SEARCH · PubMed Health

Results for “Statistics, Nonparametric”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

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

At least 829 records · Page 46Linked to original sources

PubMed1998

Coverage and precision of confidence intervals for area under the curve using parametric and non-parametric methods in a toxicokinetic experimental design.

PURPOSE: The coverage and precision of parametric Bailer-type confidence intervals (CIs) for area under the curve (AUC) was compared to nonparametric bootstrap confidence intervals. METHODS: Concentration-time data was simulated using Monte Carlo simulation under a toxicokinetic paradigm with sparse (SSC) and dense sampling (DSC) conditions. AUC was calculated using the trapezoidal rule and 95% CIs were computed using various parametric and nonparametric methods. RESULTS: Under SSC, the various parametric CIs contained the true population AUC with coverage probabilities ranging from 0.77 to 0.95 with low inter-subject variation (coefficient of variation (CV) = 15%) and from 0.82 to 0.95 with high inter-subject variation (CV = 50%). The nominal value should be close to 0.95. DSC tended to increase coverage by about 0.05. Bailer's method always produced the lowest coverage of all parametric CIs examined. Under SSC, bootstrap CIs had coverage probabilities ranging from 0.62 (CV = 15%) to 0.68 (CV = 50%). DSC increased coverage to 0.77. Parametric CIs were wider than their nonparametric counterparts, often giving lower CI estimates less than zero. Bailer's method and Bailer's method using the jackknife estimate of the standard error were the worst in this respect. Bootstrap CIs never had lower CI estimates less than zero. However, SSC tends to produce bootstrap distributions that are not continuous which, if used, may produce biased CI estimates. CONCLUSIONS: Bootstrap CI estimates were judged to be the "best". However, the limitations of the bootstrap should be clearly recognized and it should not be used indiscriminately. Examination of the bootstrap distribution for its degree of discreteness must be part of the statistical process.

Area Under Curve↗

PubMed2001

Nonparametric tests for the gap time distributions of serial events based on censored data.

This article deals with the problem of comparing two populations with respect to the distribution of the gap time between two successive events when each subject can experience a series of events and when the event times are potentially right censored. Several families of nonparametric tests are developed, all of which allow arbitrary distributions and dependence structures for the serial events. The asymptotic and small-sample properties of the proposed tests are investigated. An illustration with data taken from a colon cancer study is provided. The related problem of testing the independence of two successive gap times is also studied.

Chemotherapy, Adjuvant↗

PubMed2005

Enamel opacities removal using two different acids: an in vivo comparison.

Computerized analysis assessed quantitatively the efficacy of microabrasion using 37% phosphoric and 18% hydrochloric acids with pumice on removal of enamel opacities. Baseline and after one month photos were taken and analyzed by Paint Shop Pro 7 software and Image Pro Express 4.0. Nonparametric tests were used. Results depicted significant differences immediately versus one month post treatment for both acids. It was concluded that both acids can be used, and as time passes enamel color improvement occurs.

Adolescent↗

PubMed2006

Modelling SF-6D health state preference data using a nonparametric Bayesian method.

This paper reports on the findings from applying a new approach to modelling health state valuation data. The approach applies a nonparametric model to estimate SF-6D health state utility values using Bayesian methods. The data set is the UK SF-6D valuation study where a sample of 249 states defined by the SF-6D (a derivative of the SF-36) was valued by a representative sample of the UK general population using standard gamble. The paper presents the results from applying the nonparametric model and comparing it to the original model estimated using a conventional parametric random effects model. The two models are compared theoretically and in terms of empirical performance. The paper discusses the implications of these results for future applications of the SF-6D and further work in this field.

Bayes Theorem↗

PubMed2000

Bootstrap confidence intervals for adaptive cluster sampling.

Consider a collection of spatially clustered objects where the clusters are geographically rare. Of interest is estimation of the total number of objects on the site from a sample of plots of equal size. Under these spatial conditions, adaptive cluster sampling of plots is generally useful in improving efficiency in estimation over simple random sampling without replacement (SRSWOR). In adaptive cluster sampling, when a sampled plot meets some predefined condition, neighboring plots are added to the sample. When populations are rare and clustered, the usual unbiased estimators based on small samples are often highly skewed and discrete in distribution. Thus, confidence intervals based on asymptotic normal theory may not be appropriate. We investigated several nonparametric bootstrap methods for constructing confidence intervals under adaptive cluster sampling. To perform bootstrapping, we transformed the initial sample in order to include the information from the adaptive portion of the sample yet maintain a fixed sample size. In general, coverages of bootstrap percentile methods were closer to nominal coverage than the normal approximation.

Animals↗

PubMed2002

Risk-taking attitudes among young drivers: the psychometric qualities and dimensionality of an instrument to measure young drivers' risk-taking attitudes.

Adolescents are proportionately more frequently involved in traffic accidents than are other age groups. A strategy for promoting road safety is to change the attitudes likely to influence driving behavior. However, the lack of valid and reliable instruments to measure risk-taking attitudes makes it difficult to evaluate the effects of measures aimed at changing attitudes among young drivers and their passengers. The present study tested the psychometric qualities of a scale intended to measure adolescents' risk-taking attitudes to driving. The results are based on a self-completion questionnaire survey carried out among 3,942 adolescents and young adults, aged 16-23 years, in Norway in 1998/1999. Using both exploratory and confirmatory factor analyses, 11 dimensions of risk-taking attitudes were identified. Parametric as well as nonparametric methods were applied to test the homogeneity of items within each attitude dimension. The reliability and validity of the dimensions were satisfactory. The attitude dimensions were significantly correlated with self-reported driving behavior, as well as accident frequency. The application of the new measurement instrument in studies aimed at evaluating safety campaigns is discussed.

Accidents, Traffic↗

PubMed2006

Instruments for causal inference: an epidemiologist's dream?

The use of instrumental variable (IV) methods is attractive because, even in the presence of unmeasured confounding, such methods may consistently estimate the average causal effect of an exposure on an outcome. However, for this consistent estimation to be achieved, several strong conditions must hold. We review the definition of an instrumental variable, describe the conditions required to obtain consistent estimates of causal effects, and explore their implications in the context of a recent application of the instrumental variables approach. We also present (1) a description of the connection between 4 causal models-counterfactuals, causal directed acyclic graphs, nonparametric structural equation models, and linear structural equation models-that have been used to describe instrumental variables methods; (2) a unified presentation of IV methods for the average causal effect in the study population through structural mean models; and (3) a discussion and new extensions of instrumental variables methods based on assumptions of monotonicity.

Bias↗

PubMed2004

Semiparametric models for cumulative incidence functions.

In analyses of time-to-failure data with competing risks, cumulative incidence functions may be used to estimate the time-dependent cumulative probability of failure due to specific causes. These functions are commonly estimated using nonparametric methods, but in cases where events due to the cause of primary interest are infrequent relative to other modes of failure, nonparametric methods may result in rather imprecise estimates for the corresponding subdistribution. In such cases, it may be possible to model the cause-specific hazard of primary interest parametrically, while accounting for the other modes of failure using nonparametric estimators. The cumulative incidence estimators so obtained are simple to compute and are considerably more efficient than the usual nonparametric estimator, particularly with regard to interpolation of cumulative incidence at early or intermediate time points within the range of data used to fit the function. More surprisingly, they are often nearly as efficient as fully parametric estimators. We illustrate the utility of this approach in the analysis of patients treated for early stage breast cancer.

Analysis of Variance↗

PubMed2004

[Socio-economic characteristics of pregnant women with preeclampsia: a gypsies population survey].

Preeclampsia as a serious syndrome affecting both the mother and the baby is associated with a number of socio-economic, demographic and clinical characteristics. We have filed the distribution of major epidemiological parameters in two gypsy groups, comprising 58 preeclamptic and 118 control cases. Only the classical features of preeclampsia as well as single liveborns were included in our study. Data are taken from the medical files and from a direct interview with the patients. Nonparametric and multivariate analysis are applied. The two study groups show insignificant differences in the distribution of the major epidemiological factors. Significant p-values are obtained referring to maternal age, parity, obesity. Speculations as to gypsy lifestyle are discussed as possible explanation to the achieved results.

Adult↗

PubMedDate not supplied

Air vs ground transport and outcome in trauma patients requiring urgent operative interventions.

OBJECTIVE: To study trauma patients requiring urgent operative interventions to determine whether transport mode was associated with outcome difference. METHODS: Eligible patients were injured adults and children who presented over a 57-month period to the emergency department (ED) at the study hospital (annual ED census 36,000) after air or ground transport from trauma scenes or referring hospitals. Patients included were those whose ED lengths of stay were <60 minutes prior to transfer to an operating room. Data collected included injury severity score (ISS), ED and hospital lengths of stay, and mortality. Continuous data, which were not distributed normally, were analyzed using Wilcoxon nonparametric analysis. Categorical variables were analyzed using chi-square testing. Multivariate logistic regression was used to account for confounding variables and isolate the effects of transport mode on mortality. Alpha for all tests was set at 0.05. RESULTS: 272 patients were eligible for study; 168 air medical and 104 ground transports. No between-group differences were found for ED length of stay, ISS, or mortality. A significantly longer hospital stay was found for air-transported patients. Subgroup analysis of patients with penetrating trauma and ISS of > or =25 revealed mortalities of 28% and 45% for air- and ground-transported patients, respectively; this difference was not statistically significant (p = 0.24), but the study had a power of only 22% to detect a difference at this magnitude. CONCLUSION: This study failed to identify, but had insufficient power to rule out, outcome benefit to air medical transport in a subset of trauma patients requiring urgent operative interventions.

Adolescent↗

PubMedDate not supplied

Confidence intervals for cost-effectiveness ratios: a comparison of four methods.

We evaluated four methods for computing confidence intervals for cost-effectiveness ratios developed from randomized controlled trials: the box method, the Taylor series method, the nonparametric bootstrap method and the Fieller theorem method. We performed a Monte Carlo experiment to compare these methods. We investigated the relative performance of each method and assessed whether or not it was affected by differing distributions of costs (normal and log normal) and effects (10% absolute difference in mortality resulting from mortality rates of 25% versus 15% in the two groups as well as from mortality rates of 55% versus 45%) or by differing levels of correlation between the costs and effects (correlations of -0.50, -0.25, 0.0, 0.25 and 0.50). The principal criterion used to evaluate the performance of the methods was the probability of miscoverage. Symmetrical miscoverage of the intervals was used as a secondary criterion for evaluating the four methods. Overall probabilities of miscoverage for the nonparametric bootstrap method and the Fieller theorem method were more accurate than those for the other the methods. The Taylor series method had confidence intervals that asymmetrically underestimated the upper limit of the interval. Confidence intervals for cost-effectiveness ratios resulting from the nonparametric bootstrap method and the Fieller theorem method were more dependably accurate than those estimated using the Taylor series or box methods. Routine reporting of these intervals will allow individuals using cost-effectiveness ratios to make clinical and policy judgments to better identify when an intervention is a good value for its cost.

Computer Simulation↗

PubMed2004

Estimation of fish abundance indices based on scientific research trawl surveys.

The fish abundance index over an ocean region is defined here to be the integral of expected catch per unit effort (CPUE), approximated by the sum of expected CPUE over grid squares. When trawl surveys are done within grid squares selected according to a probability sampling design, several other sources of variation such as the fish population dynamics and the catching process are also involved. In such situations model-assisted methods for estimating abundance, assessed under both design and model perspectives, have some advantages over purely design-based methods such as the Horvitz-Thompson (HT) estimator or purely model-based prediction approaches. This article develops model-assisted empirical likelihood (EL) methods via loglinear regression and nonparametric smoothing. The methods are applied to grid surveys of the Grand Bank region carried out annually by Fishery Products International from 1996 through 2002. The HT and EL methods produce similar point estimates of abundance indices. Simulation results, however, indicate that the EL estimator under local linear smoothing is associated with smaller standard errors.

Animals↗

PubMed2002

Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression.

We propose a new method for constructing genetic network from gene expression data by using Bayesian networks. We use nonparametric regression for capturing nonlinear relationships between genes and derive a new criterion for choosing the network in general situations. In a theoretical sense, our proposed theory and methodology include previous methods based on Bayes approach. We applied the proposed method to the S. cerevisiae cell cycle data and showed the effectiveness of our method by comparing with previous methods.

Bayes Theorem↗

PubMed1997

Regression analysis of interval-censored survival data with covariates using log-linear models.

We considered the regression analysis of the event time data with left-, right-, or interval-censored observations. We extended life-table techniques for censored survival data using log-linear models to incorporate interval-censored failures. The EM algorithm was used to calculate maximum likelihood estimates for the parameters. We assumed that the hazard function was a stepwise function over disjoint intervals of time; thus, the nonparametric model, the parametric exponential model, and the semiparametric Cox proportional hazard model were easily implemented as special cases. We adapted the restricted EM algorithm to test hypotheses and to construct confidence intervals for the parameters. These methods were applied in an analysis of the recurrence time for treated melanoma patients.

Adult↗

PubMed2005

Effect of root canal filling/sealer systems on apical endotoxin penetration: a coronal leakage evaluation.

Endotoxin, elaborated by gram-negative organisms, is an important factor in apical periodontitis. The objective of this study was to evaluate the magnitude of endotoxin penetration through root canal treated teeth using a dual chamber model system. Forty-four maxillary anterior teeth were prepared endodontically and canals filled either by lateral condensation or a warm thermoplasticized technique in combination with either Roth's 801 or AH 26 sealer. Teeth were suspended in the model system with a mixed anaerobic bacterial suspension in the upper chamber and HBSS in the lower chamber. The QCL-1000 LAL assay was used to measure endotoxin at 0, 1, 7, 14, and 21 days. Response feature analysis using trapezoidal area under the curve was performed; the four treatment groups were compared using nonparametric methods. Groups differed (p = 0.028), with thermoplasticized root canal filling/Roth's 801 sealer permitting the least apical endotoxin penetration. Results suggest that Roth's 801 sealer may have a role in inhibiting endotoxin penetration.

Analysis of Variance↗

PubMed1999

Analysis of simulated data: evidence for genetic and environmental effects.

We approached the simulation as though it were an international study with similar but not identical information being collected from different populations. In keeping with this we analyzed one replicate from each population. Initially we examined the risk of disease in relatives of cases to determine whether the disease appeared to be "more genetic" in one population than in the others and we examined the evidence for environmental risk factors in each population. Nonparametric linkage analysis and transmission/disequilibrium testing (TDT) were used to search for loci linked to the disease in each population. Using these methods we identified several candidate regions for a susceptibility gene which on examination of the answers are explicable in terms of the underlying model.

Environment↗

PubMed2003

Nonparametric quantile estimation with correlated failure time data.

In biomedical studies, correlated failure time data arise often. Although point and confidence interval estimation for quantiles with independent censored failure time data have been extensively studied, estimation for quantiles with correlated failure time data has not been developed. In this article, we propose a nonparametric estimation method for quantiles with correlated failure time data. We derive the asymptotic properties of the quantile estimator and propose confidence interval estimators based on the bootstrap and kernel smoothing methods. Simulation studies are carried out to investigate the finite sample properties of the proposed estimators. Finally, we illustrate the proposed method with a data set from a study of patients with otitis media.

Child, Preschool↗

PubMed2005

In vivo debridement efficacy of ultrasonic irrigation following hand-rotary instrumentation in human mandibular molars.

This study histologically compared the in vivo debridement efficacy of hand/rotary canal preparation versus a hand/rotary/ultrasound technique in mesial root canals of vital mandibular molars. Group 1 consisted of 16 teeth prepared with a hand/rotary technique whereas group 2 consisted of 15 teeth prepared in similar fashion but followed by 1 min of ultrasonic irrigation, per canal, utilizing an ultrasonic needle in a MiniEndo unit. Five uninstrumented mandibular molars served as histologic controls. After extraction and histologic preparation, 0.5 microm cross-sections, taken every 0.2 mm from the 1- to 3-mm apical levels, were evaluated for percentage of tissue removal. Nonparametric analysis revealed mean percent canal and isthmus cleanliness values to be significantly higher for group 2 at all levels evaluated, except one. In conclusion, the 1 min use of the ultrasonic needle after hand/rotary instrumentation resulted in significantly cleaner canals and isthmuses in the mesial roots of mandibular molars.

Humans↗