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Cox regression (also called proportional hazards regression) is used to analyze the effect of several variables on survival or other time-to-event outcomes.
Join Elaine Eisenbeisz to learn about:
— Review of the Kaplan-Meier method with log-rank test
— Review of Interpreting Survival curves
— Assumptions for Cox proportional hazards models
— Analysis & Interpretation of Cox proportional hazards models.
Anyone is welcome to attend the webinar. However knowledge of multiple linear regression and logistic regression will be useful for attendees in understanding the concepts presented.
There is always some time for Q&A during the live event so bring your questions!
All registrants will receive handouts and a link to a recording of the webinar. So if you cannot attend the live event, you can watch later at your convenience.
The webinar and recording are FREE! But space is limited, so register today!
Feel free to click on any of the webinars listed below to be directed to the Omega Statistics channel on YouTube to view for free!
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I’d love to hear from you about statistical topics or analyses you’d like to see in more detail so send me an email at [email protected] with your ideas and/or suggestions!
You see them reported in the literature. You see them in your computer output. You add them to your reports.
You even (kinda) understand them!
Or, maybe, not quite?
Please join Elaine Eisenbeisz, Owner and Principal of Omega Statistics, as she presents an overview of the how and why of various ratios we use often in statistical practice.
Which ones you ask? How about:
Elaine takes some time during the event to show examples of some of the ratios “at work.”
Unlike Frequentist statistics, in which we assume we know nothing about phenomena until we sample and test it, Bayesian statistics allow us to take into account information we already know in our analysis and our conclusions.
Please join Elaine Eisenbeisz, Owner and Principal of Omega Statistics, as she presents an overview of Bayesian thought and techniques. Emphasis will be on applications to diagnostic tests and genetics.
Want to know how to find the right sample size for your study? Too few subjects and you’ll waste time. Too many subjects and you’ll waste time and money!
Please join Elaine Eisenbeisz, Owner and Principal of Omega Statistics, as she presents an overview of power analysis techniques for determining sample size (a priori techniques). Elaine will take some time during the event to work examples in GPower software.
You’ve developed some possible study questions and are well into your literature review. It is time to start building your study “for reals”.
The next step will be to define your research design and research method. What design should you use to best inform your research? What method would work best with what you have available? What is the dang difference between design and method? (Oh yes, there is a difference).
Please join Elaine Eisenbeisz, Owner and Principal of Omega Statistics, as she presents an overview of the types of research designs and methods which are the foundation of building a solid structure for collecting, analyzing, and interpreting your research.
A thorough and solid literature review should be your first step in designing a study. Join Elaine Eisenbeisz of Omega Statistics as she shares the tips and tools for navigating the literature review.
Contrary to popular opinion, the p-value is not the most important way, and in many cases not the best way, to support a study’s findings. Find out why effect sizes ARE important to research and why they are becoming more so.
In Back-to-Basics I, you learned the Why. Now understand the How. Elaine will present the processes of statistics as relates to the Scientific Method. This webinar is a must-see if you struggle with concepts of hypothesis testing and inference.
Why Statistics? Statistics are just guesses. Good guesses, if applied properly. Learn the value of, and limitations of, statistics in application. Elaine will also explain what some of those pesky statistics terms mean in the real world.
Wondering what to do with those categorical variables? Please join Elaine Eisenbeisz of Omega Statistics as she introduces you to chi-square and similar tests of association using contingency tables. She will also cover the use of contingency tables involving repeated measures.
Logistic Regression is something every researcher should have in his or her statistics toolbox.
Join Elaine Eisenbeisz of Omega Statistics as she presents an overview of logistic regression. Learn how to code your data properly, check your assumptions for analysis, and how to understand and use odds ratios and likelihoods.
Multiple Regression is one of the most informative data analysis techniques there is!
Join Elaine Eisenbeisz of Omega Statistics as she presents an overview of multiple regression using ordinary least squares techniques (OLS). Learn how to code your data properly, check your assumptions for analysis, and how to use a regression model to predict outcomes.
So many correlations, so little time!
Join Elaine Eisenbeisz of Omega Statistics as she presents an overview of various correlational analysis techniques. Learn which correlation fits your data best. You will also learn how correlation, chi-squares, and t-tests are related!
It will sound like cheating, but it isn’t. It’s so righteous dude! Multiple imputation (MI) is an effective and responsible way to handle data which is missing at random (MAR). You’ll find out what that means too…
Please join Elaine Eisenbeisz, Owner and Principal of Omega Statistics, as she presents an overview of MI concepts.
Please join Elaine Eisenbeisz, Owner and Principal of Omega Statistics, as she presents a few tools and techniques for assuring your data entry and statistical analyses are in good form.
You have an idea of what you want to do for your research, but where do you start with the analysis plan? Start with knowing about the types of research designs and their applications. Join Elaine Eisenbeisz of Omega Statistics as she presents a brief yet useful overview of study design.