Efficacy of two treatments on the change of a biomarker between two time points
In the context of randomized clinical trials, health care professionals are asked to compare two different treatments or one treatment with a placebo in terms of their effectiveness in improving the levels of a biomarker between two time points (e.g. start of study – after 6 months of treatment administration).
Examples: A research group wishes to develop a new treatment that will contribute to the reduction of LDL-cholesterol levels after 6 months of administration, and in order to support its necessity and utility in the treatment of patients at high cardiovascular risk, they will it needs to compare it with usual therapeutic practice and demonstrate that it leads to a significantly greater reduction in LDL-cholesterol levels.
The problem
In the context of randomized clinical trials, healthcare professionals are asked to compare two different treatments or one treatment with a placebo in terms of their effectiveness in improving the levels of a biomarker between two time points (e.g. start of study – after 6 months of treatment administration). For example, a research group wishes to develop a new treatment that will contribute to the reduction of LDL-cholesterol levels after 6 months of administration, and in order to support its necessity and utility in the treatment of patients at high cardiovascular risk, they will it needs to compare it with usual therapeutic practice and demonstrate that it leads to a significantly greater reduction in LDL-cholesterol levels.
The purpose
To indicate the most effective treatment in regards to the improvement of the levels of a biomarker between two time points in subjects participating in a clinical trial.
The solution
With the use of the appropriate statistical methodology (Independent samples t-test, Mann Whitney U test) and after the test of the assumptions, the two therapeutic approaches that the user will insert in the algorithm will be compared in terms of the improvement of a biomarker between two time points of individuals, and the most effective treatment will be identified.
The benefit
Healthcare professionals will be able to easily, quickly and without prior statistical knowledge, compare the two therapeutic approaches they wish to improve a biomarker between two time points of the individuals participating in their study, and thus arrive at the conclusion about which treatment is most effective.
Indicative presentation of the data needed
- The first column of the data file must contain the patient code (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 the information about the treatment to which each person has been subjected (e.g. 0: Standard treatment, 1: New suggested treatment).
- The following two columns of the file must contain the values of the biomarker of interest to the user, such as: LDL-cholesterol, both at the start of the study and after a period of treatment administration (e.g. after 6 months of administration).
Table 1. Indicative table of input data from the application user
|
Patient Code |
Treatment |
LDL-cholesterol |
LDL-cholesterol |
|
1 |
Standard treatment |
100 |
100 |
|
2 |
Standard treatment |
120 |
115 |
|
3 |
Standard treatment |
150 |
145 |
|
4 |
Suggested treatment |
110 |
90 |
|
5 |
Suggested treatment |
125 |
95 |
|
6 |
Suggested treatment |
145 |
100 |
System Prerequisites:
- Toolbox accepts xlsx or csv files.
- The variable denoting the treatment must not contain missing data.
- The two variables denoting the biomarker levels must not have missing data. If at least one of the two time fields has missing data, then that subject is removed from the analysis.
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.
