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A review of studies examining the relationship between progression ...

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effect on PFS and treatment effect on OS with three using individual patient data, whilst <strong>the</strong>remainder used aggregate data from multiple trials. A variety <strong>of</strong> statistical techniques were employedwithin <strong>the</strong>se <strong>studies</strong>, with <strong>the</strong> most commonly used being rank correlation coefficients, linearregression and landmark analysis. The lack <strong>of</strong> a standardised approach made it difficult to establishwhe<strong>the</strong>r <strong>the</strong>re is a consistent <strong>relationship</strong> <strong>between</strong> PFS/TTP and OS. The majority <strong>of</strong> <strong>the</strong> <strong>studies</strong>found a positive correlation <strong>between</strong> PFS/TTP and OS for individual patients, individual trial armsand <strong>the</strong> treatment effect <strong>between</strong> trial arms, although, <strong>the</strong> size <strong>of</strong> <strong>the</strong> correlation and its statisticalsignificance varied considerably across <strong>studies</strong>. This is not surprising given <strong>the</strong> variety <strong>of</strong> methodsemployed and <strong>the</strong> variation in <strong>studies</strong> characteristics such as differences in <strong>the</strong> tumour type and <strong>the</strong>line <strong>of</strong> <strong>the</strong>rapy considered.ConclusionThis <strong>review</strong> suggests that <strong>the</strong> level <strong>of</strong> evidence available supporting a <strong>relationship</strong> <strong>between</strong> PFS/TTPand OS varies considerably by cancer type and is not always consistent even within one specificcancer type. Fur<strong>the</strong>rmore, even where strong consistent evidence supporting a correlation <strong>between</strong><strong>the</strong> treatment effects (i.e Level 1 evidence according to Elston and Taylor’s framework) is available,it is unclear how that should be converted into a quantified <strong>relationship</strong> <strong>between</strong> PFS and OStreatment effects within a cost-effectiveness model. Therefore, any cost-effectiveness analysis whichmakes a strong assumption regarding <strong>the</strong> <strong>relationship</strong> <strong>between</strong> PFS and OS should be treated withcaution. We would support Elston and Taylor in recommending that any cost-effectiveness analysisbased on a surrogate <strong>relationship</strong> <strong>between</strong> PFS and OS should be supported with a transparentexplanation <strong>of</strong> how <strong>the</strong> <strong>relationship</strong> is quantified in <strong>the</strong> model and should be accompanied bysensitivity analysis exploring <strong>the</strong> uncertainty associated with that <strong>relationship</strong> and a systematic<strong>review</strong> <strong>of</strong> papers <strong>examining</strong> <strong>the</strong> <strong>relationship</strong> <strong>between</strong> PFS and OS in <strong>the</strong> relevant setting. This wouldallow decision makers to judge <strong>the</strong> appropriateness <strong>of</strong> <strong>the</strong> model in light <strong>of</strong> <strong>the</strong> evidence available inthat specific disease area.4

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