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Results for “Statistics, Nonparametric”

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At least 811 records · Page 45Linked to original sources

PubMed2005

Nonparametric estimation of asymptomatic duration from a randomized prospective cancer screening trial.

We propose a nonparametric estimation of preclinical duration distribution in cancer based on data from a randomized early detection trial. In cancer screening studies, the preclinical duration of a disease is of great interest for better understanding the natural history of the disease, and for developing optimal screening strategies. To estimate the sojourn time distribution nonparametrically, we first estimate the distribution of the age at onset of preclinical disease nonparametrically using data from the screening arm in a randomized screening trial, and the distribution for the age at onset of clinical disease from the control arm of the randomized screening trial. Finally, by using deconvolution the two estimated distributions lead to a nonparametric estimate of the distribution for the gap time between the onset of preclinical disease and the onset of clinical disease. We illustrate the methodology using data from a randomized breast cancer screening trial.

Adult↗

PubMed2003

Linkage mapping of total cholesterol level in a young cohort via nonparametric regression.

BACKGROUND: Compared to model-based approaches, nonparametric methods for quantitative trait loci mapping are more robust to deviations in distributional assumptions. In this study, we modify a nonparametric regression method and the "contrast function"- based regression method to analyze total cholesterol level in the younger cohort (the offspring generation) of the Genetic Analysis Workshop 13 simulated data set. RESULTS: We obtained significant evidence of linkage near four of the six non-sex-specific genes in at least 30% of the replicates. CONCLUSIONS: The proposed nonparametric method seems to be a powerful robust alternative to distribution-based methods.

Cardiovascular Diseases↗

PubMed2005

A nonparametric comparison of conditional distributions with nonnegligible cure fractions.

Survival data with nonnegligible cure fractions are commonly encountered in clinical cancer clinical research. Recently, several authors (e.g. Kuk and Chen, Biometrika 79 (1992) 531; Maller and Zhou, Journal of Applied Probability, 30 (1993) 602; Peng and Dear, Biometrics, 56 (2000) 237; Sy and Taylor, Biometrics 56 (2000) 227) have proposed to use semiparametric cure models to analyze such data. Much of the existing work has been emphasized on cure detections and regression techniques. In contrast, this project focuses on the hypothesis testing in the presence of a cure fraction. Specifically, our interest lies in detecting whether there exists survival differences among noncured patients between treatment arms. For this purpose, we investigate the use of a modified Cramér-von Mises statistic for two-sample survival comparisons within the framework of cure models. Such a test has been studied by Tamura et al., (Statistics in Medicine 19, 2000, 2169) using bootstrap procedure. We will focus on developing asymptotic theory and convergent algorithms in this paper. We show that the limiting distributions of the Cramér-von Mises statistic under the null hypothesis can be represented by stochastic integrals and a weighted noncentral chi-squares. Both representations lead to concrete numerical schemes for computing the limiting distributions. The algorithms can be easily implemented for data analysis and significantly reduce computing time compared to the bootstrap approach. For illustrative purposes, we apply the proposed test to a published clinical trial.

Algorithms↗

PubMed1996

GELSTATS: a computer program for population genetics analyses using VNTR multilocus probe data.

GELSTATS, a computer program for population genetics analyses utilizing genetic markers revealed with variable number tandem repeat (VNTR) multilocus probes, is described and made available (both as C++ source code and as an executable DOS program). The program calculates several population genetics parameters, including: (i) individual and population band numbers; (ii) population bands exhibiting complete linkage (redundant examples of such bands can be removed in subsequent analyses); (iii) similarity (fraction of bands shared) between individuals and average similarity within and between designated groups; (iv) estimated probability that two individuals chosen at random will have identical band profiles; (v) heterozygosity estimates for designated groups; and (vi) Fst estimates. Nonparametric permutation methods are used to assess the significance of differences in both within- and between-group similarity. A jackknife test for heterozygosity differences between groups is also computed. Examples of GELSTATS analyses illustrate some features of the program.

Animals↗

PubMed2000

Nonparametric tests for stratum effects in the Cox model.

This paper considers the stratified proportional hazards model with a focus on the assessment of stratum effects. The assessment of such effects is often of interest, for example, in clinical trials. In this case, two relevant tests are the test of stratum interaction with covariates and the test of stratum interaction with baseline hazard functions. For the test of stratum interaction with covariates, one can use the partial likelihood method (Kalbfleisch and Prentice, 1980; Lin, 1994). For the test of stratum interaction with baseline hazard functions, however, there seems to be no formal test available. We consider this problem and propose a class of nonparametric tests. The asymptotic distributions of the tests are derived using the martingale theory. The proposed tests can also be used for survival comparisons which need to be adjusted for covariate effects. The method is illustrated with data from a lung cancer clinical trial.

Humans↗

PubMed2005

Nonparametric estimation of ROC curves in the absence of a gold standard.

In the evaluation of diagnostic accuracy of tests, a gold standard on the disease status is required. However, in many complex diseases, it is impossible or unethical to obtain such a gold standard. If an imperfect standard is used, the estimated accuracy of the tests would be biased. This type of bias is called imperfect gold standard bias. In this article we develop a nonparametric maximum likelihood method for estimating ROC curves and their areas of ordinal-scale tests in the absence of a gold standard. Our simulation study shows that the proposed estimators for the ROC curve areas have good finite-sample properties in terms of bias and mean squared error. Further simulation studies show that our nonparametric approach is comparable to the binormal parametric method, and is easier to implement. Finally, we illustrate the application of the proposed method in a real clinical study on assessing the accuracy of seven specific pathologists in detecting carcinoma in situ of the uterine cervix.

Algorithms↗

PubMed1998

Microleakage reduction from newer esthetic restorative materials in permanent molars.

Through use of new technologies, such as air-abrasive devices, caries detector dyes, modern technology can increase the speed, comfort and success of dental restorations. However the issue of marginal adaptation has been an ongoing technical problem. Today with the more frequent use of posterior composite resins, poor marginal adaptation of resin is still the major cause of failure of the restoration. Material contraction from polymerization and possibly inappropriate preparation design are key factors in the longevity of the restorations. This study attempts to determine the more effective material and technique for reducing microleakage in composite restorations. An in vitro study using three hybrid composite resins was done to evaluate marginal microleakage. Extracted molars were chosen and evaluated for caries through the use of caries detector dye. Using air-abrasion, caries were removed and minimal preparations were done. The samples were divided into two groups. First group was restored with Herculite XRV and Heliomolar paste composites resins. The second group was restored with Revolution flowable composite resin. Also Opti-bond and Fortify were used as a bonding agent and surface penetrating sealant respectively. The teeth were properly finished, thermocycled and evaluated for leakage using 2% basic fuschin detector dye. All data were subjected to the nonparametric Mann-Whitney Rank Sum Test. The results revealed that Herculite XRV and Heliomolar paste composites had significantly higher microleakage formation (P < 0.03), and also they presented higher void formation (P < 0.0001) than Revolution flowable composite resin.

Acrylic Resins↗

PubMed2000

Nonparametric and semiparametric trend analysis for stratified recurrence times.

Recurrent event data are frequently encountered in longitudinal follow-up studies when the occurrences of multiple events are considered as the major outcomes. Suppose that the recurrent events are of the same type and the variable of interest is the recurrence time between successive events. In many applications, the distributional pattern of recurrence times can be used as an index for the progression of a disease. Such a distributional pattern is important for understanding the natural history of a disease or for confirming long-term treatment effect. In this article, we discuss and define the comparability of recurrence times. Nonparametric and semiparametric methods are developed for testing trend of recurrence time distributions and estimating trend parameters in regression models. The construction of the methods is based on comparable recurrence times from stratified data. A real data example is presented to illustrate the use of methodology.

Biometry↗

PubMed2004

Mixed effects multivariate adaptive splines model for the analysis of longitudinal and growth curve data.

In this article, I review the use of nonparametric methods in the analysis of longitudinal and growth curve data, particularly the multivariate adaptive splines models for the analysis of longitudinal data (MASAL). These methods combine nonparametric techniques (B-splines, kernel smoothing, piecewise polynomials) and models with random effects, and provide fruitful alternatives to mixed effects linear models. Similarities, differences, strengths and limitations among these methods are presented. The analysis of a real example is also presented to illustrate the application and interpretation of MASAL. Open questions are posed for further investigation.

Biomedical Research↗

PubMed2000

The load-displacement characteristics of neonatal rat cranial sutures.

OBJECTIVE: Recently several centers have attempted to distract the craniofacial skeleton in infants with craniosynostosis. To effectively achieve this goal, we must first understand the normal sutural response to tensile forces. The objective of this study was to determine the load-displacement characteristics of neonatal rat sutures. METHODS: Thirty cranial sutures were harvested from 1-week-old Wistar rats (10 each coronal, posterior frontal, and sagittal). The width of the harvested bone-suture-bone construct was standardized to 4 mm. The specimens, kept moist, were mounted fresh and distracted at 10 microm/sec until rupture using a Vitrodyne V1000 universal tester. Standard load-displacement curves were constructed. The stiffness, defined as tensile force/change in suture length, and the ultimate stress, defined as tensile force at suture rupture/cross sectional area, were calculated. RESULTS: These sutures demonstrated classical viscoelastic behavior. During the elastic phase, they elongated approximately 1 microm for every 1 g of force (10(4) N/m). The ultimate tensile stress was approximately 4 MN/m2. The estimated mean elastic modulus was 10 megapascals. The posterior frontal sutures were significantly less stiff than the other two sutures (Kruskal-Wallis nonparametric analysis of variance, p = .0023). The difference in the ultimate stress was also significant (p = .0201). CONCLUSIONS: This study provides data regarding the basic mechanical behavior of neonatal cranial sutures in a mammalian system.

Analysis of Variance↗

PubMed2001

A robust method for the analysis of experiments with ordered treatment levels.

A robust approach for the analysis of experiments with ordered treatment levels is presented as an alternative to existing approaches such as the parametric Abelson-Tukey test for monotone alternatives and the nonparametric Terpstra-Jonckheere test. The method integrates the familiar Spearman rank-order correlation with bootstrap routines to provide magnitude of association measures, p values, and confidence intervals for magnitude of association measures. The advantages of this method relative to five alternative approaches are pointed out.

Analysis of Variance↗

PubMed1999

The importance of watching our weights: how the choice of weights for non-independent sib pairs can dramatically alter results.

Handling non-independent sib pairs in families with multiple affected sibs presents a problem in likelihood-based nonparametric linkage analyses. We contrast the more stable partial-likelihood solution in MAPMAKER/SIBS with the extremely variable partial-likelihood approach used in ASPEX, and the potential inflation of lods when the problem is ignored as in BETA.

Alcoholism↗

PubMed2002

A nonparametric Bayesian modeling approach for cytogenetic dosimetry.

In cytogenetic dosimetry, samples of cell cultures are exposed to a range of doses of a given agent. In each sample at each dose level, some measure of cell disability is recorded. The objective is to develop models that explain cell response to dose. Such models can be used to predict response at unobserved doses. More important, such models can provide inference for unknown exposure doses given the observed responses. Typically, cell disability is viewed as a Poisson count, but in the present work, a more appropriate response is a categorical classification. In the literature, modeling in this case is very limited. What exists is purely parametric. We propose a fully Bayesian nonparametric approach to this problem. We offer comparison with a parametric model through a simulation study and the analysis of a real dataset modeling blood cultures exposed to radiation where classification is with regard to number of micronuclei per cell.

Bayes Theorem↗

PubMed2005

Recursive partitioning models for linkage in COGA data.

We have developed a recursive-partitioning (RP) algorithm for identifying phenotype and covariate groupings that interact with the evidence for linkage. This data-mining approach for detecting gene x environment interactions uses genotype and covariate data on affected relative pairs to find evidence for linkage heterogeneity across covariate-defined subgroups. We adapted a likelihood-ratio based test of linkage parameterized with relative risks to a recursive partitioning framework, including a cross-validation based deviance measurement for choosing optimal tree size and a bootstrap sampling procedure for choosing robust tree structure. ALDX2 category 5 individuals were considered affected, categories 1 and 3 unaffected, and all others unknown. We sampled non-overlapping affected relative pairs from each family; therefore, we used 144 affected pairs in the RP model. Twenty pair-level covariates were defined from smoking status, maximum drinks, ethnicity, sex, and age at onset. Using the all-pairs score in GENEHUNTER, the nonparametric linkage tests showed no regions with suggestive linkage evidence. However, using the RP model, several suggestive regions were found on chromosomes 2, 4, 6, 14, and 20, with detection of associated covariates such as sex and age at onset.

Alcoholism↗

PubMed1997

GENEHUNTER: application to analysis of bipolar pedigrees and some extensions.

A newly developed nonparametric multipoint linkage analysis (NPL) by Kruglyak et al. [1996] was applied to the data set from the Johns Hopkins Bipolar Study available through the GAW10 workshop. Our results from GENEHUNTER do not support the analyses of Stine et al. [1995]. Some possible extensions to incorporate unaffecteds are also discussed.

Bipolar Disorder↗

PubMed2006

The Wilcoxon signed rank test for paired comparisons of clustered data.

The Wilcoxon signed rank test is a frequently used nonparametric test for paired data (e.g., consisting of pre- and posttreatment measurements) based on independent units of analysis. This test cannot be used for paired comparisons arising from clustered data (e.g., if paired comparisons are available for each of two eyes of an individual). To incorporate clustering, a generalization of the randomization test formulation for the signed rank test is proposed, where the unit of randomization is at the cluster level (e.g., person), while the individual paired units of analysis are at the subunit within cluster level (e.g., eye within person). An adjusted variance estimate of the signed rank test statistic is then derived, which can be used for either balanced (same number of subunits per cluster) or unbalanced (different number of subunits per cluster) data, with an exchangeable correlation structure, with or without tied values. The resulting test statistic is shown to be asymptotically normal as the number of clusters becomes large, if the cluster size is bounded. Simulation studies are performed based on simulating correlated ranked data from a signed log-normal distribution. These studies indicate appropriate type I error for data sets with > or =20 clusters and a superior power profile compared with either the ordinary signed rank test based on the average cluster difference score or the multivariate signed rank test of Puri and Sen. Finally, the methods are illustrated with two data sets, (i) an ophthalmologic data set involving a comparison of electroretinogram (ERG) data in retinitis pigmentosa (RP) patients before and after undergoing an experimental surgical procedure, and (ii) a nutritional data set based on a randomized prospective study of nutritional supplements in RP patients where vitamin E intake outside of study capsules is compared before and after randomization to monitor compliance with nutritional protocols.

Humans↗

PubMed2006

Nonparametric estimation of stage occupation probabilities in a multistage model with current status data.

Multistage models are used to describe individuals (or experimental units) moving through a succession of "stages" corresponding to distinct states (e.g., healthy, diseased, diseased with complications, dead). The resulting data can be considered to be a form of multivariate survival data containing information about the transition times and the stages occupied. Traditional survival analysis is the simplest example of a multistage model, where individuals begin in an initial stage (say, alive) and move irreversibly to a second stage (death). In this article, we consider general multistage models with a directed tree structure (progressive models) in which individuals traverse through stages in a possibly non-Markovian manner. We construct nonparametric estimators of stage occupation probabilities and marginal cumulative transition hazards. Empirical calculations of these quantities are not possible due to the lack of complete data. We consider current status information which represents a more severe form of censoring than the commonly used right censoring. Asymptotic validity of our estimators can be justified using consistency results for nonparametric regression estimators. Finite-sample behavior of our estimators is studied by simulation, in which we show that our estimators based on these limited data compare well with those based on complete data. We also apply our method to a real-life data set arising from a cardiovascular diseases study in Taiwan.

Biometry↗

PubMed2000

Nonparametric evaluation of birth cohort trends in disease rates.

BACKGROUND: Although interpretation of age-period-cohort analyses is complicated by the non-identifiability of maximum likelihood estimates, changes in the slope of the birth-cohort effect curve are identifiable and have potential aetiologic significance. METHODS: A nonparametric test for a change in the slope of the birth-cohort trend has been developed. The test is a generalisation of the sign test and is based on permutational distributions. A method for identifying interactions between age and calendar-period effects is also presented. RESULTS: The nonparametric method is shown to be powerful in detecting changes in the slope of the birth-cohort trend, although its power can be reduced considerably by calendar-period patterns of risk. The method identifies a previously unidentified decrease in the birth-cohort risk of lung-cancer mortality from 1912 to 1919, which appears to reflect a reduction in the initiation of smoking by young men at the beginning of the Great Depression (1930s). The method also detects an interaction between age and calendar period in leukemia mortality rates, reflecting the better response of children to chemotherapy. CONCLUSION: The proposed nonparametric method provides a data analytic approach, which is a useful adjunct to log-linear Poisson analysis of age-period-cohort models, either in the initial model building stage, or in the final interpretation stage.

Adult↗