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SUMMARY:Constructing Confidence Interval for RMST under Group Sequential Setting
DESCRIPTION:Frontiers in Biostatistics Seminar\nOctober 13\, 2020\n1:00PM \nLu Tian\, PhD\nAssociate Professor of Biomedical Data Science in the School of Medicine\nStanford University \nIt is appealing to compared survival distributions based on restricted mean survival time (RMST)\, since it generates a clinically interpretable summary of the treatment effect\, which can be estimated nonparametrically without assuming restrictive model assumptions such as the proportional hazards assumption. However\, there are special challenges in designing and analyzing group sequential study based on RMST\, because the truncation timepoint of the RMST in the interim analysis often differs from that in the final analysis. A valid test controls the unconditional type one error has been developed in the past. However\, there is no appropriate statistical procedure for constructing the confidence interval for the treatment effect measured by a contrast in RMST\, while it is crucial for informative clinical decision making. In this talk\, I will review some important design issues for study based on RMST. I will then discuss how to conduct hypothesis testing and how to construct confidence intervals for the difference RMST in a group sequential setting. Examples and numerical studies will be presented to illustrate the method. \nA recording of this seminar is available on your YouTube Channel.
URL:https://ds.dfci.harvard.edu/event/constructing-confidence-interval-for-rmst-under-group-sequential-setting/
CATEGORIES:Seminar
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SUMMARY:Computational Biology of DNA Repair in Cancer
DESCRIPTION:Data Science Seminar \nOctober 15\, 2020\n1:00PM ET \nDominik Glodzik\, PhD\nRepare Therapeutics \nZoom link: https://bit.ly/DSOct15 \nAbstract: Whole genome sequences contain within them signatures of mutational processes. In particular\, some of the mutation signatures relate to impaired DNA-repair in cancer cells. Accurate measurement of mutation signatures reveals the role of DNA-repair deficiencies in etiology and progression of cancer. \nWe extended the computational methods for analysis of mutation signatures in order to describe patterns of chromosomal rearrangements. In particular\, the rearrangement signatures enable the assessment of proficiency of homologous recombination (HR). HRDetect\, an algorithm we developed\, predicts probability of HR-deficiency\, and is based on holistic portrayal of mutational signatures across different classes of somatic mutations. Around 20% of breast cancers contain signatures of HR-deficiency\, and this group is wider than the group of carriers of BRCA1/2 mutations. By contrast to adult cancers\, pediatric cancers with known DNA-repair defects display variation of mutational signatures\, hinting at tissue-specificity of mutational signatures. Finally\, in the chromosomally unstable cancers\, we identified structural rearrangements\, in coding and non-coding regions\, that can act as cancer drivers. Altogether\, these results indicate that computational assessment of DNA-repair capacity of tumor cells is now possible. The methods will be crucial to understanding of the DNA-repair mechanisms and tissue-specificity of mutational processes. \nBio: Dominik Glodzik received his PhD in Computational Biology from the University of Edinburgh\, and held a postdoctoral position at Wellcome Trust Sanger Institute\, before moving to a staff scientist position at Memorial Sloan Kettering Cancer Center. Currently he is a Principal Bioinformatician at Repare Therapeutics in Cambridge.
URL:https://ds.dfci.harvard.edu/event/computational-biology-of-dna-repair-in-cancer/
CATEGORIES:Seminar
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