Dear %$firstname$%,

You may have noticed that we've been growing, adding two new members to our team. Now that we've settled into fall a bit, I thought you'd find it helpful to have a little overview of the team and how to contact each one of us.

We've updated our Team page, so you can keep track of who does what around here and can catch a glimpse of our smiling faces.

We've also gotten a new toll-free phone system that allows you to reach any one of us at the same central phone number. (Crazy, huh?) The phone number is 877-272-8096, and if you ever need to find it, it's on the updated Contact page of our website, along with all of our email addresses.

And speaking of new team members, statistical consultant Lucy Fike will be presenting this month's Craft of Statistical Analysis webinar. She will give you an overview of Survival Analysis--the trickiest part is often recognizing when to use it. It's free to attend, so join us, meet Lucy if you haven't already, and learn something new. Register here.

She also wrote this month's newsletter article on Censoring in Time to Event Analyses. I hope you enjoy it. If you have questions or comments, we've also posted it on our blog. Go leave a comment here.

We've gotten great feedback on our new workshop format. Participants really enjoyed having the flexibility of viewing the video material on their own time, doing the exercises, and asking questions at the weekly Q&A sessions. They said it kept them involved and doing the exercises weekly. They also appreciated having both an evening and daytime option for the Q&A sessions.

So we're doing it again. Starting December 2nd, we'll have a 3-hour workshop on Assumptions of the General Linear Model and How to Check Them. Advance Discount registration begins Nov 11. Join the Advance Discount list to get the discount. Get details and register here.

Happy analyzing,
Karen

Time to event analyses (aka, Survival Analysis and Event History Analysis) are used often within medical, social science, marketing, and epidemiological research. Some examples of time-to-event analysis are measuring the median time to death after being diagnosed with a heart condition, comparing male and female time to purchase after being given a coupon and estimating time to infection after exposure to a disease.

Survival time has two components that must be clearly defined: a beginning point and an end point that is reached either when the event occurs or when the follow-up time has ended.

One basic concept needed to understand time-to-event (TTE) analysis is censoring.

In simple TTE, you should have two types of observations:

1. The event occurred, and we are able to measure when it occurred OR
   
   
2. The event did NOT occur during the time we observed the individual, and we only know the total number of days in which it didn't occur. (CENSORED).

Again you have two groups, one where the time-to-event is known exactly and one where it is not. The latter group is only known to have a certain amount of time where the event of interest did not occur. We don't know if it would have occurred had we observed the individual longer. But knowing that it didn't occur for so long tells us something about the risk of the envent for that person.

For example, let the time-to-event be a person's age at onset of cancer. If you stop following someone after age 65, you may know that the person did NOT have cancer at age 65, but you do not have any information after that age.

You know that their age of getting cancer is greater than 65. But you do not know if they will never get cancer or if they'll get it at age 66, only that they have a "survival" time greater than 65 years. They are censored because we did not gather information on that subject after age 65.

So one cause of censoring is merely that we can't follow people forever. At some point you have to end your study, and not all people will have experienced the event.

But another common cause is that people are lost to follow-up during a study. This is called random censoring. It occurs when follow-up ends for reasons that are not under control of the investigator.

In survival analysis, censored observations contribute to the total number at risk up to the time that they ceased to be followed. One advantage here is that the length of time that an individual is followed does not have to be equal for everyone. All observations could have different amounts of follow-up time, and the analysis can take that into account.

Allison, P. D. (1995). Survival Analysis Using SAS. Cary, NC: SAS Institute Inc.

Hosmer, D. W. (2008). Applied Survival Analysis (2nd ed.). Hoboken, NJ: John Wiley & Sons, Inc.

by Judith D. Singer & John B. Willett. (2008). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence.

Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence
by Judith D. Singer & John B. Willett

One of the reasons we love this book is that it's really two exceptionally understandable books in one. The first half is all about running mixed models for longitudinal data, specifically individual growth models.

The second half is about Event History Analysis. Both halves start from the beginning and assume you've never used it before. The authors are Education researchers, not statisticians, and are exceptionally good at making the information accessible and intuitive to researchers.

The link is an Amazon affiliate link, but there's a good chance that if you're at a university, your library has it.

The next Craft of Statistical Analysis Webinar:

Survival Analysis - An Introduction to Time-to-Event Data

This webinar will give an introduction to survival analysis. I will discuss when it is appropriate to use time-to-event analysis – it’s not just for counting deaths! We will interpret survival distributions and hazard curves. We will also discuss testing for differences in survivor functions and for the effect of covariates. We will use both business and medical related data to explore the different areas in which time-to-event analyses can be useful.

Get more information and register here.

The next Workshop:

Assumptions of the General Linear Model and How to Check Them

In this workshop, we will investigate each of the assumptions of the GLM--what they mean, why they're important, how to read the plots and tests that check them, how to remedy assumption failures and when to just let it go.

Begins December 2nd. Student discounts apply.

Registration opens November 11th. Get more information and sign up for an advance registration discount here.

What is The Analysis Factor? The Analysis Factor is the difference between knowing about statistics and knowing how to use statistics in data analysis. It acknowledges that statistical analysis is an applied skill. It requires learning how to use statistical tools within the context of a researcher’s own data, and supports that learning.

The Analysis Factor, the organization, offers statistical consulting, resources, and learning programs that empower researchers to become confident, able, and skilled statistical practitioners. Our aim is to make your journey acquiring the applied skills of statistical analysis easier and more pleasant.

Karen Grace-Martin, the founder, spent seven years as a statistical consultant at Cornell University. While there, she learned that being a great statistical advisor is not only about having excellent statistical skills, but about understanding the pressures and issues researchers face, about fabulous customer service, and about communicating technical ideas at a level each client understands. 

You can learn more about Karen Grace-Martin and The Analysis Factor at analysisfactor.com.

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