Meta-analysis
Meta-analysis (MA) is a statistical technique which pools data from published studies to provide a precise estimate of pair-wise treatment effect size between two interventions. MAs typically follow the completion of a systematic literature review and are widely used throughout the scientific community to report comparative efficacy and safety of medical treatments or diagnostic tests and tools. By pooling the estimate of the effect size across multiple trials, MAs can provide a stronger statistical power compared to individual trials. MAs can also be used to understand possible correlations between trial endpoints. Evidinno’s research team have abundant experience conducting such analyses and conforms to recommended guidelines for conducting MA, including the Cochrane Handbook for Systematic Reviews of Interventions.
Indirect Treatment Comparison and Network Meta-analysis
Indirect treatment comparison (ITC) is a more complex form of MA that is used to compare the efficacy and/or safety of interventions that have only been directly compared to a common comparator (a placebo or a different intervention), but not to each other. Network meta-analysis (NMA) is an umbrella term that is used when the evidence involves comparison of multiple randomized controlled trials and interventions. NMAs are readily used and accepted by health technology assessment agencies, policymakers, payers, pharmaceuticals, physicians as well as patients in selecting the best treatment. Evidinno’s research team has conducted and published a large number of ITCs and NMAs and conforms to recommended guidelines for conducting ITC and NMA, including the Cochrane Handbook for Systematic Reviews of Interventions.
Matching-Adjusted Indirect Comparison and Simulated Treatment Comparison
When using NMA to compare different trials, an underlying assumption is that the population characteristics in the selected trials are similar enough so that they do not impact the treatment effect. However, if this assumption does not hold true, the trial population differences need to be accounted for with a matching-adjusted indirect comparison (MAIC) or simulated treatment comparison (STC). Both MAIC and STC are conducted to mitigate the effect of population differences on treatment comparisons. These unique techniques can be used as part of the submission package to health technology assessment agencies. Evidinno can conduct MAICs and STCs for clients using individual patient level data.
Surrogate Endpoint Analysis
It happens often in oncological trials that a specific endpoint is the gold standard; however, data on that particular endpoint may take significant amount of time to materialize, and thus reliance on maturation of such data could delay the introduction of possibly beneficial treatments. Additionally, many randomized controlled trials have also failed to demonstrate a benefit in long-term study endpoints for investigational drugs, despite improvements in other endpoints that may become available sooner in the course of the study. In such cases, a surrogate endpoint analysis is a very useful set of tools that can help define and assess the validity as well as robustness of a surrogacy relationship between two endpoints. An example of such an analysis would be the investigation of the surrogacy relationship between overall survival and progression-free survival in oncological studies. Evidinno’s expert team of statisticians and clinical epidemiologists have abundant experience conducting and publishing surrogacy analysis across therapeutic areas and can help achieve our client’s objectives.
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Evidinno provides high quality health outcome and research consulting services for clients worldwide.