Haresh D. Rochani
Assistant Professor, Biostatistics, Epidemiology, and Environmental Health Sciences | Georgia Southern University
Statesboro, GA, UNITED STATES
Haresh Rochani works with bio-statistics and sampling designs to improve public health.
Spotlight
Biography
Haresh Rochani is a DrPH of Biostatistics and currently working as an Assistant Professor (Tenured track position) and the Director of the Karl Peace Center for Biostatistics of at Jian Ping HSu college of Public Health at Georgia Southern University. He has a undergraduate MBBS degree from Baroda Medical College, Maharaja Sayaji Rao University along with ECFMG (Educational Commission for Foreign Medical Graduates) certification.
Areas of Expertise (6)
Generalized Linear Models
Longitudinal Data Analysis
Multiple Imputations
Missing Data in Diagnostic Medicine
Categorical Missing Data
Log-Linear Models
Accomplishments (1)
Award for Excellence in Scholarship, Jiann Ping Hsu College of Public Health
2018
Education (3)
Georgia Southern University: Dr.P.H., Doctor of Public Health with Bio-Statistics 2014
Georgia Southern University: M.P.H., Master of Public Health 2010
Maharaja Sayajirao University: M.B.B.S., Bachelor of Medicine & Surgery 2007
Links (1)
Articles (3)
On Kernel-Based Mode Estimation Using Different Stratified Sampling Designs
Journal of Statistical Theory and PracticeHani Samawi, Haresh Rochani, JingJing Yin, Robert Vogel
2019 In the literature, the properties and the application of mode estimation is considered under simple random sampling and ranked set sampling (RSS). We investigate some of the asymptotic properties of kernel density-based mode estimation using stratified simple random sampling (SSRS) and stratified ranked set sampling designs (SRSS). We demonstrate that kernel density-based mode estimation using SRSS and SSRS is consistent, asymptotically normally distributed and using SRSS has smaller variance than that under SSRS. Improved performance of the mode estimation using SRSS compared to SSRS is supported through a simulation study. We will illustrate the method by using biomarker data collected in China Health and Nutrition Survey data.
Reducing sample size needed for cox-proportional hazards model analysis using more efficient sampling method
Communications in Statistics-Theory and MethodsHani M Samawi, Lili Yu, Haresh Rochani, Robert Vogel
2018 In general, survival data are time-to-event data, such as time to death, time to appearance of a tumor, or time to recurrence of a disease. Models for survival data have frequently been based on the proportional hazards model, proposed by Cox. The Cox model has intensive application in the field of social, medical, behavioral and public health sciences. In this paper we propose a more efficient sampling method of recruiting subjects for survival analysis. We propose using a Moving Extreme Ranked Set Sampling (MERSS) scheme with ranking based on an easy-to-evaluate baseline auxiliary variable known to be associated with survival time. This paper demonstrates that this approach provides a more powerful testing procedure as well as a more efficient estimate of hazard ratio than that based on simple random sampling (SRS). Theoretical derivation and simulation studies are provided. The Iowa 65+ Rural study data are used to illustrate the methods developed in this paper.
Application of the Misclassification Simulation Extrapolation Procedure to Log-Logistic Accelerated Failure Time Models in Survival Analysis
Journal of Statistical Theory and PracticeVaradan Sevilimedu, Lili Yu, Hani Samawi, Haresh Rochani
2018 Misclassification of binary covariates is pervasive in survival data, leading to inaccurate parameter estimates. Despite extensive research of misclassification error in Cox proportional hazards models, it has not been adequately researched in the context of accelerated failure time models. The log-logistic distribution plays an important role in evaluating non-monotonic hazards. However, the performance of misclassification correction methods has not been explored in such scenarios. We aim to fill this gap in the literature by investigating a method involving the simulation and extrapolation algorithm, to correct for misclassification error in log-logistic AFT models and later apply this method in real survival data.
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