The Analysis Factor
Statistical Consulting, Resources, and Statistics Workshops for Researchers
by Jeff Meyer
As mentioned in a previous post, there is a significant difference between truncated and censored data.
Truncated data eliminates observations from an analysis based on a maximum and/or minimum value for a variable.
Censored data has limits on the maximum and/or minimum value for a variable but includes all observations in the analysis.
As a result, the models for analysis of these data are different. [Read more…] about Statistical Models for Truncated and Censored Data
Imagine this scenario:
This year’s flu strain is very vigorous. The number of people checking in at hospitals is rapidly increasing. Hospitals are desperate to know if they have enough beds to handle those who need their help.
You have been asked to analyze a previous year’s hospitalization length of stay by people with the flu who had been admitted to the hospital. The predictors in your data set are age group, gender and race of those admitted. You also have an indicator that signifies whether the hospital was privately or publicly run.
[Read more…] about The Problem with Linear Regression for Count Data
by Jeff Meyer
It’s that time of year: flu season.
Let’s imagine you have been asked to determine the factors that will help a hospital determine the length of stay in the intensive care unit (ICU) once a patient is admitted.
The hospital tells you that once the patient is admitted to the ICU, he or she has a day count of one. As soon as they spend 24 hours plus 1 minute, they have stayed an additional day.
Clearly this is count data. There are no fractions, only whole numbers.
To help us explore this analysis, let’s look at real data from the State of Illinois. We know the patients’ ages, gender, race and type of hospital (state vs. private).
A partial frequency distribution looks like this: [Read more…] about Analyzing Zero-Truncated Count Data: Length of Stay in the ICU for Flu Victims
by Jeff Meyer
A normally distributed variable can have values without limits in both directions on the number line. While most variables have practical limitations, most of the time, this assumption of infinite tails is quite reasonable as there is no real boundary.
Air temperature is an example of a variable that can extend far from its mean in either direction.
But for other variables, there is a practical beginning or ending point. Age is left-bounded. It starts at zero.
The number of wins that a baseball team can have in a season is bounded on the upper end by the number of games played in a season.
The temperature of water as a liquid is bound on the low end at zero degrees Celsius and on the high end at 100 degrees Celsius.
There are two types of bounded data that have direct implications for how to work with them in analysis: censored and truncated data. Understanding the difference is a critical first step when working with these variables.
[Read more…] about The Difference Between Truncated and Censored Data
Statistically speaking, when we see a continuous outcome variable we often worry about outliers and how these extreme observations can impact our model.
But have you ever had an outcome variable with no outliers because there was a boundary value at which accurate measurements couldn’t be or weren’t recorded?
Examples include:
These are all examples of data that are truncated or censored. Failing to incorporate the truncation or censoring will result in biased results.
This webinar will discuss what truncated and censored data are and how to identify them.
There are several different models that are used with this type of data. We will go over each model and discuss which type of data is appropriate for each model.
We will then compare the results of models that account for truncated or censored data to those that do not. From this you will see what possible impact the wrong model choice has on the results.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
[Read more…] about Member Training: Working with Truncated and Censored Data
by Jeff Meyer
In a previous post we explored bounded variables and the difference between truncated and censored. Can we ignore the fact that a variable is bounded and just run our analysis as if the data wasn’t bounded? [Read more…] about Issues with Truncated Data