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Grant Details

Grant Number: 5R03CA205018-02 Interpret this number
Primary Investigator: Lipsitz, Stuart
Organization: Brigham And Women'S Hospital
Project Title: Analyzing Complex Cancer Studies with Skewed Responses
Fiscal Year: 2017


 DESCRIPTION (provided by applicant): Many of the outcomes in seminal cancer studies are highly skewed. Moreover, the data are clustered within oncologist, practice, or hospital, and often the outcomes are right or interval censored, and there are a large number of predictors of interest. Because of the skewness in the outcomes, medians and quantiles of the outcome as a function of covariates is of interest. There is very limited current literature available to deal particularly with statistical models and analysis of clustered skewed response data. Here, to analyze such data, we propose methods in five aims that will have a high impact on clinical and biostatistical sciences and future cancer studies. In particular, the four aims are: 1).Quantile regression for highly skewed clustered outcomes (censored and not censored); 2) Methods for interval-censored data with a log-linear median; 3) Estimating covariate effects on quantiles of highly-skewed mixed response data (including zero-inflated type models); 4) Estimation and prediction for skewed responses when there are a large number of covariates. An additional goal is to make the newly developed statistical/epidemiological methodology widely accessible to nonstatisticians. For the methods described in each aim, we plan to create macros and procedures which can be used with existing, widely-used statistical packages (e.g., SAS and R). Statistical macros and procedures will be made available on our website, together with documentation on how to apply these macros to the examples analyzed in the resulting publications. The approaches we propose are specifically developed to answer important clinical questions that our clinical collaborators need to publish future clinical papers.


Semiparametric analysis of clustered interval-censored survival data using soft Bayesian additive regression trees (SBART).
Authors: Basak P. , Linero A. , Sinha D. , Lipsitz S. .
Source: Biometrics, 2021-04-17; , .
EPub date: 2021-04-17.
PMID: 33864633
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Bayesian analysis of survival data with missing censoring indicators.
Authors: Brownstein N.C. , Bunn V. , Castro L.M. , Sinha D. .
Source: Biometrics, 2021 Mar; 77(1), p. 305-315.
EPub date: 2020-05-04.
PMID: 32282929
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Joint analysis of recurrence and termination: A Bayesian latent class approach.
Authors: Xu Z. , Sinha D. , Bradley J.R. .
Source: Statistical methods in medical research, 2021 Feb; 30(2), p. 508-522.
EPub date: 2020-10-13.
PMID: 33050774
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A New Bayesian Single Index Model with or without Covariates Missing at Random.
Authors: Dhara K. , Lipsitz S. , Pati D. , Sinha D. .
Source: Bayesian analysis, 2020 Sep; 15(3), p. 759-780.
EPub date: 2019-08-06.
PMID: 33692872
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Semiparametric Bayesian latent variable regression for skewed multivariate data.
Authors: Bhingare A. , Sinha D. , Pati D. , Bandyopadhyay D. , Lipsitz S.R. .
Source: Biometrics, 2019 06; 75(2), p. 528-538.
EPub date: 2019-03-29.
PMID: 30365158
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Approximate median regression for complex survey data with skewed response.
Authors: Fraser R.A. , Lipsitz S.R. , Sinha D. , Fitzmaurice G.M. , Pan Y. .
Source: Biometrics, 2016 12; 72(4), p. 1336-1347.
EPub date: 2016-04-08.
PMID: 27062562
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Sex offender registration and notification policy increases juvenile plea bargains.
Authors: Letourneau E.J. , Armstrong K.S. , Bandyopadhyay D. , Sinha D. .
Source: Sexual abuse : a journal of research and treatment, 2013 Apr; 25(2), p. 189-207.
EPub date: 2012-08-22.
PMID: 22915204
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