noah simon

Reframing proportional-hazards modeling for large time-to-event datasets with applications to deep learning

Frontiers in Biostatistics Seminar

Noah Simon, Ph.D.
Associate Professor
Department of Biostatistics
University of Washington

To build inferential or predictive survival models, it is common to assume proportionality of hazards and fit a model by maximizing the partial likelihood. This has been combined with non-parametric and high dimensional techniques, eg. spline expansions and penalties, to flexibly build survival models.

New challenges require extension and modification of that approach. In a number of modern applications there is interest in using complex features such as images to predict survival. In these cases, it is necessary to connect more modern backends to the partial likelihood (such as deep learning infrastructures based on eg. convolutional/recurrent neural networks). In such scenarios, large numbers of observations are needed to train the model. However, in cases where those observations are available, the structure of the partial likelihood makes optimization difficult (if not completely intractable).

In this talk we show how the partial likelihood can be simply modified to easily deal with large amounts of data. In particular, with this modification, stochastic gradient-based methods, commonly applied in deep learning, are simple to employ. This simplicity holds even in the presence of left truncation/right censoring, and time-varying covariates. This can also be applied relatively simply with data stored in a distributed manner.

Frontiers in Biostatistics: Group Sequential Design Assuming Delayed Benefit

February 9, 2021
1:00PM ET

Keaven Anderson, PhD
Scientific AVP, Methodology Research, Biostatistics at Merck

Group Sequential Design Assuming Delayed Benefit

Abstract: We consider an asymptotic approach to design of group sequential trials with a potentially delayed effects. Logrank, weighted logrank tests and combination tests are of primary interest, but we also consider restricted mean survival. The asymptotic approach allows both quick derivation of study design properties that are also easily verified using simulation. We rely heavily on work done by Tsiatis, 1981 published while he was at the HSPH. The impact of a potential delay in treatment effect on timing of analyses, study boundaries and sample size are demonstrated. The value of a robust design that is well powered under a variety of assumptions is emphasized. Open source software is provided for implementation. Given the current regulatory climate, logrank testing may still be preferred for these trials, but we hope that efficiencies and Type I error control associated with alternatives may make other options more acceptable for future consideration.

 

Alejandra Avalos-Pacheco, PhD Wins 2020 Savage Award

Alejandra Avalos-Pacheco, PhD, a postdoctoral fellow in the Lorenzo Trippa lab and the Harvard Program in Therapeutic Science (HiTS) within the Harvard-MIT Center for Regulatory Science (CRS), has won the 2020 International Society of Bayesian Analysis Savage Award. The Savage Award, named for pioneering statistician Leonard J. Savage, recognizes two out-standing doctoral dissertations in Bayesian econometrics and statistics, one each in Theory & Methods and Applied Methodology.

Dr. Avalos-Pacheco won in the category Applied Methodology for her PhD thesis on“Factor regression for dimensionality reduction and data integration techniques with applications to cancer data.” She defended her thesis on March 2019 at the University of Warwick as part of her PhD studies in the joint Oxford-Warwick PhD program on Big Data, and was supervised by Richard Savage (University of Warwick) and David Rossell (Universitat Pompeu Fabra). “My thesis develops a practical solution to address heterogenous high-dimensional data integration using Bayesian techniques, while also offering practical foundational and computational developments,”  says Dr. Avalos-Pacheco. “ My results show that batch effects and data integration are practically-relevant in cancer genomics and applies the methodology to ovarian, lung and pancreatic datasets.”

Congratulations, Dr. Avalos-Pacheco!

Collin Tokheim Headshot

Collin Tokheim, PhD Wins New Damon Runyon Cancer Research Foundation Quantitative Biology Fellowship

Collin Tokheim, PhD, a postdoc in the X. Shirley Liu lab, has been awarded a Damon Runyon Cancer Research Foundation Quantitative Biology Fellowship. Dr. Tokheim’s mentors are X. Shirley Liu, PhD, and Eric S. Fischer, PhD, at Dana-Farber Cancer Institute, Boston.

“The first class of Damon Runyon Quantitative Biology Fellowship Awardees launched their research in novel directions that may lead to the next breakthroughs in cancer research. Nine brilliant young scientists will apply their quantitative skills to design innovative experiments and interpret massive data sets that may help solve important biological and clinical problems. The awardees were selected by a distinguished committee of leaders in the field.

Dr. Tokheim is developing computational models that can identify degradable proteins that are linked to the development of human cancers. Unlike traditional drugs that bind and block the activity of key proteins in cancer cells, a new generation of drugs can eliminate proteins by hijacking the protein degradation machinery within cells. By leveraging big data from thousands of tumor profiles and a novel statistical and deep learning model, he will conduct an unbiased search for candidate proteins that can be verified experimentally. This research may lead to the development of drugs targeting protein degradation as a potent and selective way to treat a variety of human cancers.

Each postdoctoral scientist selected for this unique three-year award will receive independent funding ($240,000 total) to train under the joint mentorship of an established computational scientist and a cancer biologist. Damon Runyon has created this new funding mechanism to encourage quantitative scientists (from fields such as mathematics, physics, computer science and engineering) to pursue careers in cancer research. This support will help create an elite cadre of computational biology leaders trained in quantitative and biological sciences—scientists who are capable of traversing both worlds with ease and are comfortable speaking both languages fluently.

“Because this is in essence a new field at the nexus of traditional cancer research and data science, it is critical to draw fearless and brilliant young computational scientists to these problems to drive the field forward,” said Aviv Regev, PhD, of the Broad Institute and inaugural Chair of the Quantitative Biology Fellowship Award Committee.”

Full release: Damon Runyon Cancer Research Foundation

rafael irizarry dfci headshot

Rafael Irizarry, PhD Elected International Society of Computational Biology Fellow

Rafa Irizarry, PhD, Professor of Biostatistics at Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, has been elected a 2020 International Society of Computational Biology Fellow.

From ISCB:

“The ISCB Fellows program was created to honor members who have distinguished themselves through outstanding contributions to the fields of computational biology and bioinformatics. Begun in 2009, 2020 marks the 11th anniversary of the program. In early December of each year, ISCB has sought Fellow’s nominations from our members, with eligibility restrictions based on selection criteria focused most heavily on the significance of scientific contributions.

Dr. Irizarry was selected for his pioneering work in expression analysis and development of statistical methods for expression analysis, which are some of the most impactful in the entire field.”