Time to response of one treatment

In many studies, researchers are interested in evaluating a treatment or line of treatment in terms of response time, that is, the time until a complete or partial response to the drug occurs.

Progression-free survival (PFS) of two or more treatments

In the context of many studies, interventional or non-interventional, researchers are interested in the long-term effectiveness of therapeutic treatments, in terms of the probability of survival without progression of the disease.

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.

Overall response rate of one treatment

In many studies, researchers are interested in evaluating a treatment or line of treatment, in terms of response rate, both complete and partial, after a certain period of time.

Effectiveness of one treatment in terms of the change of a biomarker between two time points

In the context of prospective studies, interventional or non-interventional, health professionals are asked to evaluate the effectiveness of a therapeutic treatment in terms of the change in the values ​​of basic biomarkers, between two points in time (e.g. the start of the study and after a certain period of treatment administration).

Overall response rate of two or more treatments

In the context of many clinical trials, particularly phase III, researchers are interested in comparing the two, under evaluation, treatments, in terms of the response rate, both complete and partial, after a certain period of time.

Time to response of two or more treatments

In the context of many clinical trials, particularly phase III, researchers are interested in comparing the two treatments under evaluation, in terms of response time, i.e. the time until the appearance of a complete or partial response to the drug.

Safety analysis of two or more treatments

In the context of randomized clinical trials, healthcare professionals are required to evaluate the safety of a new suggested therapeutic treatment and for this reason compare it with other pre-existing treatments, in terms of the occurrence of an adverse event after a certain period of administration.

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).

Efficacy of three or more treatments on the change in a biomarker between two time points

In the context of randomized clinical trials, health care professionals are often asked to compare a new therapeutic approach, both with the already existing one, and with a placebo in terms of their effectiveness in improving the levels of a biomarker between two points in time (eg study start – after 12 months of treatment).

Predicting appointment cancellations

Patients who fail to show up for scheduled appointments or cancel at the last minute - do not give the health center the opportunity to fill the appointment slot, resulting in both a loss of time and money for the health center, as well as disruption of the rectal and timely care of the remaining patients. There are many reasons why patients miss their appointments. They may have forgotten it, have problems with transportation due to weather, or may not be able to leave work on time. It is therefore considered to be of major importance that both private doctors and health care units be able to predict the no-shows of their patients, so that they can replace the canceled appointment in time and no loss of time and money is noted.