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PubMed1954

Objectives underlying future patterns of work of national committees on vital and health statistics.

As a basis for considering the forms and patterns of national committees on vital and health statistics in the future, the author first discusses the six main objectives of national systems of those statistics. Turning then to the aims of the national committees themselves, he observes that such is the variety of statistcal needs in different countries in view of their different stages of economic development that the work of the national committees at present inevitably differs greatly from country to country and must continue to do so as development continues. However, the common purpose of the committees is to help in developing, or in strengthening, the statistical structure of each country. Any activity or objective of a national committee that furthers one or more of the country's objectives can be regarded as worthwhile and legitimate.

Health Services Needs and Demand↗
PubMed1992

The effect of trial size on statistical power.

Many research studies produce results that falsely support a null hypothesis due to a lack of statistical power. The purpose of this research was to demonstrate selected relationships between single subject (SS) and group analyses and the importance of data reliability (trial size) on results. A computer model was developed and used in conjunction with Monte Carlo procedures to study the effects of sample size (subjects and trials), within- and between-subject variability, and subject performance strategies on selected statistical evaluation procedures. The inherent advantages of the approach are control and replication. Selected results are presented in this paper. Group analyses on subjects using similar performance strategies identified 10, 5, and 3 trials for sample sizes of 5, 10, and 20, respectively, as necessary to achieve statistical power values greater than 90% for effect sizes equal to one standard deviation of the condition distribution. SS analyses produced results exhibiting considerably less power than the group results for corresponding trial sizes, indicating how much more difficult it is to detect significant differences using a SS design. These results should be of concern to all investigators especially when interpreting nonsignificant findings.

Data Interpretation, Statistical↗
PubMed2004

[Statistical notes. Absolute and relative effect measures].

The aim of these statistical notes was to give the cardiologist the suitable tools for the understanding of the statistical aspects in the reading of papers presenting the results of randomized controlled clinical trials, in the most simple and intuitive way, without any previous knowledge of basic statistics. The fundamentals of the most common tools for the comparison of two experimental treatments, are developed by getting cue from classical cardiological examples and by guiding the reader, step by step, toward the understanding and critical discussion of the proposed examples. In particular, this note focuses the attention on the main "indicators" of absolute and relative effect measures by evidencing their most important differences.

Cardiology↗
PubMed1992

[Data analysis by statistical models].

The basic idea for the realization of effective statistical data analysis is illustrated with an example. The use of statistical models is explained and the feasibility of objective comparison of the models by an information criterion AIC is demonstrated. Further, the possibility of practical use of Bayesian models for complex data analysis is explained. Finally, the necessity of cooperation between the experts of respective fields and statisticians for further development of statistical data analysis is mentioned.

Adult↗
PubMedDate not supplied

[Choice and consequence: measurement level determines the statistical tool-box].

Various types of data are used in health and behavioural sciences. Some of the variables, such as blood variables, can be measured by standardised, calibrated methods while others are based on subjective judgements from an expert or from the patients. There is a strong link between the measurement process and the choice of statistical toolbox. The statistically important measurement levels are; categorical, ordinal, quantitative discrete and quantitative continuous data. Special attention should be paid to non-negative quantitative data, and to ordinal data which are very common in medicine. In this paper the properties of the measurement levels are given and their consequences on the choice of statistical methods for description and analysis.

Data Interpretation, Statistical↗
PubMed2006

[Possibilities and limitations of statistical regression models for the calculation of threshold values for minimum provider volumes].

Inadequate statistical procedures are often applied for the derivation of threshold values in various medical research areas. The frequently applied method to establish threshold values on the basis of simple comparisons between arbitrarily defined low-volume and high-volume groups may be misleading because the result depends on the preceding classification. In this paper, the possibilities and limitations of statistical regression models for the calculation of threshold values are described. The features of these models for the selection of minimum volumes for hospitals or physicians are discussed. Simulated data examples are used to demonstrate that the definition of a useful minimum provider volume should not be based upon a calculated value of purely mathematical meaning without clinically assessing the risk curve. In the application of statistical regression models to retrospective observational data it should be noticed that calculated threshold values are only of a hypothesis-generating character. In order to verify that a minimum provider volume leads to the expected quality improvement, a prospective intervention study is required.

Data Interpretation, Statistical↗
PubMed2006

Statistical approaches to handle proportions as characters.

The handling of morphometric proportions as characters in phylogenetics is methodically hardly elaborated. Common approaches are either to search for statistically significant differences in "index"-values, usually quotients of measures, or to compare regression lines for logarithmic values. The former have their weakness in an insufficient consideration of allometric proportions, the latter in a lack of appropriate methods for testing statistical significance of differences. This contribution presents a methodical approach which considers both allometry and statistical significance. Moreover, not only significance, but also the size of mean differences, as compared to standard deviations, is calculated. If differences are too small, they do not allow to draw a clear distinction between compared groups, and therefore have a low quality as characters. At the beginning of the procedure a test for gradient differences between allometry lines is applied, and if no such are found, a second test for parallel shift between the two lines. If a difference is found, the extent of which exceeds a prearranged limit, it is accepted as a character difference suitable for phylogenetic reconstruction. This method was practically applied by Wiesemüller & Rothe (1999) in a phylogenetic investigation of New World monkeys (Platyrrhini, Primates).

Animals↗
PubMed1991

Statistical conclusion validity: an empirical analysis of multiplicity in mental retardation research.

The statistical conclusion validity of mental retardation research was evaluated by reviewing 41 recently published articles in the American Journal on Mental Retardation and the Journal of Mental Deficiency Research. The analysis was focused on the incidence of Type I error as reflected by three different measures: the experiment-wise error rate, the error rate per experiment, and the percentage error rate. Values for each of these measures indicated that the probability of committing a Type I error was considerably greater than the traditionally assumed level of p less than .05, or 5%. The percentage error rate computed for the 41 articles (369 statistical tests) suggested that approximately 20% of the statistically significant results may be erroneous. Procedures to evaluate the impact of Type I errors in mental retardation research were presented and briefly discussed.

Activities of Daily Living↗
PubMed1990

Harnessing information technology to revolutionise teaching, research and consultative service in health statistics.

Computers have traditionally been associated with health statistics because of the need to manage, process and analyse data. Over the last two years, the Division of Biostatistics and Health Informatics of the Department of Community, Occupational and Family Medicine at the National University of Singapore has been actively looking into ways in which informatics and telematics could be usefully applied to teaching and research in health statistics and in the provision of a statistical information service. This paper describes three projects, originating from the National University of Singapore, which provide an effective demonstration of the application of modern technology to an age-old discipline.

Computer-Assisted Instruction↗
PubMed1994

A physician-based architecture for the construction and use of statistical models.

Physicians need specially tailored computer tools to take advantage of published research results. We present a knowledge-based computer framework--the physician-based (PB) architecture--for constructing such tools, and we use the problem of physicians' interpretation of two-arm parallel randomized clinical trials (TAPRCT) as a working example. Statistical models are represented by influence diagrams. The interpretation of influence-diagram elements are mapped into users' language in a domain-specific, physician-based user interface, called a patient-flow diagram. Statistical-model transformations that maintain the semantic relationships of the model and that embody clinical-epidemiological knowledge are encoded in a mediating structure called the cohort-state diagram. The algorithm that coordinates the interactions among the knowledge representations uses modular actions called construction steps. This architecture has been implemented in a Bayesian system, called THOMAS, that supports physician decision making in light of TAPRCT data. This support entails assessing clinical significance, prior beliefs, and methodological concerns. We suggest that the PB architecture applies to a wide range of statistical tools and users.

Algorithms↗
PubMedDate not supplied

[A statistic approach to interpretation, results processing and hypo thesis testing in experiments on exposuring the human brain to perception of low level signals].

There are shown the limitations of using the generally accepted approaches to statistic data processing at the perception of low level signals on Bernully scheme when specific nature on the phenomenon (the human ability to perception is not constant, uncontrolled and manifests itself for a short time) isn't taken into account. A new approach to the statistic processing of measurement results is also proposed. It is based on the sequence analysis of correct answers in a series, the use of sequential statistic procedures (criteria), maximally taking into account the perception specificity and the measurement features. The estimate of first kind error in proposed method has been carried out.

Brain↗
PubMed1985

A proposed solution to the base rate problem in the kappa statistic.

Because it corrects for chance agreement, kappa (kappa) is a useful statistic for calculating interrater concordance. However, kappa has been criticized because its computed value is a function not only of sensitivity and specificity, but also the prevalence, or base rate, of the illness of interest in the particular population under study. For example, it has been shown for a hypothetical case in which sensitivity and specificity remain constant at .95 each, that kappa falls from .81 to .14 when the prevalence drops from 50% to 1%. Thus, differing values of kappa may be entirely due to differences in prevalence. Calculation of agreement presents different problems depending on whether one is studying reliability or validity. We discuss quantification of agreement in the pure validity case, the pure reliability case, and those studies that fall somewhere between. As a way of minimizing the base rate problem, we propose a statistic for the quantification of agreement (the Y statistic), which can be related to kappa but which is completely independent of prevalence in the case of validity studies and relatively so in the case of reliability.

Epidemiologic Methods↗
PubMed1992

Measuring and managing quality of surgery. Statistical vs incidental approaches.

New rules for quality assurance provoked a comparison of effects of two approaches used concurrently for 14 years. In an incidental approach, a multidisciplinary conference reviewed all postoperative complications as they occurred and attributed each to one of six causes. Remedies were instituted and data were filed. In a statistical approach, death and complication rates were computed annually and compared with previous years' rates and with rates reported to Congress as national norms. Statistics suggested acceptable quality in each specialty but calculations were tedious and differences achieved significance too rarely or too slowly to identify problems, protect patients, and improve care. The incidental approach was popular and produced immediate improvements in patient care. Conferees attributed one half of complications to errors. Frequent acknowledgment of susceptibility to error may contribute to the safety and quality shown by our statistics.

Humans↗
PubMed1998

Effect of the statistical significance of results on the time to completion and publication of randomized efficacy trials.

CONTEXT: Medical evidence may be biased over time if completion and publication of randomized efficacy trials are delayed when results are not statistically significant. OBJECTIVE: To evaluate whether the time to completion and the time to publication of randomized phase 2 and phase 3 trials are affected by the statistical significance of results and to describe the natural history of such trials. DESIGN: Prospective cohort of randomized efficacy trials conducted by 2 trialist groups from 1986 to 1996. SETTING: Multicenter trial groups in human immunodeficiency virus infection sponsored by the National Institutes of Health. PATIENTS: A total of 109 efficacy trials (total enrollment, 43708 patients). MAIN OUTCOME MEASURES: Time from start of enrollment to completion of follow-up and time from completion of follow-up to peer-reviewed publication assessed with survival analysis. RESULTS: The median time from start of enrollment to publication was 5.5 years and was substantially longer for negative trials than for results favoring an experimental arm (6.5 vs 4.3 years, respectively; P<.001; hazard ratio for time to publication for positive vs negative trials, 3.7; 95% confidence interval [CI], 1.8-7.7). This difference was mostly attributable to differences in the time from completion to publication (median, 3.0 vs 1.7 years for negative vs positive trials; P<.001). On average, trials with significant results favoring any arm completed follow-up slightly earlier than trials with nonsignificant results (median, 2.3 vs 2.5 years; P=.045), but long-protracted trials often had low event rates and failed to reach statistical significance, while trials that were terminated early had significant results. Positive trials were submitted for publication significantly more rapidly after completion than were negative trials (median, 1.0 vs 1.6 years; P=.001) and were published more rapidly after submission (median, 0.8 vs 1.1 years; P=.04). CONCLUSION: Among randomized efficacy trials, there is a time lag in the publication of negative findings that occurs mostly after the completion of the trial follow-up.

Bias↗
PubMed1999

Nonparametric simulation-based statistical analyses for bipolar affective disorder locus on chromosome 21q22.3.

Straub et al. [1994: Nat Genet 8:291-296] reported a candidate bipolar affective disorder (BAD) locus on chromosome 21q22.3. As a replication study, we analyzed 12 Australian BAD pedigrees for the presence of excess allele sharing and cosegregation with the putative chromosome 21q22.3 BAD locus, using six microsatellite markers. The nonparametric simulation-based statistic SimAPM produced positive results for the marker PFKL (P < 0.001) and D21S198 (P = 0.007). PFKL also demonstrated linkage (P < 0.001) when analyzed using the more conservative statistic, SimIBD. Comparable results were obtained when using the original APM statistic (P = 0.02 for D21S198). However, other nonparametric analyses such as GENEHUNTER and model-free linkage (MFLINK) analysis did not yield significant results. Combined LOD scores for the 12 families were strongly negative for all six markers under six genetic models. Two-point and multipoint analyses of individual families revealed one family, family 17, with maximal LOD scores greater than 1.41 for the 10.5-cM region between PFKL and D21S198. This report provides additional support for the suggestive linkage of a susceptibility locus for BAD on chromosome 21q22.3.

Bipolar Disorder↗
PubMed2000

Statistical power comparisons among alternative morphometric methods.

This paper compares the statistical power of various tests that have been proposed to test for equality of shape in two populations. Power surfaces are computed with emphasis on the simplest case of three points in the plane (i.e., landmarks at the vertices of a triangle). Goodall's ([1991] J Roy Stat Soc Serb 53:285-339) F-test was found to have the highest power followed by T(2)-tests using Kendall tangent space coordinates. Power for T(2)-tests using Bookstein shape coordinates was good if the baseline was not the shortest side of the triangle. The Rao and Suryawanshi ([1996] Proc Natl Acad Sci 93:12132-12136 and Rao and Suryawanshi [1998] Proc Natl Acad Sci 95:4121-4125) shape variables had much lower power when triangles were not close to being equilateral. Power surfaces for the EDMA-I T statistic revealed very low power for many shape comparisons including those between very different shapes. Power surface for the EDMA-II Z statistic were also complicated and depended strongly on the choice of baseline used for size scaling. The type I error rate was also often not correct for this method. Results for more than three landmarks are also presented. The implications of the results for practical applications of morphometrics are discussed.

Anthropology, Physical↗
PubMed1998

Statistical reviewing for medical journals.

This paper reviews the difficulties associated with being a statistical reviewer for a medical journal. As background, I consider first the use of statistical reviewers by medical journals, medical journals' policies on statistical peer review, and the limited evidence of its effectiveness. The assessment of a manuscript is considered under the headings of design, methods of analysis, presentation and interpretation, with many illustrative examples of the difficulties to be overcome. I emphasize the judgemental nature of many aspects. I suggest how to present and structure the reviewer's report to the editor. Finally, I consider wider issues, including the various other ways in which statisticians can interact with medical journals.

Peer Review, Research↗
PubMedDate not supplied

Issues in the statistical analysis of small area health data.

The availability of geographically indexed health and population data, with advances in computing, geographical information systems and statistical methodology, have opened the way for serious exploration of small area health statistics based on routine data. Such analyses may be used to address specific questions concerning health in relation to sources of pollution, to investigate clustering of disease or for hypothesis generation. We distinguish four types of analysis: disease mapping; geographic correlation studies; the assessment of risk in relation to a prespecified point or line source, and cluster detection and disease clustering. A general framework for the statistical analysis of small area studies will be considered. This framework assumes that populations at risk arise from inhomogeneous Poisson processes. Disease cases are then realizations of a thinned Poisson process where the risk of disease depends on the characteristics of the person, time and spatial location. Difficulties of analysis and interpretation due to data inaccuracies and aggregation will be addressed with particular reference to ecological bias and confounding. The use of errors-in-variables modelling in small area analyses will be discussed.

Bayes Theorem↗