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Statistical inference and forensic evidence: evaluating a bullet lead match.

This experiment tested the ability of undergraduate mock jurors (N=295) to draw appropriate conclusions from statistical data on the diagnostic value of forensic evidence. Jurors read a summary of a homicide trial in which the key evidence was a bullet lead "match" that was either highly diagnostic, non-diagnostic, or of unknown diagnostic value. There was also a control condition in which the forensic "match" was not presented. The results indicate that jurors as a group used the statistics appropriately to distinguish diagnostic from non-diagnostic forensic evidence, giving considerable weight to the former and little or no weight to the latter. However, this effect was attributable to responses of a subset of jurors who expressed confidence in their ability to use statistical data. Jurors who lacked confidence in their statistical ability failed to distinguish highly diagnostic from non-diagnostic forensic evidence; they gave no weight to the forensic evidence regardless of its diagnostic value. Confident jurors also gave more weight to evidence of unknown diagnostic value. Theoretical and legal implications are discussed.

Female↗

P300 in schizophrenia: confirmation and statistical validation of temporal region deficit in P300 topography.

Comparison of normal and medicated schizophrenic groups on the auditory P300 component of the event-related potential confirmed our earlier finding of a left temporal deficit in P300 amplitude in schizophrenia. A difference in P300 topography between groups was evident in both color mapping and in grand-averaged waveforms, which was statistically validated by the presence of a group-by-scalp region interaction (p less than 0.05). The left temporal area in schizophrenics was denoted as the region of greatest deficit and of maximal statistical separation (p less than 0.05) relative to normals by t statistic mapping (SPM), Hotelling's T-squared "protected" contrasts of individual scalp regions, and the relative ratio of left scalp amplitudes to right scalp amplitudes. The left temporal scalp region yielding maximal group separation in the previous study also statistically separated the schizophrenic group from the normal group. This feature correctly differentiated 9 of 11 schizophrenics and 7 of 9 controls. These findings are compatible with other histological, metabolic, and electrophysiological studies suggesting temporal lobe abnormality in schizophrenia.

Adult↗

Extended relational operators for statistical data manipulations in medical databases.

Conventional relational database management systems fail to address three features of statistical data management in a biomedical/clinical database, namely, that (1) statistical and medical data (SMD) require a great deal of space and need to be stored in a reduced form with minimal duplication; indeed, SMD have many derived/calculated and summary statistics that make the number of attributes in a relation (i.e., a set of records) grow rapidly and dynamically; (2) most SMD have hierarchical structures that are difficult to manage using the relational data model since SMD are stored in separate relations for duplication and space considerations; and (3) the management of SMD is made easier if it is possible to reorganize relations or group data, a capability lacking in conventional relational database management systems. In this paper, we (1) introduce five extended relational operators, (lattice) NEST, (lattice) UNNEST, MERGE, SPREAD, and GEN, to reorganize relations; (2) integrate the extended operators with conventional relational algebra and introduce the concept of the lattice relational model; and (3) give applications of the extended relational operators and the lattice relational model in solving the problems of statistical data manipulation in medical databases.

Database Management Systems↗

Multivariate statistical analysis of mutational spectra of alkylating agents.

A series of multivariate statistical methods were used to explore the current knowledge on the mutational spectra of alkylating agents (AA) in bacterial and mammalian cells. The data relative to lac I and gpt genes of Escherichia coli were considered. The analysis focused on the distribution of GC to AT transitions, which account for the majority of AA-induced mutations. The statistical analysis of 15 different mutational spectra obtained by various laboratories pointed to a number of biological factors involved in the mutational process. First of all, factor and cluster analyses demonstrated that the mutational profiles obtained in mammalian cells form a homogeneous cluster different from the cluster formed by the bacterial cell mutational spectra. SN1-type AAs give rise to classes of mutational spectra statistically different from the spectra induced by the SN2-type AAs. The analysis of the mutated sequences of both genes pointed to a correlation between mutation induction by SN1 AAs, which react through a positively charged alkylating intermediate, and the occurrence of mutations at guanines preceded 5' by a purine. Moreover, our statistical analysis showed that the distribution of AA-induced mutations is not affected by the transcriptional activity of the target gene, but is strongly determined by the sequence specificity of AA-induced mutagenesis and by the structure of the target proteins. The agreement of our results with the findings of previous studies indicates that the multivariate data analysis methods are a sensitive and reliable tool for exploring the mechanisms underlying complex biological processes. The novelty of the present results lies in their quantitative character, and in the clarity of the graphical displays. We propose the use of this methodological approach to explore the large bulk of information available on mutational spectra.

Alkylating Agents↗

IntroStat: a hypertext-based design for an electronic textbook to introduce biomedical statistics.

A hypertext-based system called IntroStat has been developed to introduce fundamental methods of biomedical statistics. The system has been developed on a Macintosh II using HyperCard. It is written mainly in Hypertalk, a scripting language of HyperCard. Being an electronic textbook of probability and statistics, the system features computational capabilities. At the end of the sections describing basic methods of statistics, pages are provided where a user may enter data and try the methods right there. Some means for branching are provided for easy access to a page of interest, including branching by contents, by index, and by keyword reference. The system design is proposed as a model of an electronic textbook for biomedical statistics.

Biometry↗

Factors involved in burn mortality: a multivariate statistical approach based on discriminant analysis.

This article suggests an alternative statistical model for studying the mortality of burned patients: discriminant analysis. This model was applied to our population of 532 patients among whom 71 died. It is not the first time that this model has been applied to assess burn mortality, although it is not frequently used for that application. We found four factors that are statistically significant: age, TBSA, inhalation injury and sex (female). Discriminant analysis allowed us to demonstrate an impressive correlation between death, age and TBSA; inhalation injury by itself and sex, the two other significant factors in our study, seem to have a minor influence on the final outcome of the burned patients and their predictive value is virtually nil. The advantages of this statistical model are compared with logistic regression, the commonly chosen statistical method.

Adult↗

Statistical methods for analysis of in vitro antigen quantification data for veterinary biologic products.

In vitro assay systems that measure the antigen content of veterinary biologic products use a statistical model for quantification. The most prevalent assay system for measuring veterinary biologics in the United States compares a reference, demonstrated to be protective in the host species, to the serial being tested. An ideal statistical model evaluates the similarity of response and compares the amount of antigen of a serial to the reference. This method is defined as the relative potency approach. Various statistical methods have been proposed for relative potency determinations. The standard curve, slope ratio assay, parallel line assay, four-parameter (logistics) fit, and integration of the area under the curve have been used. Each statistical method has strengths and weaknesses. The parallel line assay accepts data from assays with non-ideal dose response curves, provides a similarity comparison, and evaluates the relative potency in the most linear portion of the curve. The parallel line method is recommended for evaluation of in vitro assays for potency determination of U.S. licensed veterinary biologics.

Animal Testing Alternatives↗

Statistical quantification of 24-hour and monthly variabilities of spontaneous otoacoustic emission frequency in humans.

Previous evidence has suggested a relationship between spontaneous otoacoustic emissions (SOAEs) and established, biological cycles, although detailed statistical quantifications of the suggested relationships do not exist in the literature. In an attempt to statistically quantify the purported circadian and monthly influences on this phenomenon, two experiments were undertaken. The first experiment was conducted over eight weeks, investigating 31 SOAEs recorded from eight women and two men. Time series statistical analysis examined whether daily, weekly, and/or monthly cycles characterized SOAE frequency variability. Results yielded a significant monthly cycle for the majority of SOAEs recorded from the women but for none of the SOAEs recorded from the men. These results suggest the possibility that SOAE frequency fluctuation in women may be entrained to the monthly menstrual cycle. In the second experiment, hourly SOAE frequency stability was examined over a 24-h period to ascertain the nature of the daily frequency variation as precisely as possible. Four SOAEs from two subjects were examined, and time series analysis of these data included (1) modelling the autocorrelation structure of the measurements, (2) resolving each 24-h series of measurements into cyclical components of various periodicities, and (3) testing the statistical significance of given cycles within the spectrum of each series. Findings included a significant 24-h variability of frequency for each SOAE, suggesting the possibility of a circadian influence on frequency fluctuation. Results from the two experiments provide quantitative evidence supporting a hypothetical relationship between SOAEs and established, biological cycles.

Acoustics↗

Breast lesion discrimination using statistical analysis and shape measures on magnetic resonance imagery.

Magnetic resonance images of intact human breast tissue are evaluated using statistical measures and shape analysis. In this paper, the Mahalanobis distance measurement and a related F-statistical value demonstrate that breast lesions are statistically separable from normal breast tissue. The minimum set of parameters to provide first order statistical separability between fibroadenomas, cysts, and carcinomas are T1-weighted, T2-weighted, and Dixon opposed pulse sequences. Tumor shape is quantified by development of a compactness measure and a spatial frequency analysis of the lesion boundary. Malignant lesions are shown to be separable from benign lesions based on quantitative shape measures.

Adenofibroma↗

The t-CWT: a new ERP detection and quantification method based on the continuous wavelet transform and Student's t-statistics.

OBJECTIVE: This study was aimed at developing a method for extraction and assessment of event-related brain potentials (ERP) from single-trials. This method should be applicable in the assessment of single persons' ERPs and should be able to handle both single ERP components and whole waveforms. METHODS: We adopted a recently developed ERP feature extraction method, the t-CWT, for the purposes of hypothesis testing in the statistical assessment of ERPs. The t-CWT is based on the continuous wavelet transform (CWT) and Student's t-statistics. The method was tested in two ERP paradigms, oddball and semantic priming, by assessing individual-participant data on a single-trial basis, and testing the significance of selected ERP components, P300 and N400, as well as of whole ERP waveforms. The t-CWT was also compared to other univariate and multivariate ERP assessment methods: peak picking, area computation, discrete wavelet transform (DWT) and principal component analysis (PCA). RESULTS: The t-CWT produced better results than all of the other assessment methods it was compared with. CONCLUSIONS: The t-CWT can be used as a reliable and powerful method for ERP-component detection and testing of statistical hypotheses concerning both single ERP components and whole waveforms extracted from either single persons' or group data. SIGNIFICANCE: The t-CWT is the first such method based explicitly on the criteria of maximal statistical difference between two average ERPs in the time-frequency domain and is particularly suitable for ERP assessment of individual data (e.g. in clinical settings), but also for the investigation of small and/or novel ERP effects from group data.

Acoustic Stimulation↗

A statistical evaluation of microtensile bond strength methodology for dental adhesives.

OBJECTIVES: The purpose of this study was to determine the correlation between beams from the same tooth in a microtensile bond strength study and to examine their effect on the interpretation of results. METHODS: A flat occlusal dentin surface was exposed using wet 180, 240, and 320-grit SiC paper on caries-free human molar teeth. Adhesive was applied followed by 6mm of composite (CoreRestore2) applied in 2mm increments. Four adhesives were used: Optibond FL, light-cure, dual-cure, and self-cure Optibond Solo Plus. Nine beams (1 mm2) were obtained from fifteen teeth for each group, labeled to indicate the orientation of the beams to each other and stored in artificial saliva at 37 degrees C for 1 week or 3 months. Microtensile dentin bond strengths were determined using a non-trimming technique. Statistical comparisons between adhesive-storage combinations were performed using ANOVA. Analyses were performed assuming statistical independence between all beams and then repeated using a random tooth effect to account for correlations between beams from the same tooth. RESULTS: Significant correlations were found between beams. No pattern was observed in the correlations related to the orientations of the beams to each other. Conclusions regarding statistical significance of comparisons (at alpha=0.05) were sometimes affected by the incorrect assumption of independent beams. The degree of correlation was large enough to severely affect power and sample size calculations. SIGNIFICANCE: Analyses of microtensile dentin bond strength studies need to account for correlations between beams to avoid over-stating statistical significance of study results.

Adhesiveness↗

Optimization and statistical evaluation of dissolution tests for indinavir sulfate capsules.

An optimization, statistically based on t-student test, to set up dissolution test conditions for indinavir sulfate capsules is presented. Three dissolution media, including that reported in United States Pharmacopeial Forum, and two apparatus, paddle and basket, were applied. Two different indinavir sulfate capsules, products A and B, were evaluated. For a reliable statistical analysis eighteen capsules were assayed in each condition based on the combination of dissolution medium and apparatus. All tested media were statistically equivalent (P > 0.05) for both drug products when paddle apparatus was employed at the stirring speed of 50 rpm. The use of basket apparatus at the stirring speed of 50 rpm caused significant decrease in the drug release percent for the product B (P < 0.05). The best dissolution conditions tested, for products A and B, were applied to evaluate capsules dissolution profiles. Twelve dosage units were assayed and dissolution efficiency concept was used, for each condition, to obtain results with statistical significance (P > 0.05). Optimal conditions to carry out the dissolution test were 900 ml of 0.1 M hydrochloric acid as dissolution medium, basket at 100 rpm stirring speed and 260 nm ultraviolet detection.

Capsules↗

A Bayesian statistical analysis of human T-cell lymphotropic virus evolutionary rates.

HTLV is a genetically-stable retrovirus that is considered to have evolved partly in concert with human migrations. Its rate of evolution is low and therefore, difficult to estimate reliably. In the first part of this study, we provide an improved estimate of HTLV evolutionary rate using anthropological calibration of phylogenetic nodes. We investigate two different anthropological calibrations using a Bayesian method that implements a relaxed molecular clock model and can combine data from multiple genes. The analysis shows that the two calibrations are compatible. In the second part, we develop a Bayesian statistical model to combine and compare the anthropology-based estimates of evolutionary rate with a rate recently calculated using pedigree data from vertically HTLV-infected families. We compare the statistical power of the two estimates and show that the current pedigree estimate, although resulting in considerably higher evolutionary rates, is too statistically weak to warrant a re-examination of the commonly used anthropology-based estimates. Statistical uncertainty burdens HTLV rate estimates based on both anthropological calibrations and on pedigree data; the former method rests on an untested assumption, whilst that latter is affected by small sample sizes.

Bayes Theorem↗

Application of public-domain statistical analysis software for evaluation and comparison of comet assay data.

A novel approach for statistical analysis of comet assay data (i.e.: tail moment) is proposed, employing public-domain statistical software, the R system. The analytical strategy takes into account that the distribution of comet assay data, like the tail moment, is usually skewed and do not follow a normal distribution. Probability distributions used to model comet assay data included: the Weibull, the exponential, the logistic, the normal, the log normal and log-logistic distribution. In this approach it was also considered that heterogeneity observed among experimental units is a random feature of the comet assay data. This statistical model can be characterized with a location parameter m(ij), a scale parameter r and a between experimental units variability parameter theta. In the logarithmic scale, the parameter m(ij) depends additively on treatment and random effects, as follows: log(m(ij)) = a0 + a1x(ij) + b(i), where exp(a0) represents approximately the mean value of the control group, exp(a1) can be interpreted as the relative risk of damage with respect to the control group, x(ij) is an indicator of experimental group and exp(b(i)) is the individual risk effects assume to follows a Gamma distribution with mean 1 and variance theta. Model selection is based on Akaike's information criteria (AIC). Real data coming from comet analysis of blood samples taken from the flounder Paralichtys orbignyanus (Teleostei: Paralichtyidae) and from samples of cells suspension obtained from the estuarine polychaeta Laeonereis acuta (Nereididae) were employed. This statistical approach showed that the comet assay data should be analyzed under a modeling framework that take into account the important features of these measurements. Model selection and heterogeneity between experimental units play central points in the analysis of these data.

Animals↗

ROC evaluation of statistical wavelet-based analysis of brain activation in [15O]-H2O PET scans.

This paper presents and evaluates a wavelet-based statistical analysis of PET images for the detection of brain activation areas. Brain regions showing significant activations were obtained by performing Student's t tests in the wavelet domain, reconstructing the final image from only those wavelet coefficients that passed the statistical test at a given significance level, and discarding artifacts introduced during the reconstruction process. Using Receiver Operating Characteristic (ROC) curves, we have compared this statistical analysis in the wavelet domain to the conventional image-domain Statistical Parametric Mapping (SPM) method. For obtaining an accurate assessment of sensitivity and specificity, we have simulated realistic single subject [15O]-H2O PET studies with different hyperactivation levels of the thalamic region. The results obtained from an ROC analysis show that the wavelet approach outperforms conventional SPM in identifying brain activation patterns. Using the wavelet method, activation areas detected were closer in size and shape to the region actually activated in the reference image.

Algorithms↗

Pathology of Barrett's esophagus by proton magnetic resonance spectroscopy and a statistical classification strategy.

BACKGROUND: Barrett's esophagus is thought to be a precursor of adenocarcinoma. The incidence of adenocarcinoma of the lower esophagus in the Western world is rising and accounts for more than 40% of esophageal carcinomas in males. It is not possible to identify which Barrett's patients are at high risk of developing malignancy. Here we applied a statistical classification strategy to the analysis of magnetic resonance spectroscopy and histopathological data from esophageal biopsies to ascertain whether this risk could be identified in Barrett's patients. METHODS: Tissue specimens from 72 patients (29 noncancer-bearing and 43 cancer-bearing) were analyzed by one-dimensional proton magnetic resonance spectroscopy at 8.5 Tesla. Diagnostic correlation was performed between the magnetic resonance spectra and histopathology. The magnetic resonance magnitude spectra were preprocessed, followed by identification of optimal spectral regions, and were then classified by cross-validated linear discriminant analysis of rank orders of the first derivative of magnetic resonance spectra. RESULTS: Magnetic resonance spectroscopy combined with a statistical classification strategy analysis distinguished normal esophagus from adenocarcinoma and Barrett's epithelium with an accuracy of 100%. Barrett's epithelium and adenocarcinoma were distinguished with an accuracy of 98.6% but only when 4 of the Barrett's specimens and 7 of the carcinoma specimens, determined to be "fuzzy" (ie, unable to be accurately assigned to either class) were withdrawn. The 7 cancer and 4 Barrett's specimens, determined to be "fuzzy" using the Barrett's versus cancer (B versus C) classifier, were submitted to the other two classifiers (Barrett's versus normal [B versus N] and normal versus cancer [N versus C], respectively). The 4 Barrett's specimens were assigned to Barrett's by the N versus B classifier and to normal (n = 2) or cancer (n = 2) classes by the N versus C classifier. The 7 cancer specimens were crisply assigned to the cancer class (N versus C), or for the B versus N classifier, to the Barrett's class (ie, more similar to Barrett's than to normal tissue). Visual inspection of the spectra from histologically identified Barrett's epithelium showed a gradation from normal to carcinoma. CONCLUSIONS: Proton magnetic resonance spectroscopy of esophageal biopsies combined with a statistical classification strategy data analysis provides a robust diagnosis with a high degree of accuracy for discriminating normal epithelium from esophageal adenocarcinoma and Barrett's esophagus. Different spectral categories of Barrett's epithelium were identified both by visual inspection and by statistical classification strategy, possibly reflecting the risk of future malignant transformation.

Adenocarcinoma↗

Cardiac surgery report cards: comprehensive review and statistical critique.

Public report cards and confidential, collaborative peer education represent distinctly different approaches to cardiac surgery quality assessment and improvement. This review discusses the controversies regarding their methodology and relative effectiveness. Report cards have been the more commonly used approach, typically as a result of state legislation. They are based on the presumption that publication of outcomes effectively motivates providers, and that market forces will reward higher quality. Numerous studies have challenged the validity of these hypotheses. Furthermore, although states with report cards have reported significant decreases in risk-adjusted mortality, it is unclear whether this improvement resulted from public disclosure or, rather, from the development of internal quality programs by hospitals. An additional confounding factor is the nationwide decline in heart surgery mortality, including states without quality monitoring. Finally, report cards may engender negative behaviors such as high-risk case avoidance and "gaming" of the reporting system, especially if individual surgeon results are published. The alternative approach, continuous quality improvement, may provide an opportunity to enhance performance and reduce interprovider variability while avoiding the unintended negative consequences of report cards. This collaborative method, which uses exchange visits between programs and determination of best practice, has been highly effective in northern New England and in the Veterans Affairs Administration. However, despite their potential advantages, quality programs based solely on confidential continuous quality improvement do not address the issue of public accountability. For this reason, some states may continue to mandate report cards. In such instances, it is imperative that appropriate statistical techniques and report formats are used, and that professional organizations simultaneously implement continuous quality improvement programs. The statistical methodology underlying current report cards is flawed, and does not justify the degree of accuracy presented to the public. All existing risk-adjustment methods have substantial inherent imprecision, and this is compounded when the results of such patient-level models are aggregated and used inappropriately to assess provider performance. Specific problems include sample size differences, clustering of observations, multiple comparisons, and failure to account for the random component of interprovider variability. We advocate the use of hierarchical or multilevel statistical models to address these concerns, as well as report formats that emphasize the statistical uncertainty of the results.

Bias↗

Subjective versus statistical model assessment of mortality risk in open heart surgical procedures.

BACKGROUND: The aim of this study was to compare the predictive accuracy for open heart surgical mortality between a statistical model based on collection of clinical data and surgeons' subjective risk assessment. METHODS: Predictive discrimination of both risk assessments (surgeons' and model) was compared through the area under the receiver operating characteristic curve. Logistic regression analysis was used to assess the relation between surgeons' and model predictions to actual outcomes. Calibration of the subjective estimates was evaluated with a chi2 test. RESULTS: Overall, the area under the receiver operating characteristic curve was 0.76 for the statistical model and 0.70 for the subjective assessment. Logistic regression analysis showed that the statistical model remained significant after accounting for the subjective assessment. Calibration of subjective mortality predictions was poor. CONCLUSIONS: Surgeons' risk assessment tends to cluster in the middle ranges of risk. Subjective assessment seems accurate in identifying the two extremes of risk but is inaccurate for intermediate risk levels. A multivariate statistical model improves the accuracy of subjective predictions.

Aged↗