One might ask the question why not have a direct comparison. The reason behind this is such direct comparisons are often misleading & inappropriate & they should be incorporated only for exploratory purposes & only when there are no further options at disposal. Although certain indirect comparisons are now being the commonly accepted method. They make use of the links through one or many comparator (placebo) drugs.
Mixed treatment comparisons (MTCs) utilize the Bayesian theorem to include all the data that is present on a drug. This includes all that data too which is not related to the comparator drug. Mixed treatment comparisons decrease the uncertainty, although such treatment systems are not yet accepted by scientists & researchers. These are not even accepted by authorities in the drug regulation. Such indirect analyses are dependent on the similar underlying postulation as meta-analyses, that is the population’s understudy in the trials that are compared are similar.
Although, information about the efficacies between or within the different drugs is required to make decisions that are involved in clinical practice, &in public health and health policy.
Example of drug therapy comparison:
One such example of drug therapy comparison is the classic case of type 2 diabetes mellitus (also known as T2DM). When various new drug classes were added over the past few years, there was seen a rise in the therapeutic choices made. Some of the few added new classes are notably dipeptidyl peptidase 4 [DPP4] inhibitors and glucagon-like peptide-1 [GLP-1] analogs. It was not clearly understood what the relative efficacies of the different agents are. As a result, there was uncertainty regarding where these drug agents are a proper fit in the complete treatment or the path of therapy. Relatively fewer DPP4 inhibitors and GLP-1 analogs have been compared in a head-to-head trial. Such uncertainty is a great challenge for health policymakers, clinicians, & most importantly the patients.
Methods of comparison:
When the head-to-head clinical trial data is not available, various statistical methods are used. These statistical methods are developed over the last few years. They enable indirect comparison between various drugs or other interventions.
There are five prime statistical methods. They are:
NaÃ¯ve direct comparisons
Head-to-head clinical trial
Mixed treatment comparisons
Multiple adjusted indirect comparisons
NaÃ¯ve direct comparisons:
NaÃ¯ve direct comparison within two drugs is an analysis where the outcome for one drug in a clinical trial is compared directly with results for another drug in another clinical trial. In these cases, there are efforts taken for adjustment of inconsistency in comparators between or among the trials.
Adjusted indirect comparisons:
Adjusted indirect comparisons collate the amount effect of two treatments that are related to a common comparator. This generally links the magnitude between these two treatments.
Multiple adjusted indirect comparisons:
In multiple adjusted indirect comparisons, there is uncertainty resent at every such link.
Mixed treatment comparisons:
Mixed treatment comparisons are done by using Bayesian models. The idea is that any comparison that has either one drug that is compared had information that can be used to describe the correlation between the pair. This can be simplified with the explanation of an example. To form relative efficacy in between drug B and drug A, any trial that included either drug B or drug A will give information for the comparison between B and A, irrespective of the comparators.
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