ArticleComputational statistics & data analysis2009
The use of plasmodes as a supplement to simulations: A simple example evaluating individual admixture estimation methodologies.
Article in Computational statistics & data analysis, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Estimating real-world treatment effects in the presence of measurement error and sparse outcome data using propensity score methods.Frontiers in pharmacology · 2026Article
- Causal Forests Versus Inverse Probability of Treatment Weighting to Adjust for Cluster-Level Confounding: A Parametric and Plasmode Simulation Study Based on US Hospital Electronic Health Record Data.Pharmacoepidemiology and drug safety · 2025Article
- Beware of counter-intuitive levels of false discoveries in datasets with strong intra-correlations.Genome biology · 2025Article
- A Framework for Generating Realistic Synthetic Tabular Data in a Randomized Controlled Trial Setting.Statistics in medicine · 2025Article
- Cardinality matching versus propensity score matching for addressing cluster-level residual confounding in implantable medical device and surgical epidemiology: a parametric and plasmode simulation study.BMC medical research methodology · 2024Article
- Unraveling the genomic diversity and admixture history of captive tigers in the United States.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Comparison of two propensity score-based methods for balancing covariates: the overlap weighting and fine stratification methods in real-world claims data.BMC medical research methodology · 2024Article
- Longitudinal plasmode algorithms to evaluate statistical methods in realistic scenarios: an illustration applied to occupational epidemiology.BMC medical research methodology · 2023Article
- Information sharing in high-dimensional gene expression data for improved parameter estimation in concentration-response modelling.PloS one · 2023Article
- A Framework for Using Real-World Data and Health Outcomes Modeling to Evaluate Machine Learning-Based Risk Prediction Models.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2022Article
- Causal simulation experiments: Lessons from bias amplification.Statistical methods in medical research · 2022Article
- Implementing Multiple Imputation for Missing Data in Longitudinal Studies When Models are Not Feasible: An Example Using the Random Hot Deck Approach.Clinical epidemiology · 2022Article
- Evaluating the Utility of Coarsened Exact Matching for Pharmacoepidemiology Using Real and Simulated Claims Data.American journal of epidemiology · 2020Article
- Cost-Constrained feature selection in binary classification: adaptations for greedy forward selection and genetic algorithms.BMC bioinformatics · 2020Article
- Evaluating large-scale propensity score performance through real-world and synthetic data experiments.International journal of epidemiology · 2018Article
- Bulk development and stringent selection of microsatellite markers in the western flower thrips Frankliniella occidentalis.Scientific reports · 2016Article
- Assessing Dissimilarity Measures for Sample-Based Hierarchical Clustering of RNA Sequencing Data Using Plasmode Datasets.PloS one · 2015Article
- Mapping asthma-associated variants in admixed populations.Frontiers in genetics · 2015Review
- Challenges in analysis and interpretation of microsatellite data for population genetic studies.Ecology and evolution · 2014Review
- A free-knot spline modeling framework for piecewise linear logistic regression in complex samples with body mass index and mortality as an example.Frontiers in nutrition · 2014Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
With the advent of powerful computers, simulation studies are becoming an important tool in statistical methodology research. However, computer simulations of a specific process are only as good as our understanding of the underlying mechanisms. An attractive supplement to simulations is the use of plasmode datasets. Plasmodes are data sets that are generated by natural biologic processes, under experimental conditions that allow some aspect of the truth to be known. The benefit of the plasmode approach is that the data are generated through completely natural processes, thus circumventing the common concern of the realism and accuracy of computer simulated data. The estimation of admixture, or the proportion of an individual's genome that originates from different founding populations, is a particularly difficult research endeavor that is well suited to the use of plasmodes. Current methods have been tested with simulations of complex populations where the underlying mechanisms such as the rate and distribution of recombination are not well understood. To demonstrate the utility of this method data derived from mouse crosses is used to evaluate the effectiveness of several admixture estimation methodologies. Each cross shares a common founding population so that the ancestry proportion for each individual is known, allowing for the comparison of true and estimated individual admixture values. Analysis shows that the different estimation methodologies (Structure, AdmixMap and FRAPPE) examined all perform well with simple datasets. However, the performance of the estimation methodologies varied greatly when applied to a plasmode consisting of three founding populations. The results of these examples illustrate the utility of plasmodes in the evaluation of statistical genetics methodologies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.