Abstract
PurposeDespite the plethora of randomized controlled trial (RCT) data, most cancer treatment recommendations are formulated by experts. Alternatively, network meta-analysis (NMA) is one method of analyzing multiple indirect treatment comparisons. However, NMA does not account for mixed end points or temporality. Previously, we described a prototype information theoretical approach for the construction of ranked chemotherapy treatment regimen networks. Here, we propose modifications to overcome an apparent straw man effect, where the most studied regimens were the most negatively valued.MethodsRCTs from two scenarios—upfront treatment of chronic myelogenous leukemia and relapsed/refractory multiple myeloma—were assembled into ranked networks using an automated algorithm based on effect sizes, statistical significance, surrogacy of end points, and time since RCT publication. Vertex and edge color, transparency, and size were used to visually analyze the network. This analysis led to the additional incorporation of value propagation.ResultsA total of 18 regimens with 42 connections (chronic myelogenous leukemia) and 28 regimens with 25 connections (relapsed/refractory multiple myeloma) were analyzed. An initial negative correlation between vertex value and size was ameliorated after value propagation, although not eliminated. Updated rankings were in close agreement with published guidelines and NMAs.ConclusionStraw man effects can distort the comparative efficacy of newer regimens at the expense of older regimens, which are often cheaper or less toxic. Using an automated method, we ameliorated this effect and produced rankings consistent with common practice and published guidelines in two distinct cancer settings. These findings are likely to be generalizable and suggest a new means of ranking efficacy in cancer trials.
Highlights
Health care data can be highly convoluted, given the significant dimensionality, nonlinearity, and temporality present in most clinical contexts
randomized controlled trial (RCT) from two scenarios—upfront treatment of chronic myelogenous leukemia and relapsed/ refractory multiple myeloma—were assembled into ranked networks using an automated algorithm based on effect sizes, statistical significance, surrogacy of end points, and time since RCT publication
The RCTs that were previously identified in the context of CML-1 were used in this study, along with several newly published RCTs
Summary
Health care data can be highly convoluted, given the significant dimensionality, nonlinearity, and temporality present in most clinical contexts. Work by others has shown that network meta-analysis applied to RCTs can yield powerful insights[2,3,4,5,6,7]; the networks in these studies have been relatively simple, do not allow for mixed end points (eg, overall survival and response rate), and do not account for temporal factors. Layout, animation, and visual parameters such as size and color take on increasing importance.[8] For example, visual analytics have been successfully applied to temporal associations of laboratory results, phenotype relationship networks, and patterns of publication by biomedical specialty and primary degree.[9,10,11] Visual analysis of networked RCT data may help uncover previously underappreciated biases
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