New models for describing outliers in meta-analysis.
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ABSTRACT:
SUBMITTER: Baker R
PROVIDER: S-EPMC4964911 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundMeta-analysis is a useful tool for combining evidence from multiple studies to estimate a pooled treatment effect. An extension of meta-analysis, network meta-analysis, is becoming more commonly used as a way to simultaneously compare multiple treatments in a single analysis. Despite the variety of approaches available for presenting fitted models, ascertaining an intuitive understanding of these models is often difficult. This is especially challenging in large networks with many different treatments. Here we propose two visualisation methods, so that network meta-analysis models can be more easily interpreted.MethodsOur methods can be used irrespective of the statistical model or the estimation method used and are grounded in network analysis. We define three types of distance measures between the treatments that contribute to the network. These three distance measures are based on 1) the estimated treatment effects, 2) their standard errors and 3) the corresponding p-values. Then, by using a suitable threshold, we categorise some treatment pairs as being "close" (short distances). Treatments that are close are regarded as "connected" in the network analysis theory. Finally, we group the treatments into communities using standard methods for network analysis. We are then able to identify which parts of the network are estimated to have similar (or different) treatment efficacy and which parts of the network are better identified. We also propose a second method using parametric bootstrapping, where a heat map is used in the visualisation. We use the software R and provide the code used.ResultsWe illustrate our new methods using a challenging dataset containing 22 treatments, and a previously fitted model for this data. Two communities of treatments that appear to have similar efficacy are identified. Furthermore using our methods we can identify parts of the network that are better (and less well) identified.ConclusionsOur new visualisation approaches may be used by network meta-analysts to gain an intuitive understanding of the implications of their fitted models. Our visualisation methods may be used informally, to identify the most salient features of the fitted models that can then be reported, or more formally by presenting the new visualisation devices within published reports.
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Project description:Connectivity of terrestrial deep biosphere microbial communities with surface communities
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Project description:When addressing spatial biological questions using mathematical models, symmetries within the system are often exploited to simplify the problem by reducing its physical dimension. In a reduced-dimension model molecular movement is restricted to the reduced dimension, changing the nature of molecular movement. This change in molecular movement can lead to quantitatively and even qualitatively different results in the full and reduced systems. Within this manuscript we discuss the condition under which restricted molecular movement in reduced-dimension models accurately approximates molecular movement in the full system. For those systems which do not satisfy the condition, we present a general method for approximating unrestricted molecular movement in reduced-dimension models. We will derive a mathematically robust, finite difference method for solving the 2D diffusion equation within a 1D reduced-dimension model. The methods described here can be used to improve the accuracy of many reduced-dimension models while retaining benefits of system simplification.
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Project description:Background:To elucidate molecular features associated with disproportionate survival of glioblastoma (GB) patients, we conducted deep genomic comparative analysis of a cohort of patients receiving standard therapy (surgery plus concurrent radiation and temozolomide); "GB outliers" were identified: long-term survivor of 33 months (LTS; n = 8) versus short-term survivor of 7 months (STS; n = 10). Methods:We implemented exome, RNA, whole genome sequencing, and DNA methylation for collection of deep genomic data from STS and LTS GB patients. Results:LTS GB showed frequent chromosomal gains in 4q12 (platelet derived growth factor receptor alpha and KIT) and 12q14.1 (cyclin-dependent kinase 4), and deletion in 19q13.33 (BAX, branched chain amino-acid transaminase 2, and cluster of differentiation 33). STS GB showed frequent deletion in 9p11.2 (forkhead box D4-like 2 and aquaporin 7 pseudogene 3) and 22q11.21 (Hypermethylated In Cancer 2). LTS GB showed 2-fold more frequent copy number deletions compared with STS GB. Gene expression differences showed the STS cohort with altered transcriptional regulators: activation of signal transducer and activator of transcription (STAT)5a/b, nuclear factor-kappaB (NF-?B), and interferon-gamma (IFNG), and inhibition of mitogen-activated protein kinase (MAPK1), extracellular signal-regulated kinase (ERK)1/2, and estrogen receptor (ESR)1. Expression-based biological concepts prominent in the STS cohort include metabolic processes, anaphase-promoting complex degradation, and immune processes associated with major histocompatibility complex class I antigen presentation; the LTS cohort features genes related to development, morphogenesis, and the mammalian target of rapamycin signaling pathway. Whole genome methylation analyses showed that a methylation signature of 89 probes distinctly separates LTS from STS GB tumors. Conclusion:We posit that genomic instability is associated with longer survival of GB (possibly with vulnerability to standard therapy); conversely, genomic and epigenetic signatures may identify patients where up-front entry into alternative, targeted regimens would be a preferred, more efficacious management.
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Project description:Data on functional disability are of widespread policy interest in the United States, especially with respect to planning for Medicare and Social Security for a growing population of elderly adults. We consider an extract of functional disability data from the National Long Term Care Survey (NLTCS) and attempt to develop disability profiles using variations of the Grade of Membership (GoM) model. We first describe GoM as an individual-level mixture model that allows individuals to have partial membership in several mixture components simultaneously. We then prove the equivalence between individual-level and population-level mixture models, and use this property to develop a Markov Chain Monte Carlo algorithm for Bayesian estimation of the model. We use our approach to analyze functional disability data from the NLTCS.
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Project description:Gels are a drug delivery platform that is being evaluated for application of active pharmaceutical ingredients, termed microbicides, that act topically against vaginal and rectal mucosal infection by sexually transmitted HIV. Despite success in one Phase IIb trial of a vaginal gel delivering tenofovir, problems of user adherence to designed gel application scheduling have compromised results in two other trials. The microbicides field is responding to this dilemma by expanding behavioral analysis of the determinants of adherence while simultaneously improving the pharmacological, biochemical, and biophysical analyses of the determinants of microbicide drug delivery. The intent is to combine results of these two complementary perspectives on microbicide performance and epidemiological success to create an improved product design paradigm. Central to both user sensory perceptions and preferences, key factors that underlie adherence, and to vaginal gel mucosal drug delivery, that underlies anti-HIV efficacy, are gel properties (e.g. rheology) and volume. The specific engineering problem to be solved here is to develop a model for how gel rheology and volume, interacting with loaded drug concentration, govern the transport of the microbicide drug tenofovir into the vaginal mucosa to its stromal layer. These are factors that can be controlled in microbicide gel design. The analysis here builds upon our current understanding of vaginal gel deployment and drug delivery, incorporating key features of the gel's environment, the vaginal canal, fluid production and subsequent gel dilution, and vaginal wall elasticity. These have not previously been included in the modeling of drug delivery. We consider the microbicide drug tenofovir, which is the drug most completely studied for gels: in vitro, in animal studies in vivo, and in human clinical trials with both vaginal or rectal gel application. Our goal is to contribute to improved biophysical and pharmacological understanding of gel functionality, providing a computational tool that can be used in future vaginal microbicide gel design.
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Project description:Notwithstanding the rapid developments in sequencing techniques, Y and W sex chromosomes have still been mostly excluded from whole genome sequencing projects due to their high repetitive DNA content. Therefore, Y and W chromosomes are poorly described in most species despite their biological importance. Several methods were developed for identifying Y or W-linked sequences among unmapped scaffolds. However, it is not enough to discover functional regions from short unmapped scaffolds. Here, we provide a new and simple strategy based on k-mer comparison for comprehensive analysis of the W chromosome in Bombyx mori. Using this novel method, we effectively assembled de novo 1281?W-derived genome contigs (totaling 1.9?Mbp), and identified 156?W-linked transcript RNAs and 345?W-linked small RNAs. This method will help in the elucidation of mechanisms of sexual development and exploration of W chromosome biological functions, and provide insights into the evolution of sex chromosomes. Moreover, we showed this method can be employed in identifying heterogametic sex chromosomes (W and Y chromosomes) in many other species where genomic information is still scarce.
| S-EPMC6105102 | biostudies-literature
Project description:Dependence between studies in meta-analysis is an assumption which is imposed on the structure of hierarchical Bayesian meta-analytic models. Dependence in meta-analysis can occur as a result of study reports using the same data or from the same authors. In this paper, the hierarchical Bayesian delta-splitting (HBDS) model (Steven and Taylor, 2009), which allows for dependence between studies and sub-studies by introducing dependency at the sampling and hierarchical levels, is developed using Bayesian approaches. Parameter estimation obtained from the joint posterior distributions of all parameters for the HBDS model was conducted using the Metropolis within Gibbs algorithm. The estimation of parameters for simulation studies using R code confirmed the consistency of the model parameters. These parameters were then tested successfully on studies to assess the effects of native-language vocabulary aids on second language reading as a case study.
| S-EPMC7511738 | biostudies-literature
Project description:The objective of the present study was to evaluate mechanistic pharmacokinetic models describing active renal secretion and reabsorption over a range of Michaelis-Menten parameter estimates and doses. Plasma concentration and urinary excretion profiles were simulated and renal clearance (CL(r)) was calculated for two pharmacokinetic models describing active renal reabsorption (R1/R2), two models describing active secretion (S1/S2), and a model containing both processes. A range of doses (1-1,000 mg/kg) was evaluated, and V (max) and K (m) parameter estimates were varied over a 100-fold range. Similar CL(r) values were predicted for reabsorption models (R1/R2) with variations in V (max) and K (m). Tubular secretion models (S1/S2) yielded similar relationships between Michaelis-Menten parameter perturbations and CL(r), but the predicted CL(r) values were threefold higher for model S1. For both reabsorption and secretion models, the greatest changes in CL(r) were observed with perturbations in V (max), suggesting the need for an accurate estimate of this parameter. When intrinsic clearance was substituted for Michaelis-Menten parameters, it failed to predict similar CL(r) values even within the linear range. For models S1 and S2, renal secretion was predominant at low doses, whereas renal clearance was driven by fraction unbound in plasma at high doses. Simulations demonstrated the importance of Michaelis-Menten parameter estimates (especially V (max)) for determining CL(r). K (m) estimates can easily be obtained directly from in vitro studies. However, additional scaling of in vitro V (max) estimates using in vitro/in vivo extrapolation methods are required to incorporate these parameters into pharmacokinetic models.
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Project description:Rich meta-epidemiological data sets have been collected to explore associations between intervention effect estimates and study-level characteristics. Welton et al proposed models for the analysis of meta-epidemiological data, but these models are restrictive because they force heterogeneity among studies with a particular characteristic to be at least as large as that among studies without the characteristic. In this paper we present alternative models that are invariant to the labels defining the 2 categories of studies. To exemplify the methods, we use a collection of meta-analyses in which the Cochrane Risk of Bias tool has been implemented. We first investigate the influence of small trial sample sizes (less than 100 participants), before investigating the influence of multiple methodological flaws (inadequate or unclear sequence generation, allocation concealment, and blinding). We fit both the Welton et al model and our proposed label-invariant model and compare the results. Estimates of mean bias associated with the trial characteristics and of between-trial variances are not very sensitive to the choice of model. Results from fitting a univariable model show that heterogeneity variance is, on average, 88% greater among trials with less than 100 participants. On the basis of a multivariable model, heterogeneity variance is, on average, 25% greater among trials with inadequate/unclear sequence generation, 51% greater among trials with inadequate/unclear blinding, and 23% lower among trials with inadequate/unclear allocation concealment, although the 95% intervals for these ratios are very wide. Our proposed label-invariant models for meta-epidemiological data analysis facilitate investigations of between-study heterogeneity attributable to certain study characteristics.
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