Florida State University


Welcome to Dr. Sinha's research page. On this page, you will find his research interests, accomplishments, current and former student information, and published research. You will also find scripts involved in his research.

Dr. Debajyoti Sinha

Ron & Carolyn Hobbs Endowed Chair/ Professor, Department of Statistics.

Research interests

  • Survival analysis and Bayesian analysis.
  • Bayesian Biostatistics.
  • Modeling Cancer prevention data.
  • Cure rate and survival data.
  • Modeling Cancer relapse data and recurrence data.
  • Semiparametric empirical Bayes.
  • Methods for skewed and heteroscedastic response.

Curriculum Vitae



Please click here to view my most recent CV.

Code for computing the empirical power and type I error for Proportional Odds Model when the data are generated from proportional odds model or proportional hazard model respectively.

One of functions we used in calculate the MLE of effect size beta.

A function for using Newton-Raphson algorithm to estimate maximum likelihood estimation (MLE) of beta of under proportional odds model. The input include patient survival time, censored status and covariates. The output is the point estimate for the covariate effects beta.

One of functions we used in calculate the MLE of effect size beta.

A function for generating data from univariate proportional hazard model. The input includes the number of samples n and the true effect size beta. Then output data are the survival times for every patients..

A function for generating data from univariate proportional odds survival model. The input includes the number of samples n and the true effect size beta. Then output data are the survival times for every patients.

A function for calculating the standard deviation for the estimation of covariate effects beta.

R code used for IMR prior for Cure Rate survival model. Given the survival data, the output for the function includes the posterior samples for the covariates effects using IM prior given the input data.

R code used for IMR prior for proportional hazard model. The baseline hazard function is assumed to be piecewise constant function. Given the survival data, the output for the function includes the posterior samples for the covariates effects using IM prior given the input data.

R code used for gprior for proportional hazard model. Given the survival data, the output for the function includes the posterior samples for the covariates effects using IM prior given the input data. This method is used as a reference method to evaluate the performance of IMR prior.

R code used for Gaussian prior for proportional hazard model. Given the survival data, the output for the function includes the posterior samples for the covariates effects using IM prior given the input data. This method is used as a reference method to evaluate the performance of IMR prior.

Generates data from proportional hazard model with piecewised baseline hazard function. We used these data in our simulation study. The input includes the covariates matrix, the coefficients for covariates, number of sample size, and the baseline hazard function. The output is the survival time for all patients.

The code below corresponds to methodology discussed in the following paper:

Bayesian Partial Linear Model for skewed longitudinal Data

[Status: Submitted to Journal of the American statisticial assosciation on 12/12/12]

The zip folder contains scripts (JAGS,R) for the following:
Simulation Study:
R code is for generating data, theJAGS code is for the model and prior.
Data Example:
R code is to read the data, reshape the data, standardize the data. JAGS code covers the model and prior.

The code below corresponds to methodology discussed in the following paper:

Bayesian variable selection for skewed heteroscedastic error

[Status: In progress]

The zip folder contains scripts (JAGS,R) for the following:
Simulation Study:
R code for generating data, JAGS code for model and prior.
Data Example:
R code for cleaning, reading data, JAGS code for MCMC update.

The code below corresponds to methodology discussed in the following paper:

Bayesian regression for univariate skewed heteroscedastic error

[Status: In progress]

The zip folder contains scripts (MATLAB) for simulation studies and a data example.

The code below corresponds to methodology discussed in the following paper:

Empirical Bayes estimation for additive hazards regression models.

Sinha D, McHenry MB, Lipsitz SR, Ghosh M.

[Status: Published; Biometrika. 2009 Sep;96(3):545-558. Epub 2009 Jun 26.]

The code below corresponds to methodology discussed in the following paper:

Flexible Cure Rate Modelling Under Latent Activation Schemes

Cooner F., Banerjee S., Carlin B.P, and Sinha D.

[Status: Published; Journal of the American Statistical Association, 2006]

The code below corresponds to methodology discussed in the following paper:

Changing approaches of prosecutors towards juvenile repeated sex-offenders: A Bayesian evaluation

D.Bandyopadhyay, D. Sinha, S.Lipsitz, and E. Letourneau

[Status: Published; Annals of Applied Statistics, 2010; doi: 10.1214/09-AOAS295]

The code below corresponds to methodology discussed in the following paper:

Association Models for Clustered Data with Binary and Continuous Responses

L.J. Lin, D. Bandyopadhyay, S.R. Lipsitz, and D.Sinha.

[Status: Published; Biometrics, 2010 Mar;66(1):287-93. doi: 10.1111/j.1541-0420.2008.01232.x. Epub 2009 May 7.]

The code below corresponds to methodology discussed in the following paper:

Bayesian analysis of recurrent event with dependent termination: an application to a heart transplant study

Ouyang B, Sinha D, Slate EH, Van Bakel AB.

[Status: Published; Stat Med. 2012 Dec 19. doi: 10.1002/sim.5717. [Epub ahead of print]]

The code below corresponds to methodology discussed in the following paper:

An extension of the Wilcoxon rank sum test for complex sample survey data

[Status: Published;Journal of Royal Statistical Society, Series C, 2012, pp 653-664]

The code below corresponds to methodology discussed in the following paper:

Bias correction for the proportional odds logistic regression model with application to a study of surgical complications

[Status: Published;Journal of Royal Statistical Society, Series C, 2013, pp 233-250]