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Results for “confidence limits”

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At least 73 records · Page 4Linked to original sources
PubMed1981

A computer program for estimating LD50 and its confidence limits using modified Behrens-Reed-Muench cumulant method.

A FORTRAN IV program is developed for simple and quick estimation of LD50 and its confidence intervals from quantal dose-response data. The algorithm is based on a modification of the Behrens-Reed-Muench cumulant method. Under ideal experimental design, the method produces results comparable to the ones by statistically more accurate maximum likelihood method using the Logit or the Probit transformation. The procedure, as implemented, can also be easily performed on a small hand-held calculator.

Computers↗
PubMed1997

Accurate confidence limits for quantiles under random censoring.

In survival analysis, estimates of median survival times in homogeneous samples are often based on the Kaplan-Meier estimator of the survivor function. Confidence intervals for quantiles, such as median survival, are typically constructed via large sample theory or the bootstrap. The former has suspect accuracy for small sample sizes under moderate censoring and the latter is computationally intensive. In this paper, improvements on so-called test-based intervals and reflected intervals (cf., Slud, Byar, and Green, 1984, Biometrics 40, 587-600) are sought. Using the Edgeworth expansion for the distribution of the studentized Nelson-Aalen estimator derived in Strawderman and Wells (1997, Journal of the American Statistical Association 92), we propose a method for producing more accurate confidence intervals for quantiles with randomly censored data. The intervals are very simple to compute, and numerical results using simulated data show that our new test-based interval outperforms commonly used methods for computing confidence intervals for small sample sizes and/or heavy censoring, especially with regard to maintaining specified coverage.

Biometry↗
PubMedDate not supplied

Confidence limits for estimates of gene linkage based on analysis of recombinant inbred strains.

Recombinant inbred (RI) strains are extremely useful for genetic mapping. This paper presents a simple method for determining confidence intervals for linkage estimates based on analysis of RI strains. The results show that such confidence intervals are usually large with the currently available numbers of RI strains. Therefore, map positions based only on analysis of RI strains should be interpreted with caution. To facilitate interpretation of linkage data derived from RI strains, a table is presented giving the 95 percent and 99 percent confidence intervals for all possible linkages detected with up to 45 RI strains.

Animals↗
PubMed2005

Placing confidence limits on the molecular age of the human-chimpanzee divergence.

Molecular clocks have been used to date the divergence of humans and chimpanzees for nearly four decades. Nonetheless, this date and its confidence interval remain to be firmly established. In an effort to generate a genomic view of the human-chimpanzee divergence, we have analyzed 167 nuclear protein-coding genes and built a reliable confidence interval around the calculated time by applying a multifactor bootstrap-resampling approach. Bayesian and maximum likelihood analyses of neutral DNA substitutions show that the human-chimpanzee divergence is close to 20% of the ape-Old World monkey (OWM) divergence. Therefore, the generally accepted range of 23.8-35 millions of years ago for the ape-OWM divergence yields a range of 4.98-7.02 millions of years ago for human-chimpanzee divergence. Thus, the older time estimates for the human-chimpanzee divergence, from molecular and paleontological studies, are unlikely to be correct. For a given the ape-OWM divergence time, the 95% confidence interval of the human-chimpanzee divergence ranges from -12% to 19% of the estimated time. Computer simulations suggest that the 95% confidence intervals obtained by using a multifactor bootstrap-resampling approach contain the true value with >95% probability, whether deviations from the molecular clock are random or correlated among lineages. Analyses revealed that the use of amino acid sequence differences is not optimal for dating human-chimpanzee divergence and that the inclusion of additional genes is unlikely to narrow the confidence interval significantly. We conclude that tests of hypotheses about the timing of human-chimpanzee divergence demand more precise fossil-based calibrations.

Animals↗
PubMed1987

A fast algorithm for the computation of the plasma concentration time curve and confidence limits in linear pharmacokinetics.

An algorithm is developed for the computation of the plasma concentration time curve for a drug administration regime involving fast injection (bolus) and/or continuous infusion. The effect of the pharmacokinetic model parameter variations on the plasma concentration time curve is analyzed and efficient algorithms for the computation of the parameter-induced first-order variation and confidence bounds of the plasma concentration time curve are also presented. The application of the method is illustrated with pharmacokinetic data for the etomidate.

Algorithms↗
PubMed1989

Using permutation tests and bootstrap confidence limits to analyze repeated events data from clinical trials.

In clinical trials comparing treatments for superficial bladder cancer, patients are at risk of repeated recurrences of their disease. Statistical methods of analyzing such data are required. This article presents a nonparametric approach. A statistical test to compare the recurrence or tumor rates in two treatment groups, using the randomization distribution, is described. Confidence intervals for the rate ratio are determined from the bootstrap distribution. The implementation of both requires Monte Carlo methods. Computer simulations support the use of these nonparametric methods when there are more than 60 recurrences in each treatment group. An example illustrating their use is given. The strategy adopted for analysis of these data could be applied to other clinical trials where standard methodology is inappropriate.

Biometry↗
PubMed2006

Upper susceptibility threshold limits with confidence intervals: a method to identify normal and abnormal population values for laboratory toxicological parameters, based on acetylcholinesterase activities in sea lice.

The interpretation and importance of comparing field values of susceptibility to pesticides with a laboratory reference strain that might bear little resemblance to the actual situation in the field are problematic and a continuing subject of debate. In this paper a procedure for defining a 'normal sensitive' population from a field study of 383 individuals to provide a basis for analysing and interpreting in vitro results is described and examined. Instead of using only the 95th percentile, the upper and lower confidence limits for the 95th percentile were also compared to select the best estimation of the limit for the normal material. A field population constrained by the upper confidence limit for the 95th percentile provides appropriate descriptions of the normal material in this study. This approach should prove useful in studies of pesticide resistance in field populations.

Acetylcholinesterase↗
PubMed1993

Determining confidence limits for drug potency in immunoassay.

Nonlinear models have frequently been used to characterize dose-response data obtained from biological assays. The effect of a bioactive agent is observed and the model allows prediction of the dose required to obtain the observed effect (the 'inverse prediction'). The precision of this estimate is important in potency determination. Here, a general method is presented for calculating the inverse confidence intervals for estimates of dose potencies obtained from nonlinear models often used to describe these tests. The approach is demonstrated with application of data sets to the negative exponential and four-parameter logistic regression models. Necessary theory is presented and followed by detailed discussion in which estimation strategies are explained and intermediate quantities calculated.

Biological Assay↗
PubMed2000

Improved bootstrap confidence limits in large-scale phylogenies, with an example from Neo-Astragalus (Leguminosae).

Phylogenetic analyses of large data sets pose special challenges, including the apparent tendency for the bootstrap support for a clade to decline with increased taxon sampling of that clade. We document this decline in data sets with increasing numbers of taxa in Astragalus, the most species-rich angiosperm genus. Support for one subclade, Neo-Astragalus, declined monotonically with increased sampling of taxa inside Neo-Astragalus, irrespective of whether parsimony or neighbor-joining methods were used or of which particular heuristic search algorithm was used (although more stringent algorithms tended to yield higher support). Three possible explanations for this decline were examined, including (1) mistaken assignment of the most recent common ancestor of the taxon sample (and its bootstrap support) with the most recent common ancestor of the clade from which it was sampled; (2) computational limitations of heuristic search strategies; and (3) statistical bias in bootstrap proportions, especially that from random homoplasy distributed among taxa. The best explanation appears to be (3), although computational shortcomings (2) may explain some of the problem. The bootstrap proportion, as currently used in phylogenetic analysis, does not accurately capture the classical notion of confidence assessments on the null hypothesis of nonmonophyly, especially in large data sets. More accurate assessments of confidence as type I error levels (relying on iterated bootstrap methods) remove most of the monotonic decline in confidence with increasing numbers of taxa.

Algorithms↗