1,721,028 research outputs found
Discrete Approximation of a Mixture Distribution via Restricted Divergence
No full textMixture distributions arise in many application areas, for example, as marginal distributions or convolutions of distributions. We present a method of constructing an easily tractable discrete mixture distribution as an approximation to a mixture distribution with a large to infinite number, discrete or continuous, of components. The proposed DIRECT (divergence restricting conditional tesselation) algorithm is set up such that a prespecified precision, defined in terms of Kullback–Leibler divergence between true distribution and approximation, is guaranteed. Application of the algorithm is demonstrated in two examples. Supplementary materials for this article are available online.</p
Supplemental material for Group sequential designs for negative binomial outcomes
No full textSupplemental material for Group sequential designs for negative binomial outcomes by Tobias Mütze, Ekkehard Glimm, Heinz Schmidli and Tim Friede in Statistical Methods in Medical Research</p
Supplemental material for Group sequential designs with robust semiparametric recurrent event models
No full textSupplemental material for Group sequential designs with robust semiparametric recurrent event models by Tobias Mütze, Ekkehard Glimm, Heinz Schmidli and Tim Friede in Statistical Methods in Medical Research</p
Likelihood-based random-effects meta-analysis with few studies: empirical and simulation studies
Full text linkBackground: Standard random-effects meta-analysis methods perform poorly when applied to few studies only. Such settings however are commonly encountered in practice. It is unclear, whether or to what extent small-sample-size behaviour can be improved by more sophisticated modeling.
Methods: We consider likelihood-based methods, the DerSimonian-Laird approach, Empirical Bayes, several adjustment methods and a fully Bayesian approach. Confidence intervals are based on a normal approximation, or on adjustments based on the Student-t-distribution. In addition, a linear mixed model and two generalized linear mixed models (GLMMs) assuming binomial or Poisson distributed numbers of events per study arm are considered for pairwise binary meta-analyses. We extract an empirical data set of 40 meta-analyses from recent reviews published by the German Institute for Quality and Efficiency in Health Care (IQWiG). Methods are then compared empirically as well as in a simulation study, based on few studies, imbalanced study sizes, and considering odds-ratio (OR) and risk ratio (RR) effect sizes. Coverage probabilities and interval widths for the combined effect estimate are evaluated to compare the different approaches.
Results: Empirically, a majority of the identified meta-analyses include only 2 studies. Variation of methods or effect measures affects the estimation results. In the simulation study, coverage probability is, in the presence of heterogeneity and few studies, mostly below the nominal level for all frequentist methods based on normal approximation, in particular when sizes in meta-analyses are not balanced, but improve when confidence intervals are adjusted. Bayesian methods result in better coverage than the frequentist methods with normal approximation in all scenarios, except for some cases of very large heterogeneity where the coverage is slightly lower. Credible intervals are empirically and in the simulation study wider than unadjusted confidence intervals, but considerably narrower than adjusted ones, with some exceptions when considering RRs and small numbers of patients per trial-arm. Confidence intervals based on the GLMMs are, in general, slightly narrower than those from other frequentist methods. Some methods turned out impractical due to frequent numerical problems.
Conclusions: In the presence of between-study heterogeneity, especially with unbalanced study sizes, caution is needed in applying meta-analytical methods to few studies, as either coverage probabilities might be compromised, or intervals are inconclusively wide. Bayesian estimation with a sensibly chosen prior for between-trial heterogeneity may offer a promising compromise
MSJ794063_supplementary_material – Supplemental material for Over three decades study populations in progressive multiple sclerosis have become older and more disabled, but have lower on-trial progression rates: A systematic review and meta-analysis of 43 randomised placebo-controlled trials
No full textSupplemental material, MSJ794063_supplementary_material for Over three decades study populations in progressive multiple sclerosis have become older and more disabled, but have lower on-trial progression rates: A systematic review and meta-analysis of 43 randomised placebo-controlled trials by Richard S Nicholas, Erika Han, Joel Raffel, Jeremy Chataway and Tim Friede in Multiple Sclerosis Journal</p
Supplemental material for Robustness of testing procedures for confirmatory subpopulation analyses based on a continuous biomarker
No full textSupplemental Material for Robustness of testing procedures for confirmatory subpopulation analyses based on a continuous biomarker by Alexandra Christine Graf, Gernot Wassmer, Tim Friede, Roland Gerard Gera and Martin Posch in Statistical Methods in Medical Research</p
Changing EDSS Progression in Placebo Cohorts in Relapsing MS: A Systematic Review and Meta-Regression
Full text linkBackground
Recent systematic reviews of randomised controlled trials (RCTs) in relapsing multiple sclerosis (RMS) revealed a decrease in placebo annualized relapse rates (ARR) over the past two decades. Furthermore, regression to the mean effects were observed in ARR and MRI lesion counts. It is unclear whether disease progression measured by the expanded disability status scale (EDSS) exhibits similar features.
Methods
A systematic review of RCTs in RMS was conducted extracting data on EDSS and baseline characteristics. The logarithmic odds of disease progression were modelled to investigate time trends. Random-effects models were used to account for between-study variability; all investigated models included trial duration as a predictor to correct for unequal study durations. Meta-regressions were conducted to assess the prognostic value of a number of study-level baseline variables.
Results
The systematic literature search identified 39 studies, including a total of 19,714 patients. The proportion of patients in placebo controls experiencing a disease progression decreased over the years (p<0.001). Meta-regression identified associated covariates including the size of the study and its duration that in part explained the time trend. Progression probabilities tended to be lower in the second year of a study compared to the first year with a reduction of 28% in progression odds from year 1 to year 2 (p = 0.017).
Conclusion
EDSS disease progression exhibits similar behaviour over time as the ARR and point to changes in trial characteristics over the years. This needs to be considered in comparisons between historical and recent trials
sj-docx-2-msj-10.1177_13524585231201089 – Supplemental material for Mobile health interventions in multiple sclerosis: A systematic review
No full textSupplemental material, sj-docx-2-msj-10.1177_13524585231201089 for Mobile health interventions in multiple sclerosis: A systematic review by Christoph Heesen, Thomas Berger, Karin Riemann-Lorenz, Nicole Krause, Tim Friede, Jana Pöttgen, Björn Meyer and Dagmar Lühmann in Multiple Sclerosis Journal</p
sj-docx-1-msj-10.1177_13524585231201089 – Supplemental material for Mobile health interventions in multiple sclerosis: A systematic review
No full textSupplemental material, sj-docx-1-msj-10.1177_13524585231201089 for Mobile health interventions in multiple sclerosis: A systematic review by Christoph Heesen, Thomas Berger, Karin Riemann-Lorenz, Nicole Krause, Tim Friede, Jana Pöttgen, Björn Meyer and Dagmar Lühmann in Multiple Sclerosis Journal</p
sj-csv-2-smm-10.1177_09622802221133557 - Supplemental material for Regularization approaches in clinical biostatistics: A review of methods and their applications
No full textSupplemental material, sj-csv-2-smm-10.1177_09622802221133557 for Regularization approaches in clinical biostatistics: A review of methods
and their applications by Sarah Friedrich, Andreas Groll, Katja Ickstadt, Thomas Kneib, Markus Pauly, Jörg Rahnenführer and Tim Friede in Statistical Methods in Medical Research</p
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