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

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 Methods for meta-analysis of pharmacodynamic dose–response data with application to multi-arm studies of alogliptin

2016 ◽  
Vol 27 (2) ◽  
pp. 564-578 ◽  
Author(s):  
Oliver Langford ◽  
Jeffrey K Aronson ◽  
Gert van Valkenhoef ◽  
Richard J Stevens
Keyword(s):  
Randomized Controlled Trials ◽  
Least Squares ◽  
Dose Response ◽  
Meta Analysis ◽  
Weighted Least Squares ◽  
Controlled Trials ◽  
Response Model ◽  
Response Data ◽  
Randomized Controlled ◽  
The Mean

Standard methods for meta-analysis of dose–response data in epidemiology assume a model with a single scalar parameter, such as log-linear relationships between exposure and outcome; such models are implicitly unbounded. In contrast, in pharmacology, multi-parameter models, such as the widely used Emax model, are used to describe relationships that are bounded above and below. We propose methods for estimating the parameters of a dose–response model by meta-analysis of summary data from the results of randomized controlled trials of a drug, in which each trial uses multiple doses of the drug of interest (possibly including dose 0 or placebo). We assume that, for each randomized arm of each trial, the mean and standard error of a continuous response measure and the corresponding allocated dose are available. We consider weighted least squares fitting of the model to the mean and dose pairs from all arms of all studies, and a two-stage procedure in which scalar inverse-variance meta-analysis is performed at each dose, and the dose–response model is fitted to the results by weighted least squares. We then compare these with two further methods inspired by network meta-analysis that fit the model to the contrasts between doses. We illustrate the methods by estimating the parameters of the Emax model to a collection of multi-arm, multiple-dose, randomized controlled trials of alogliptin, a drug for the management of diabetes mellitus, and further examine the properties of the four methods with sensitivity analyses and a simulation study. We find that all four methods produce broadly comparable point estimates for the parameters of most interest, but a single-stage method based on contrasts between doses produces the most appropriate confidence intervals. Although simpler methods may have pragmatic advantages, such as the use of standard software for scalar meta-analysis, more sophisticated methods are nevertheless preferable for their advantages in estimation.

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 A Bayesian dose–response meta-analysis model: A simulations study and application

2021 ◽  
pp. 096228022098264
Author(s):  
Tasnim Hamza ◽  
Andrea Cipriani ◽  
Toshi A Furukawa ◽  
Matthias Egger ◽  
Nicola Orsini ◽  
...  
Keyword(s):  
Dose Response ◽  
Small Sample Size ◽  
Meta Analysis ◽  
Antidepressant Drugs ◽  
Bayesian Framework ◽  
Small Sample ◽  
Response Model ◽  
Analysis Model ◽  
One Stage ◽  
Specific Outcome

Dose–response models express the effect of different dose or exposure levels on a specific outcome. In meta-analysis, where aggregated-level data is available, dose–response evidence is synthesized using either one-stage or two-stage models in a frequentist setting. We propose a hierarchical dose–response model implemented in a Bayesian framework. We develop our model assuming normal or binomial likelihood and accounting for exposures grouped in clusters. To allow maximum flexibility, the dose–response association is modelled using restricted cubic splines. We implement these models in R using JAGS and we compare our approach to the one-stage dose–response meta-analysis model in a simulation study. We found that the Bayesian dose–response model with binomial likelihood has lower bias than the Bayesian model with normal likelihood and the frequentist one-stage model when studies have small sample size. When the true underlying shape is log–log or half-sigmoid, the performance of all models depends on choosing an appropriate location for the knots. In all other examined situations, all models perform very well and give practically identical results. We also re-analyze the data from 60 randomized controlled trials (15,984 participants) examining the efficacy (response) of various doses of serotonin-specific reuptake inhibitor (SSRI) antidepressant drugs. All models suggest that the dose–response curve increases between zero dose and 30–40 mg of fluoxetine-equivalent dose, and thereafter shows small decline. We draw the same conclusion when we take into account the fact that five different antidepressants have been studied in the included trials. We show that implementation of the hierarchical model in Bayesian framework has similar performance to, but overcomes some of the limitations of the frequentist approach and offers maximum flexibility to accommodate features of the data.

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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