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PubMedDate not supplied

CAN'T MISS--conquer any number task by making important statistics simple. Part 3. Standard error, estimation, and confidence intervals.

Healthcare quality improvement professionals need to understand and use inferential statistics to interpret sample data from their organizations. In quality improvement and healthcare research studies all the data from a population often are not available, so investigators take samples and make inferences about the population by using inferential statistics. This three-part series will give readers an understanding of the concepts of inferential statistics as well as the specific tools for calculating confidence intervals for samples of data. This article, Part 3, describes standard error and margin of error for a continuous variable and how they are calculated from the sample size and standard deviation of a sample. The article then demonstrates how the standard error and margin of error are used to calculate the confidence interval for estimating a population mean based on a sample mean.

Analysis of Variance↗
PubMedDate not supplied

CAN'T MISS--conquer any number task by making important statistics simple. Part 1. Types of variables, mean, median, variance, and standard deviation.

Healthcare quality improvement professionals need to understand and use inferential statistics to interpret sample data from their organizations. In quality improvement and healthcare research studies all the data from a population often are not available, so investigators take samples and make inferences about the population by using inferential statistics. This three-part series will give readers an understanding of the concepts of inferential statistics as well as the specific tools for calculating confidence intervals for samples of data. This article, Part 1, presents basic information about data including a classification system that describes the four major types of variables: continuous quantitative variable, discrete quantitative variable, ordinal categorical variable (including the binomial variable), and nominal categorical variable. A histogram is a graph that displays the frequency distribution for a continuous variable. The article also demonstrates how to calculate the mean, median, standard deviation, and variance for a continuous variable.

Analysis of Variance↗
PubMed1994

Guidelines for experimental studies.

OBJECTIVES: The purpose of this document is to present guidelines for the training of researchers and for the planning of experiments that ensure optimum interaction and communication among the investigators and statisticians of the research team. METHODS: The protocol described in this document is designed to systematically guide a researcher through a sequence of planning steps and checkpoints to ensure proper experimental design, data management, statistical analyses, and interpretation. RESULTS: The investigator and the statistician, as a collaborative team, will be able to determine the most effective and appropriate procedures and tools necessary for the successful completion of a research project. SIGNIFICANCE: Planning an experimental study necessitates much thought. Lack of essential communication with a statistician in the initial stages of the experimental design can doom the experiment to failure. Flawed research studies may be associated with one or more of the following: failure of the established testable hypothesis, uncontrolled biases, insufficient sample size, lack of appropriate controls, variability in specimen preparation, improper calibration of instruments or standardization of measurements, incorrect data entries, inappropriate statistical analyses, errors in interpretation of statistical analyses, and erroneously drawn conclusions. Standard guidelines will eliminate many of these potential problems.

Data Collection↗
PubMed1994

Improvements in biological X-ray microanalysis: cryoembedding for specimen preparation and multivariate statistical analysis for data interpretation.

For biological X-ray microanalysis, cryoembedding (CE) combined with cryofixation (CF) and cryodehydration (CD) was already proposed as an alternative method to freeze-dried cryosections in 1984 by Wróblewski and Wroblewski. CD by freeze-drying (FD) is usually recommended because it provides better retention of diffusible elements. CD by freeze-substitution (FS) has the advantage of being simpler, giving more reproducible preservation of ultrastructure and causing fewer problems for resin infiltration. We have increased the retention of diffusible elements by using home-made devices for CS and CE in the new Lowicryl K11M and HM23 resins. These resins allow samples to be kept at a maximum temperature of 213K and 193K respectively. Application of multivariate statistical analysis (MSA) to X-ray data (spectra and maps) allows the study of correlations between the analyzed elements in different nuclear areas and in the cytoplasm. The "factorial" images, obtained with MSA, display the compartments of strong correlation between P and K (nucleic acids) and the compartments of strong correlation between S and K (proteins). We suggest that the future application of MSA methods will provide increased knowledge of the physio-pathological compartmentation of diffusible elements at the subcellular level.

Animals↗
PubMed2025

Direct estimation and inference of higher-level correlations from lower-level measurements with applications in gene-pathway and proteomics studies.

This paper tackles the challenge of estimating correlations between higher-level biological variables (e.g. proteins and gene pathways) when only lower-level measurements are directly observed (e.g. peptides and individual genes). Existing methods typically aggregate lower-level data into higher-level variables and then estimate correlations based on the aggregated data. However, different data aggregation methods can yield varying correlation estimates as they target different higher-level quantities. Our solution is a latent factor model that directly estimates these higher-level correlations from lower-level data without the need for data aggregation. We further introduce a shrinkage estimator to ensure the positive definiteness and improve the accuracy of the estimated correlation matrix. Furthermore, we establish the asymptotic normality of our estimator, enabling efficient computation of P-values for the identification of significant correlations. The effectiveness of our approach is demonstrated through comprehensive simulations and the analysis of proteomics and gene expression datasets. We develop the R package highcor for implementing our method.

Proteomics↗
PubMed2000

Progress report on the guidance for industry for statistical aspects of the design, analysis, and interpretation of chronic rodent carcinogenicity studies of pharmaceuticals.

The U.S. Food and Drug Administration (FDA) is in the process of preparing a draft Guidance for Industry document on the statistical aspects of carcinogenicity studies of pharmaceuticals for public comment. The purpose of the document is to provide statistical guidance for the design of carcinogenicity experiments, methods of statistical analysis of study data, interpretation of study results, presentation of data and results in reports, and submission of electronic study data. This article covers the genesis of the guidance document and some statistical methods in study design, data analysis, and interpretation of results included in the draft FDA guidance document.

Animals↗
PubMed1993

Is statistical significance testing useful in interpreting data?

Although P values and statistical significance testing have become entrenched in the practice of biomedical research, their usefulness and drawbacks should be reconsidered, particularly in observational epidemiology. The central role for the null hypothesis, assuming an infinite number of replications, and the dichotomization of results as positive or negative are argued to be detrimental to the proper design and evaluation of research. As an alternative, confidence intervals for estimated parameters convey some information about random variation without several of these limitations. Elimination of statistical significance testing as a decision rule would encourage those who present and evaluate research to more comprehensively consider the methodologic features that may yield inaccurate results and shift the focus from the potential influence of random error to a broader consideration of possible reasons for erroneous results.

Epidemiologic Methods↗
PubMed2005

Behavioral test methods workshop.

A one and a half day workshop on behavioral testing was conducted in order to discuss experimental procedures and practices that may help enhance the utility of behavioral data as a reliable index of neurotoxicity and in the safety evaluation of chemical substances. The workshop was open to participation by all sectors of the neuroscience community including academia, government, testing laboratories, and industry. The level of confidence with which changes in behavior can reliably signal adverse effects on the nervous system depends, in part, on the scientific quality of the data generated. With an emphasis on education and problem solving, the workshop focused on the practical aspects and scientific rationale underlying valid and high quality testing. In behavioral testing, there are numerous experimental factors that may impact on the quality of data. These include such elements as experimental design, selection of test methods, the care and precision in the conduct of behavioral testing, procedures to minimize bias and potential confounds, appropriateness of statistical analyses, and data interpretation. In plenary session investigators experienced in behavioral testing discussed the significance of these various experimental factors to data quality, outlined problematic issues, and presented a synopsis of approaches for addressing each of the factors as outlined in a draft of a primer developed by the Interagency Committee on Neurotoxicology (ICON). During the remainder of the workshop, open discussions in small breakout groups were used to address the problematic issues identified by the plenary speakers and explore alternative approaches for dealing with them. Finally, all workshop participants were reconvened in plenary session for summation of breakout group discussions and final recommendations. Information from the workshop was used to form the basis of this manuscript and will be used to help finalize a behavioral test methods primer being drafted by the ICON. The overall conclusions from the workshop were that consensus can be reached on the fundamentals of behavioral assessment, and that aspects of behavioral assessment including experimental design, test method selection, training, validation, control of confounds, data variability, data analysis, and data interpretation need to be carefully considered in the planning and conduct of behavioral safety assessments.

Animals↗
PubMed1999

Does environmental data collection need statistics?

The term 'statistics' with reference to environmental science and policymaking might mean different things: the development of statistical methodology, the methodology developed by statisticians to interpret and analyse such data, or the statistical data that are needed to understand environmental pollution and to identify possible policy options. This may and indeed has led to confusion on the part of the users of 'statistics' in environmental science and policymaking. This paper focuses on some of the needs of environmental scientists for statistical methodologies to help us do our jobs better. Statisticians and statistics can contribute to environmental science and the environmental policymaking process in three main ways. First, in describing phenomena: this may in some cases be a non-trivial application of statistical methods. Second, by assessing causal relations: (multivariate) analysis techniques can be applied in establishing the (causal) relations between pressures, state and impact. Third, by setting and checking of standards: uncertainties between the dose-effect relations and the formulation of the standard, and the standard and the state of the environment should be carefully dealt with. Statisticians may help in formulating the standards in such a way that standard checking is as straightforward as possible.

Data Collection↗
PubMed2003

An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets.

Analysis and interpretation of functional MRI (fMRI) data have traditionally been based on identifying areas of significance on a thresholded statistical map of the entire imaged brain volume. This form of analysis can be likened to a "fishing expedition." As we become more knowledgeable about the structure-function relationships of different brain regions, tools for a priori hypothesis testing are needed. These tools must be able to generate region of interest masks for a priori hypothesis testing consistently and with minimal effort. Current tools that generate region of interest masks required for a priori hypothesis testing can be time-consuming and are often laboratory specific. In this paper we demonstrate a method of hypothesis-driven data analysis using an automated atlas-based masking technique. We provide a powerful method of probing fMRI data using automatically generated masks based on lobar anatomy, cortical and subcortical anatomy, and Brodmann areas. Hemisphere, lobar, anatomic label, tissue type, and Brodmann area atlases were generated in MNI space based on the Talairach Daemon. Additionally, we interfaced these multivolume atlases to a widely used fMRI software package, SPM99, and demonstrate the use of the atlas tool with representative fMRI data. This tool represents a necessary evolution in fMRI data analysis for testing of more spatially complex hypotheses.

Brain↗
PubMed2002

Likelihood methods for measuring statistical evidence.

Focused on interpreting data as statistical evidence, the evidential paradigm uses likelihood ratios to measure the strength of statistical evidence. Under this paradigm, re-examination of accumulating evidence is encouraged because (i) the likelihood ratio, unlike a p-value, is unaffected by the number of examinations and (ii) the probability of observing strong misleading evidence is naturally low, even for study designs that re-examine the data with each new observation. Further, the controllable probabilities of observing misleading and weak evidence provide assurance that the study design is reliable without affecting the strength of statistical evidence in the data. This paper illustrates the ideas and methods associated with using likelihood ratios to measure statistical evidence. It contains a comprehensive introduction to the evidential paradigm, including an overview of how to quantify the probability of observing misleading evidence for various study designs. The University Group Diabetes Program (UGDP), a classic and still controversial multi-centred clinical trial, is used as an illustrative example. Some of the original UGDP results, and subsequent re-analyses, are presented for comparison purposes.

Biometry↗
PubMed2001

Multivariate statistical interpretation of coastal sediment monitoring data.

Multivariate statistical analysis of sediment data (input matrix 122 x 15) collected from 122 sampling sites from the western coastline of the USA and analyzed for 15 analytes indicates that the data structure could be explained by four latent factors. These factors are conditionally named "anthropogenic", "organic", "natural", and "hot spots". They explain over 85% of the total variance of the data system, which is an acceptable value for the PCA model. The receptor models obtained after regression of the mass on the absolute principal components scores ensures reliable estimation of the contribution of each possible natural or anthropogenic source to the mass of each chemical component. It can be concluded that the region of interest reveals a different pattern of pollution compared with the eastern coastline treated statistically in a previous study.

Journal Article↗
PubMedDate not supplied

Classical detection theory and the cryo-EM particle selection problem.

Particle selection is an essential but tedious step in the determination of macromolecular structures by single particle reconstruction. This paper presents an automatic, multi-reference particle detection scheme that is based on the classical matched filter principle. It makes use of a pre-whitening filter to standardize the noise, a reduced representation of the references by means of principal component analysis, and a statistic for distinguishing particles from image artifacts. Standardizing the noise allows the noise-induced false-positive frequency to be estimated, and also allows the distribution of the discrimination statistic to be calculated a priori. The method is demonstrated with an annotated dataset of cryo-EM images.

Algorithms↗