A Model-Based Dose–Response Meta-Analysis of Ocular Hypotensive Agents as a Drug Development Tool to Evaluate New Therapies in Glaucoma

2015 ◽  
Vol 31 (4) ◽  
pp. 189-197 ◽  
Author(s):  
Susan Raber ◽  
Jaap W. Mandema ◽  
Hanbin Li ◽  
Dana J. Nickens
Keyword(s):  
Drug Development ◽  
Dose Response ◽  
Meta Analysis ◽  
New Therapies ◽  
Model Based ◽  
Development Tool

scholarly journals Model-Based Meta-Analysis for Quantifying Paclitaxel Dose Response in Cancer Patients

2014 ◽  
Vol 3 (5) ◽  
pp. 115 ◽  
Author(s):  
D Lu ◽  
A Joshi ◽  
H Li ◽  
N Zhang ◽  
MM Ren ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Dose Response ◽  
Meta Analysis ◽  
Model Based

scholarly journals Model-Based Meta-Analysis for Multiple Myeloma: A Quantitative Drug-Independent Framework for Efficient Decisions in Oncology Drug Development

2017 ◽  
Vol 11 (2) ◽  
pp. 218-225 ◽  
Author(s):  
Zhaoyang Teng ◽  
Neeraj Gupta ◽  
Zhaowei Hua ◽  
Guohui Liu ◽  
Vivek Samnotra ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Drug Development ◽  
Meta Analysis ◽  
Model Based ◽  
Oncology Drug ◽  
Oncology Drug Development

scholarly journals Joining the Dots: Linking Disconnected Networks of Evidence Using Dose-Response Model-Based Network Meta-Analysis

2021 ◽  
Vol 41 (2) ◽  
pp. 194-208
Author(s):  
Hugo Pedder ◽  
Sofia Dias ◽  
Meg Bennetts ◽  
Martin Boucher ◽  
Nicky J. Welton
Keyword(s):  
Dose Response ◽  
Meta Analysis ◽  
Relative Effectiveness ◽  
Data Sets ◽  
Response Relationship ◽  
Phase Ii Trials ◽  
Dose Response Relationship ◽  
Data Set ◽  
Model Based ◽  
Multiple Doses

Background Network meta-analysis (NMA) synthesizes direct and indirect evidence on multiple treatments to estimate their relative effectiveness. However, comparisons between disconnected treatments are not possible without making strong assumptions. When studies including multiple doses of the same drug are available, model-based NMA (MBNMA) presents a novel solution to this problem by modeling a parametric dose-response relationship within an NMA framework. In this article, we illustrate several scenarios in which dose-response MBNMA can connect and strengthen evidence networks. Methods We created illustrative data sets by removing studies or treatments from an NMA of triptans for migraine relief. We fitted MBNMA models with different dose-response relationships. For connected networks, we compared MBNMA estimates with NMA estimates. For disconnected networks, we compared MBNMA estimates with NMA estimates from an “augmented” network connected by adding studies or treatments back into the data set. Results In connected networks, relative effect estimates from MBNMA were more precise than those from NMA models (ratio of posterior SDs NMA v. MBNMA: median = 1.13; range = 1.04–1.68). In disconnected networks, MBNMA provided estimates for all treatments where NMA could not and were consistent with NMA estimates from augmented networks for 15 of 18 data sets. In the remaining 3 of 18 data sets, a more complex dose-response relationship was required than could be fitted with the available evidence. Conclusions Where information on multiple doses is available, MBNMA can connect disconnected networks and increase precision while making less strong assumptions than alternative approaches. MBNMA relies on correct specification of the dose-response relationship, which requires sufficient data at different doses to allow reliable estimation. We recommend that systematic reviews for NMA search for and include evidence (including phase II trials) on multiple doses of agents where available.

Clinical dose–response for a broad set of biological products: A model-based meta-analysis

2017 ◽  
Vol 27 (9) ◽  
pp. 2694-2721 ◽  
Author(s):  
Joseph Wu ◽  
Anindita Banerjee ◽  
Bo Jin ◽  
Sandeep M Menon ◽  
Steven W Martin ◽  
...  
Keyword(s):  
Small Molecules ◽  
Dose Response ◽  
Meta Analysis ◽  
Small Sample ◽  
Model Parameters ◽  
Maximal Effect ◽  
Clinical Dose ◽  
Model Based ◽  
Effect Model ◽  
Dose Ranging

Characterizing clinical dose–response is a critical step in drug development. Uncertainty in the dose–response model when planning a dose-ranging study can often undermine efficiency in both the design and analysis of the trial. Results of a previous meta-analysis on a portfolio of small molecule compounds from a large pharmaceutical company demonstrated a consistent dose–response relationship that was well described by the maximal effect model. Biologics are different from small molecules due to their large molecular sizes and their potential to induce immunogenicity. A model-based meta-analysis was conducted on the clinical efficacy of 71 distinct biologics evaluated in 91 placebo-controlled dose–response studies published between 1995 and 2014. The maximal effect model, arising from receptor occupancy theory, described the clinical dose–response data for the majority of the biologics (81.7%, n = 58). Five biologics (7%) with data showing non-monotonic trend assuming the maximal effect model were identified and discussed. A Bayesian model-based hierarchical approach using different joint specifications of prior densities for the maximal effect model parameters was used to meta-analyze the whole set of biologics excluding these five biologics ( n = 66). Posterior predictive distributions of the maximal effect model parameters were reported and they could be used to aid the design of future dose-ranging studies. Compared to the meta-analysis of small molecules, the combination of fewer doses, narrower dosing ranges, and small sample sizes further limited the information available to estimate clinical dose–response among biologics.

scholarly journals Methods to assess evidence consistency in dose‐response model based network meta‐analysis

2021 ◽  
Author(s):  
Hugo Pedder ◽  
Sofia Dias ◽  
Martin Boucher ◽  
Meg Bennetts ◽  
David Mawdsley ◽  
...  
Keyword(s):  
Dose Response ◽  
Meta Analysis ◽  
Response Model ◽  
Model Based

Daytime napping, daytime sleepiness and the risk of metabolic diseases: dose-response meta-analysis

2016 ◽  
Author(s):  
Tomohide Yamada ◽  
Nobuhiro Shojima ◽  
Toshimasa Yamauchi ◽  
Takashi Kadowaki
Keyword(s):  
Dose Response ◽  
Daytime Sleepiness ◽  
Metabolic Diseases ◽  
Meta Analysis
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