Robert Tibshirani
Stanford University
H-index: 179
North America-United States
Description
Robert Tibshirani, With an exceptional h-index of 179 and a recent h-index of 119 (since 2020), a distinguished researcher at Stanford University, specializes in the field of Statistics, data science, Machine Learning.
His recent articles reflect a diverse array of research interests and contributions to the field:
Smooth multi-period forecasting with application to prediction of COVID-19 cases
CAR19 monitoring by peripheral blood immunophenotyping reveals histology-specific expansion and toxicity.
Intraoperative Evaluation of Breast Tissues During Breast Cancer Operations Using the MasSpec Pen
Evaluating a shrinkage estimator for the treatment effect in clinical trials
Artificial Intelligence Identifies Factors Associated with Blood Loss and Surgical Experience in Cholecystectomy
Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank
Pretraining and the Lasso
Prognostic pan-cancer and single-cancer models: A large-scale analysis using a real-world clinico-genomic database
Professor Information
University | Stanford University |
---|---|
Position | Professor of Biomedical Data Sciences and of Statistics |
Citations(all) | 488983 |
Citations(since 2020) | 186494 |
Cited By | 377286 |
hIndex(all) | 179 |
hIndex(since 2020) | 119 |
i10Index(all) | 539 |
i10Index(since 2020) | 416 |
University Profile Page | Stanford University |
Research & Interests List
Statistics
data science
Machine Learning
Top articles of Robert Tibshirani
Smooth multi-period forecasting with application to prediction of COVID-19 cases
Forecasting methodologies have always attracted a lot of attention and have become an especially hot topic since the beginning of the COVID-19 pandemic. In this article we consider the problem of multi-period forecasting that aims to predict several horizons at once. We propose a novel approach that forces the prediction to be “smooth” across horizons and apply it to two tasks: point estimation via regression and interval prediction via quantile regression. This methodology was developed for real-time distributed COVID-19 forecasting. We illustrate the proposed technique with the COVIDcast dataset as well as a small simulation example. Supplementary materials for this article are available online.
Authors
Elena Tuzhilina,Trevor J Hastie,Daniel J McDonald,J Kenneth Tay,Robert Tibshirani
Journal
Journal of Computational and Graphical Statistics
Published Date
2024/1/5
CAR19 monitoring by peripheral blood immunophenotyping reveals histology-specific expansion and toxicity.
Chimeric antigen receptor (CAR) T cells directed against CD19 (CAR19) are a revolutionary treatment for B-cell lymphomas. CAR19 cell expansion is necessary for CAR19 function but is also associated with toxicity. To define the impact of CAR19 expansion on patient outcomes, we prospectively followed a cohort of 236 patients treated with CAR19 (brexucabtagene autoleucel or axicabtagene ciloleucel) for mantle cell (MCL), follicular (FL), and large B-cell lymphoma (LBCL) over the course of five years and obtained CAR19 expansion data using peripheral blood immunophenotyping for 188 of these patients. CAR19 expansion was higher in patients with MCL compared to other lymphoma histologic subtypes. Notably, patients with MCL had increased toxicity and required four-fold higher cumulative steroid doses than patients with LBCL. CAR19 expansion was associated with the development of cytokine release …
Authors
Mark P Hamilton,Erin Craig,Cesar Gentille,Alain Mina,John Tamaresis,Nadia Kirmani,Zachary Ehlinger,Shriya Syal,Zinaida Good,Brian Sworder,Joseph Schroers-Martin,Ying Lu,Lori Muffly,Robert S Negrin,Sally Arai,Robert Lowsky,Everett Meyer,Andrew R Rezvani,Judith Shizuru,Wen-Kai Weng,Parveen Shiraz,Surbhi Sidana,Sushma Bharadwaj,Melody Smith,Saurabh Dahiya,Bita Sahaf,David M Kurtz,Crystal L Mackall,Robert Tibshirani,Ash A Alizadeh,Matthew J Frank,David B Miklos
Journal
Blood Advances
Published Date
2024/3/20
Intraoperative Evaluation of Breast Tissues During Breast Cancer Operations Using the MasSpec Pen
ImportanceSurgery with complete tumor resection remains the main treatment option for patients with breast cancer. Yet, current technologies are limited in providing accurate assessment of breast tissue in vivo, warranting development of new technologies for surgical guidance.ObjectiveTo evaluate the performance of the MasSpec Pen for accurate intraoperative assessment of breast tissues and surgical margins based on metabolic and lipid information.Design, Setting, and ParticipantsIn this diagnostic study conducted between February 23, 2017, and August 19, 2021, the mass spectrometry–based device was used to analyze healthy breast and invasive ductal carcinoma (IDC) banked tissue samples from adult patients undergoing breast surgery for ductal carcinomas or nonmalignant conditions. Fresh-frozen tissue samples and touch imprints were analyzed in a laboratory. Intraoperative in vivo and ex vivo …
Authors
Kyana Y Garza,Mary E King,Chandandeep Nagi,Rachel J DeHoog,Jialing Zhang,Marta Sans,Anna Krieger,Clara L Feider,Alena V Bensussan,Michael F Keating,John Q Lin,Min Woo Sun,Robert Tibshirani,Christopher Pirko,Kirtan A Brahmbhatt,Ahmed R Al-Fartosi,Alastair M Thompson,Elizabeth Bonefas,James Suliburk,Stacey A Carter,Livia S Eberlin
Journal
JAMA Network Open
Published Date
2024/3/4
Evaluating a shrinkage estimator for the treatment effect in clinical trials
The main objective of most clinical trials is to estimate the effect of some treatment compared to a control condition. We define the signal‐to‐noise ratio (SNR) as the ratio of the true treatment effect to the SE of its estimate. In a previous publication in this journal, we estimated the distribution of the SNR among the clinical trials in the Cochrane Database of Systematic Reviews (CDSR). We found that the SNR is often low, which implies that the power against the true effect is also low in many trials. Here we use the fact that the CDSR is a collection of meta‐analyses to quantitatively assess the consequences. Among trials that have reached statistical significance we find considerable overoptimism of the usual unbiased estimator and under‐coverage of the associated confidence interval. Previously, we have proposed a novel shrinkage estimator to address this “winner's curse.” We compare the performance of our …
Authors
Erik W van Zwet,Lu Tian,Robert Tibshirani
Journal
Statistics in Medicine
Published Date
2024/2/28
Artificial Intelligence Identifies Factors Associated with Blood Loss and Surgical Experience in Cholecystectomy
BackgroundLaparoscopic surgery videos offer valuable insights into the intraoperative skills of surgeons. Traditionally, skill assessment has focused on trainees, but analyzing the operative techniques of established surgeons can reveal behaviors that are associated with surgical expertise. Computer vision (CV), a domain of artificial intelligence (AI), facilitates scalable, video-based assessment, enabling the discovery of novel associations between surgical skill and clinical outcomes. For this study, we developed an AI-powered CV model capable of autonomously recognizing fine-grained surgical actions in laparoscopic videos and uncovering associations between these actions and operative blood loss and surgical experience.MethodsWe utilized a dataset of laparoscopic surgical videos from 243 patients who underwent cholecystectomy. We used a subset of these videos to train an AI-powered CV model to …
Authors
Josiah G Aklilu,Min Woo Sun,Shelly Goel,Sebastiano Bartoletti,Anita Rau,Griffin Olsen,Kay S Hung,Sophie L Mintz,Vicki Luong,Arnold Milstein,Mark J Ott,Robert Tibshirani,Jeffrey K Jopling,Eric C Sorenson,Dan E Azagury,Serena Yeung-Levy
Journal
NEJM AI
Published Date
2024/1/25
Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank
Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals, underscoring a critical gap in genetic research. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data. We evaluate the performance of Group-LASSO INTERaction-NET (glinternet) and pretrained lasso in disease prediction focusing on diverse ancestries in the UK Biobank. Models were trained on data from White British and other ancestries and validated across a cohort of over 96,000 individuals for 8 diseases. Out of 96 models trained, we report 16 with statistically significant incremental predictive performance in terms of ROC-AUC scores. These findings suggest that advanced statistical methods that borrow information across multiple ancestries may improve disease risk prediction, but with limited benefit.
Authors
Thomas Le Menestrel,Erin Craig,Robert Tibshirani,Trevor Hastie,Manuel Rivas
Journal
arXiv preprint arXiv:2404.17626
Published Date
2024/4/26
Pretraining and the Lasso
Pretraining is a popular and powerful paradigm in machine learning. As an example, suppose one has a modest-sized dataset of images of cats and dogs, and plans to fit a deep neural network to classify them from the pixel features. With pretraining, we start with a neural network trained on a large corpus of images, consisting of not just cats and dogs but hundreds of other image types. Then we fix all of the network weights except for the top layer (which makes the final classification) and train (or "fine tune") those weights on our dataset. This often results in dramatically better performance than the network trained solely on our smaller dataset. In this paper, we ask the question "Can pretraining help the lasso?". We develop a framework for the lasso in which an overall model is fit to a large set of data, and then fine-tuned to a specific task on a smaller dataset. This latter dataset can be a subset of the original dataset, but does not need to be. We find that this framework has a wide variety of applications, including stratified models, multinomial targets, multi-response models, conditional average treatment estimation and even gradient boosting. In the stratified model setting, the pretrained lasso pipeline estimates the coefficients common to all groups at the first stage, and then group specific coefficients at the second "fine-tuning" stage. We show that under appropriate assumptions, the support recovery rate of the common coefficients is superior to that of the usual lasso trained only on individual groups. This separate identification of common and individual coefficients can also be useful for scientific understanding.
Authors
Erin Craig,Mert Pilanci,Thomas Le Menestrel,Balasubramanian Narasimhan,Manuel Rivas,Roozbeh Dehghannasiri,Julia Salzman,Jonathan Taylor,Robert Tibshirani
Published Date
2024/1/23
Prognostic pan-cancer and single-cancer models: A large-scale analysis using a real-world clinico-genomic database
Prognostic models in oncology have a profound impact on personalized cancer care and patient profiling, but tend to be heterogeneously developed and implemented in narrow patient cohorts. Here, we develop and benchmark multiple machine learning models to predict survival in pan-cancer and 16 single-cancer settings using a de-identified clinico-genomic database of 28,079 US patients with cancer. We identify key predictors of cancer prognosis, including 15 shared across seven or more cancer types, revealing strong consistency in cancer prognostic factors. We demonstrate that pan-cancer models generally outperform or match single-cancer models in predicting survival and risk stratifying patients, especially in smaller cancer cohorts, suggesting a unique transfer learning advantage of pan-cancer models. This work demonstrates the potential of pan-cancer approaches in enhancing the accuracy and applicability of prognostic models in oncology, paving the way for more personalized and effective cancer care strategies.
Authors
Sarah F McGough,Svetlana Lyalina,Devin Incerti,Yunru Huang,Stefka Tyanova,Kieran Mace,Chris Harbron,Ryan Copping,Balasubramanian Narasimhan,Robert Tibshirani
Journal
medRxiv
Published Date
2023
Professor FAQs
What is Robert Tibshirani's h-index at Stanford University?
The h-index of Robert Tibshirani has been 119 since 2020 and 179 in total.
What are Robert Tibshirani's top articles?
The articles with the titles of
Smooth multi-period forecasting with application to prediction of COVID-19 cases
CAR19 monitoring by peripheral blood immunophenotyping reveals histology-specific expansion and toxicity.
Intraoperative Evaluation of Breast Tissues During Breast Cancer Operations Using the MasSpec Pen
Evaluating a shrinkage estimator for the treatment effect in clinical trials
Artificial Intelligence Identifies Factors Associated with Blood Loss and Surgical Experience in Cholecystectomy
Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank
Pretraining and the Lasso
Prognostic pan-cancer and single-cancer models: A large-scale analysis using a real-world clinico-genomic database
...
are the top articles of Robert Tibshirani at Stanford University.
What are Robert Tibshirani's research interests?
The research interests of Robert Tibshirani are: Statistics, data science, Machine Learning
What is Robert Tibshirani's total number of citations?
Robert Tibshirani has 488,983 citations in total.
What are the co-authors of Robert Tibshirani?
The co-authors of Robert Tibshirani are David Botstein, Trevor Hastie, Ronald Levy, B Efron, Matt van de Rijn, Ash A. Alizadeh, MD, PhD.