Statistical issues in longitudinal data analysis for treatment efficacy studies in the biomedical sciences.
Ontology highlight
ABSTRACT:
SUBMITTER: Liu C
PROVIDER: S-EPMC2956920 | biostudies-other | 2010 Sep
REPOSITORIES: biostudies-other
Similar Datasets
Project description:The microbiome is a complex and dynamic community of microorganisms that co-exist interdependently within an ecosystem, and interact with its host or environment. Longitudinal studies can capture temporal variation within the microbiome to gain mechanistic insights into microbial systems; however, current statistical methods are limited due to the complex and inherent features of the data. We have identified three analytical objectives in longitudinal microbial studies: (1) differential abundance over time and between sample groups, demographic factors or clinical variables of interest; (2) clustering of microorganisms evolving concomitantly across time and (3) network modelling to identify temporal relationships between microorganisms. This review explores the strengths and limitations of current methods to fulfill these objectives, compares different methods in simulation and case studies for objectives (1) and (2), and highlights opportunities for further methodological developments. R tutorials are provided to reproduce the analyses conducted in this review.
| S-EPMC9294433 | biostudies-literature
Project description:BackgroundIn the context of ever-growing health expenditure and limited resources, economic evaluations aid in making evidence-informed policy decisions. Cost-utility analysis (CUA) is often used, and CUA data synthesis is also desirable, but methodological issues are challenged. Hence, we aim to provide a step-by-step process to prepare the CUA data for meta-analysis.MethodsData harmonisation methods were constructed specifically considering CUA methodology, including inconsistent reports, economic parameters, heterogeneity (i.e., country's income, time horizon, perspective, modelling approaches, currency, willingness to pay). An incremental net benefit (INB) and its variance were estimated and pooled across studies using a basic meta-analysis by COMER.ResultsFive scenarios show how to obtain INB and variance with various reported data: Study reports the mean and variance (Scenario 1) or 95% confidence interval (Scenario 2) of ΔC, ΔE, and ICER for INB/variance calculations. Scenario 3: ΔC, ΔE, and variances are available, but not for the ICER; a Monte Carlo was used to simulate ΔC and ΔE data, variance and covariance can be then estimated leading INB calculation. Scenario-4: Only the CE plane was available, ΔC and ΔE data can be extracted; means of ΔC, ΔE, and variance/covariance can be estimated accordingly, leading to INB/variance estimates. Scenario-5: Only mean cost/outcomes and ICER are available but not for variance and the CE-plane. A variance INB can be borrowed from other studies which are similar characteristics, including country income, ICERs, intervention-comparator, time period, country region, and model type and inputs (i.e., discounting, time horizon).ConclusionOut data harmonisation and meta-analytic methods should be useful for researchers for the synthesis of economic evidence to aid policymakers in decision making.
| S-EPMC8845252 | biostudies-literature
Project description:Longitudinal neuroimaging (LNI) studies are rapidly becoming more prevalent and growing in size. Today, no standardized computational tools exist for the analysis of LNI data and widely used methods are sub-optimal for the types of data encountered in real-life studies. Linear Mixed Effects (LME) modeling, a mature approach well known in the statistics community, offers a powerful and versatile framework for analyzing real-life LNI data. This article presents the theory behind LME models, contrasts it with other popular approaches in the context of LNI, and is accompanied with an array of computational tools that will be made freely available through FreeSurfer - a popular Magnetic Resonance Image (MRI) analysis software package. Our core contribution is to provide a quantitative empirical evaluation of the performance of LME and competing alternatives popularly used in prior longitudinal structural MRI studies, namely repeated measures ANOVA and the analysis of annualized longitudinal change measures (e.g. atrophy rate). In our experiments, we analyzed MRI-derived longitudinal hippocampal volume and entorhinal cortex thickness measurements from a public dataset consisting of Alzheimer's patients, subjects with mild cognitive impairment and healthy controls. Our results suggest that the LME approach offers superior statistical power in detecting longitudinal group differences.
| S-EPMC3586747 | biostudies-literature
Project description:The number of in vitro fertilization (IVF) cycles in the United States increased from fewer than 46,000 in 1995 to more than 120,000 in 2005. IVF and other assisted reproductive technology (ART) data are routinely collected and used to identify outcome predictors. However, researchers do not always make full use of the data due to their complexity. Design approaches have included restriction to first-cycle attempts only, which reduces power and identifies effects only of those factors associated with initial success. Many statistical techniques have been used or proposed for analysis of IVF data, ranging from simple t tests to sophisticated models designed specifically for IVF. We applied several of these methods to data from a prospective cohort of 2687 couples undergoing ART from 1994 through 2003. Results across methods are compared and the appropriateness of the various methods is discussed with the intent to illustrate methodologic validity. We observed a remarkable similarity of coefficient estimates across models. However, each method for dealing with multiple cycle data relies on assumptions that may or may not be expected to hold in a given IVF study. The robustness and reported magnitude of effect for individual predictors of IVF success may be inflated or attenuated due to violation of statistical assumptions, and should always be critically interpreted. Given that risk factors associated with IVF success may also advance our understanding of the physiologic processes underlying conception, implantation, and gestation, the application of valid methods to these complex data is critical.
| S-EPMC3402159 | biostudies-literature
Project description:Arguments are proposed that researchers using longitudinal data should consider more and less complex statistical model alternatives to their initially chosen techniques in an effort to "right-size" the model to the data at hand. Such model comparisons may alert researchers who use poorly fitting, overly parsimonious models to more complex, better-fitting alternatives and, alternatively, may identify more parsimonious alternatives to overly complex (and perhaps empirically underidentified and/or less powerful) statistical models. A general framework is proposed for considering (often nested) relationships between a variety of psychometric and growth curve models. A 3-step approach is proposed in which models are evaluated based on the number and patterning of variance components prior to selection of better-fitting growth models that explain both mean and variation-covariation patterns. The orthogonal free curve slope intercept (FCSI) growth model is considered a general model that includes, as special cases, many models, including the factor mean (FM) model (McArdle & Epstein, 1987), McDonald's (1967) linearly constrained factor model, hierarchical linear models (HLMs), repeated-measures multivariate analysis of variance (MANOVA), and the linear slope intercept (linearSI) growth model. The FCSI model, in turn, is nested within the Tuckerized factor model. The approach is illustrated by comparing alternative models in a longitudinal study of children's vocabulary and by comparing several candidate parametric growth and chronometric models in a Monte Carlo study.
| S-EPMC4679639 | biostudies-literature
Project description:Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.
| S-EPMC6018996 | biostudies-literature
Project description:Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries.
| S-EPMC4990152 | biostudies-literature
Project description:Numerous statistical methods have been published for designing and analyzing microarray projects. Traditional genome-wide microarray platforms (such as Affymetrix, Illumina, and DASL) measure the expression level of tens of thousands genes. Since the sets of genes included in these array chips are selected by the manufacturers, the number of genes associated with a specific disease outcome is limited and a large portion of the genes are not associated. nCounter is a new technology by NanoString to measure the expression of a selected number (up to 800) of genes. The list of genes for nCounter chips can be selected by customers. Due to the limited number of genes and the price increase in the number of selected genes, the genes for nCounter chips are carefully selected among those discovered from previous studies, usually using traditional high-throughput platforms, and only a small number of definitely unassociated genes, called control genes, are included to standardize the overall expression level across different chips. Furthermore, nCounter chips measure the expression level of each gene using a counting observation while the traditional high-throughput platforms produce continuous observations. Due to these differences, some statistical methods developed for the design and analysis of high-throughput projects may need modification or may be inappropriate for nCounter projects. In this paper, we discuss statistical methods that can be used for designing and analyzing nCounter projects.
| S-EPMC4266201 | biostudies-literature
Project description:We propose a novel framework to learn the spatiotemporal variability in longitudinal 3D shape data sets, which contain observations of objects that evolve and deform over time. This problem is challenging since surfaces come with arbitrary parameterizations and thus, they need to be spatially registered. Also, different deforming objects, hereinafter referred to as 4D surfaces, evolve at different speeds and thus they need to be temporally aligned. We solve this spatiotemporal registration problem using a Riemannian approach. We treat a 3D surface as a point in a shape space equipped with an elastic Riemannian metric that measures the amount of bending and stretching that the surfaces undergo. A 4D surface can then be seen as a trajectory in this space. With this formulation, the statistical analysis of 4D surfaces can be cast as the problem of analyzing trajectories embedded in a nonlinear Riemannian manifold. However, performing the spatiotemporal registration, and subsequently computing statistics, on such nonlinear spaces is not straightforward as they rely on complex nonlinear optimizations. Our core contribution is the mapping of the surfaces to the space of Square-Root Normal Fields (SRNF) where the [Formula: see text] metric is equivalent to the partial elastic metric in the space of surfaces. Thus, by solving the spatial registration in the SRNF space, the problem of analyzing 4D surfaces becomes the problem of analyzing trajectories embedded in the SRNF space, which has a euclidean structure. In this paper, we develop the building blocks that enable such analysis. These include: (1) the spatiotemporal registration of arbitrarily parameterized 4D surfaces even in the presence of large elastic deformations and large variations in their execution rates; (2) the computation of geodesics between 4D surfaces; (3) the computation of statistical summaries, such as means and modes of variation, of collections of 4D surfaces; and (4) the synthesis of random 4D surfaces. We demonstrate the performance of the proposed framework using 4D facial surfaces and 4D human body shapes.
| S-EPMC9937914 | biostudies-literature
Project description:Advancement of gene expression measurements in longitudinal studies enables the identification of genes associated with disease severity over time. However, problems arise when the technology used to measure gene expression differs between time points. Observed differences between the results obtained at different time points can be caused by technical differences. Modeling the two measurements jointly over time might provide insight into the causes of these different results. Our work is motivated by a study of gene expression data of blood samples from Huntington disease patients, which were obtained using two different sequencing technologies. At time point 1, DeepSAGE technology was used to measure the gene expression, with a subsample also measured using RNA-Seq technology. At time point 2, all samples were measured using RNA-Seq technology. Significant associations between gene expression measured by DeepSAGE and disease severity using data from the first time point could not be replicated by the RNA-Seq data from the second time point. We modeled the relationship between the two sequencing technologies using the data from the overlapping samples. We used linear mixed models with either DeepSAGE or RNA-Seq measurements as the dependent variable and disease severity as the independent variable. In conclusion, (1) for one out of 14 genes, the initial significant result could be replicated with both technologies using data from both time points; (2) statistical efficiency is lost due to disagreement between the two technologies, measurement error when predicting gene expressions, and the need to include additional parameters to account for possible differences.
| S-EPMC8049011 | biostudies-literature