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Assessing the benefit-risk of new treatments using generalised pairwise comparisons: the case of erlotinib in pancreatic cancer.

ABSTRACT: Efficacy and safety are the two considerations when characterising the effects of a new therapy. We sought to apply an innovative method of assessing the benefit-risk balance using data from a completed randomised controlled trial that compared erlotinib vs placebo added to gemcitabine in patients with advanced pancreatic cancer (NCIC CTG PA.3).We applied generalised pairwise comparisons with several prioritised outcome measures (e.g., one or more benefit outcomes and one or more risk outcomes). Here, the first priority outcome was overall survival (OS) time. Differences in OS that exceeded 2 months were considered clinically meaningful. The second priority outcome was toxicity. The overall treatment effect was quantified using the proportion in favour of erlotinib, which can be interpreted as the net proportion of patients who have a better overall outcome with erlotinib as compared with placebo. Sensitivity analyses were performed.In this trial 569 patients were randomly assigned in a 1 : 1 ratio to receive gemcitabine plus either erlotinib or a matched placebo. Overall, the method indicated no statistically significant overall treatment effect in favour of erlotinib; if anything, the point estimate of the net proportion leaned in favour of the placebo group (overall proportion in favour of erlotinib=-3.6%, 95% CI, -14.2- 7.1%; P=0.51). The net proportion was never in favour of the erlotinib group throughout all sensitivity analyses.Generalised pairwise comparisons make it possible to assess the benefit-risk balance of new treatments using a single statistical test for any number of prioritised outcomes. The benefit-risk assessment was not in favour of adding erlotinib to gemcitabine for the treatment of patients with advanced pancreatic cancer.

SUBMITTER: Peron J

PROVIDER: S-EPMC4366896 | biostudies-other | 2015 Mar

REPOSITORIES: biostudies-other

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Project description:BACKGROUND:Researchers commonly analyze lists of differentially expressed entities (DEEs), such as differentially expressed genes (DEGs), differentially expressed proteins (DEPs), and differentially methylated positions/regions (DMPs/DMRs), across multiple pairwise comparisons. Large biological studies can involve multiple conditions, tissues, and timepoints that result in dozens of pairwise comparisons. Manually filtering and comparing lists of DEEs across multiple pairwise comparisons, typically done by writing custom code, is a cumbersome task that can be streamlined and standardized. RESULTS:A-Lister is a lightweight command line and graphical user interface tool written in Python. It can be executed in a differential expression mode or generic name list mode. In differential expression mode, A-Lister accepts as input delimited text files that are output by differential expression tools such as DESeq2, edgeR, Cuffdiff, and limma. To allow for the most flexibility in input ID types, to avoid database installation requirements, and to allow for secure offline use, A-Lister does not validate or impose restrictions on entity ID names. Users can specify thresholds to filter the input file(s) by column(s) such as p-value, q-value, and fold change. Additionally, users can filter the pairwise comparisons within the input files by fold change direction (sign). Queries composed of intersection, fuzzy intersection, difference, and union set operations can also be performed on any number of pairwise comparisons. Thus, the user can filter and compare any number of pairwise comparisons within a single A-Lister differential expression command. In generic name list mode, A-Lister accepts delimited text files containing lists of names as input. Queries composed of intersection, fuzzy intersection, difference, and union set operations can then be performed across these lists of names. CONCLUSIONS:A-Lister is a flexible tool that enables the user to rapidly narrow down large lists of DEEs to a small number of most significant entities. These entities can then be further analyzed using visualization, pathway analysis, and other bioinformatics tools.

Project description:IntroductionThe long-term benefit-risk profiles of licensed and investigational treatments for moderate-to-severe plaque psoriasis have not been fully characterized.MethodsRandomized controlled trials (RCTs) of licensed and investigational treatments for moderate-to-severe plaque psoriasis were identified through a systematic literature review through 2 May 2021. Bayesian network meta-analyses (NMAs) were conducted to compare the efficacy (Psoriasis Area and Severity Index [PASI] 75/90/100 [at least a 75/90/100% reduction in PASI score from baseline] response) and safety outcomes (any adverse event [AE], any serious AE [SAE], and AEs leading to treatment discontinuation) of each treatment evaluated between weeks 48 and 56 after baseline. Surfaces under the cumulative ranking curves (SUCRAs) were calculated to evaluate the relative ranking of treatments. The benefit-risk profiles of treatments were assessed by bidimensional plots of the NMA-estimated efficacy and safety outcomes.ResultsIn the efficacy NMA (N = 14 RCTs), the relative rankings for PASI 75/90/100 responses by weeks 48-56 were the highest for risankizumab (SUCRA: 98.5%) and bimekizumab (83.8% for dosing every 4 weeks [Q4W], 72.7% for dosing Q4W then every 8 weeks). The PASI response rates did not differ significantly between risankizumab and the two bimekizumab regimens. Additionally, risankizumab was associated with significantly higher PASI response rates than brodalumab, guselkumab, ixekizumab, secukinumab, ustekinumab, adalimumab, and etanercept. In the safety NMAs (N = 8 RCTs), risankizumab had the highest relative rankings for all three outcomes (SUCRA: 92.1%, 82.0%, and 91.0% for any AE, any SAE, and AEs leading to treatment discontinuation, respectively). Risankizumab had a significantly lower rate of any AE than bimekizumab, ustekinumab, and secukinumab.ConclusionsRisankizumab was associated with the most favorable long-term benefit-risk profile for the treatment of moderate-to-severe plaque psoriasis. Although ixekizumab and bimekizumab had favorable efficacy profiles, both treatments had lower rankings for safety outcomes.

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Assessing the benefit-risk of new treatments using generalised pairwise comparisons: the case of erlotinib in pancreatic cancer.

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