Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “robustness”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources
PubMed2026

Robust optimisation for photon radiotherapy: A scoping review of models, paradigms, and reporting.

BACKGROUND AND PURPOSE: Robust optimisation offers an alternative to conventional margin-based photon radiotherapy planning by explicitly modelling uncertainty, but practice is variable and not standardised. MATERIALS AND METHODS: A scoping review was conducted to map robust optimisation for photon external beam radiotherapy. Electronic searches of Scopus, PubMed and Google Scholar (2000-2025, English language) identified planning studies that incorporated modelled uncertainties into the optimisation process and reported at least one robustness-related outcome. Data were charted on clinical context, uncertainty models, optimisation paradigms, robustness metrics and evidence for clinical implementation. RESULTS: Seventy-one studies were included. Most investigated prostate, breast or lung cancer and used intensity-modulated radiotherapy or volumetric-modulated arc therapy in commercial or research treatment planning systems. Scenario-based worst-case (minimax) optimisation was the dominant paradigm in clinically oriented work, while chance-constrained, conditional value at-risk, distributionally robust and adaptive formulations were confined to small methodological series. Uncertainty modelling focused mainly on rigid set-up error; fewer studies incorporated respiratory motion, inter-fraction anatomical change, dose-calculation uncertainty or biological variation. Robustness was evaluated with diverse scenario-based dose-volume metrics, probabilistic coverage measures, composite robustness indices and, less often, biological endpoints. Direct clinical implementation reports were scarce. CONCLUSION: Robust photon planning is technically feasible and generally maintains or improves target coverage and organ sparing compared with margin-based planning. However, heterogeneity in uncertainty models, optimisation configuration and robustness reporting limits comparison and synthesis. Pragmatic minimum standards are proposed to support future consensus and wider clinical adoption.

Humans↗
PubMedDate not supplied

Robust multivariate methods in laboratory techniques and in assisting medical diagnosis.

The usefulness of robust multivariate methods in medical applications, for example, the indirect estimation of total lung capacity by robust regression and the assistance of medical diagnosis in obstructive airways disease using robust discriminant functions, is discussed. The results of robust methods that consist in downweighting the influence of the multivariate outliers are compared with the outcomes of classical procedures. The advantages of modern robust algorithms are proved in the present study. It is planned to include the methods in the system for the computerized consulting unit for respiratory diseases that is being set up in Wrocław.

Algorithms↗
PubMed2026

Robust prioritization of genomic features with stability selection.

MOTIVATION: The heterogeneity of complex diseases including cancer leads to heavy-tailed distributions in the disease traits. In such settings, non-robust variable selection methods are inherently susceptible to data contamination and can yield unstable or misleading results. This vulnerability becomes more severe for recently proposed approaches that introduce pseudo-features as negative controls, as these methods further amplify the curse of dimensionality by expanding the genotype matrix in the presence of outliers and high-dimensional genomic features. RESULTS: We develop a robust variable selection framework with stability selection to prioritize genomic features in the presence of contamination. In contrast to existing approaches that rely on pseudo-features for error control, the proposed method achieves double robustness. First, it adopts least absolute deviation (LAD) LASSO to ensure robustness against outliers and heavy-tailed errors in disease traits. Second, it avoids augmenting the genotype matrix with pseudo-features, thereby mitigating the curse of dimensionality that is particularly problematic in high-dimensional genomic data. The proposed method has been extensively evaluated in simulation studies to demonstrate its effectiveness over multiple competing methods for variable selection. In addition, we have applied the proposed method and competing approaches to two real-data case studies: the The Cancer Genome Atlas (TCGA) Skin Cutaneous Melanoma (SKCM) dataset and an eQTL dataset. The results demonstrate that the proposed method achieves superior performance by identifying genomic features with higher reproducibility. AVAILABILITY AND IMPLEMENTATION: The source code for implementing the proposed methods is publicly available at https://github.com/cenwu/RSS with an archival DOI https://doi.org/10.6084/m9.figshare.32306883.

Genomics↗
PubMed1991

Scaled test statistics and robust standard errors for non-normal data in covariance structure analysis: a Monte Carlo study.

Research studying robustness of maximum likelihood (ML) statistics in covariance structure analysis has concluded that test statistics and standard errors are biased under severe non-normality. An estimation procedure known as asymptotic distribution free (ADF), making no distributional assumption, has been suggested to avoid these biases. Corrections to the normal theory statistics to yield more adequate performance have also been proposed. This study compares the performance of a scaled test statistic and robust standard errors for two models under several non-normal conditions and also compares these with the results from ML and ADF methods. Both ML and ADF test statistics performed rather well in one model and considerably worse in the other. In general, the scaled test statistic seemed to behave better than the ML test statistic and the ADF statistic performed the worst. The robust and ADF standard errors yielded more appropriate estimates of sampling variability than the ML standard errors, which were usually downward biased, in both models under most of the non-normal conditions. ML test statistics and standard errors were found to be quite robust to the violation of the normality assumption when data had either symmetric and platykurtic distributions, or non-symmetric and zero kurtotic distributions.

Analysis of Variance↗
PubMed1978

Sexual dimorphism of skeletal robusticity in several mammalian orders.

This study tries to determine whether a phylogenetic trend exists in the sexual dimorphism of skeletal robusticity and whether domestication has an effect on it. The problems were studied in the metatarsals of marsupials, insectivores, rodents and primates including Homo, and in various skeletal sections of wild and domesticated rats and dogs. No clear phylogenetic trend could be found and domestication cannot be shown to have an effect on diminishing skeletal robusticity. Sexual dimorphism of skeletal robusticity seems to be highly species-specific and to have a mosaic distribution in a given taxon. Conclusions on phylogenetic trends of sexual dimorphism of skeletal robusticity and the effect of culture on it seem to be premature.

Animals↗
PubMed1987

Robust inference for variance components models in families ascertained through probands: I. Conditioning on proband's phenotype.

A robust approach for estimating standard errors of variance components by using quantitative phenotypes from families ascertained through a proband with an extreme phenotypic value is presented. Estimators that use the multivariate normal distribution as a "working likelihood" are obtained by computing conditional ln-likelihoods, conditional first and second derivatives in a Newton-Raphson approach. Robust estimates of standard errors about the estimators are also provided. Tests of hypotheses are based on a modification of the score test, which allows the assumption of multivariate normality to be relaxed. Conditional goodness-of-fit statistics are proposed that can be used to examine the fit of separate pedigrees to the overall model. This robust approach for estimating the standard errors for variance components by conditioning on the proband's phenotype will allow general inferences to be made from the analysis of families ascertained through probands with extreme or unusual phenotypes and should be most appropriate for studying many physiological traits that may be intrinsically nonnormal.

Analysis of Variance↗
PubMed1992

LMSMVE: a program for least median of squares regression and robust distances.

The program LMSMVE performs robust regression analysis by using the method of the least median of squares. It also computes robust distances to locate leverage points, that is, outliers with respect to the set of independent variables. LMSMVE constructs plots of least median of squares residuals against robust distances. Both methods can tolerate up to half the data being outliers before they fail to give results that describe the bulk of the data. A complete system that operates directly on SYSTAT files is available for the IBM PC and compatibles; it includes a utility that converts ASCII files to SYSTAT format.

Animals↗
PubMed2025

Protein overabundance is driven by growth robustness.

Protein expression levels optimize cell fitness: Too low an expression level of essential proteins will slow growth by compromising essential processes; whereas overexpression slows growth by increasing the metabolic load. This trade-off naïvely predicts that cells maximize their fitness by sufficiency, expressing just enough of each essential protein for function. We test this prediction in the naturally-competent bacterium Acinetobacter baylyi by characterizing the proliferation dynamics of essential-gene knockouts at a single-cell scale (by imaging) as well as at a genome-wide scale. In these experiments, cells proliferate for multiple generations as target protein levels are diluted from their endogenous levels. This approach facilitates a proteome-scale analysis of the fitness landscape with respect to protein abundance. We find that most essential proteins are subject to a threshold-like fitness landscape: growth is independent of protein abundance above a critical threshold and arrests below that threshold. We have recently analyzed the implications of this landscape for growth robustness. Confirming signature predictions of this model, we find that (i) roughly 70% of essential proteins are overabundant, (ii) overabundance increases as the expression level decreases and (iii) the lowest abundance proteins are in vast excess (>10×) of what is required for growth in the typical cell. These results reveal that robustness plays a fundamental role in determining the expression levels of essential genes and that overabundance is a key mechanism for ensuring robust growth.

Journal Article↗
PubMed1985

Use of robust variance components models to analyse triglyceride data in families.

A robust approach for analysis of variance components models is presented which does not rely on the assumption of multivariate normality for its validity. This approach uses the multivariated normal distribution as a 'working model' but obtains standard errors for the final estimators which do not depend on this underlying distribution. By using the observed variance in the first derivatives of the multivariate normal 'working model' to modify the conventional score test, hypotheses regarding specific components can also be tested without relying directly on the assumption of multivariate normality. A special case is presented where both the modified score test and the likelihood ratio test are equally robust, and simulated data are used to illustrate this situation. Measurements of triglyceride levels in 391 individuals in 60 families randomly selected from the membership of a health maintenance organization are used to illustrate this robust approach to variance components.

Genetic Variation↗
PubMed1986

Robust spectral analysis of the EEG.

Robust methods for the spectral analysis of time series are briefly reviewed and seen to have applications in the field of EEG. After presenting two simple schemes for outliers (artifacts) generation and discussing their implications for estimation of the spectral density, the robust filtering algorithm of Kleiner et al. [J.R. Statist. Soc. Ser. B, 41: 313-351, 1979] is introduced and shown to work well for simulated data and for true EEG data containing artifacts. A new use of the robust methods for the detection of artifacts and possibly other transients in long-time EEG recordings is suggested and a preliminary implementation illustrated.

Electroencephalography↗
PubMed2025

A novel and robust feature selection method with FDR control for omics-wide association analysis.

Omics-wide association analysis is a very important tool for medicine and human health study. However, the modern omics data sets collected often exhibit the high-dimensionality, unknown distribution response, unknown distribution features and unknown complex association relationships between the response and its explanatory features. Reliable association analysis results depend on an accurate modeling for such data sets. Most of the existing association analysis methods rely on the specific model assumptions and lack effective false discovery rate (FDR) control. To address these limitations, the paper firstly applies a single index model for omics data. The model shows robust performance in allowing the relationships between the response variable and linear combination of covariates to be connected by any unknown monotonic link function, and both the random error and the covariates can follow any unknown distribution. Then based on this model, the paper combines rank-based approach and symmetrized data aggregation approach to develop a novel and robust feature selection method for achieving fine-mapping of risk features while controlling the false positive rate of selection. The theoretical results support the proposed method and the analysis results of simulated data show the new method possesses effective and robust performance for all the scenarios. The new method is also used to analyze the two real datasets and identifies some risk features unreported by the existing finds.

Humans↗
PubMed1988

Alleged synapomorphy of the M1/I1 eruption pattern in robust australopithecines and Homo: evidence from high-resolution computed tomography.

Ever since Broom and Robinson (1951) published their claim that the eruption pattern of permanent incisors in robust australopithecines was most similar to that of modern man and different from that of gracile australopithecines and apes, the accuracy of this observation has been the subject of periodic debate (e.g., Wallace: Ph.D. thesis, 1972; Dean: Am. J. Phys. Anthropol. 67:251-257, 1985; Grine: Am. J. Phys. Anthropol. 72:353-359, 1987). Part of the problem is that the developing incisors in one of the specimens most crucial to this argument (SK61) are difficult to visualize clearly by conventional radiographic techniques because of the heavy mineralization in the fossil. This study reanalyzes SK 61 by high-resolution computed tomography in order to contribute to the final resolution of its incisor development. Grine's (op. cit.) assessment of the incisors as the deciduous ones, not the permanent ones, is fully confirmed. This fact, in conjunction with the observation that permanent incisor root formation had only just commenced in this specimen, further weakens the argument of M1/I1 eruption pattern synapomorphy between Homo and robust australopithecines.

Fossils↗
PubMed1989

A more powerful robust sib-pair test of linkage for quantitative traits.

A more powerful robust test for linkage is developed from the methodology of Haseman and Elston [Behav Genet 2(1):3-19, 1972]. This new robust test uses weighted least-squares (WLS) methods to detect linkage between a quantitative trait and a polymorphic marker. For comparison, the characteristics of a test for linakge that uses known trait genotypes for the parents are also studied. Sample sizes needed to detect linkage, calculated using asymptotic results, are compared for 1) the usual Haseman-Elston method, 2) the WLS method, and 3) the method that uses parental trait genotype data. The WLS method needs at most twice the number of sib pairs as does the method that uses information on the trait genotypes of the parents. The small sample properties of the Haseman-Elston (H-E) and WLS tests are investigated by simulation. The power calculations for the H-E method are found to be accurate. The power of the WLS method is overestimated when fewer than 300 sib pairs are studied, but the WLS method is nonetheless more powerful than the usual H-E method. In samples of fewer than 300 sib pairs, the WLS test tends to be anticonservative. Treating all sib pairs from sibships of size 3 or 5 as independent does not increase the significance of the tests.

Alleles↗
PubMedDate not supplied

Robustness of the two independent samples t-test when applied to ordinal scaled data.

One may encounter the application of the two independent samples t-test to ordinal scaled data (for example, data that assume only the values 0, 1, 2, 3) from small samples. This situation clearly violates the underlying normality assumption for the t-test and one cannot appeal to large sample theory for validity. In this paper we report the results of an investigation of the t-test's robustness when applied to data of this form for samples of sizes 5 to 20. Our approach consists of complete enumeration of the sampling distributions and comparison of actual levels of significance with the significance level expected if the data followed a normal distribution. We demonstrate under general conditions the robustness of the t-test in that the maximum actual level of significance is close to the declared level.

Biometry↗
PubMed1987

OMNE: a new robust membership-set estimator for the parameters of nonlinear models.

A new method for estimating parameters and their uncertainty is presented. Data are assumed to be corrupted by a noise whose statistical properties are unknown but for which bounds are available at each sampling time. The method estimates the set of all parameter vectors consistent with this hypothesis. Its results are compared with those of the weighted least squares, extended least squares, and biweight robust regression approaches on two data sets, one of which includes 33% outliers. On the basis of these preliminary results, the new method appears to have attractive properties of reliability and robustness.

Kinetics↗
PubMed1988

Robust regression in biological assay: application to the evaluation of alternative experimental techniques.

Robust Huber type regression and testing of linear hypotheses are adapted to statistical analysis of parallel line and slope ratio assays. They are applied in the evaluation of results of several experiments carried out in order to compare and validate alternatives to animal experimentation based on embryo and cell cultures. Computational procedures necessary for the application of robust methods of analysis used the conversational statistical package ROBSYS. Special commands for the analysis of parallel line and slope ratio assays have been added to ROBSYS.

Animal Testing Alternatives↗
PubMed1991

Robustness of maximum likelihood tree estimation against different patterns of base substitutions.

In the maximum likelihood (ML) method for estimating a molecular phylogenetic tree, the pattern of nucleotide substitutions for computing likelihood values is assumed to be simpler than that of the actual evolutionary process, simply because the process, considered to be quite devious, is unknown. The problem, however, is that there has been no guarantee to endorse the simplification. To study this problem, we first evaluated the robustness of the ML method in the estimation of molecular trees against different nucleotide substitution patterns, including Jukes and Cantor's, the simplest ever proposed. Namely, we conducted computer simulations in which we could set up various evolutionary models of a hypothetical gene, and define a true tree to which an estimated tree by the ML method was to be compared. The results show that topology estimation by the ML method is considerably robust against different ratios of transitions to transversions and different GC contents, but branch length estimation is not so. The ML tree estimation based on Jukes and Cantor's model is also revealed to be resistant to GC content, but rather sensitive to the ratio of transitions to transversions. We then applied the ML method with different substitution patterns to nucleotide sequence data on tax gene from T-cell leukemia viruses whose evolutionary process must have been more complicated than that of the hypothetical gene. The results are in accordance with those from the simulation study, showing that Jukes and Cantor's model is as useful as a more complicated one for making inferences about molecular phylogeny of the viruses.

Base Composition↗
PubMed1985

A robust microcomputer routine for the identification of outlying and influential points in radioimmunoassay standard curves.

This paper describes a suite of robust logit-log optimization routines for the fitting of a linear relation to laboratory data. The microcomputer routines assign a weighting value to each data point corresponding to the relative importance of that point. This procedure ensures that the routines identify and deal with any outlying data points. The routines are designed to generate the results of both a standard, unweighted least-squares algorithm and a robust, weighted algorithm thus providing the laboratory analyst with invaluable information concerning the data and the fitted relation. The routines are thoroughly tested on "perfect" and perturbed laboratory data.

Biometry↗