant data meta-analysis with mixed-effects transformation models 🔓 Bálint Tamási, Michael Crowther, Milo Alan Puhan, Ewout Steyerberg, Torsten Hothorn doi.org/10.1093/biostatistic…

Individual participant data meta-analysis with mixed-effects transformation models

Summary. One-stage meta-analysis of individual participant data (IPD) poses several statistical and computational challenges. For time-to-event outcomes, t

academic.oup.com
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New in @biostatistics: Evaluating replicability in microbiome data 🔓 David Clausen, Amy Willis doi.org/10.1093/biostatistic…
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New in @biostatistics: Integrated causal-predictive machine learning models for tropical cyclone epidemiology🔓 Rachel Nethery, Nina Katz-Christy, Marianthi-Anna Kioumourtzoglou, Robbie Parks, Andrea Schumacher, G Brooke Anderson doi.org/10.1093/biostatistic…

Integrated causal-predictive machine learning models for tropical cyclone epidemiology

Summary. Strategic preparedness reduces the adverse health impacts of hurricanes and tropical storms, referred to collectively as tropical cyclones (TCs),

academic.oup.com
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New in @biostatistics: Multivariate Bayesian spatio-temporal P-spline models to analyze crimes against women 🔓 Gonzalo Vicente, Tomás Goicoa, María Dolores Ugarte doi.org/10.1093/biostatistic…

Multivariate Bayesian spatio-temporal P-spline models to analyze crimes against women

Summary. Univariate spatio-temporal models for areal count data have received great attention in recent years for estimating risks. However, models for stu

academic.oup.com
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New in @biostatistics: Two-Stage TMLE to reduce bias and improve efficiency in cluster randomized trials 🔓 Laura Balzer, Mark van der Laan, James Ayieko, Moses Kamya, Gabriel Chamie, Joshua Schwab, Diane Havlir, Maya Petersen doi.org/10.1093/biostatistic…

Two-Stage TMLE to reduce bias and improve efficiency in cluster randomized trials

Summary. Cluster randomized trials (CRTs) randomly assign an intervention to groups of individuals (e.g., clinics or communities) and measure outcomes on i

academic.oup.com
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Biostatistics retweeted
Daniel Nevo@DanielNevo3 Jan 2022
Our new paper @biostatistics is out: Causal inference for semi-competing risks data 📄: doi.org/10.1093/biostatistic… Joint work with @GorfineMalka #stattwitter #causaltwitter Short thread about the paper👇 1/10
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New in @biostatistics: Two-phase stratified sampling and analysis for predicting binary outcomes Yaqi Cao, Sebastien Haneuse, Yingye Zheng, Jinbo Chen doi.org/10.1093/biostatistic…

Two-phase stratified sampling and analysis for predicting binary outcomes

Summary. The two-phase study design is a cost-efficient sampling strategy when certain data elements are expensive and, thus, can only be collected on a su

academic.oup.com
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New in @biostatistics: Overcoming the impacts of two-step batch effect correction on gene expression estimation and inference 🔓 Tenglong Li, Yuqing Zhang, Prasad Patil, W Evan Johnson doi.org/10.1093/biostatistic…

Overcoming the impacts of two-step batch effect correction on gene expression estimation and...

Summary. Nonignorable technical variation is commonly observed across data from multiple experimental runs, platforms, or studies. These so-called batch ef

academic.oup.com
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Looking for some light vacation reading? Two-Stage TMLE is officially published! Tackles differential missingness & adaptive adjustment for precision in cluster (group) randomized trials academic.oup.com/biostatisti… #causalinference #missingdata #epitwitter #machinelearning #TMLE

Two-Stage TMLE to reduce bias and improve efficiency in cluster randomized trials

Summary. Cluster randomized trials (CRTs) randomly assign an intervention to groups of individuals (e.g., clinics or communities) and measure outcomes on i

academic.oup.com
Laura B. Balzer@LauraBBalzer1 Jul 2021
After 10+yrs in the works, I'm THRILLED: "Two-Stage TMLE to Reduce Bias & Improve Efficiency in Cluster Randomized Trials" is finally here: arxiv.org/abs/2106.15737 #epitwitter #statstwitter #causalinference #machinelearning #DataScience #missingdata @UCBerkeleySPH @UCSF_HIVIDGM

Two-Stage TMLE to Reduce Bias and Improve Efficiency in Cluster...

Cluster randomized trials (CRTs) randomly assign an intervention to groups of individuals (e.g., clinics or communities) and measure outcomes on individuals in those groups. While offering many...

arxiv.org
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New in @biostatistics: Joint frailty modeling of time-to-event data to elicit the evolution pathway of events: a generalized linear mixed model approach 🔓 Shu Kay Ng, Richard Tawiah, Geoffrey Mclachlan, Vinod Gopalan doi.org/10.1093/biostatistic…

Joint frailty modeling of time-to-event data to elicit the evolution pathway of events: a general...

Summary. Multimorbidity constitutes a serious challenge on the healthcare systems in the world, due to its association with poorer health-related outcomes,

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