Yahtzee: an anonymized group level matching procedure.
Ontology highlight
ABSTRACT:
SUBMITTER: Jones JJ
PROVIDER: S-EPMC3564933 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BACKGROUND:Randomized controlled trials are the gold-standard for clinical trials. However, randomization is not always feasible. In this article we propose a prospective and adaptive matched case-control trial design assuming that a control group already exists. METHODS:We propose and discuss an interim analysis step to estimate the matching rate using a resampling step followed by a sample size recalculation. The sample size recalculation is based on the observed mean resampling matching rate. We applied our approach in a simulation study and to a real data set to evaluate the characteristics of the proposed design and to compare the results to a naive approach. RESULTS:The proposed design achieves at least 10% higher matching rate than the naive approach at final analysis, thus providing a better estimation of the true matching rate. A good choice for the interim analysis seems to be a fraction of around [Formula: see text] to [Formula: see text] of the control patients. CONCLUSION:The proposed resampling step in a prospective matched case-control trial design leads to an improved estimate of the final matching rate and, thus, to a gain in power of the approach due to sensible sample size recalculation.
| S-EPMC6636117 | biostudies-literature
Project description:Pooling is an attractive strategy in screening infected specimens, especially for rare diseases. An essential step of performing the pooled test is to determine the group size. Sometimes, equal group size is not appropriate due to population heterogeneity. In this case, varying group sizes are preferred and could be determined while individual information is available. In this study, we propose a sequential procedure to determine varying group sizes through fully utilizing available information. This procedure is data driven. Simulations show that it has good performance in estimating parameters.
| S-EPMC6466917 | biostudies-literature
Project description:PrecisThe exclusion of unmatched observations in propensity score matching has implications for the generalizability of causal effects. Machine learning methods can help to identify how the study population differs from the unmatched subpopulation.BackgroundThere has been widespread use of propensity scores in evaluating the effect of cancer treatments on survival, particularly in administrative databases and cancer registries. A byproduct of certain matching schemes is the exclusion of observations. Borrowing an analogy from clinical trials, one can view these exclusions as subjects that do not satisfy eligibility criteria.MethodsDeveloping identification rules for these "data-driven eligibility criteria" in observational studies on both population and individual levels helps to ascertain the population on which causal effects are being made. This article presents a machine learning method to determine the representativeness of causal effects in two different datasets from the National Cancer Database.ResultsDecision trees reveal that groups with certain features have a higher probability of inclusion in the study population than older patients. In the first dataset, younger age categories had an inclusion probability of at least 0.90 in all models, while the probability for the older category ranged from 0.47 to 0.65. Most trees split once more on an even higher age at a lower node, suggesting that the oldest patients are the least likely to be matched. In the second set of data, both age and surgery status were associated with inclusion.ConclusionThe methodology presented in this paper underscores the need to consider exclusions in propensity score matching procedures as well as complementing matching with other propensity score adjustments.
| S-EPMC9630947 | biostudies-literature
Project description:To understand the molecular pathogenesis of human disease, precision analyses to define alterations within and between disease-associated cell populations are desperately needed. Single-cell genomics represents an ideal platform to enable the identification and comparison of normal and diseased transcriptional cell populations. We created cellHarmony, an integrated solution for the unsupervised analysis, classification, and comparison of cell types from diverse single-cell RNA-Seq datasets. cellHarmony efficiently and accurately matches single-cell transcriptomes using a community-clustering and alignment strategy to compute differences in cell-type specific gene expression over potentially dozens of cell populations. Such transcriptional differences are used to automatically identify distinct and shared gene programs among cell-types and identify impacted pathways and transcriptional regulatory networks to understand the impact of perturbations at a systems level. cellHarmony is implemented as a python package and as an integrated workflow within the software AltAnalyze. We demonstrate that cellHarmony has improved or equivalent performance to alternative label projection methods, is able to identify the likely cellular origins of malignant states, stratify patients into clinical disease subtypes from identified gene programs, resolve discrete disease networks impacting specific cell-types, and illuminate therapeutic mechanisms. Thus, this approach holds tremendous promise in revealing the molecular and cellular origins of complex disease.
| S-EPMC6868361 | biostudies-literature
Project description:Tremors are a common movement disorder with a spectrum of benign and pathological causes, including neurodegenerative disease, alcohol withdrawal, and physical overexertion. Studies of tremors in clinical practice are limited in size and scope and depend on explicit tracking of tremor characteristics by clinicians. Data drawn from small numbers of patients observed in short-duration sessions pose challenges for understanding the nature and distribution of tremors over a large population. Methods are presented to estimate hand tremors based on anonymized computer mouse cursor movement data collected from millions of users of a web search engine. To determine the feasibility of using this signal for the estimation of the prevalence of tremors over a large population, the characteristics of tremor-like movements are computed and compared against user data that can be interpreted as self-reports, the findings of published clinical studies, and a target selection study where participants self-report hand tremors and known causes. The results demonstrate significant alignment between estimated tremors and both self-reports and clinical findings. Those with cursor tremor events are more likely to report tremor-related search interests. Variations in cursor tremor quantity and cursor tremor frequency with demographics mirror those from clinical studies. Distributions of cursor tremor frequencies vary as expected for different medical conditions. Overall, the study finds evidence for the validity of harnessing anonymized mouse cursor motion as a population-scale tremor sensor for epidemiologic studies. Feasible future applications include opt-in services for screening and for monitoring the progression of illness.
| S-EPMC6760188 | biostudies-literature
Project description:Here we demonstrate that highly β-selective glycosylation reactions can be achieved when the electronics of a sulfonyl chloride activator and the reactivity of a glycosyl donor hemiacetal are matched. While these reactions are compatible with the acid- and base-sensitive protecting groups that are commonly used in oligosaccharide synthesis, these protecting groups are not relied upon to control selectivity. Instead, β-selectivity arises from the stereoinversion of an α-glycosyl arylsulfonate in an SN2-like mechanism. Our mechanistic proposal is supported by NMR studies, kinetic isotope effect (KIE) measurements, and DFT calculations.
| S-EPMC6814073 | biostudies-literature
Project description:The brain can be considered as an information processing network, where complex behavior manifests as a result of communication between large-scale functional systems such as visual and default mode networks. As the communication between brain regions occurs through underlying anatomical pathways, it is important to define a "traffic pattern" that properly describes how the regions exchange information. Empirically, the choice of the traffic pattern can be made based on how well the functional connectivity between regions matches the structural pathways equipped with that traffic pattern. In this paper, we present a multimodal connectomics paradigm utilizing graph matching to measure similarity between structural and functional connectomes (derived from dMRI and fMRI data) at node, system, and connectome level. Through an investigation of the brain's structure-function relationship over a large cohort of 641 healthy developmental participants aged 8-22 years, we demonstrate that communicability as the traffic pattern describes the functional connectivity of the brain best, with large-scale systems having significant agreement between their structural and functional connectivity patterns. Notably, matching between structural and functional connectivity for the functionally specialized modular systems such as visual and motor networks are higher as compared to other more integrated systems. Additionally, we show that the negative functional connectivity between the default mode network (DMN) and motor, frontoparietal, attention, and visual networks is significantly associated with its underlying structural connectivity, highlighting the counterbalance between functional activation patterns of DMN and other systems. Finally, we investigated sex difference and developmental changes in brain and observed that similarity between structure and function changes with development.
| S-EPMC6688960 | biostudies-literature
Project description:The Saccharomyces cerevisiae gene deletion collection is widely used for functional gene annotation and genetic interaction analyses. However, the standard G418-resistance cassette used to produce knockout mutants delivers strong regulatory elements into the target genetic loci. To date, its side effects on the expression of neighboring genes have never been systematically assessed. Here, using ribosome profiling data, RT-qPCR, and reporter expression, we investigated perturbations induced by the KanMX module. Our analysis revealed significant alterations in the transcription efficiency of neighboring genes and, more importantly, severe impairment of their mRNA translation, leading to changes in protein abundance. In the 'head-to-head' orientation of the deleted and neighboring genes, knockout often led to a shift of the transcription start site of the latter, introducing new uAUG codon(s) into the expanded 5' untranslated region (5' UTR). In the 'tail-to-tail' arrangement, knockout led to activation of alternative polyadenylation signals in the neighboring gene, thus altering its 3' UTR. These events may explain the so-called neighboring gene effect (NGE), i.e. false genetic interactions of the deleted genes. We estimate that in as much as ∼1/5 of knockout strains the expression of neighboring genes may be substantially (>2-fold) deregulated at the level of translation.
| S-EPMC8565318 | biostudies-literature
Project description:Group selection models combine selection pressure at the individual level with selection pressure at the group level. Cooperation can be costly for individuals, but beneficial for the group, and therefore, if individuals are sufficiently much assorted, and cooperators find themselves in groups with disproportionately many other cooperators, cooperation can evolve. The existing literature on group selection generally assumes that competition between groups takes place in a well-mixed population of groups, where any group competes with any other group equally intensely. Competition between groups however might very well occur locally; groups may compete more intensely with nearby than with far-away groups. We show that if competition between groups is indeed local, then the evolution of cooperation can be hindered significantly by the fact that groups with many cooperators will mostly compete against neighboring groups that are also highly cooperative, and therefore harder to outcompete. The existing empirical method for determining how conducive a group structured population is to the evolution of cooperation also implicitly assumes global between-group competition, and therefore gives (possibly very) biased estimates.
| S-EPMC7496855 | biostudies-literature
Project description:Decoding brain imaging data are gaining popularity, with applications in brain-computer interfaces and the study of neural representations. Decoding is typically subject-specific and does not generalise well over subjects, due to high amounts of between subject variability. Techniques that overcome this will not only provide richer neuroscientific insights but also make it possible for group-level models to outperform subject-specific models. Here, we propose a method that uses subject embedding, analogous to word embedding in natural language processing, to learn and exploit the structure in between-subject variability as part of a decoding model, our adaptation of the WaveNet architecture for classification. We apply this to magnetoencephalography data, where 15 subjects viewed 118 different images, with 30 examples per image; to classify images using the entire 1 s window following image presentation. We show that the combination of deep learning and subject embedding is crucial to closing the performance gap between subject- and group-level decoding models. Importantly, group models outperform subject models on low-accuracy subjects (although slightly impair high-accuracy subjects) and can be helpful for initialising subject models. While we have not generally found group-level models to perform better than subject-level models, the performance of group modelling is expected to be even higher with bigger datasets. In order to provide physiological interpretation at the group level, we make use of permutation feature importance. This provides insights into the spatiotemporal and spectral information encoded in the models. All code is available on GitHub (https://github.com/ricsinaruto/MEG-group-decode).
| S-EPMC10619368 | biostudies-literature