Browsing by Subject "Network meta‐analysis"

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Extensions and applications of generalized linear mixed models for network meta-analysis of randomized controlled trials
(2022) Wiksten, Anna; Piepho, Hans-Peter
Network meta-analyses of published clinical trials has received increased attention over the past years with some meta-analytic publications having had a big impact on the cost-benefit assessment of important drugs. Much of the research has been based on Bayesian analysis using so called base-line contrast model. The research in network meta-analysis methodology has in parts been isolated from other fields of mathematical statistics and is lacking an integrative framework clearly separating statistical models and assumptions, inferential principles, and computational algorithms. The very extensive past research on ANOVA and MANOVA of un- balanced designs, variance component models, generalised linear models with fixed and/or random effects, provides a wealth of useful approaches and insights. These models are especially common in agricultural statistics and this thesis extended the use of the general statistical methods mainly applied in agricultural statistics to applications of network meta-analysis of clinical trials. The methods were applied to four different research problems in separate manuscripts. The first manuscript was based on a simulated case (based on real example) where some of the trials provided individual patient data and some only aggregated data. The outcome type considered was continuous normally distributed data. This manuscript provides models for jointly model the individual patient data and aggregated data. It was also explored how much information is lost if data is aggregated and how to quantify the amount of lost information. The second manuscript was based a real life dataset with pain medications used in acute postoperative pain. The outcome of interest was binomial, whether a subject experienced pain relief or not. The dataset used for NMA included 261 trials with 52 different treatment and dose combinations, making it extraordinarily rich and large network. The third manuscript developed methods for a case of time-to-event-outcome extracted from published Kaplan-Meier curves of survival analyses. This re-generated individual patient data was then used to model and compare the Kaplan-Meier curves and hazards of different treatments. The fourth manuscript of the thesis was tackling the problem of between-trial variance estimation for a specific method of Hartung-Knapp in classical two-treatment meta-analysis. The main finding of the paper was that in some cases random effect meta-analysis using Hartung-Knapp method may yield shorter confidence intervals for combined treatment effect than fixed effect meta-analysis and therefore the recommendation is to always compare results from Hartung-Knapp method with fixed effect meta-analysis. This thesis explored and developed the use of generalized linear mixed models in a setting of network meta-analysis of randomized clinical trials. In practice the most popular analysis method in the field of network meta-analysis has been the baseline contrast model which is usually fitted in a Bayesian framework. The baseline contrast model and Bayesian estimation provides great flexibility, but also come with some unnecessary complications for certain types of analyses. This thesis showed how methods originally developed and extensively used in agricultural research can be used in other field providing efficient calculation, estimation, and inference. Some of the examples used in this thesis arose from analyses needed for real applications in drug development and were directly used in medical research.
How to observe the principle of concurrent control in an arm‐based meta‐analysis using SAS procedures GLIMMIX and BGLIMM
(2022) Piepho, Hans‐Peter; Madden, Laurence V.
Network meta‐analysis is a popular method to synthesize the information obtained in a systematic review of studies (e.g., randomized clinical trials) involving subsets of multiple treatments of interest. The dominant method of analysis employs within‐study information on treatment contrasts and integrates this over a network of studies. One advantage of this approach is that all inference is protected by within‐study randomization. By contrast, arm‐based analyses have been criticized in the past because they may also recover inter‐study information when studies are modeled as random, which is the dominant practice, hence violating the principle of concurrent control, requiring treated individuals to only be compared directly with randomized controls. This issue arises regardless of whether analysis is implemented within a frequentist or a Bayesian framework. Here, we argue that recovery of inter‐study information can be prevented in an arm‐based analysis by adding a fixed study main effect. This simple device means that it is possible to honor the principle of concurrent control in a two‐way analysis‐of‐variance approach that is very easy to implement using generalized linear mixed model procedures and hence may be particularly welcome to those not well versed in the more intricate coding required for a contrast‐based analysis.