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At least 109 records · Page 6Linked to original sources
PubMed2004

Confidence interval for rate ratio in a 2 x 2 table with structural zero: an application in assessing false-negative rate ratio when combining two diagnostic tests.

In this article, we consider problems with correlated data that can be summarized in a 2 x 2 table with structural zero in one of the off-diagonal cells. Data of this kind sometimes appear in infectious disease studies and two-step procedure studies. Lui (1998, Biometrics54, 706-711) considered confidence interval estimation of rate ratio based on Fieller-type, Wald-type, and logarithmic transformation statistics. We reexamine the same problem under the context of confidence interval construction on false-negative rate ratio in diagnostic performance when combining two diagnostic tests. We propose a score statistic for testing the null hypothesis of nonunity false-negative rate ratio. Score test-based confidence interval construction for false-negative rate ratio will also be discussed. Simulation studies are conducted to compare the performance of the new derived score test statistic and existing statistics for small to moderate sample sizes. In terms of confidence interval construction, our asymptotic score test-based confidence interval estimator possesses significantly shorter expected width with coverage probability being close to the anticipated confidence level. In terms of hypothesis testing, our asymptotic score test procedure has actual type I error rate close to the pre-assigned nominal level. We illustrate our methodologies with real examples from a clinical laboratory study and a cancer study.

Biometry↗
PubMed2006

Profile likelihood-based confidence intervals using Monte Carlo integration for population pharmacokinetic parameters.

Population pharmacokinetic (PPK) analysis usually employs nonlinear mixed effects models using first-order linearization methods. It is well known that linearization methods do not always perform well in actual situations. To avoid linearization, the Monte Carlo integration method has been proposed. Moreover, we generally utilize asymptotic confidence intervals for PPK parameters based on Fisher information. It is known that likelihood-based confidence intervals are more accurate than those from the usual asymptotic confidence intervals. We propose profile likelihood-based confidence intervals using Monte Carlo integration. We have evaluated the performance of the proposed method through a simulation study, and analyzed the erythropoietin concentration data set by the method.

Computer Simulation↗
PubMed2003

Likelihood-based confidence intervals for a log-normal mean.

To construct a confidence interval for the mean of a log-normal distribution in small samples, we propose likelihood-based approaches - the signed log-likelihood ratio and modified signed log-likelihood ratio methods. Extensive Monte Carlo simulation results show the advantages of the modified signed log-likelihood ratio method over the signed log-likelihood ratio method and other methods. In particular, the modified signed log-likelihood ratio method produces a confidence interval with a nearly exact coverage probability and highly accurate and symmetric error probabilities even for extremely small sample sizes. We then apply the methods to two sets of real-life data.

Biomedical Research↗
PubMed2004

Estimation of a parameter and its exact confidence interval following sequential sample size reestimation trials.

For confirmatory trials of regulatory decision making, it is important that adaptive designs under consideration provide inference with the correct nominal level, as well as unbiased estimates, and confidence intervals for the treatment comparisons in the actual trials. However, naive point estimate and its confidence interval are often biased in adaptive sequential designs. We develop a new procedure for estimation following a test from a sample size reestimation design. The method for obtaining an exact confidence interval and point estimate is based on a general distribution property of a pivot function of the Self-designing group sequential clinical trial by Shen and Fisher (1999, Biometrics55, 190-197). A modified estimate is proposed to explicitly account for futility stopping boundary with reduced bias when block sizes are small. The proposed estimates are shown to be consistent. The computation of the estimates is straightforward. We also provide a modified weight function to improve the power of the test. Extensive simulation studies show that the exact confidence intervals have accurate nominal probability of coverage, and the proposed point estimates are nearly unbiased with practical sample sizes.

Biometry↗
PubMed1998

Approximate standard errors and confidence intervals for indices of positive and negative agreement.

Indices of positive and negative agreement for observer reliability studies, in which neither observer can be regarded as the standard, have been proposed. In this article, it is demonstrated by means of an example and a small simulation study that a recently published method for constructing confidence intervals for these indices leads to intervals that are too wide. Appropriate asymptotic (i.e., large sample) variance estimates and confidence intervals for the positive and negative agreement indices are presented and compared with bootstrap confidence intervals. We also discuss an alternative method of interval estimation motivated from a Bayesian viewpoint. The asymptotic intervals performed adequately for sample sizes of 200 or more. For smaller samples, alternative confidence intervals such as bootstrap intervals or Bayesian intervals should be considered.

Confidence Intervals↗
PubMedDate not supplied

Use of jackknife resampling techniques to estimate the confidence intervals of fMRI parameters.

PURPOSE: The objective of this study was to determine the reliability and the confidence intervals of task-activated functional MRI (fMRI) parameters using a computer-intensive resampling technique. The jackknife, a commonly used method for resampling mathematical data, was used to calculate the confidence interval of fMRI parameters for a simple bilateral finger-tapping paradigm. METHOD: Four healthy test subjects (three men, one woman) were used to test the correlation coefficient and variability in the data. Each subject performed 4.5 cycles, each cycle having 20 s of bilateral finger tapping alternating with rest periods of equal time, producing 90 images. One additional scan of 10 cycles (200 images) was used to test the stability of the method itself. One thousand jackknifed resampled data sets of 85 elements each (from 90 original points) were generated, and the correlation coefficient was determined using an idealized "on/off" box-car reference waveform. RESULTS: Activation maps were generated that had the same confidence intervals at each pixel. These maps were more localized with less extraneous activated pixels than the maps generated with a fixed correlation coefficient threshold. There was no significant difference in the distribution of correlation coefficients between the 85, 90, and 95 element, jackknifed data sets; similar robustness was seen, as well. CONCLUSION: The jackknife resampling technique for data analysis produced reliable distributions and statistical parameters. The jackknife estimates were shown to be stable, even from a small initial sample size. This method may be used in lieu of test-retest analysis.

Adolescent↗
PubMedDate not supplied

Pulling cost-effectiveness analysis up by its bootstraps: a non-parametric approach to confidence interval estimation.

The statistic of interest in the economic evaluation of health care interventions is the incremental cost effectiveness ratio (ICER), which is defined as the difference in cost between two treatment interventions over the difference in their effect. Where patient-specific data on costs and health outcomes are available, it is natural to attempt to quantify uncertainty in the estimated ICER using confidence intervals. Recent articles have focused on parametric methods for constructing confidence intervals. In this paper, we describe the construction of non-parametric bootstrap confidence intervals. The advantage of such intervals is that they do not depend on parametric assumptions of the sampling distribution of the ICER. We present a detailed description of the non-parametric bootstrap applied to data from a clinical trial, in order to demonstrate the strengths and weaknesses of the approach. By examining the bootstrap confidence limits successively as the number of bootstrap replications increases, we conclude that percentile bootstrap confidence interval methods provide a promising approach to estimating the uncertainty of ICER point estimates. However, successive bootstrap estimates of bias and standard error suggests that these may be unstable; accordingly, we strongly recommend a cautious interpretation of such estimates.

Bias↗
PubMed1976

Symmetrical confidence intervals for bioequivalence trials.

The conventional method of setting confidence intervals for the difference of the means of two normal populations gives an interval which is not, in general, symmetrical about zero. A modification of the conventional method which leads to symmetry about zero is discussed and is recommended as particularly appropriate for use in bioequivalence trials. This modification has the effect of decreasing the "effective" length of the confidence interval, on which the decision concerning bioequivalence is based, while increasing the confidence coefficient.

Methods↗
PubMed2005

Review and evaluation of methods for computing confidence intervals for the ratio of two proportions and considerations for non-inferiority clinical trials.

This article reviews several methods for forming confidence intervals for a risk ratio of two independent binomial proportions (which are both less than 0.50) and evaluates their statistical performance. These methods include use of a Taylor Series expansion to estimate variance, solutions to a quadratic equation, and maximum likelihood methods. In addition, for improvement of the properties of the methods based on large sample approximations, situations where either binomial count was less than or equal to 3 were managed conservatively by having an exact confidence interval for the odds ratio become the confidence interval for its risk ratio counterpart. Methods were initially evaluated by computing confidence limits for certain cases. Second, simulations were used to identify the better methods for controlling the Type I error rate while maintaining power. Last, relationships between methods were evaluated by calculating the percent of disagreement in the decision made regarding non inferiority. Methods in the group using a Taylor Series expansion in variance estimation perform similarly to the Pearson method preferred in the literature. In addition, the group of methods using a Taylor Series expansion are most easily computed. Applications of these findings are discussed for ratios that arise in randomized clinical trials that are conducted to show noninferiority of a new medical product to a reference control. Consideration is given as well to sample size calculations for noninferiority clinical trials.

Chi-Square Distribution↗
PubMed1997

Confidence intervals for relative risk estimates from affected-sib-pair data.

Confidence intervals for relative risk parameters estimated using affected-sib-pair data are derived and evaluated for two markers showing previous evidence of linkage to bipolar illness. For D18S41 we found some evidence, and for D18S37 stronger evidence, of relative risks greater than 1, although in both cases the estimated confidence intervals for the parameters are wide.

Bipolar Disorder↗
PubMedDate not supplied

Confidence intervals and p-values for Williams' and other step-down multiple comparison tests against control.

When single-step multiple comparison tests against control, such as Dunnett's test, are used, p-values and confidence intervals can be reported. However, Williams' test and other step-down multiple comparison tests only provide results in terms of statistical significance. In this paper, approximate confidence intervals are proposed for some step-down multiple comparison tests and their coverage properties are assessed by simulation; p-values are readily calculated. The proposed simultaneous confidence intervals associated with Williams' test, Dunnett's step-down test, and the closed t test are all found to have good coverage, typically between 94% and 96% for a nominal value of 95%. Thus practicing statisticians can now quote p-values for these tests and use simple confidence intervals to aid interpretation of test results.

Computer Simulation↗
PubMed1999

Bootstrap approach for constructing confidence intervals for population pharmacokinetic parameters. I: A use of bootstrap standard error.

In population pharmacokinetic studies, one of the main objectives is to estimate population pharmacokinetic parameters specifying the population distributions of pharmacokinetic parameters. Confidence intervals for population pharmacokinetic parameters are generally estimated by assuming the asymptotic normality, which is a large-sample property, that is, a property which holds for the cases where sample sizes are large enough. In actual clinical trials, however, sample sizes are limited and not so large in general. Likelihood functions in population pharmacokinetic modelling include a multiple integral and are quite complicated. We hence suspect that the sample sizes of actual trials are often not large enough for assuming the asymptotic normality and that the asymptotic confidence intervals underestimate the uncertainties of the estimates of population pharmacokinetic parameters. As an alternative to the asymptotic normality approach, we can employ a bootstrap approach. This paper proposes a bootstrap standard error approach for constructing confidence intervals for population pharmacokinetic parameters. Comparisons between the asymptotic and bootstrap confidence intervals are made through applications to a simulated data set and an actual phase I trial.

Adult↗
PubMed2005

In vitro versus canine data for predicting input profiles of isosorbide-5-mononitrate from oral extended release products on a confidence interval basis.

The objective of this study was to assess the relative usefulness of in vitro and canine data sets in the prediction of input rates of isosorbide-5-mononitrate (ISMN) from extended release (ER) products on a confidence interval basis. ISMN is a highly soluble compound with high permeability in the upper GI tract but not in the colon. Two ISMN ER tablet formulations were studied: Imdur and an osmotic pump. Differences between amount of drug absorbed versus time predicted from in vitro and canine data and results observed in humans were assessed using the estimated median value of the difference factor, f(1), and its associated 90% bootstrap confidence intervals. For both products, median f(1) values and their associated confidence intervals for the comparisons between in vitro and human profiles and between canine and human profiles were similar. Although the median difference between the human input profiles of the two ISMN ER products was equally well predicted by in vitro and canine data, the 90% confidence intervals for this difference were better predicted by the canine data.

Administration, Oral↗
PubMed2007

Covariates-dependent confidence intervals for the difference or ratio of two median survival times.

In this paper, we are concerned with the estimation of the discrepancy between two treatments when right-censored survival data are accompanied with covariates. Conditional confidence intervals given the available covariates are constructed for the difference between or ratio of two median survival times under the unstratified and stratified Cox proportional hazards models, respectively. The proposed confidence intervals provide the information about the difference in survivorship for patients with common covariates but in different treatments. The results of a simulation study investigation of the coverage probability and expected length of the confidence intervals suggest the one designed for the stratified Cox model when data fit reasonably with the model. When the stratified Cox model is not feasible, however, the one designed for the unstratified Cox model is recommended. The use of the confidence intervals is finally illustrated with a HIV+ data set.

Confidence Intervals↗
PubMed1997

A SAS macro for constructing simultaneous confidence intervals for multinomial proportions.

Quesenberry and Hurst (1964), Goodman (1965) and Fitzpatrick and Scott (1987) proposed simultaneous construction of confidence intervals for multinomial proportions, however. statistical computing packages do not generally give one the option of specifying the type of construction to be used. We have written a SAS macro using PROC IML that takes multinomial cell counts as input and returns simultaneous confidence intervals with the user-specified coverage probability. Two main features of the macro are its ease of use and its flexibility in allowing the user to choose among six methods of constructing confidence intervals for multinomial proportions. Based on simulation May and Johnson (1997) recommended the intervals proposed by Goodman (1965) in most practical applications.

Binomial Distribution↗
PubMed1991

[Effect measures and confidence intervals].

In order to exemplify the use of effect measures and confidence intervals in epidemiology, it is assumed that a group of patients treated with antiflogistic drugs were compared by repeated gastroscopy with a control group. The effect measures "incidence rate difference" and "incidence rate ratio" are used to compare ulcer incidence in the two groups of patients, and the corresponding confidence intervals are stated. On the other hand, if the registered parameter is prevalence rather than incidence rate, for instance the occurrence of prepyloric erosions at a certain time, the effect measures employed are "risk difference" and "risk ratio". An alternative to the latter is "odds ratio".

Confidence Intervals↗
PubMed2000

Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians.

Since the early 1980s, a bewildering array of methods for constructing bootstrap confidence intervals have been proposed. In this article, we address the following questions. First, when should bootstrap confidence intervals be used. Secondly, which method should be chosen, and thirdly, how should it be implemented. In order to do this, we review the common algorithms for resampling and methods for constructing bootstrap confidence intervals, together with some less well known ones, highlighting their strengths and weaknesses. We then present a simulation study, a flow chart for choosing an appropriate method and a survival analysis example.

Algorithms↗