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Results for “statistical inference”

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PubMed1978

Effects of serial dependency on the agreement between visual and statistical inference.

Comparisons between visual and time-series inferences from behavioral data show that serial dependency in scores is likely to disrupt agreement between the two methods of analysis. If researchers follow an earlier recommendation that time-series analysis be used to supplement or confirm visual analysis, this study's findings suggest that the two methods will disagree most often when the data contain high levels of autocorrelation and when reliable behavorial changes are indicated by time-series analysis.

Journal Article↗
PubMed2000

Resampling approach to statistical inference: bootstrapping from event-related potentials data.

We propose the use of the bootstrap resampling technique as a tool to assess the within-subject reliability of experimental modulation effects on event-related potentials (ERPs). The assessment of the within-subject reliability is relevant in all those cases when the subject score is obtained by some estimation procedure, such as averaging. In these cases, possible deviations from the assumptions on which the estimation procedure relies may lead to severely biased results and, consequently, to incorrect functional inferences. In this study, we applied bootstrap analysis to data from an experiment aimed at investigating the relationship between ERPs and memory processes. ERPs were recorded from two groups of subjects engaged in a recognition memory task. During the study phase, subjects in Group A were required to make an orthographic judgment on 160 visually presented words, whereas subjects in Group B were only required to pay attention to the words. During the test phase all subjects were presented with the 160 previously studied words along with 160 new words and were required to decide whether the current word was "old" or "new." To assess the effect of word imagery value, half of the words had a high imagery value and half a low imagery value. Analyses of variance performed on ERPs showed that an imagery-induced modulation of the old/new effect was evident only for subjects who were not engaged in the orthographic task during the study phase. This result supports the hypothesis that this modulation is due to some aspect of the recognition memory process and not to the stimulus encoding operations that occur during the recognition memory task. However, bootstrap analysis on the same data showed that the old/new effect on ERPs was not reliable for all the subjects. This result suggests that only a cautious inference can be made from these data.

Adult↗
PubMed1991

Exact statistical inference for group sequential trials.

This paper considers clinical trials comparing two treatments with dichotomous responses where the data are examined periodically for early evidence of treatment difference. The existing group sequential methods for such trials are based on the large-sample normal approximation to the joint distribution of the estimators of treatment difference over interim analyses. We demonstrate through extensive numerical studies that, for small and even moderate-sized trials, these approximate procedures may lead to tests with supranominal size (mainly when unpooled estimators of variance are used) and confidence intervals with under-nominal coverage probability. We then study exact methods for group sequential testing, repeated interval estimation, and interval estimation following sequential testing. The new procedures can accommodate any treatment allocation rules. An example using real data is provided.

Biometry↗
PubMed1988

Multifactorial analysis of family data ascertained through truncation: a comparative evaluation of two methods of statistical inference.

When family data are ascertained through single selection based on truncation, a prevailing method of analysis is to condition the likelihood function on the proband's actual phenotypic value. An alternative method conditions the likelihood function on the event that the proband's measurement lies in the truncation region. Both methods are contrasted here by using Monte Carlo simulations; identical sets of data were analyzed using both methods. The results suggest that, under either method, (1) parameter estimates are nearly unbiased and (2) likelihood-ratio tests of null hypotheses are approximately distributed as chi 2. However, conditioning on the proband's actual phenotypic value yields considerably less efficient estimates and reduced power for hypothesis tests. A corresponding result also holds under complete ascertainment. It is argued, therefore, that whenever sufficient information is available on the nature of truncation, the alternative approach should be used.

Computer Simulation↗
PubMed2006

HLA-A, -B, and -DRB1 polymorphism defined by sequence-based typing of the Han population in Northern China.

DNA typing for human leukocyte antigen (HLA)-A, -B and -DRB1 was performed using polymerase chain reaction-sequence-based typing method on 618 randomly selected healthy individuals of the Han population in Northern China. Allele frequencies and haplotypes were statistically analyzed. A total of 84 HLA-A alleles, 143 B alleles, and 122 DRB1 alleles were detected, and 853 A-B-DRB1 haplotypes, 473 A-B haplotypes, and 551 B-DRB1 haplotypes were statistically inferred. Statistical analysis of three-locus haplotypes showed that A*0207-B*4601-DRB1*0901 (3.06%) was the most predominant. Gene frequencies and haplotypic associations within HLA-A, -B, and -DRB1 loci were determined at a high-resolution (four digit) allelic level and should provide useful information in anthropology, bone marrow donor registry, legal medicine, and disease association studies.

Alleles↗
PubMed2006

Biological parametric mapping: A statistical toolbox for multimodality brain image analysis.

In recent years, multiple brain MR imaging modalities have emerged; however, analysis methodologies have mainly remained modality-specific. In addition, when comparing across imaging modalities, most researchers have been forced to rely on simple region-of-interest type analyses, which do not allow the voxel-by-voxel comparisons necessary to answer more sophisticated neuroscience questions. To overcome these limitations, we developed a toolbox for multimodal image analysis called biological parametric mapping (BPM), based on a voxel-wise use of the general linear model. The BPM toolbox incorporates information obtained from other modalities as regressors in a voxel-wise analysis, thereby permitting investigation of more sophisticated hypotheses. The BPM toolbox has been developed in Matlab with a user-friendly interface for performing analyses, including voxel-wise multimodal correlation, ANCOVA, and multiple regression. It has a high degree of integration with the SPM (statistical parametric mapping) software relying on it for visualization and statistical inference. Furthermore, statistical inference for a correlation field, rather than a widely used T-field, has been implemented in the correlation analysis for more accurate results. An example with in vivo data is presented, demonstrating the potential of the BPM methodology as a tool for multimodal image analysis.

Brain↗
PubMed2005

A Monte Carlo analysis technique applied to cerebral perfusion SPECT scans.

OBJECTIVE: Voxel-based techniques are used to provide objective analyses of SPECT scans. The aim of this study was to develop a voxel-based analysis technique that uses a Monte Carlo method of statistical inference and assess this analysis technique for application to cerebral perfusion SPECT scans. METHODS: Assessment of the validity of this non-parametric, Monte Carlo method of statistical inference has been performed for a range of study designs, image characteristics and analysis parameters using phantom SPECT and Gaussian images. The Monte Carlo method of statistical inference and the voxel-based analysis technique were clinically evaluated for the analysis of individual cerebral perfusion SPECT scans using control subject data. In addition, a comparison has been performed with an existing analysis package that uses a theoretical parametric method of statistical inference (statistical parametric mapping). RESULTS: The Monte Carlo method was found to provide accurate statistical inference for phantom SPECT and Gaussian images independent of degrees of freedom, acquired counts, image smoothness and voxel significance level threshold. The clinical evaluation of the analysis of individual cerebral perfusion SPECT scans demonstrated satisfactory statistical inference and characterization of perfusion deficits. CONCLUSION: An analysis method incorporating a Monte Carlo method of statistical inference has been successfully applied for the analysis of cerebral perfusion SPECT scans.

Algorithms↗
PubMed2001

A general goodness-of-fit approach for inference procedures concerning the kappa statistic.

The kappa statistic is frequently used as a measure of agreement among two or more raters. Although considerable research on statistical inferences for this statistic has been published for the case of two raters and a binary outcome, relatively little work has appeared on inference problems for the case of multiple raters and/or polytomous nominal outcome categories. In this paper we propose a new procedure for constructing inferences for the kappa statistic that may be applied to this general case. The procedure is based on a chi-square goodness-of-fit test as applied to the Dirichlet multinomial model, and is a natural extension of previously proposed procedures that apply to more restricted cases. A simulation study shows that the new procedure provides confidence interval coverage levels and type I error rates close to nominal over a wide range of parameter combinations. We also present a sample size formula which may be used to determine the required number of subjects and raters for a given number of outcome categories.

Bias↗