Quantitaive Analysis Unit


Analytical Quantitative Unit

In this unit various sources of big data are gathered for mining the health indices. These indices offer a wide insight of current situation of national health system in the first step, which can be used for appropriate interventions planning.  These Various sources of big data are such as: Census, House Hold Expenditure, outpatient data, death registry, cancer registry, DHS, Step, and NHS. For several areas in the country, Data specific to those areas such as cohort studies are being used. By collecting these data, the most comprehensive databases will be available for the NCDRC.  

In order to prepare the results needed, our statisticians made enormous effort and developed advance models namely: Gaussian process Regression (GPR), Bayesian Multilevel, Spatiotemporal, Machine Learning, Data Mining, and Bayesian Modelling Average (BMA). Several bias in raw data, which make them useless, force the expertise of NCDRC to consider the complex and accurate processing of cleaning data in their agenda.

By accomplishment of many projects regarding current situation of health in the country, the approach of this unit tends to provide solution to overcome health system issues and to improve health. This is realized by designing new studies such as cross-sectional survey, cohort survey and interventional studies like clinical trial studies.

 

 

 

Members


 

Ali sheidaei

Master graduate of  Biostatistics, Consultant in the field of statistical analysis

Kimiya Gohari

Master graduate of Biostatistics

Moein Yoosefi

Master graduate of Biostatistics

Parinaz Mahdipour

Master graduate of Biostatistics

Mehrdad Azmin

BEng. MSc. PhD.

Quantitative Analysis Unit Manager

Contact Info

  • Non-Communicable Diseases Research Center, Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences

    No. 10, Al-e-Ahmad and Chamran Highway intersection, Tehran

    Postcode: 1411713136

  • +9821-88631293
  • This email address is being protected from spambots. You need JavaScript enabled to view it.

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