Estimation Techniques

ITC (Indirect Treatment Comparison)


1. Description

ITC (Indirect Treatment Comparison) is a meta-analytic method to synthesise different pieces of evidence into a coherent set of estimates (of treatment effects).[1][2][3] In the absence of direct comparative evidence between two treatments, ITC can infer their relationship through a common comparator-for example, both treatments may have been directly compared against a placebo. Indirect comparisons are subject to greater statistical uncertainty than direct comparisons, and this effect on uncertainty is captured by ITC. See also MTC.

2. Evaluation


2.1 Principle
  • ITC is based on probabilities and is a meta-analytic technique.
  • ITC offers increased transparency when the sources of evidence, bias, and uncertainties from the data are documented carefully.
  • ITC can calculate statistical uncertainties around the mean effect size.
  • There is no concept of incorporating value judgements in standard ITC literature, because it is not a specific method for benefit-risk analysis.
  • There are greater biases in using indirect comparisons than direct comparisons.
  • There are greater uncertainties involved in using indirect evidence.
2.2 Features
  • ITC borrows the strengths from related trials in a network of indirect evidence of benefits and risks of treatments, where there is a common comparator.
  • Multiple criteria of benefits and risks as well as multiple treatment options can also be taken into consideration and be estimated simultaneously in a single ITC model.
  • ITC are used in combination with other benefit-risk approaches to characterise the benefit-risk profile of treatments.
  • Sensitivity analyses can be performed on the various parameters in the model.
2.3 Visualisation
  • A network diagram was suggested to visualise the structure of an ITC model.
  • Visual representations of the results are dependent on the actual metric use to quantify benefits and risks.
    • 2.4 Assessability and accessibility
    • The parameters and results from ITC analysis are simple to understand without in-depth technical knowledge of the method itself.
    • The variance modelling and comprehension in ITC requires greater level of statistical understanding.
    • The flexibility in the application of ITC means it may be suitable to most stakeholders.
    • ITC provides decision-makers with a general and flexible technique to assess benefits and risks to support decision making when used appropriately.

    This method was tested in the Natalizumab and Rimonabant case studies.

    3. References


    [1] Lumley T. Network meta-analysis for indirect treatment comparisons. Stat Med 2002;21(16):2313-24.
    [2] Lu G, Ades AE. Combination of direct and indirect evidence in mixed treatment comparisons. Stat Med 2004;23(20):3105-24.
    [3] Nixon RM, Bansback N, Brennan A. Using mixed treatment comparisons and meta-regression to perform indirect comparisons to estimate the efficacy of biologic treatments in rheumatoid arthritis. Stat Med 2007;26(6):1237-54.