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PhD (Biostatistics)


In order to develop manpower in the area of Biostatistics, the department trains research students for doctoral degree.  The students are admitted every year and the details of this programme are as follows.

 

Program of Study:  Development and application of biostatistics in the field of mental health and  neurosciences.

 

Number of seats:  01* Seat under Institute Fellowship (Candidates with external fellowship are also taken).

 

Duration:    3 – 5 Years

 

Minimum Qualification for Admission:  M.Stat (ISI) / M.Sc in Statistics / Biostatistics / Medical Statistics / Health Statistics / Agricultural Statistics/ Applied Statistics through regular course from recognized Universities/ Institutions.

 

 Mode of selection:  The candidates will be selected through entrance examination followed by interview.

 

 Fellowship:  The selected candidates will receive fellowship as per NIMHANS guidelines (details in NIMHANS Prospectus)

 

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So far, 15 PhD degrees have been awarded and the following is the list of research areas of the completed and on-going research.

 

  1. Multivariate analysis to predict Prognosis of Patients with Epilepsy.
  2. Statistical  studies on rabies.
  3. Application of Path coefficient analyses in the field of psychiatry, neurology and neurosurgery.
  4. Empirical study on the performance of the linear discriminant function.
  5. Cluster analytic approach in psychiatry with a special reference to child psychiatry.
  6. Survival regression and their application in mental health and neurosciences.
  7. Development of latent structure model for the computation of disability adjusted life years.
  8. Prognostic, calibration and validation of survival models for epilepsy.
  9. Relative performance of artificial neural network and logistic regression for outcome prediction in schizophrenia.
  10. Meta-analytical approach to estimate pattern of prevalence of schizophrenia and epilepsy In India.
  11. Evaluation of clustering methods for pattern recognition in scholastic improvement of rural children.
  12. Cluster Formation in Latent Class and Finite Mixture Models.
  13. On Certain Statistical Methods to Handle Incomplete Data in Research Studies.
  14. Categorical Data Modelling and their Application in Biomedical Research.
  15. A Comparison of Clustering Algorithms for Classification and Prognosis in Microarray Data.
  16. Utility of Multidimensional Scaling and Causal Modelling in the Interpretation of IMRI Data.
  17. On Certain Linear and Non-Linear Statistical Models for Measurement of Change.
  18. Exploration of quantile regression methods and their applications in Biomedical Research.
  19. A Comparative Study on Imputation Techniques in Incomplete Data Analysis.
  20. A Study on Poisson and Negative Binomial regression Models for Count Data in Biomedical Research.
  21. Comparison of Probit and Logistic Regression in Fixed effects and Mixed effects Models.
  22. Survival Analysis and the Related Prognostic Factors of Post-operative fossa tumour using Statistical Modeling.