Our focus at Analyse-it has always been on the development and improvement of our software. While we provide extensive help, tutorials, and technical support for Analyse-it, one area we do not cover is training and consultancy. As many of you will know we are based in England in the United Kingdom, and providing training and consultancy is often done better locally, in-person.
Instead we partner with experts who can provide training and consultancy in various disciplines, in local language, and geographically near (or at least nearer) to our customers. You can always find a list of current consultant and training partners at https://analyse-it.com/support/training
One of the experts we have had a long relationship with is Dr. Thomas Keller. Dr Keller is an independent statistician and has run ACOMED statistik for 15 years. One his many areas of expertise is the planning and evaluation of experiments for method validation and he has been involved in international working groups (IFCC, CLSI) in the fields of clinical chemistry and laboratory medicine. Dr. Keller was actually a customer and started to provide training in Analyse-it shortly after. His reputation is second to none in the industry and he has provided consultancy and training to many companies using Analyse-it. See an example of a course on Method Validation according to CLSI guidelines offered by Dr. Keller. He also provides WebEx and telephone consultations for anything from simple questions to full courses for individuals and small groups.
Dr. Keller is providing a training course in April on how to use Analyse-it in bio-statistical analysis. The course will be in Germany, in Leipzig, and details are below. If you would like to attend, would like to consult with Dr. Keller, or maybe even arrange a similar course for your colleagues, please contact Dr. Keller at https://analyse-it.com/support/training#drThomasKeller
Course details taken from:
3rd April 2019, 9am to 5pm
BIO CITY Leipzig, Deutscher Platz 5c, 04103 Leipzig, Germany
The evaluation of data from clinical laboratories and biomedical research requires a sound statistical basis. In addition to the generation of research results, statistical methods also play a central role in quality assurance and method validation. A suitable software for this is the Excel-Add-On Analyse-It®, which provides basic statistical evaluations regarding estimation and testing as well as method validation. The software is based on the methodology described in the CLSI (Clinical Laboratory Standard Institute).
The seminar will introduce basic methods of bio-statistics. Data from method validation experiments will mainly be used as examples. The aim of the seminar is to introduce scientific staff and laboratory assistants to basic statistical approaches and at the same time to apply and deepen this knowledge in exercises.
The event is aimed at scientists and laboratory assistants from biomedical research as well as clinical laboratories and comparable institutions who use Microsoft Excel and, if applicable, Analyse-It® for their analyses or are planning to do so.
The workshop will be provided in German language only. In the seminar exercises with the statistics software Analyse-It® will form an important part. Participants should bring their own laptop with MS Excel installed. In addition, a temporary license for the Analyse-It® software will be provided in advance.
Part 1: Parametric and non-parametric description of data, introduction to the Analyse-It® software
Introduction to the Analyse-It® software
Structure of the data for evaluations with Analyse-It®
Mean value and standard deviation
Median and percentiles
Suitable graphical representations
Deviation from normal distribution
Application: Data from clinical studies, reference limits
Part 2: Statistical estimation
Standard error and confidence interval
Statistical testing with confidence intervals
Test for difference vs. test for equivalence
Application: Robustness investigations, method comparisons by means of difference plot
Part 3: Statistical hypothesis tests
How do hypothesis tests work?
How do I select the appropriate test?
How do I interpret the test results?
Application: Data from clinical studies
Part 4: Linear Regression and ANOVA
Linear and Polynomial Regression
Application: Linearity (Demonstration)
ANOVA and variance components
Application: Precision investigations (demonstration)
For pricing and information on how to book please see http://www.biosaxony-tools.de/limesurvey/index.php/764944/lang-de and contact Dr. Keller directly at https://analyse-it.com/support/training#drThomasKeller
Prediction intervals on Deming regression are a major new feature in the Analyse-it Method Validation Edition version 4.90, just released.
A prediction interval is an interval that has a given probability of including a future observation(s). They are very useful in method validation for testing the commutability of reference materials or processed samples with patient samples. Two CLSI protocols, and both use prediction intervals.
Often we collect a sample of data not to make statements about that particular sample but to generalize our statements to say something about the population. Estimation is the process of making inferences about an unknown population parameter from a random sample drawn from the population of interest. An estimator is a method for arriving at an estimate of the value of an unknown parameter. Often there are many competing estimators for the population parameter that differ based on the underlying statistical theory.
A critical feature of any analytical and statistical software is accuracy. You are making decisions based on the statistics obtained and you need to know you can rely on them.
We have documented our previously, but another good benchmark to test statistical software against is the NIST StRD. The Statistical Engineering and Mathematical and Computational Sciences Divisions of NIST’s Information Technology Laboratory developed datasets with certified values for a variety of statistical methods against which statistical software packages can be benchmarked. The certified values are computed using ultra-high precision floating point arithmetic and are accurate to 15 significant digits.
The recent of passing of Professor Rick Jones (see ) caused me to reflect on the past.
I was very fortunate to earn a work placement with Dr Rick Jones at The University of Leeds in the summer of 1990. Rick was enthusiastic about the role of IT in medicine, and after securing funding for a full-time position he employed me as a computer programmer. Early projects included software for automating the monitoring of various blood marker tests and software to diagnose Down’s syndrome. At the time many hospitals had in-house solutions for diagnosing Down’s syndrome, and although the project took many years and the help of many other people to complete, it eventually gained widespread adoption.
Today we released version 3.80 of the Analyse-it Standard edition.
The new release includes Principal Component Analysis (PCA), an extension to the multivariate analysis already available in Analyse-it. It also includes probably the most advanced implementation of biplots available in any commercial package.
New features include:
The tutorial walks you through a guided example looking at how to use correlation and principal component analysis to discover the underlying relationships in data about New York Neighbourhoods. It demonstrates the amazing new features and helps you understand how to use them. You can either follow the tutorial yourself, at your own pace, or .
What is a sample quantile or percentile? Take the 0.25 quantile (also known as the 25th percentile, or 1st quartile) -- it defines the value (let’s call it x) for a random variable, such that the probability that a random observation of the variable is less than x is 0.25 (25% chance).
A simple question, with a simple definition? The problem is calculating quantiles. The formulas are simple enough, but a take a quick look on Wikipedia and you’ll see there are at least 9 alternative methods . Consequently, statistical packages use different formulas to calculate quantiles. And we're sometimes asked why the quantiles calculated by Analyse-it sometimes don’t agree with Excel, SAS, or R.
In a previous post, , we explained the tests provided in Analyse-it to determine if a sample has normal distribution. In that post, we mentioned that although hypothesis tests are useful you should not solely rely on them. You should always look at the histogram and, maybe more importantly, the normal plot.
The beauty of the normal plot is that it is designed specifically for judging normality. The plot is very easy to interpret and lets you see where the sample deviates from normality.