Medical Statistics for Big Data



Overview

The course aims to give students the skills needed to analyse data from electronic health record studies and to interpret the results. Lectures will cover logistic and Poisson regression, and conditional regression methods for self-controlled and matched study designs. Sessions on missing data and multiple imputation will cover all necessary steps from investigation of missingness and multiple imputation of missing data, through to analysis and sensitivity analysis.  Other lectures will include introductions to the statistical analysis of interrupted time series and propensity scores and causal inference.  The in-depth practical sessions will give students the opportunity to implement these methods using R or Stata. 

Module coordinators Dr Margaret Smith and Dr Emily McFadden both have extensive experience in the use of routine healthcare data for research.  Margaret is a senior statistician and epidemiologist within Oxford’s CPRD research group, Deputy Course Director of the MSc in EBHC Medical Statistics and a member of a CPRD Research and Data Governance (RDG) Expert Review Committee. Emily is a Department Lecturer in Clinical Epidemiology and senior statistical epidemiologist within the Centre for Evidence-Based Medicine, Chair of one of the CPRD's RDG Expert Review Committees and a member of the Central Advisory Committee.  Tutors include Professor Richard Stevens and Dr Francesca Little. Richard is Course Director of the MSc in EBHC Medical Statistics and was involved in the CPRD's RDG process for over 10 years. Francesca is a senior statistician in the Nuffield Department of Primary Care Health Sciences with many years’ experience in statistical modelling applied to health sciences data and the teaching of medical statistics.

The last date for receipt of complete applications is 5pm, Friday 19th June 2026. Regrettably, late applications cannot be accepted.
 

Programme details

This module is run over an eight week cycle where the first week is spent working on introductory activities using a Virtual Learning Environment, the second week is spent in Oxford for the face to face teaching week (this takes place on the dates advertised), there are then four Post-Oxford activities (delivered through the VLE) which are designed to help you write your assignment. You then have a week of personal study and you will be required to submit your assignment electronically the following week (usually on a Tuesday at 14:00 UK Local Time).

Fees

Description Costs
Short Course in Health Sciences £3390.00

Funding

Details of funding opportunities, including grants, bursaries, loans, scholarships and benefit information are available on our financial assistance page.

Discounts

If you are an employee of the University of Oxford and have a valid University staff card you may be eligible to receive a 10% discount on the full stand-alone fee. To take advantage of this offer please submit a scan/photocopy of your staff card along with your application. Your card should be valid for a further six months after attending the course.

 

If you are a current employee of the NHS, you may be eligible for our new NHS Short Course Bursary (or “NHS Bursary”), which offers a limited number of places at a 20% fee reduction. Please click on the link here to find out more.

Tutors

Dr Margaret Smith

Module Coordinator

Dr Margaret Smith is a Senior Statistician and Epidemiologist at the Nuffield Dept of Primary Care Health Sciences, University of Oxford, and the Deputy Course Director of the MSc in EBHC (Medical Statistics).

Dr Emily McFadden

Module Coordinator

Dr Emily McFadden is a Departmental Lecturer and Senior Statistical Epidemiologist at the Nuffield Dept of Primary Care Health Sciences, University of Oxford.

Course aims

The course aims to give students the skills needed to plan and execute statistical analyses for data from electronic health record studies, including from specialised study designs. The module will emphasise the practical application of statistical methods to real-life problems in large health databases. Students will learn what to consider when choosing an appropriate analysis method and to conduct the analyses using appropriate software and to interpret the results.

This course complements the Big Data Epidemiology module which aims to provide competence in the design, execution and interpretation of analyses of large databases of routinely collected data, such as the Clinical Practice Research Datalink (CPRD), but does not emphasise the application of statistical methods of analysis.  For a detailed comparison of these two courses please click here

This course is designed for health care professionals or graduate students who already have some knowledge of designing studies using electronic health record data as well as some previous experience of statistical analysis. Applicants must provide information on their statistical experience and their experience of research utilising electronic health record data in their personal statement section of the application form. Students who have taken the Essential Medical Statistics and Big Data Epidemiology will meet these requirements, but this is not a requirement for acceptance.
 

Learning outcomes

By the end of the course the student will be expected to:

  • Understand the statistical concepts and assumptions underlying generalised linear models such as Poisson regression.
  • Be able to apply conditional methods to specialised study designs such as case-crossover designs and self-controlled case-series. 
  • Understand the principles of multiple imputation for missing data, when it is appropriate, and how to analyse data after multiple imputation.
  • Have an awareness of the principles underlying causal inference and how propensity-scores can be used in this context.
  • Understand how to extend regression approaches to the analysis of interrupted time series. Have an awareness of some of the methodological issues such as autocorrelation.
  • Be able to choose appropriate statistical analyses for questions using EHR data.
  • Have gained experience conducting statistical analyses using methods taught on the course and be able to interpret the results
     

Assessment methods

Assessment will be based on submission of a written assignment which should not exceed 4,000 words.

Academic Credit

Applicants may take this course for academic credit. The University of Oxford Department for Continuing Education offers Credit Accumulation and Transfer Scheme (CATS) points for this course. Participants attending at least 80% of the taught course and successfully completing assessed assignments are eligible to earn credit equivalent to 20 CATS points which may be counted towards a postgraduate qualification.

Applicants can choose not to take the course for academic credit and will therefore not be eligible to undertake the academic assignment offered to students taking the course for credit. Applicants cannot receive CATS (Credit Accumulation and Transfer Scheme) points or equivalence. Credit cannot be attributed retrospectively. CATS accreditation is required if you wish for the course to count towards a further qualification in the future.

A Certificate of Completion is issued at the end of the course.

Applicants registered to attend ‘not for credit’ who subsequently wish to register for academic credit and complete the assignment are required to submit additional information, which must be received one calendar month in advance of the course start date. Please contact us for more details.

Please contact cpdhealth@conted.ox.ac.uk if you have any questions.

Application

This course requires you to complete the application form and to attach a copy of your CV. If you are applying to take this course for academic credit you will also be required to provide a reference. Please note that if you are not applying to take the course for academic credit then you do not need to submit a reference.

Please ensure you read the guidance notes which appear when you click on the symbols as you progress through the application form, as any errors resulting from failure to do so may delay your application.

Selection criteria

To apply for this course you should be able to demonstrate evidence of statistical experience,  including the following: 

  • Statistical concepts - p-values, confidence intervals, standard error.
  • Multivariable linear, logistic and Cox regression.
  • Competence in statistical software.
  • Some experience of using electronic health record data in research.

 

Applicants should also:

  • Be a graduate or have successfully completed a professional training course.
  • Have professional work experience in the health service or a health-related field.
  • Be able to combine intensive classroom learning with the application of the principles and practices of evidence-based health care within the work place.
  • Have a good working knowledge of email, internet, word processing and Windows applications (for communications with course members, course team and administration).
  • Show evidence of the ability to commit time to study and an employer's commitment to make time available to study, complete course work and attend course and university events and modules.
  • Be able to demonstrate English Language proficiency at the University’s higher level. 
     

Accommodation

Accommodation is available at the Rewley House Residential Centre, within the Department for Continuing Education, in central Oxford. The comfortable, en-suite, study-bedrooms have been rated as 4-Star Campus accommodation under the Quality In Tourism scheme, and come with tea- and coffee-making facilities, free Wi-Fi access and Freeview TV. Guests can take advantage of the excellent dining facilities and common room bar, where they may relax and network with others on the programme.  Accommodation is not included in the course fee.

IT requirements

Please ensure that you have access to a computer that meets the specifications detailed on our technical support page.

Terms & conditions for applicants and students

Information on financial support