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PubMedDate not supplied

Statistics: can we prove an association for a rare complication?

BACKGROUND AND OBJECTIVES: Microcatheters for continuous spinal anesthesia were withdrawn from the market after an apparent increase in the incidence of cauda equina syndrome (CES) associated with spinal anesthesia after introduction of these catheters. The objective of this review is to evaluate the historical data on CES after spinal anesthesia and to compare the result to the recent data. METHODS: The literature on the use of statistics and on complications associated with spinal anesthesia was reviewed. Poisson probabilities were calculated to assess the probability of seeing the recent cases of CES, given the historical data. The sample size required for a prospective study was calculated. RESULTS: Statistics cannot "prove" an hypothesis but can only support or fail to support it. Use and interpretation of the p value are discussed, as are possible problems with interpretation of the p value. The difference between causal and statistical inference is discussed. Probabilities for the occurrence of CES after microcatheter use were calculated. The sample size for a prospective study of this problem was calculated; a large sample is required. CONCLUSIONS: Statistics alone cannot support an association of microcatheters with CES after spinal anesthesia. Additional considerations suggest a possible association, but further study is required.

Anesthesia, Spinal↗
PubMed1993

Data explorer: a prototype expert system for statistical analysis.

The inadequate analysis of medical research data, due mainly to the unavailability of local statistical expertise, seriously jeopardizes the quality of new medical knowledge. Data Explorer is a prototype Expert System that builds on the versatility and power of existing statistical software, to provide automatic analyses and interpretation of medical data. The system draws much of its power by using belief network methods in place of more traditional, but difficult to automate, classical multivariate statistical techniques. Data Explorer identifies statistically significant relationships among variables, and using power-size analysis, belief network inference/learning and various explanatory techniques helps the user understand the importance of the findings. Finally the system can be used as a tool for the automatic development of predictive/diagnostic models from patient databases.

Artificial Intelligence↗
PubMed1994

The validity of Maori hospital discharge statistics for ischaemic heart disease in New Zealand.

Data from an Auckland coronary heart disease register for the years 1983 to 1991 have been used to assess the validity of routine national statistics on Maori hospital discharge rates for ischaemic heart disease. Ethnicity as recorded on the hospital admission record was compared with self defined ethnicity as recorded by register interviewers. Unlike routine New Zealand mortality statistics, where there is marked underreporting of Maori mortality, it appears that hospital discharge statistics are not markedly affected by misclassification of ethnicity. Approximately 12% of those classified on the admission record as Maori considered themselves to be of a different ethnicity, and 0.5% of those classified as 'other' considered themselves to be Maori. Because of the small proportion of the population (and of ischaemic heart disease deaths) who are Maori, the two misclassifications cancel out and the overall routinely reported hospital morbidity rates are similar to rates based on self reported ethnicity. For example, in 1990, routine national statistics show that there were 345 Maori hospital discharges or deaths due to ischaemic heart disease, and 8946 events among other ethnic groups. After adjustment using the register figures to reflect self defined ethnicity, the figures were 347 and 8944 respectively.

Humans↗
PubMed1993

A look at the statistical overview (or meta-analysis)

In essence, the statistical overview (or meta-analysis) does little more than many medical scientists and practising clinicians have been attempting for years: estimating the average size of an effect from an apparently disparate medical literature. The statistical overview provides a method of pooling the results of comparable trials (or observational studies) which, on their own, may be too small to demonstrate a 'statistically significant' effect. The role for the overview is obvious, the method is relatively simple, but the findings have to be interpreted cautiously, since joining the hypothesis post hoc contravenes one of the essential principles of scientific enquiry. The use of the statistical overview and some of its pros and cons are illustrated in four clinical examples.

Cholesterol↗
PubMed1993

Statistical testing of genetic linkage under heterogeneity.

Recent advances in human genetics have led to a renewed interest in statistical methods for the detection of linkage from family data--for example, between marker loci and disease traits. Statistical analysis of linkage between two loci is carried out almost exclusively by means of the lod (log-odds) score test, equivalent to a likelihood ratio test. The current practice is to declare genetic linkage between loci when the maximum lod score exceeds 3. As will be discussed here, the lod-score approach is not appropriate for the detection of linkage from heterogeneous data, e.g., when families consist of a mixture of linked and unlinked types. Heterogeneity may arise, for example, when rare mutations at different genetic loci are responsible for the same disease trait. As an alternative approach to account for possible heterogeneity in the detection of linkage, we propose the application of large-sample test statistics that are members of Neyman's class of C(alpha), or partial score tests. The convergence of the proposed test statistics to their asymptotic distributions is investigated via Monte Carlo simulation for typical study designs applicable in human genetics.

Biometry↗
PubMed1996

Statistical analysis of heterogeneous pharmacokinetic data from the literature.

To obtain the reliable pharmacokinetic quantities necessary for the adjustment of individual drug dosages, the pharmacokinetic data in the literature must be analysed by various appropriate statistical methods. Generally, pharmacokinetic quantities have been calculated from small sample sizes and published in different ways (the coefficient of variation of an estimated pharmacokinetic quantity is often higher than 40 percent). In the present study data were therefore structured and clustered according to the important influence variables. The important influence variables were obtained by known multivariate statistical methods. To obtain optimal estimators (uniformly minimum variance unbiased estimators) for the quantities inside the clusters, goodness of fit tests must be performed. Skewed distributions and samples with heterogeneous variances must be subjected to transformation procedures to obtain normally distributed values with homogeneous variances. The population-derived estimate of the a priori knowledge of pharmacokinetic quantities can be obtained by a statistical algorithm proposed in this paper. Information on the quantities half-life, total body clearance and volume of distribution of vancomycin were extracted from over 200 publications. These data were analysed with the proposed statistical algorithm, yielding population-based estimates for half-life, clearance and volume of vancomycin, taking into consideration the detected influence variables--renal function and the age of the patient. Important dependencies between different variables are discussed.

Age Factors↗
PubMedDate not supplied

[Clinical interpretation of statistical significance].

In any clinical study which compares versus an established therapy, the results will be expressed in terms of statistical significance. For this reason, it is important that every clinician interested in a judicious interpretation of the scientific literature, understands the origin and interpretation of statistical significance. The present paper addresses physicians attempting to give practical meaning to research results by discriminative and critical reading. The main aim is to explain in a simple way the meaning of the P-value, confidence intervals, and the required number to treat. Using practical examples, the most common mistakes in the interpretation of statistical significance are analyzed. The difference between statistical and clinical significance is emphasized and a new way to express and compare the practical utility of clinical trial results and the required number to treat is given.

Clinical Trials as Topic↗
PubMed1997

Use of statistical analysis of cytologic interpretation to determine the causes of interobserver disagreement and in quality improvement.

BACKGROUND: Disagreements in cytologic interpretation can have several causes, including differences in diagnostic threshold and diagnostic accuracy. These can be distinguished by a combination of statistical analyses. METHODS: For demonstration purposes, a nonrandom collection of 80 cervicovaginal smears, the majority of which (74) were originally diagnosed as atypical cells of undetermined significance (ASCUS), were reviewed by 3 separate observers and classified as either negative, negative and reactive, ASCUS favor reactive, ASCUS not otherwise specified, ASCUS suggestive of a squamous intraepithelial lesion (SIL), low grade SIL, or high grade SIL. The results were compared with corresponding biopsies and analyzed with distribution analysis, the kappa statistic, threshold analysis, and receiver operating characteristic (ROC) curve analysis. RESULTS: Distribution analysis of diagnoses from the three observers demonstrated statistically significant differences in how cases were classified and a low level of agreement. Kappa analysis confirmed a very poor interobserver agreement. Threshold analysis revealed that one observer used a threshold between negative and ASCUS that was statistically more specific but less sensitive than the other observers. ROC curve analysis showed that another observer was more accurate than this observer. CONCLUSIONS: Variation in cytologic interpretation may have several causes. Distribution, threshold, and ROC analysis allow distinction between differences in diagnostic accuracy and diagnostic thresholds. This approach to analyzing cytologic interpretation may be useful for quality improvement efforts.

Cytological Techniques↗
PubMed1998

Concept-oriented standardization and statistics-oriented classification: continuing the classification versus nomenclature controversy.

Nowadays, most activities on controlled medical vocabularies focus on the provision of a sufficient atomic-level granularity for representing clinical data. Amongst others, clinical vocabularies should be concept oriented, compositional and should also reject "Not Elsewhere Classified". We strongly share the opinion that there is a need to deal with serious deficits of existing manually created vocabularies and with new demands for computer-based advanced processing and exchange of medical language data. However, we do not share the opinion that methodological requirements like observational and structural comparability needed for sound statistics should not be included in desiderata of controlled medical vocabularies. Statistical-oriented classifications are not developed for representing detailed clinical data but for providing purpose-dependent classes where cases of interest are assigned uniquely. Either statistical classifications are not included into the set of controlled medical vocabularies in the sense of Cimino, or his desiderata are misleading. We argue that statistical classifications should be linked to (formal) concept systems, but again this linkage does not change their different natures. With this article we continue the "classification versus nomenclature" controversy referring to Coté.

Data Interpretation, Statistical↗
PubMed1999

A statistical approach to data analysis and 3-D geometric description of the human head and face.

Many analytical biomechanical methods require extensive three-dimensional statistical description of anatomical geometry. In particular, to design personal protective items for the human head and face, where good fit is critical, it is inevitable that a three-dimensional statistical description of this complicated structure will be needed. The work here offers an approach to this problem. This approach consists of three steps: (1) osteometric scaling, (2) normative specimen accumulation and (3) statistical testing. Three groups of facial data (24 Asian, 29 Black, and 29 White) were digitized. The effectiveness and accuracy of the statistical approach was tested on these three different experimental specimen sets. The method was found to be very accurate in modelling the most complicated human body parts--head and face. The availability of this detailed geometric information will also open many doors for future research and development of muscle controlled prostheses, repair of ligament damage, and in-vivo bone remodelling.

Asian People↗
PubMed2006

A statistical methodology for analyzing co-occurrence data from a large sample.

Determining important associations among items in a large database is challenging due to multiple simultaneous hypotheses and the ability to select weak associations that are statistically but not clinically significant. The simple application of the chi2 test among all possible pairs of items results in mostly inappropriate associations surpassing the traditional (alpha=.05, chi2=3.94) threshold. One can choose a stricter threshold to find stronger associations, but the choice may be arbitrary. We combined the volume test of Diaconis and Efron with a p-value plot to select a more rigorous and less arbitrary threshold. The volume test adjusts the p-value of the chi2-statistic. A plot of adjusted p-values (1 - p versus N(p)), where N(p) is the number of test statistics with a p-value greater than p, should be linear if there are no true associations. The point where the plot deviates from a line can be used as a threshold. We used linear regression to select the threshold in a reproducible fashion. In one experiment, we found that the method selected a threshold similar to that previously obtained by manually reviewing associations.

Calibration↗
PubMed2005

Rank-based methods as a non-parametric alternative of the T-statistic for the analysis of biological microarray data.

We have recently introduced a rank-based test statistic, RankProducts (RP), as a new non-parametric method for detecting differentially expressed genes in microarray experiments. It has been shown to generate surprisingly good results with biological datasets. The basis for this performance and the limits of the method are, however, little understood. Here we explore the performance of such rank-based approaches under a variety of conditions using simulated microarray data, and compare it with classical Wilcoxon rank sums and t-statistics, which form the basis of most alternative differential gene expression detection techniques. We show that for realistic simulated microarray datasets, RP is more powerful and accurate for sorting genes by differential expression than t-statistics or Wilcoxon rank sums - in particular for replicate numbers below 10, which are most commonly used in biological experiments. Its relative performance is particularly strong when the data are contaminated by non-normal random noise or when the samples are very inhomogenous, e.g. because they come from different time points or contain a mixture of affected and unaffected cells. However, RP assumes equal measurement variance for all genes and tends to give overly optimistic p-values when this assumption is violated. It is therefore essential that proper variance stabilizing normalization is performed on the data before calculating the RP values. Where this is impossible, another rank-based variant of RP (average ranks) provides a useful alternative with very similar overall performance. The Perl scripts implementing the simulation and evaluation are available upon request. Implementations of the RP method are available for download from the authors website (http://www.brc.dcs.gla.ac.uk/glama).

Algorithms↗
PubMed1987

Population registers and population statistics.

The authors examine the use of population registers in the production of demographic statistics. "To do this, we first describe these registers; we then examine their further development as an independent source of population statistics--including current population estimates, vital and migration statistics--and as an alternative to population censuses. In conclusion, we consider a number of essential features of registers, including the original collection of data, the system's lifetime, privacy protection, and the government's ability to bring about a balance among conflicting interests regarding the collection and use of the data." The discussion is illustrated using examples from the Netherlands and Denmark.

Censuses↗
PubMed1995

Predictive accuracy of the TRISS survival statistic is improved by a modification that includes admission pH.

OBJECTIVE: To determine if pH measured at the time of hospital admission and corrected for PCO2 was an independent predictor of trauma survival. DESIGN: Phase 1 was a retrospective case-control analysis of 1708 patients, followed by multivariate multiple logistic regression analysis of a subset of 919 patients for whom the Revised Trauma Score (RTS), Injury Severity Score (ISS), and pH were available. Phase 2 was a prospective comparison of a mathematical model of survival derived in phase 1 (pH-TRISS) with the TRISS method in 508 of 1325 subsequently admitted trauma patients. SETTING: Urban level 1 trauma center. PATIENTS: All patients admitted with blunt or penetrating trauma during the study period. MAIN OUTCOME MEASURES: Survival vs mortality. RESULTS: In phase 1, factors significantly associated with mortality by t test and chi 2 analysis included the RTS, ISS< Glasgow Coma Scale, corrected pH (CpH), and sum of the head, chest, and abdominal components of the Abbreviated Injury Scale-85 (AIS85) (HCAISS) (for all, P < .0001). The TRISS statistic was also a significant predictor of survival (P < .004). Age, sex, and the extremity and soft tissue components of the AIS85 were not associated with mortality. In a multivariate analysis of the RTS, HCAISS, and CpH, all were significant predictors of mortality. Even when controlling for RTS and HCAISS, CpH remained a significant predictor of mortality (P < .008). In phase 2, when pH-TRISS was tested prospectively against TRISS in a new group of patients, the new statistic appeared to provide a more accurate prediction of survival. CONCLUSIONS: The arterial pH measurement obtained on hospital arrival and corrected for PCO2 is a significant independent predictor of survival and adds to the predictive accuracy of the TRISS survival statistic. Age, sex, and the extremity and soft tissue components of the AIS85 did not contribute to the accuracy of the TRISS statistic in this patient population.

Abbreviated Injury Scale↗
PubMed2002

Association between time interval to publication and statistical significance.

CONTEXT: Studies have shown that reports from clinical trials with statistically significant results tend to be submitted and published more rapidly than reports from studies with null or insignificant findings. METHODS: We wanted to determine whether manuscripts reporting positive results from controlled clinical trials are published more quickly than those reporting negative results. We tracked manuscripts from submission to JAMA until the publication decision. We classified results as positive if a statistically significant difference was reported for the primary outcome. Manuscripts were further classified according to indicators of methodologic quality and other study characteristics. We included manuscripts if they were submitted from February 1996 through August 1999, reported results of a prospective study in which participants were assigned to a treatment or comparison group, used statistical tests to compare differences between groups, and were accepted for publication. RESULTS: One hundred thirty-three manuscripts met our inclusion criteria, 78 (59%) reported positive results, 51 (38%) reported negative results. We were unable to classify the direction of results for 4 articles (3%). The time interval between submission and publication was not associated with positive results: median time between submission and publication was 7.8 months for reports with positive vs 7.6 months for reports with negative results (P =.44). Time to publication also was not associated with any marker of study quality or study characteristic. CONCLUSIONS: Among 133 published controlled trials, time to publication was not associated with statistical significance, methodologic quality, or other study characteristics.

Controlled Clinical Trials as Topic↗
PubMed1998

A practical guide to Papanicolaou smear rescreens: how many slides must be reevaluated to make a statistically valid assessment of screening performance?

BACKGROUND: The question of the minimum number of Papanicolaou (Pap) smear slides that must be rescreened to draw statistically valid conclusions regarding the accuracy of screening often is raised. No method for generating answers in varying laboratory circumstances has achieved widespread application; standard statistical sample size calculations may represent such a resource. METHODS: A series of tables was constructed to display minimum required numbers of rescreens, with each table representing differing hypothetical laboratory circumstances. To use each table, assumptions must be specified in advance as to prevalence of abnormality, definition of error, baseline false-negative proportions (FNPs) of performance, and a degree of increase in FNPs that is considered a departure from baseline warranting concern, among others. RESULTS: The authors constructed four sample tables displaying minimum numbers of slides that must be rescreened in differing specified laboratory scenarios. Depending on assumed conditions and predetermined levels of satisfactory and unsatisfactory accuracy, the range of numbers is very broad (38-10,000). One example representing likely conditions indicates that 1040 slides must be reexamined; in another scenario, a sample size of 300 is sufficient. CONCLUSIONS: The minimum number of rescreened slides needed to draw statistically valid conclusions regarding Pap smear screening accuracy can be calculated using standard statistical methods. However, a number of assumptions must be detailed in advance. The authors offer this as a practical guide and a continuation of a general inquiry regarding Pap smear error rate measurement and display. The use of these tables raises at least as many questions as it answers, but still may represent a significant advance. Future efforts at further numeric characterization of aspects of Pap smear screening performance are warranted to enable rational decision making when performance is examined in the course of quality assurance, and during quality control and regulatory activities. [See editorial on pages 127-9, this issue.]

Evaluation Studies as Topic↗
PubMed2000

On the use of the Kolmogorov-Smirnov statistical test for immunofluorescence histogram comparison.

BACKGROUND: The problem considered is the quantitative comparison of immunofluorescence frequency distributions in order to detect their differences of biological significance, i.e., to evaluate the potential positivity of a cell sample with respect to negative control cells. The Kolmogorov-Smirnov (KS) statistical test, proposed in the literature for this purpose, is examined and discussed through its application to a set of experimental measurements. It is shown that even differences due to the stain procedure or to instrumental biases may be considered significant by the test implemented in the standard form. METHODS: In order to ensure valid results, it is necessary to take into account the various sources of variation in the specific experimental context. A procedure is proposed that uses the KS statistics as a reference for determining an appropriate estimate of the overall variability in the control data. This estimate is derived from the comparisons of the cumulative distributions associated with repeated measurements of the negative cell sample. RESULTS AND CONCLUSIONS: The KS-related index thus defined provides a tool for assessing the potential positivity of a cell sample, since it allows to distinguish between statistical and biological significance of the difference between the histogram to be tested and the set of control data. In particular, if a cell sample is not included in the control variability, either a positive cell subpopulation is present, or all cells are positive. Instead, for a sample included in the control variability, the difference will be not biologically meaningful, even if statistically significant. Moreover, when a purely positive control sample is also available, it is possible to derive a measure of the precision at which a true biological positivity can be detected. Finally, since the index is not absolute, but relative to the features of the instrumentation, of the antibodies and of the fluorochromes used, it represents a quantitative measure of the stability and reproducibility of the measurement process and could be used for quality control of flow cytometric experiments in immunofluorescence.

Confidence Intervals↗
PubMed1993

Statistics of RNA secondary structures.

A statistical reference for RNA secondary structures with minimum free energies is computed by folding large ensembles of random RNA sequences. Four nucleotide alphabets are used: two binary alphabets, AU and GC, the biophysical AUGC and the synthetic GCXK alphabet. RNA secondary structures are made of structural elements, such as stacks, loops, joints, and free ends. Statistical properties of these elements are computed for small RNA molecules of chain lengths up to 100. The results of RNA structure statistics depend strongly on the particular alphabet chosen. The statistical reference is compared with the data derived from natural RNA molecules with similar base frequencies. Secondary structures are represented as trees. Tree editing provides a quantitative measure for the distance dt, between two structures. We compute a structure density surface as the conditional probability of two structures having distance t given that their sequences have distance h. This surface indicates that the vast majority of possible minimum free energy secondary structures occur within a fairly small neighborhood of any typical (random) sequence. Correlation lengths for secondary structures in their tree representations are computed from probability densities. They are appropriate measures for the complexity of the sequence-structure relation. The correlation length also provides a quantitative estimate for the mean sensitivity of structures to point mutations.

Base Sequence↗