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Advanced statistical methods in Biomedicine

Svanninge Bakker

The appropriate and efficient statistical analysis of data collected in medicine often requires advanced techniques which are only covered at an elementary level in introductory statistical courses.

This course aims at medical researchers in need of an overview about appropriate analytical methods and discussions with statisticians to be able to solve their problem.

This course offers instruction in several topics within medical statistics. When registering to the course participants are asked to identify the main statistical topics relevant for their project.

The course will be organized as a ‘boot’camp’ where the participants gather for two days at a venue for fruitful and intensive interaction between medical researchers and statisticians.

List of potential topics

  1. Analysis of repeated and longitudinal measurements
    When you collect the same type of measurements on a patient repeatedly over time (longitudinal data) you face at least two statistical problems: how to account for the correlation induced by the repeated measurements on the same experimental subject and how to model the course over time in clinical relevant terms. The theory of mixed models and generalized estimating equations offer a rich class of solutions to the problems both for normally distributed responses as for categorical ones (like e.g. binary observations).
     
  2. Study design: Randomized clinical trials and observational studies
    We discus several aspect of study design, where the main distinction is between randomized clinical trials (RCT) and observational studies. RCT’s are considered the most appropriate design to analyze the effectiveness of a medical intervention allowing and providing a causal interpretation. Nevertheless, several aspects like randomization, have to performed in a proper way to be able to harvest these features from an RCT. We discuss these aspects and possible limitations of RCTs . For observational studies we review the basic concepts of cohort and cross-sectional designs. We discuss the selection of informative covariates, when to adjust for confounding factors and propose methods to reduce the influence of unmeasured confounding.
     
  3. Statistical genetics 
    Over the last decades genetic data (often high-dimensional) have become more and more common, such as genotype data or methylation data. A wide range of statistical methods (and software) have been applied and/or developed in order to handle, to explore and to draw inferences from these types of data. Examples are methods to test and predict in GWAS (Genome wide Association Studies), to analyze rare variants, depict biological pathways, investigate population structure, impute missing values or perform linkage analysis in family studies. Because of the high dimensionality of the data the topic of multiple testing deserves special attention.

  4. Survival analysis
    In survival analysis on analyses times to the occurrence of some event like the time to remission of cancer since end of the treatment period. An important feature of such data is the occurrence of censored observation times where a patient leaves the study before the event of interest has been observed. We will discuss non-model based (non-parametric) and model based approaches to analyze such time to event data.

  5. Constructing and validating scales from questionnaires
    Factor analyses and structural equation models (SEM) are useful tools for exploring and validating new scales describing underlying dimensions or latent variables among large sets of variables or questions from a questionnaire. When using established questionnaire scores or scales (e.g. SF-36, EORTC QLQ-C30) in new populations, Crohnbach’s alpha can provide a crude validation of the scale in the population, while SEM offers a more comprehensive validation.

  6. Meta analysis
    Meta analysis aims at combining and summarizing the evidence of medical effects already reported in published studies. It uses methods of fixed effects or random effect regression modelling. Meta analysis I becoming an important ingredient to present the already known knowledge in the planning phase of new studies.

  7. Causal modelling in medicine
    Clinical observational studies do not possess the same causal strength for treatment effect as randomized clinical trials. This is mainly due to the impossibility to control the confounder distribution in the treatment groups. Another problem is the time dependent treatment adjustment which often is affected by previous response. Methods like inverse probability weighting or g-estimation try to account for this imbalance to estimate a causally interpretable effect.

  8. Missing data
    Missing data –in the response and/or important explanatory variables -occur nearly inevitably in each clinical trial. The most simple solution of removing all observations with at least one missing value may seriously affect the correctness of the effect to estimate and the power to detect an effect. Some approaches to handle missing data like multiple imputation and inverse probability weighting are discussed.

Course Schedule:

Tuesday, 17.01 2017
   
9:00 - 9:30 Coffee and tea  
9:30 - 9.45 Welcome
9:45 - 11:00  Flash presentation of participants and projects  Participants give a short oral presentation of themselves and their projects
11:00 - 11:15  Coffee break   
11:15 - 12:00  Lecture 1 : Constructing and validating scales from questionaire  
12:00 - 13:00  Lunch   Optional walk to Lerbjerg from 12:30
13:00 - 13:45  Lecture 2 : Analysis of repeated and longitudinal measurements  
14:00 - 16:30 Work on projects in smaller groups  Coffee/tea and cake/fruit
will be available from 15:00 
18:00 - 19:00  Dinner  Skovlyst Restaurant (near by) 
http://www.skovlyst.dk
19:00 - 21:00  Further discussion about some projects   
Wednesday, 18.01 2017
   
9:00 - 9:15 Recollection of the previous day  
9:15 - 10.00 Lecture 3: Study design: Randomized clinical trials and observational studies
10:00 - 10:15 Coffee break   
10:15 - 11:00  Lecture 4: Analysis of mising data (taxonomy and missingness, model with IPW)
11:15 - 12:00  Work on projects in smaller groups   
12:00 - 13:00  Lunch   
13:00 - 14:45 Work on projects in smaller groups  Coffee/tea and cake/fruit
will be available from 14:00 
15:00 - 16:00  Discussion and evaluation   

Course site

The course site is the Svanninge Bjerge Forsknings- og Feltstation (http://svanninge.sdu.dk/). There will be 16 twin bedrooms available. Participants are required to share rooms.

Svanninge Bjerge Forsknings- og Feltstation

Course costs

The course is included in the course programme of the SDU PhD school and is free for PhD students. Researchers from SUND Odense and SUND Region of Southern Denmark have to pay 600 DKK and other researchers 5263 DKK.

You will recieve an invoice ultimo November which has to be paid latest December 9th, 2016.

Registration and preparations for the course:

Please send until 02. December 2016 the an e-mail to Lone Myllerup Hansen (lmyllerup@health.sdu.dk) with following content.

  1. Indicate your working place and position (PhD student or not).
  2. Choose up to three topics from the list you would like to be discussed at the course.
  3. Indicate whether you will stay at the venue overnight and whether you have special requests .e.g. food allergies or vegetarian.
  4. Fill out the LINKED document with a brief one-page description of your project.  The document will be used in the decision of the themes of the lectures and to present your project to the other participants. Please attach this document to your mail.

After the registration mail you will receive a confirmation mail.

SDU Research- & Field Center

Svanninge Bjerge

About the Center

Svanninge Bakker

Beautiful natural Danish resort

About the hills

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