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Quality management through statistics.

One of the responsibilities of a quality assurance professional is to assess patient care data in an effort to meet "outcome" requirements as established by the institution, the patient, the patient's family, the Joint Commission, the Health Care Financing Administration, the state PRO, and other regulatory agencies. A major goal of effective quality management is to assure that patients treated in a similar diagnostic category achieve similar outcomes. To accomplish this, the QA manager must gain and maintain control over patient care processes. To obtain this control, the conditions of patient care processes must be measured. Over one hundred years ago, the English scientist Lord Kelvin said, "When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot express it in numbers, your knowledge is...unsatisfactory." This article will describe some of the statistical quality control techniques that will assist in measuring the performance of specific patient care processes and outcomes and expressing them in numbers. A review of the numbers will show whether a particular patient care process is running smoothly or needs further investigation or adjustments. Using statistical quality control techniques will enable the QA manager to predict how well patient care processes and outcomes will run in the future. The simple statistical techniques discussed in this article can be used to measure the performance of patient care processes and outcomes both before and after corrective actions have been taken. They apply both to attempts to bring a specific process or outcome into "control" or to break through to a new, improved level of quality performance.

Data Interpretation, Statistical↗

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↗

A statistical framework for haplotype block inference.

The existence of haplotype blocks transmitted from parents to offspring has been suggested recently. This has created an interest in the inference of the block structure and length. The motivation is that haplotype blocks that are characterized well will make it relatively easier to quickly map all the genes carrying human diseases. To study the inference of haplotype block systematically, we propose a statistical framework. In this framework, the optimal haplotype block partitioning is formulated as the problem of statistical model selection; missing data can be handled in a standard statistical way; population strata can be implemented; block structure inference/hypothesis testing can be performed; prior knowledge, if present, can be incorporated to perform a Bayesian inference. The algorithm is linear in the number of loci, instead of NP-hard for many such algorithms. We illustrate the applications of our method to both simulated and real data sets.

Algorithms↗

Statistical issues in the analysis of neuronal data.

Analysis of data from neurophysiological investigations can be challenging. Particularly when experiments involve dynamics of neuronal response, scientific inference can become subtle and some statistical methods may make much more efficient use of the data than others. This article reviews well-established statistical principles, which provide useful guidance, and argues that good statistical practice can substantially enhance results. Recent work on estimation of firing rate, population coding, and time-varying correlation provides improvements in experimental sensitivity equivalent to large increases in the number of neurons examined. Modern nonparametric methods are applicable to data from repeated trials. Many within-trial analyses based on a Poisson assumption can be extended to non-Poisson data. New methods have made it possible to track changes in receptive fields, and to study trial-to-trial variation, with modest amounts of data.

Action Potentials↗

Comparison of 'model-free' and 'model-based' linkage statistics in the presence of locus heterogeneity: single data set and multiple data set applications.

Earlier work [Knapp et al.: Hum Hered 1994;44:44-51] focusing on affected sib pair (ASP) data established the equivalence between the mean test and a test based on a simple recessive lod score, as well as equivalences between certain forms of the maximum likelihood score (MLS) statistic [Risch: Am J Hum Genet 1990;46:242-253] and particular forms of the lod score. Here we extend the results of Knapp et al. [1994] by reconsidering these equivalences for ASP data, but in the presence of locus heterogeneity. We show that Risch's MLS statistic under the possible triangle constraints [Holmans: Am J Hum Genet 1993;52:362-374] is locally equivalent to the ordinary heterogeneity lod score assuming a simple recessive model (HLOD/R); while the one-parameter MLS assuming no dominance variance is locally equivalent to the (homogeneity) recessive lod. The companion paper (this issue, pp 199-208) showed that when considering multiple data sets in the presence of locus heterogeneity, the HLOD can suffer appreciable losses in power. We show here that in ASP data, these equivalences ensure that this same loss in power is incurred by both forms of the MLS statistic as well. The companion paper also introduced an adaptation of the lod, the compound lod score (HLOD/C). We confirm that the HLOD/C maintains higher power than these 'model-free' methods when applied to multiple heterogeneous data sets, even when it is calculated assuming the wrong genetic model.

Chromosome Mapping↗

A statistical score for assessing the quality of multiple sequence alignments.

BACKGROUND: Multiple sequence alignment is the foundation of many important applications in bioinformatics that aim at detecting functionally important regions, predicting protein structures, building phylogenetic trees etc. Although the automatic construction of a multiple sequence alignment for a set of remotely related sequences cause a very challenging and error-prone task, many downstream analyses still rely heavily on the accuracy of the alignments. RESULTS: To address the need for an objective evaluation framework, we introduce a statistical score that assesses the quality of a given multiple sequence alignment. The quality assessment is based on counting the number of significantly conserved positions in the alignment using importance sampling method in conjunction with statistical profile analysis framework. We first evaluate a novel objective function used in the alignment quality score for measuring the positional conservation. The results for the Src homology 2 (SH2) domain, Ras-like proteins, peptidase M13, subtilase and beta-lactamase families demonstrate that the score can distinguish sequence patterns with different degrees of conservation. Secondly, we evaluate the quality of the alignments produced by several widely used multiple sequence alignment programs using a novel alignment quality score and a commonly used sum of pairs method. According to these results, the Mafft strategy L-INS-i outperforms the other methods, although the difference between the Probcons, TCoffee and Muscle is mostly insignificant. The novel alignment quality score provides similar results than the sum of pairs method. CONCLUSION: The results indicate that the proposed statistical score is useful in assessing the quality of multiple sequence alignments.

Algorithms↗

LRTae: improving statistical power for genetic association with case/control data when phenotype and/or genotype misclassification errors are present.

BACKGROUND: In the field of statistical genetics, phenotype and genotype misclassification errors can substantially reduce power to detect association with genetic case/control studies. Misclassification also can bias population frequency parameters such as genotype, haplotype, or multi-locus genotype frequencies. These problems are of particular concern in case/control designs because, short of repeated sampling, there is no way to detect misclassification errors. We developed a double-sampling procedure for case/control genetic association using a likelihood ratio test framework. Different approaches have been proposed to deal with misclassification errors. We have chosen the likelihood framework because of the ease with which misclassification probabilities may be incorporated into in the statistical framework and hypothesis testing. The statistic is called the Likelihood Ratio Test allowing for errors (LRTae) and is freely available via software download. RESULTS: We applied our procedure to 10,000 replicates of simulated case/control data in which we introduced phenotype misclassification errors. The phenotype considered is Ankylosing Spondylitis (AS). The LRTae method power was always greater than LRTstd power for the significance levels considered (5%, 1%, 0.1%, 0.01%). Power gains for the LRTae method over the LRTstd method increased as the significance level became more stringent. Multi-locus genotype frequency estimates using LRTae method were more accurate than estimates using LRTstd method. CONCLUSION: The LRTae method can be applied to single-locus genotypes, multi-locus genotypes, or multi-locus haplotypes in a case/control framework and can be more powerful to detect association in case/control studies when both genotype and/or phenotype errors are present. Furthermore, the LRTae method provides asymptotically unbiased estimates of case and control genotype frequencies, as well as estimates of phenotype and/or genotype misclassification rates.

Case-Control Studies↗

Statistics in orthopaedic papers.

Although the importance of sound statistical principles in the design and analysis of data has gained prominence in recent years, biostatistics, the application of statistics to the analysis of biological and medical data, is still a subject which is poorly understood and often mishandled. This review introduces, in the context of orthopaedic research, the terminology and the principles involved in simple data analysis, and outlines areas of medical statistics that have gained prominence in recent years. It also lists and provides an insight into some of the more common errors that occur in published orthopaedic journals and which are frequently encountered at the review stage in papers submitted to the Journal of Bone and Joint Surgery.

Biometry↗

Statistical methods in G-protein-coupled receptor research.

In this chapter we provide an introduction to statistical methods appropriate in G-protein-coupled receptor research, including examples. Topics covered include the choice of appropriate averages and measures of dispersion to summarize data sets, and the choice of tests of significance, including t-tests and one- and two-way analysis of variance (ANOVA) plus posttests for normally distributed (Gaussian) data and their nonparametric equivalents. Techniques for transforming non-normally distributed data to more Gaussian distributions are discussed. Concepts of statistical power, errors, and the use of these in determining the optimal size of experiments are considered. Statistical aspects of linear and nonlinear regression are discussed, including tests for goodness-of-fit to the chosen model and methods for comparing fitted lines and curves.

Analysis of Variance↗

Statistical models to optimize production of probiotic characteristics.

The use of probiotic microorganisms has been widely promoted in the last 20 years. Probiotics have applied to the gastrointestinal tract as capsules or as fermented milks. The characteristics of the strains proposed as probiotics have been published or patented after elaboration of the product. The first step in designing a probiotic product is to isolate and characterize strains with beneficial properties. Our group has reported the isolation and characterization of 134 strains of lactobacilli from the vagina of women from our city. Surface properties and the production of antagonistic substances has also been published. From these studies, certain strains were selected because they shared probiotic characteristics. The second step in the design of a pharmaceutical product is to determine the optimal conditions for obtaining the highest amount of viable microorganisms, as well as the best conditions to produce antagonistic substances. To perform these types of experiments, laboratory assays can be carried out to determine the optimum conditions in which the microorganisms can be cultured. Growth parameters and optimal growth conditions can be studied by the classical methodology or by the application of alternative methodologies. Alternative methods have been widely used in the food industry, including experimental designs to test multiple conditions at the same time, complemented by the use of statistical models to evaluate and compare all the conditions tested. The object of this chapter is to describe the experimental designs used to study the technological characteristics of vaginal probiotic lactobacilli as well as the application of statistical models for evaluation of the different conditions tested for the production of biomass and antagonistic substances in these bacteria. With this objective in mind, in the present chapter, the following areas are explored: 1. Growth experiments performed to study the effect of temperature, pH, agitation, and culture media on the growth and production of antagonistic substances by vaginal lactobacilli. 2. Fractional and complete factorial experimental designs used to perform the growth experiments. 3. Statistical models applied to evaluate the growth parameters and their modifications under the conditions assayed.

Bacteriological Techniques↗

Methodological and statistical considerations for exercise-related hormone evaluations.

Improvements in laboratory techniques have allowed research related to exercise endocrinology to flourish. The emerging literature, however, is often inconsistent and contradictory. The discrepancies in research findings are possibly the result of poor control of confounding variables and/or inappropriate methodologies or analyses. Environmental and pretesting behavioural conditions must be standardised to minimise the influence of variables not directly related to the investigation. Environmental temperature and relative humidity, alcohol, caffeine and nicotine intake, prandial state, sleep deprivation and previous exercise can each alter hormonal responses to exercise. Both prescription and over-the-counter medications can also modify normal hormonal secretions thereby confusing exercise-induced findings. Specimen collection and analysis procedures must be controlled carefully. Changes in plasma volume related to postural changes or tourniquet-induced stasis can confound attempts to isolate exercise-related endocrine responses. The established circadian and rhythmical variations characteristic of many hormones need to be controlled. The specimen selection (plasma, serum, urine, etc), collection, storage and analysis procedures should be carefully planned and evaluated. The magnitude of haemolysis, analytical and biological variation must also be monitored. Isolating the hormonal perturbations resulting from a particular exercise variable can be very difficult. Exercise intensity, duration, mode, frequency and volume may each have specific effects on the endocrine changes seen with exercise and training. Furthermore, hormonal responses to exercise are dependent upon initial training status and fitness level. The statistical procedures and data presentation options selected to convey experimental findings can bias experimental results. The descriptive and inferential statistics to be used for data analysis should be preplanned and consistent with the underlying assumptions of the analytical procedure. Careful consideration should be given to the biological relevance of statistically significant findings. In some cases, data transformations (e.g. absolute vs relative changes, logarithmic) should be considered for analysis or presentation. Given the individual nature of hormonal responses to exercise, emphasis should be placed presenting individual data. Other considerations, including age, sex, racial origin and disease conditions need to be controlled for when trying to examine exercise-induced hormone changes.

Behavior↗

SeqState: primer design and sequence statistics for phylogenetic DNA datasets.

Choosing and designing primers based on available DNA sequence data and statistical contrasting of domains or structural features is a common routine among molecular biologists. Currently available, free software tools were found to lack desirable features related to these tasks. This was the motivation for developing a new program, SeqState. SeqState locates regions that remain to be sequenced in phylogenetic DNA datasets, evaluates user-provided primers and selects primers best suited to fill gaps in the sequences. If the primers provided by the user are unsuitable, new primers are designed. Primers can be loaded from a primer database, be supplied as part of the alignment or be entered manually. The position of internal primers is automatically localised in the loaded data file. Primers can be edited, and changes and new primers can be saved to the database. Primer sheets allow the user to view internal dimers, complements to a second primer, mismatches to all loaded sequences, and other primer characteristics. Calculation of various sequence statistics can be requested for the whole dataset or parts thereof (character sets), with standard errors estimated by bootstrapping. Insertion-deletion events can be evaluated statistically and encoded for subsequent phylogenetic analysis according to several published coding principles.

Algorithms↗

Numerical solutions for patterns statistics on Markov chains.

We propose here a review of the methods available to compute pattern statistics on text generated by a Markov source. Theoretical, but also numerical aspects are detailed for a wide range of techniques (exact, Gaussian, large deviations, binomial and compound Poisson). The SPatt package (Statistics for Pattern, free software available at http://stat.genopole.cnrs.fr/spatt) implementing all these methods is then used to compare all these approaches in terms of computational time and reliability in the most complete pattern statistics benchmark available at the present time.

Algorithms↗

Exploration of statistical dependence between illness parameters using the entropy correlation coefficient.

UNLABELLED: The entropy correlation coefficient (ECC) is a useful tool for measuring statistical dependence between variables. We employed this tool to search for pairs of variables that correlated in the chronic fatigue syndrome (CFS) Computational Challenge dataset. Highly related variables are candidates for data reduction, and novel relationships could lead to hypotheses regarding the pathogenesis of CFS. METHODS: Data for 130 female participants in the Wichita (KS, USA) clinical study [1] was coded into numerical values. Metric data was grouped using Gaussian mixture models; the number of groups was chosen using Bayesian information content. The pair-wise correlation between all variables was computed using the ECC. Significance was estimated from 1000 iterations of a permutation test and a threshold of 0.01 was used to identify significantly correlated variables. RESULTS: The five dimensions of multidimensional fatigue inventory (MFI) were all highly correlated with each other. Seven Short Form (SF)-36 measures, four CFS case-defining symptoms and the Zung self-rating depression scale all correlated with all MFI dimensions. No physiological variables correlate with more than one MFI dimension. MFI, SF-36, CDC symptom inventory, the Zung self-rating depression scale and three Cambridge Neuropsychological Test Automated Battery (CANTAB) measures are highly correlated with CFS disease status. DISCUSSION: Correlations between the five dimensions of MFI are expected since they are measured from the same instrument. The relationship between MFI and Zung depression index has been previously reported. MFI, SF-36, and Centers for Disease Control and Prevention (CDC) symptom inventory are used to classify CFS; it is not surprising that they are correlated with disease status. Only one of the three CANTAB measures that correlate with disease status has been previously found, indicating the ECC identifies relationships not found with other statistical tools. CONCLUSION: The ECC is a useful tool for measuring statistical dependence between variables in clinical and laboratory datasets. The ECC needs to be further studied to gain a better understanding of its meaning for clinical data.

Adult↗

Some fallacies of statistical inferences about psychotherapy.

Statistical methodology does not provide an effective, impartial, or objective way to determine the validity of theories of psychotherapy. Furthermore, the correlations revealed by statistical studies of psychotherapy are clinically irrelevant. Nevertheless there is a growing trend to recognise only statistically "proven" techniques of psychotherapy as legitimate medical treatment. The replacement of established medical standards for psychotherapy with this bogus new scientism is likely to lead to iniquitous clinical and administrative restrictions upon psychotherapists which will cause unnecessary suffering to patients.

Data Interpretation, Statistical↗

Technical note: estimating statistical power of mixed models used in dairy nutrition experiments.

Statistical power is defined as the probability of correctly rejecting the null hypothesis. Power calculations may be useful in planning experiments. The objective of this technical note is to outline an applied method that estimates statistical power of a dairy nutrition experiment that employs a Latin square as the experimental design. Because the SAS MIXED procedure (PROC MIXED) is commonly used to analyze data sets, this note outlines basic programming procedures that may be used to estimate statistical power of a mixed model using this procedure.

Animal Nutritional Physiological Phenomena↗

Census statistics through combined use of surveys and registers?

"In this paper we present some results from a study [concerning Norway] on the feasibility of producing census statistics through the combined use of surveys and registers. The main finding is that if one is willing to relax a little on the need for accuracy of statistics for 'small' areas, the large majority of the census statistics can be produced by such a combined use of data sources."

Censuses↗

[Statistical (Poisson) motor unit number estimation. Methodological aspects and normal results in the extensor digitorum brevis muscle of healthy subjects].

INTRODUCTION: Among the differents techniques for motor unit number estimation (MUNE) there is the statistical one (Poisson), in which the activation of motor units is carried out by electrical stimulation and the estimation performed by means of a statistical analysis based on the Poisson s distribution. OBJECTIVES: The study was undertaken in order to realize an approximation to the MUNE Poisson technique showing a coprehensible view of its methodology and also to obtain normal results in the extensor digitorum brevis muscle (EDB) from a healthy population. SUBJECTS AND METHODS: One hundred fourteen normal volunteers with age ranging from 10 to 88 years were studied using the MUNE software contained in a Viking IV system. RESULTS: The normal subjects were divided into two age groups (10 59 and 60 88 years). The EDB MUNE from all them was 184 49. Both, the MUNE and the amplitude of the compound muscle action potential (CMAP) were significantly lower in the older age group (p< 0.0001), showing the MUNE a better correlation with age than CMAP amplitude ( 0.5002 and 0.4142, respectively p< 0.0001). CONCLUSION: Statistical MUNE method is an important way for the assessment to the phisiology of the motor unit. The value of MUNE correlates better with the neuromuscular aging process than CMAP amplitude does.

Action Potentials↗