Biostatistics
@biostatistics
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Official twitter account of the journal Biostatistics. Co-Editors: @drizopoulos and @RhubbBstat
Joined February 2016
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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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
SUMMARY. Single-cell RNA sequencing (scRNA-seq) quantifies gene expression for individual cells in a sample, which allows distinct cell-type populations to
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New in @biostatistics: Reassessing pharmacogenomic cell sensitivity with multilevel statistical models Matt Ploenzke, Rafael Irizarry https://t.co/xC8mtWZNap
academic.oup.com
Summary. Pharmacogenomic experiments allow for the systematic testing of drugs, at varying dosage concentrations, to study how genomic markers correlate wi
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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 https://t.co/7acn6D0kXY
academic.oup.com
Summary. Individual-level data sharing across multiple sites can be infeasible due to privacy and logistical concerns. This article proposes a general dist
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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 https://t.co/YvSuWqyaR6
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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 https://t.co/G6lwYScPfx
academic.oup.com
Summary. Causal mediation analysis has historically been limited in two important ways: (i) a focus has traditionally been placed on binary exposures and s
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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
academic.oup.com
Summary. Causal mediation analysis has historically been limited in two important ways: (i) a focus has traditionally been placed on binary exposures and s
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New in @biostatistics: Separating and reintegrating latent variables to improve classification of genomic data Nora Yujia Payne, Johann Gagnon-Bartsch https://t.co/lTNklSIgwS
academic.oup.com
Summary. Genomic data sets contain the effects of various unobserved biological variables in addition to the variable of primary interest. These latent var
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New in @biostatistics: Estimation and false discovery control for the analysis of environmental mixtures Srijata Samanta, Joseph Antonelli https://t.co/VzIZciwhKd
academic.oup.com
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
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New in @biostatistics: A flexible Bayesian framework for individualized inference via adaptive borrowing Ziyu Ji, Julian Wolfson https://t.co/kn9JhKC5LN
academic.oup.com
Summary. The explosion in high-resolution data capture technologies in health has increased interest in making inferences about individual-level parameters
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New in @biostatistics: Causal inference for semi-competing risks data Daniel Nevo, Malka Gorfine https://t.co/IdC0Ryu5Ma
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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 https://t.co/TlmHwydDkM
academic.oup.com
Summary. One-stage meta-analysis of individual participant data (IPD) poses several statistical and computational challenges. For time-to-event outcomes, t
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New in @biostatistics: Evaluating replicability in microbiome data 🔓 David Clausen, Amy Willis https://t.co/ObSx1sVU8U
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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 https://t.co/qr2wIwXMxr
academic.oup.com
Summary. Strategic preparedness reduces the adverse health impacts of hurricanes and tropical storms, referred to collectively as tropical cyclones (TCs),
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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 https://t.co/BH283iKsWv
academic.oup.com
Summary. Univariate spatio-temporal models for areal count data have received great attention in recent years for estimating risks. However, models for stu
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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 https://t.co/zAKaqJBkUX
academic.oup.com
Summary. Cluster randomized trials (CRTs) randomly assign an intervention to groups of individuals (e.g., clinics or communities) and measure outcomes on i
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Our new paper @biostatistics is out: Causal inference for semi-competing risks data 📄: https://t.co/bu9oa5nzhf 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 https://t.co/Jd8tWem3as
academic.oup.com
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
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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 https://t.co/4hSEBIi66J
academic.oup.com
Summary. Nonignorable technical variation is commonly observed across data from multiple experimental runs, platforms, or studies. These so-called batch ef
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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 https://t.co/zo7WnUqsbH
#causalinference #missingdata #epitwitter #machinelearning #TMLE
academic.oup.com
Summary. Cluster randomized trials (CRTs) randomly assign an intervention to groups of individuals (e.g., clinics or communities) and measure outcomes on i
After 10+yrs in the works, I'm THRILLED: "Two-Stage TMLE to Reduce Bias & Improve Efficiency in Cluster Randomized Trials" is finally here: https://t.co/wMqaLhUWJ0
#epitwitter #statstwitter #causalinference #machinelearning #DataScience #missingdata @UCBerkeleySPH @UCSF_HIVIDGM
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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 https://t.co/rvfcbMqvuQ
academic.oup.com
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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