Overall survival (OS) of two or more treatments
In the context of many studies, interventional or non-interventional, researchers are interested in the long-term effectiveness of treatments, in terms of overall survival.
Examples: As part of an interventional phase III clinical trial comparing two different treatments, the researchers were interested in comparing the two treatments in terms of overall survival and determining whether and which of the two treatments was associated with a higher mortality rate. For this reason, their aim was to compare the median overall survival time observed in the two compared groups.
The problem
In many studies, interventional or non-interventional, researchers are interested in the long-term effectiveness of treatments, in terms of overall survival. For example, in an interventional phase III clinical trial comparing two different treatments, the researchers were interested in comparing the two treatments in terms of overall survival and determining whether and which of the two treatments was associated with a higher mortality rate. For this reason, their aim was to compare the median overall survival time observed in the two compared groups.
The purpose
Indication of the most effective treatment in terms of the greatest probability of overall survival.
The solution
By using the appropriate statistical methodology (Log-rank test) and after the test of the assumptions, a comparison will be made between the two compared groups, in order to enable the user to evaluate which of the two treatments under consideration is more effective as to overall survival.
The benefit
Healthcare professionals will be able to easily, quickly and without prior statistical knowledge, evaluate the effectiveness of a treatment they are interested in, in terms of overall survival.
Indicative presentation of the data needed
- The first column of the data file must contain the code of the patients receiving the treatment the user is interested in (e.g. Unique Patient Code). In case there is no patient code in the user's data file, then in the first column he should add a serial number.
- The second column should contain which medication the participant received (e.g. 0: Standard treatment, 1: Suggested treatment).
- The third column should contain the date the participant joined the study and received treatment.
- The fourth column should contain the date on which death was observed or the date on which the participant was last followed up.
- The fifth column should contain the information about whether the participant died or not (e.g. 1: Yes, 0: No).
Note: The columns that will contain the date should be in the format DD/MM/YYYY.
Table 1. Indicative table of input data from the application user
|
Patient Code |
Treatment |
Date of entry into the study |
Date of disease progression or death or date of last follow-up |
Death |
|
1 |
Standard |
01/10/2021 |
20/10/2021 |
Yes |
|
2 |
Standard |
15/10/2021 |
01/11/2021 |
Yes |
|
3 |
Standard |
05/10/2021 |
30/06/2022 |
No |
|
4 |
Suggested |
09/10/2021 |
25/04/2022 |
No |
|
5 |
Suggested |
18/10/2021 |
30/06/2022 |
No |
|
6 |
Suggested |
27/10/2021 |
22/03/2022 |
Yes |
System Prerequisites:
- Toolbox accepts xlsx or csv files.
- The two variables that denote the dates must not have missing data.
- The variable denoting the treatment must not contain missing data.
- The variable indicating death must not contain missing data.
Output:
After the data input by the user and after a short period for the automated analysis to be completed, a report of the results and the statistical methods used is extracted from the system.
Note: For any clarification you need regarding the content of the use case or any information related to the collection or validity of your data please contact us.
