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28.09.2014 admin
Survival analysis is used when the researcher is interested in whether or not an event happens, and also when it happens. Many social-science phenomena are about causal relationships, that is, that they have a progress over time.
The common denominator for the above mentioned examples is that they possess the same logical structure. We are pleased to offer a new Health Informatics Short Course on June 26, 2014 which examines informatics and data analytics for clinical and translational research. We are offering an exciting bioinformatics short course lead by Julia Chifman, PhD, at Wake Forest University School of Medicine aimed at University, Hospital and Industry based biomedical and translational researchers, educators, and clinician-scientists who are interested in bioinformatics tools and data analysis methodologies specific to gene expression microarrays, e.g. The Center for Quantitative Medicine is offering an intensive one day short course taught by a multidisciplinary team of physician-educators and informatics leaders that uses a patient case approach to introduce the structure and function  of electronic health records (EHR) systems and illustrates these key health informatics.
In principle the course will be in Danish, but if there are participants who do not speak Danish, then the course will be in English.
The idea of the course is to first present the basic concepts and tools that every student should learn in a course in modern basic survival analysis (see the list below).
The course is thought to have between 12 to 20 participants (chosen by the order of registration). The course will require relatively hard work for the teachers, but we do that with pleasure. The work load of the course is 10 days teaching (7 days direct teaching and 3 days consultation, exercises, etc) and approximately 10 working days for the project. We intend to publish the analyses produced in the applied supervised projects in the form of a common technical report. Survival analysis concerns the statistical techniques used to study the time to the occurrence of some specified events. To use survival analyses it is necessary to know how to situate the occurrence of the event of interest in time. Survival analysis allow us to use many techniques that are analogous to the common used statistical methods for standard data. The hypothetical example of time censoring that we described above (some animals did not died during the observation period) is a type of right censoring. An aspect of survival analysis that illustrate how flexible and powerful these techniques are is the capability of properly using time varying explanatory variables. Another interesting possibility open by survival analysis is that it allow us to properly treat what is called competitive risks.
As you can see the modern survival analysis provides some extremely flexible and powerful statistical tools that we believe that might be of relevance in many applications in biology and agricultural sciences.
To provide a thorough introduction to basic survival analysis concepts and methods, and to cover selected advanced issues.
While the information contained herein was correct at the date of publication, Monash reserves the right to alter procedures, fees and regulations should the need arise.
A common tool for the medicine, the social scientist community is starting to see the opportunities provided by this tool. The best non-experimental technique to study these processes of causality is by the use of survival analysis.

A sample of subjects is considered a group at risk where events, like getting married, giving birth, change job, or die from an illness, can happen in a period of risk.
We are now accepting registrations from unior and senior researchers, faculty, postdoctoral fellows, graduate students, research assistants and associates, and clinicians who conduct clinical or translational research or who are interested in health informatics. This short course surveys statistical approaches to analyzing time-to-event survival data which is frequently encountered in biomedical studies and clinical trials. Please insert in the text of your message the following information: full name, institutional affiliation (department and research group) telephone and working address. It is assumed that the participants speak english (most of the literature is in English anyway).
Once the basic techniques are dominated the participants will embrace a practical analysis under supervision and, if required or desired, study a more specialized theme in survival analysis.
The price of the course will be Kr 15.000 per person if 14 persons participate (and cheaper if more than 14 participate).
In spite of its name, survival analysis does not refer only to the study of life-times, although this is a very natural example in the field. For example, if you are studying the life-time of animals in a certain population, it is not enough to know which animals died during the observation period; you need to have some information on when the animals were alive and when they died. For example, in the study of animals life-time, it might occur that not all individuals die during the observation period of study.
Actuarial and Kaplan-Meier are classic methods for characterizing the distribution of censored times.
There are many other kind of censoring mechanisms that can be used in modern survival analysis. Suppose that during the period we observe the animals the climate changes drastically and we would like to know whether this change affects the mortality. Suppose that we would like to study the time of onset of a disease, but some animals die of other causes before the disease onset. We hope to contribute with this course to the dissemination of survival and event history methods. To provide training that enables people to perform their own survival analyses in the Stata 14 statistical software package. The strength of survival analysis is that the observations over time enable us to estimate the chain of causality with a large degree of confidence. In sociology one can mention studies of unemployment, careers, marriage, divorce, and child birth. For each subject one records whether or not the event took place within the time scope of the study, and also how long it took from when that subject entered into the risk phase.
This intensive short course examines the unique characteristics of clinical and life sciences data including the analytic principles, methods and tools for translating health data and information into actionable knowledge for improved health care.
Through lectures and hands-on computer exercises, the course will provide researchers with techniques for analyzing and interpreting survival data using survival curves and Kaplan-Meier estimators of survival functions, as well as regression methods (i.e.
Examples of survival times are time to death, flowering, onset of a disease, cure, unemployment, failure of a component, etc. Working with the own students data set in the project is not only allowed but also encouraged, however we would like to verify whether the project is viable in the framework of the course.

Recall that the largest cost involved is the time you have to set off for participating in the course. Knowing that provides much more information on the life-time of the animals than just recording which animals died, since if an animal dies in the first day of the observation period is a much different situation than if an animal dies, say, in the day 57 or 570.
It is also possible to compare different the distribution of censored times among different populations (e.g. For example, one might have the situation that the precise time of death of the animal is not known, but instead we know that the death occurred in certain period (e.g. These situation is called competitive risk and under certain basic assumptions can be properly treated using survival analysis techniques. The course is presented and cosponsored by the Center for Quantitative Medicine and the CICATS Division of Biomedical Informatics. The collection of the approved analyses generated during the second phases of the course will be published in a technical report and the students will participate as co-authors. We have seen examples of two naive practices to treat this kind of data: 1) Just eliminate the life times of the individuals that did not die from the analysis.
This procedure can clearly lead to misleading conclusions, since one might just eliminate the animals that lived longer. This censoring pattern is classified as an interval censoring and can also be treated with techniques of modern survival analysis.
We will study some techniques for using time varying (or time-dependent) explanatory variables in the course. This includes techniques for characterizing and comparing survival times among different populations and regression-like models. 2) Another practice is to count the animals that did not die as dead at the end of the observation period, i.e. Moreover, it might be known that the death occurred before a certain known time, but the precise death time is not known.
Moreover, survival analysis allows to properly study the effect of explanatory variables that vary on time. This is classified as a kind of left censoring and can also be treated with survival analysis. Clearly this practice leads to underestimation of the life-time and generates results that depend on the arbitrary way we observed the animals instead on depending on the life-time of the animals. During the course the focus will be in techniques for treating right, but left and interval censored data will also be treated. Survival analysis provides other alternatives for treating this type of data by supplying methods that allow to use the information that the life-time is at least larger than the observed time.

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