This dataset contains repeated measurements of tumor size from an animal xenograft experiment designed to compare four treatments for cancers. Study animals were n=37 mice with baseline tumor volumes of between40-60 mm3. Animals were assigned to either: 1) control (n=8); drug only (n=10); 3) radiation only (n=10); or 4) drug + radiation (n=9). Tumor size was typically measured on work days for up to four weeks, but the number of repeated measurements is variable because some animals had to be euthanized.
|Study Design||Topic||Statistical Method||Statistical Method||Statistical Method|
|Longitudinal||Tumor Growth||Linear Mixed Models||Longitudinal Data||Repeated Measures|
The Tumor Growth dataset was contributed by Dr. Constantine Daskalakis, Associate Professor, Thomas Jefferson University. Please refer to this resource as: Constantine Daskalakis, “Tumor Growth Dataset”, TSHS Resources Portal (2016). Available at https://www.causeweb.org/tshs/tumor-growth/.
When basic science research suggests a new possibility for cancer treatment, pre-clinical studies are performed to obtain preliminary assessment in vivo of the biological response of human tumors to that treatment. Such translational research often relies on tumor xenograft experiments in animal models.
The study’s two main specific aims were to assess whether: a) the drug has an effect on tumor growth; and b) the administration of the drug before radiation enhances the effect of the latter on tumor growth.
Subjects & Variables
|Subject||# Obs||# Var||Introduction||Data Dictionary|
|Tumor Growth||574||4||Tumor Growth-Introduction||Tumor Growth-Data Dictionary|
|Posting Date||Contributor (email)|
|07/19/16||Constantine Daskalakis (email@example.com)|
|Tumor Growth-R||Tumor Growth-SAS||Tumor Growth-Stata||Tumor Growth-SPSS||Tumor Growth-Minitab||Tumor Growth-Excel|
last updated on 7/19/2016
|#||Name (link)||Posting Date||Author (email)||Type||Statistical Topic||Level||Keywords|
|1||7/19/2016||Constantine Daskalakis (Constantine.Daskalakis@jefferson.edu)||Lab/Project||Inference||Intro/Intermediate||Mixed-Effects Modeling|