BookmarkSubscribeRSS Feed
Calcite | Level 5

Hello SAS Community,

I'm currently working on a project where I'm investigating the modifying effects of city parameters on the relationship between heat and the number of EMS services across 25 cities. Here's a breakdown of my approach and where I need some guidance:

  1. Initial Stage:

    • I've estimated the heat effects adjusted for the year and weekday using a negative binomial model for each city separately. This step provided city-specific estimates of the heat effects.
  2. Second Stage:

    • Now, I want to use meta-regression to explain the variance of the heat effects across the cities.
    • My challenge lies in incorporating two different types of covariates: daily parameters and yearly parameters.

Here are my specific questions:

  • Is there a way to assess the daily covariates in the meta-regression without aggregating them into a single mean?
  • How can I properly incorporate both daily and yearly parameters into the meta-regression model while accounting for the city-specific estimates obtained in the initial stage?
  • How should the data structure look like to utilize the PROC MIXED function for this purpose?

Any advice or suggestions on how to approach this would be greatly appreciated.

Thank you!

Ready to join fellow brilliant minds for the SAS Hackathon?

Build your skills. Make connections. Enjoy creative freedom. Maybe change the world. Registration is now open through August 30th. Visit the SAS Hackathon homepage.

Register today!
What is ANOVA?

ANOVA, or Analysis Of Variance, is used to compare the averages or means of two or more populations to better understand how they differ. Watch this tutorial for more.

Find more tutorials on the SAS Users YouTube channel.

Discussion stats
  • 0 replies
  • 1 in conversation