[PDF] Top 20 Handling missing continuous outcome data in a Bayesian network meta-analysis
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Handling missing continuous outcome data in a Bayesian network meta-analysis
... complete data on a treatment effect and its uncertainty usually contribute to an NMA ...the continuous outcome is more difficult to synthesize in an NMA than a binary endpoint for several ...a ... See full document
10
A systematic survey shows that reporting and handling of missing outcome data in networks of interventions is poor
... with network meta-analysis have received very little ...attention. Network meta-analysis (NMA) is an extension of pairwise meta-analysis that allows direct ... See full document
15
Comparative effectiveness of glycemic control in patients with type 2 diabetes treated with GLP-1 receptor agonists: a network meta-analysis of placebo-controlled and active-comparator trials
... trials. Continuous outcomes were analyzed using a normal model with an identity link, and dichotomous outcomes were analyzed using a binomial model with logit ...the data via Bayesian Markov chain ... See full document
12
Prokinetics for the treatment of functional dyspepsia: Bayesian network meta-analysis
... These medications are not available in some countries. Furthermore, levosulpiride is associated with drug-induced parkinsonism, inhibiting its wide application in clinical practice [54]. Prucalopride was developed and ... See full document
11
Bias in identification of the best treatment in a Bayesian network meta-analysis for binary outcome: a simulation study
... Using the parameters described above, binary data were generated from an appropriate binomial distribution. Then, the hierarchical Bayesian NMA model was fitted to perform statistical inference. In each ... See full document
10
Hypothesis testing in Bayesian network meta-analysis
... In this paper, we want to introduce a simple method to obtain an index υ that can be interpreted similarly to a fre- quentist p-value for an effect estimate within a Bayesian NMA. For this, we adapt an idea ... See full document
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Imputation strategies when a continuous outcome is to be dichotomized for responder analysis: a simulation study
... the outcome is measured for all subjects at base- line and the timepoint of interest, responder status can be calculated, and the analysis is ...However missing data are ubiquitous in ... See full document
11
A Microsoft-Excel-based tool for running and critically appraising network meta-analyses—an overview and application of NetMetaXL
... and Bayesian approaches for conduct- ing network meta-analysis are feasible ...because Bayesian methods provide greater flexibil- ity to use more complex models and different ... See full document
11
Multiple imputation for handling missing outcome data when estimating the relative risk
... the missing data mechanism is unknown) and in simu- lation scenarios with different covariate characteristics, outcome prevalences and missing data mechanisms would certainly be ...for ... See full document
10
An empirical comparison of Bayesian modelling strategies for missing binary outcome data in network meta-analysis
... with network meta-analysis (NMA) from a wide range of health-related fields, we evaluated comparatively the most frequently described Bayesian modelling strategies for MOD in terms of log odds ... See full document
16
Handling trial participants with missing outcome data when conducting a meta-analysis: a systematic survey of proposed approaches
... of handling MPD in systematic ...participant data to apply advanced statistical techniques such as multiple imputations [11], systematic reviewers can only use group level data with their inherent ... See full document
7
Comparison of statistical methods of handling missing binary outcome data in randomized controlled trials of efficacy studies
... On the other hand a number of principled approaches exist which are either multiple imputation based, maximum likelihood based or weighting based. These methods lead to valid estimates of parameters when data is ... See full document
271
Statistical analysis and handling of missing data in cluster randomized trials: a systematic review
... reporting missing data at the cluster (28 % versus 18 %) and individual levels (93 % versus 48 ...of missing data or because Diaz-Ordaz was not able to verify the amount of missing ... See full document
10
Individual patient-data meta-analysis comparing clinical outcome in patients with ST-elevation myocardial nfarction treated with percutaneous coronary intervention with or without prior thrombectomy. ATTEMPT study: A pooled Analysis of Trials on ThrombEctomy in acute Myocardial infarction based on individual PatienT data
... patient-level analysis (according to event counts reported at the longest available follow-up), and a random effect method with generic inverse variance weighting (according to risk estimates obtained with Cox ... See full document
6
Comparing Bayesian regression and classic zero-inflated negative binomial on size estimation of people who use alcohol
... count data such as alcohol consumption and its effects on strata of society such as students (13), youth, understanding social norms (14), and notice of number of the people and which one of personal and social ... See full document
7
The role of induction and adjuvant chemotherapy in combination with concurrent chemoradiotherapy for nasopharyngeal cancer: a Bayesian network meta-analysis of published randomized controlled trials
... Relevant studies were identified via search of the PubMed database with a timeframe from inception to February 1, 2015. The studies were limited to those with human subjects that were in the English language. We used the ... See full document
12
Comparative efficacy and safety of antipsychotics in the treatment of schizophrenia: a network meta-analysis in a Japanese population
... the meta-analysis was small; therefore, the current analysis had insufficient statistical power in addition to the fact that most studies included in this meta-analysis were ... See full document
22
Handling attrition and non-response in longitudinal data
... Another set of issues when using weights is that a traditional weighting approach will generally lose data information. For example, suppose we wish to regress a time 2 variable on a set of time 1 variables with ... See full document
10
Handling Missing Data: Traditional Techniques Versus Machine Learning
... handle missing data. The techniques for handling missing data used in practical analysis vary widely, from ad-hoc methods such as mean substitution, to more sophisticated ones ... See full document
9
PubMedCentral-PMC5708892.pdf
... neuroimaging analysis for the early diagnosis of mental ...brain network analysis, attempts to model the brain as a complex network and study the interactions of brain regions via ... See full document
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