Related Experiment Video
Updated: Dec 27, 2025
09:50
Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke
Published on: February 12, 2015
11.5K
A simulation study to compare different estimation approaches for network meta-analysis and corresponding methods to
Corinna Kiefer1, Sibylle Sturtz1, Ralf Bender2,3
1Institute for Quality and Efficiency in Health Care (IQWiG), Im Mediapark 8, Cologne, D-50670, Germany.
BMC Medical Research Methodology
|February 26, 2020
Summary
Network meta-analysis (NMA) estimators from netmeta and Bayesian MTC consistency models perform acceptably with moderate inconsistency. Evaluating consistency remains challenging, emphasizing focus on similarity and homogeneity assumptions in NMA.
Area of Science:
- Health technology assessment
- Systematic reviews
- Evidence synthesis
Background:
- Network meta-analysis (NMA) is increasingly utilized in health technology assessments and systematic reviews.
- Ambiguity persists regarding the properties of NMA estimation approaches and methods for evaluating consistency assumptions.
Purpose of the Study:
- To investigate the properties of different NMA methods through a simulation study.
- To provide recommendations for the practical application of NMA.
Main Methods:
- Evaluated three models for complex networks: frequentist netmeta, Bayesian mixed treatment comparisons (MTC) consistency model, and MTC with stepwise inconsistency removal.
- Conducted a simulation study for networks up to 5 interventions.
- Assessed global methods for evaluating the consistency assumption.
Main Results:
- Effect estimators showed unreliable results with high inconsistency.
- The MTC consistency model and netmeta estimators performed acceptably with moderate or no inconsistency.
- Performance depended on the amount of heterogeneity; none of the evaluated consistency evaluation methods were suitable.
Conclusions:
- Recommend the netmeta or Bayesian MTC consistency model estimators for practical NMA.
- Emphasize the importance of focusing on similarity and homogeneity assumptions due to limitations in consistency evaluation methods.
Related Concept Videos
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
205
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
205
01:14Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
413
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
413
01:16Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
388
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
388
01:15Study Design in Statistics
9.9K
A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
9.9K
01:24One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
1.0K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
On...
1.0K
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
234
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
234