When performing indirect treatment comparisons, effect modification can create complexities in the event of high study-to-study heterogeneity. A number of methods can be used to handle these complexities, but according to Dr. Ofir Harari, Senior Research Principal at Cytel, Network Meta-Interpolation overcomes the shortcomings of a number of other techniques.<\/p>\n
<\/p>\n
Understanding Complications Caused by Effect Modification<\/strong><\/p>\n Effect modification occurs when the effect of a treatment on an outcome varies along with the values that (one or more) other variables take. For example, if a treatment has less of an effect when someone is younger or of a particular sex, then age and sex are said to be effect modifiers. A robust approach would need to capture the relationship between the treatment effect and such effect modifiers in order to accurately establish the relationship between treatment and outcome.<\/p>\n Commonly used approaches assume for simplicity that the effect modifiers impact all treatments in the evidence network in the exact same way. This is known as the shared effect modification (SEM) assumption and can be handled with network meta-regression (NMR), a form of network meta-analysis that utilizes an aggregate data model. As Dr. Harari notes, the assumption of shared effect modification is \u201cextremely limiting\u201d and \u201crarely reasonable to make.\u201d<\/p>\n What other options are there?<\/p>\n <\/p>\n Multi-Level Network Meta-Aggression (ML-NMA)<\/strong><\/p>\n Assuming the availability of at least one individual patient-level data (IPD) per treatment class, this method can accommodate models that depart from the shared effect modification assumption \u2013 but this is an extremely hopeful assumption to make!<\/p>\n <\/p>\n Matching-Adjusted Indirect Comparison (MAIC)<\/strong><\/p>\n This method relies on weighting the IPD data and can \u2013 in its crude form \u2013 be used when there is one IPD study and one aggregate-level data study. Indirect treatment comparison when using this method is only valid to a population that follows the effect modifier distribution identical to that of the aggregate-level data study.<\/p>\n <\/p>\n Simulated Treatment Comparison (STC)<\/strong><\/p>\n STC, which relies on regression equations to balance the aggregate and individual patient-level datasets on effect modifiers, is limited by the same constraints as MAIC.<\/p>\n <\/p>\n Network Meta-Interpolation (NMI)<\/strong><\/p>\n NMI, a new method developed by Cytel, can bypass many of the restrictions of the methods mentioned above. Unlike MAIC and STC, NMI can model more than two studies and is not limited by the effect modification conditions of any of the studies. It takes advantage of reported subgroup analysis results \u2013 which are often available but have not been used before in ITCs \u2013 to overcome non-shared modification data generating mechanisms.<\/p>\n <\/p>","protected":false},"featured_media":3105,"parent":0,"template":"","meta":{"_acf_changed":false},"tags":[76],"by-topic":[432],"by-year":[72],"class_list":["post-3280","perspectives","type-perspectives","status-publish","has-post-thumbnail","hentry","tag-blog","by-topic-clinical-development-strategy","by-year-72"],"acf":[],"yoast_head":"\n