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PubMed2005

Statistical methods for testing plaque removal efficacy in clinical trials.

OBJECTIVES: To evaluate the ability of different statistical approaches in finding a statistically significant difference in plaque removal efficiency between brushes in clinical trials. MATERIALS AND METHODS: The approaches, which are evaluated, concern the scores after brushing only, the difference in scores before and after brushing and the relative difference scores (i.e. score before minus score after brushing divided by the score before brushing). In each case the scores before brushing may be included as a covariate. Except for the relative difference scores, the power of the test statistics of the approaches has been compared by assuming a simple statistical model. These theoretical results have been compared with the numerical results of two particular clinical trials--one with a between-subject design and one with a within-subject design. RESULTS: The numerical results of these clinical trials show that the calculated p-values support the conclusions drawn from the statistical model, i.e. the power of the F-test is highest when evaluating the data after brushing with the data before brushing included as a covariate. Using the differences in scores before and after brushing--again with the data before brushing as a covariate--does not add additional power to the test. Omitting the data before brushing as a covariate only gives satisfactory results when the variance over the subjects or the error variance is zero, which in general is not the case. CONCLUSIONS: This investigation reveals that in general the approach of analysing the scores after brushing with the scores before brushing as a covariate yields the highest chance of finding a statistically significant difference between two brushes.

Adult↗

PubMed1998

A quantile plot for simultaneous representation of clinical and statistical attributes of probing change: application to early identification of the downhill patient.

When multiple periodontal sites are observed in patients over time there is an intention to identify those sites where there is important change, typically loss of attachment or increase in probing depth. A change may be declared if it: (a) exceeds a threshold level, and/or (b) is determined to be statistically significant (e.g. regression slope different from zero), perhaps after (c) that significance level has been corrected for multiple testing. These criteria are not often considered when clinical or research decisions are made and there is no universal protocol for their evaluation. A quantile (uniform probability) plot, modified to incorporate additional information, is proposed as a graphical method for the display of changes at multiple sites within a mouth. This plot identifies, for each site, clinical changes beyond a threshold, site-wise statistical significance and statistical significance adjusted for multiple testing. These alternative criteria for attachment change are, thereby, made explicit, providing a detailed evaluative context. In addition, this methodology permits incorporation of an estimation procedure for the number of sites for which the null hypothesis of no change is false. This statistic can provide evidence of progressive disease even when no site has significant clinical or statistical change and even if the average change is zero. Use of the quantile plot was elucidated by application to simulated data, and to a clinical dataset using a BASIC program to automate the computational process. In the clinical example presented, the approach appeared more effective in detecting periodontal change than traditional clinical and statistical criteria. Pending technical refinement, this graphical approach may represent a new tool for the early identification of the downhill patient.

Adult↗

PubMed1985

Use of statistics in the Journal of School Health 1979-1983: a content analysis.

A content analysis of the statistical methods employed in the research/evaluation articles published in Volumes 49-53 of the Journal of School Health (JOSH) was performed. Questions addressed included: To what extent does JOSH publish articles having a research/evaluation theme? Does knowledge of a few basic statistical techniques provide access to the statistical components of a high percentage of the research/evaluation reports published in JOSH? Which additional, but more complex techniques are used most often by authors? Results indicated that, to have access to the statistical content of about three-fourths of the research/evaluation reports published in JOSH, a reader would have to have learned descriptive statistics, analysis of contingency tables, analysis of variance, t-tests, and multiple comparison tests. Knowledge of these and other statistical procedures might assist school health practitioners read research/evaluation reports, and adapt the findings to their respective work settings. Moreover, content analyses can be tools for assessing the state-of-the-art of a profession, as well as helping to plan for practitioners' continuing education needs.

Periodicals as Topic↗

PubMed1998

People and eyes: statistical approaches in ophthalmology.

In conclusion, when an observation by its nature involves two eyes, as for blindness, statistical analyses should be conducted on individuals rather than eyes and between eye correlation is not a problem. In other circumstances, if information on only one eye per individual is used in the analysis there is a potential "waste" of information leading to less precise estimates of effect and less power. In addition, bias may be introduced into a study if there is non-random selection of the eye for inclusion in the analysis. The use of an overall summary of ocular findings for an individual may result in "wastage" of information in a similar fashion to the use of only one eye per individual. On the other hand, an analysis of individual eyes with no allowance made for between eye correlation may result in falsely narrow confidence intervals around estimates of effect. Between eyes correlation may be assessed empirically using the kappa statistic or similar means. If between eye correlation is substantial, statistical techniques exist which can utilise all available data while allowing for the correlation. In some circumstances a powerful design may be to use the fellow eye as a "control". Two conclusions may be drawn from this review of analytical approaches to the analysis of clinical data in the BJO. Firstly, the analytical approaches employed in many studies fail to use all the data available. In other words the analysis is less than "optimal". Secondly, in a proportion of studies, inappropriate statistical methods are used which may lead the investigator to draw inappropriate conclusions. In other words, the analysis is invalid. Ophthalmic data, by their very nature, present particular statistical challenges. We emphasise the need to involve appropriate statistical expertise in the design and analysis of ophthalmic studies.

Bias↗

PubMed1999

MDL and the statistical mechanics of protein potentials.

The combination of a wealth of structural data and impressive computational power provides detailed information pertaining to the structure and dynamics of biomacromolecules. A natural inclination is to incorporate this information into models to gain added predictive power on protein folding and stability. There has been considerable recent interest in developing "knowledge-based" potentials to describe internal interactions in proteins. In these approaches, probability distribution functions are inferred from existing knowledge. A common assumption has been the "quasi-chemical approximation" or "Boltzmann device". This method relates statistical mechanical probabilities to observed frequencies. The validity of this approach is discussed in detail from a statistical mechanics perspective. Because statistical mechanics is a form of statistical inference based on a lack of knowledge of the system, the "Boltzmann device" does not have a rigorous theoretical justification. In the present work, a statistical mechanics based on partial knowledge of the system is employed. This statistical mechanical scheme uses the minimum description length (MDL) of phase space as its main tool. With this approach, "knowledge-based" potentials can be derived in a rigorous fashion. In practical calculations, these potentials are best obtained using Bayesian inference methods similar to those used in image reconstruction.

Algorithms↗

PubMed2002

Efficient score statistics for mapping quantitative trait loci with extended pedigrees.

The method of variance components is the method of choice for mapping quantitative trait loci (QTLs) with general pedigrees. Being a likelihood-based method, this method can be computation intensive even for nuclear families, and has excessive false positive rates under some situations. Here two efficient score statistics to detect QTLs are derived, one assumes that the candidate locus has no dominance effect, and the other one does not make such an assumption. These two score statistics are asymptotically equivalent to the method of variance components but they are easier to compute and more robust than the likelihood ratio statistic. The derivation of these score statistics is facilitated by separating the segregation parameters, the parameters that describe the distribution of the phenotypic value in the population, from the linkage parameters, the parameters that measure the effect of the candidate locus on the phenotypic value. Such a separation of the model parameters greatly reduces the number of parameters to be dealt with in the analysis of linkage. The asymptotic distributions of both score statistics are derived. Simulation studies indicate that, compared to the method of variance components, both score statistics have comparable or higher power, and their false-positive rates are closer to their respective nominal significance levels.

Chromosome Mapping↗

PubMed2005

Minimum follow-up time required for the estimation of statistical cure of cancer patients: verification using data from 42 cancer sites in the SEER database.

BACKGROUND: The present commonly used five-year survival rates are not adequate to represent the statistical cure. In the present study, we established the minimum number of years required for follow-up to estimate statistical cure rate, by using a lognormal distribution of the survival time of those who died of their cancer. We introduced the term, threshold year, the follow-up time for patients dying from the specific cancer covers most of the survival data, leaving less than 2.25% uncovered. This is close enough to cure from that specific cancer. METHODS: Data from the Surveillance, Epidemiology and End Results (SEER) database were tested if the survival times of cancer patients who died of their disease followed the lognormal distribution using a minimum chi-square method. Patients diagnosed from 1973-1992 in the registries of Connecticut and Detroit were chosen so that a maximum of 27 years was allowed for follow-up to 1999. A total of 49 specific organ sites were tested. The parameters of those lognormal distributions were found for each cancer site. The cancer-specific survival rates at the threshold years were compared with the longest available Kaplan-Meier survival estimates. RESULTS: The characteristics of the cancer-specific survival times of cancer patients who died of their disease from 42 cancer sites out of 49 sites were verified to follow different lognormal distributions. The threshold years validated for statistical cure varied for different cancer sites, from 2.6 years for pancreas cancer to 25.2 years for cancer of salivary gland. At the threshold year, the statistical cure rates estimated for 40 cancer sites were found to match the actuarial long-term survival rates estimated by the Kaplan-Meier method within six percentage points. For two cancer sites: breast and thyroid, the threshold years were so long that the cancer-specific survival rates could yet not be obtained because the SEER data do not provide sufficiently long follow-up. CONCLUSION: The present study suggests a certain threshold year is required to wait before the statistical cure rate can be estimated for each cancer site. For some cancers, such as breast and thyroid, the 5- or 10-year survival rates inadequately reflect statistical cure rates, and highlight the need for long-term follow-up of these patients.

Disease-Free Survival↗

PubMed2006

Current practices in spatial analysis of cancer data: mapping health statistics to inform policymakers and the public.

BACKGROUND: To communicate population-based cancer statistics, cancer researchers have a long tradition of presenting data in a spatial representation, or map. Historically, health data were presented in printed atlases in which the map producer selected the content and format. The availability of geographic information systems (GIS) with comprehensive mapping and spatial analysis capability for desktop and Internet mapping has greatly expanded the number of producers and consumers of health maps, including policymakers and the public.Because health maps, particularly ones that show elevated cancer rates, historically have raised public concerns, it is essential that these maps be designed to be accurate, clear, and interpretable for the broad range of users who may view them. This article focuses on designing maps to communicate effectively. It is based on years of research into the use of health maps for communicating among public health researchers. RESULTS: The basics for designing maps that communicate effectively are similar to the basics for any mode of communication. Tasks include deciding on the purpose, knowing the audience and its characteristics, choosing a media suitable for both the purpose and the audience, and finally testing the map design to ensure that it suits the purpose with the intended audience, and communicates accurately and effectively. Special considerations for health maps include ensuring confidentiality and reflecting the uncertainty of small area statistics. Statistical maps need to be based on sound practices and principles developed by the statistical and cartographic communities. CONCLUSION: The biggest challenge is to ensure that maps of health statistics inform without misinforming. Advances in the sciences of cartography, statistics, and visualization of spatial data are constantly expanding the toolkit available to mapmakers to meet this challenge. Asking potential users to answer questions or to talk about what they see is still the best way to evaluate the effectiveness of a specific map design.

Data Interpretation, Statistical↗

PubMed2005

DSM-oriented scales and statistically based syndromes for ages 18 to 59: linking taxonomic paradigms to facilitate multitaxonomic approaches.

We used behavioral and emotional problem items to construct (a) nosologically based Diagnostic and Statistical Manual of Mental Disorders (DSM) oriented scales from experts' ratings of the items' consistency with DSM-IV (4th ed.; American Psychiatric Association, 1994) diagnostic categories, and (b) statistically based syndromes from factor analyses of adults' self-ratings and ratings of adults by people who knew them (N = 4,628). Quantified, operationally defined, and normed DSM-oriented scales and statistically based syndromes facilitate multitaxonomic approaches to the assessment of adult psychopathology. Psychometric properties and cross-informant correlations were similar for DSM-oriented scales and statistically derived syndromes. Statistical associations between phenotypically similar DSM-oriented scales and statistically based syndromes were moderate to strong. Multitaxonomic approaches can avoid reification of provisional taxa that may result from excessive reliance on a single taxonomic paradigm.

Adolescent↗

PubMed2003

Financial statistics for public health dispensary decisions in Nigeria: insights on standard presentation typologies.

Public health dispensaries in Nigeria in recent times have demonstrated the poise to boost corporate productivity in the new millennium and to drive the nation closer to concretising the lofty goal of health-for-all. This is very pronounced considering the face-lift giving to the physical environment, increase in the recruitment and development of professionals, and upward review of financial subventions. However, there is little or no emphasis on basic statistical appreciation/application which enhances the decision making ability of corporate executives. This study used the responses from 120 senior public health officials in Nigeria and analyzed them with chi-square statistical technique. The results established low statistical aptitude, inadequate statistical training programmes, little/no emphasis on statistical literacy compared to computer literacy, amongst others. Consequently, it was recommended that these lapses be promptly addressed to enhance official executive performance in the establishments. Basic statistical data presentation typologies have been articulated in this study to serve as first-aid instructions to the target group, as they represent the contributions of eminent scholars in this area of intellectualism.

Administrative Personnel↗

PubMed1987

Statistical properties of radio-frequency and envelope-detected signals with applications to medical ultrasound.

Both radio-frequency (rf) and envelope-detected signal analyses have lead to successful tissue discrimination in medical ultrasound. The extrapolation from tissue discrimination to a description of the tissue structure requires an analysis of the statistics of complex signals. To that end, first- and second-order statistics of complex random signals are reviewed, and an example is taken from rf signal analysis of the backscattered echoes from diffuse scatterers. In this case the scattering form factor of small scatterers can be easily separated from long-range structure and corrected for the transducer characteristics, thereby yielding an instrument-independent tissue signature. The statistics of the more economical envelope- and square-law-detected signals are derived next and found to be almost identical when normalized autocorrelation functions are used. Of the two nonlinear methods of detection, the square-law or intensity scheme gives rise to statistics that are more transparent to physical insight. Moreover, an analysis of the intensity-correlation structure indicates that the contributions to the total echo signal from the diffuse scatter and from the steady and variable components of coherent scatter can still be separated and used for tissue characterization. However, this analysis is not system independent. Finally, the statistical methods of this paper may be applied directly to envelope signals in nuclear-magnetic-resonance imaging because of the approximate equivalence of second-order statistics for magnitude and intensity.

Humans↗

PubMed1998

Statistical science and quantitative understanding.

Authors of papers in biological journals need to make their uses of statistical methods and software explicit and maximally comprehensible. Editors and referees have important responsibilities for this. Inexact use of technical terminology causes confusion. Computer software is invaluable but not infallible. Graphical presentation should not conceal numerical results. Tests of statistical significance should be reserved for specific needs, which will rarely include multiple comparison procedures. Experiments that involve repeated measurements need special care in statistical analysis. Full attention should be given to principles of statistical estimation as well as to choice of appropriate statistical technique. At all times, ethical standards of scientific integrity must contribute to precision and clarity. Clinical research that neglects well-established statistical principles may be intrinsically unethical.

Publishing↗

PubMed1999

A hypertext-based tutorial with links to the Web for teaching statistics and research methods.

An online tutorial for research design and statistics is described. This tutorial provides a way for students to learn how scales of measure, research design, statistics, and graphing data are related. The tutorial also helps students determine what statistical analysis is appropriate for a given design and how the results of the analysis should be plotted in order to effectively communicate the results of a study. Initial research suggests that students using the tutorial are more accurate in their decisions about the design and statistics associated with a study. Students are also more confident in the decisions and find them easier to make when using the tutorial. Furthermore, practice with the tutorial appears to improve problem-solving ability in subsequent design and statistics scenarios. Implications for teaching statistics and research design are discussed.

Computer-Assisted Instruction↗

PubMed2004

Using a computer to perform statistical analysis.

SPSS, and other statistical packages, make it easy to perform complex statistical analysis, but even simple analysis such as tables and graphical output are much simpler using such a package. Use of such a package does, unfortunately, also allow you to perform meaningless statistics and incorrect statistical tests, and give misleading or wrong interpretations. You will still need to understand some statistics, but you will not need to be able to compute the results yourself. A statistics package allows you to concentrate on the appropriateness of a test and interpretation of the results. It does not do the whole job for you.

Bias↗

PubMed1995

Application of statistical inference techniques in health information management.

We have demonstrated that objective comparisons can be made using accepted statistical techniques. We have also shown that you can apply tests which don't meet the basic assumptions and still obtain valid results, in most cases. This robustness of statistics tests is particularly helpful with the type of data and analysis that health information management professionals typically deal with, where exactness of the results is not crucial. You can perform a quick analysis using simple statistical tools and obtain a P value that is fairly close to what it would be if you selected the tests more stringently. The examples of inferential statistics in this article demonstrate how to select tests based on characteristics of the data and how to interpret the results. The kinds of statistical analysis that can be performed in health information management are numerous. Below are some other ideas on how to use inferential statistics in HIM practice. 1. Set up an ordinal scale to evaluate coding accuracy to evaluate coders: Score 1 means the correct code was assigned for the principal diagnosis and only minor errors in coding among secondary diagnoses. Score 2 means the correct code was assigned for the principal diagnosis, but there are omissions or major errors among secondary diagnoses. Score 3 means a minor error in coding the principal diagnosis and only minor errors in secondary diagnoses. Score 4 means a minor error in coding the principal diagnosis and major errors or omissions in secondary diagnoses.(ABSTRACT TRUNCATED AT 250 WORDS)

Analysis of Variance↗

PubMed1999

[Statistically validated evaluation of clinical trials].

Data of clinical trials of medicinal products must be evaluated in statistically valid models. The statistical validity criteria are defined. Statistically invalid models will result in biased parameter and confidence interval estimations, erroneous statistical inferences and clinical interpretations. Finally, wrong decisions will call forth deleterious consequences in the judgement of the therapeutic effect and the frequency and severity of the adverse reactions of the tested new medicinal, and generic products. Statistically validated analyses will promote the international harmonization of the scientific evaluation of medicinal products according to the idea of the evidence-based-medicine. The study presents examples of clinical trials evaluated with a software checking statistical validity assumptions while performing evaluation of data.

Clinical Trials as Topic↗

PubMed2001

[Significance of medical statistics in insurance medicine].

Knowledge of medical statistics is of great benefit to every insurance medical officer as they facilitate communication with actuaries, allow officers to make their own calculations and are the basis for correctly interpreting medical journals. Only about 20% of original work in medicine today is published without statistics or only with descriptive statistics--and this trend is falling. The reader of medical publications should be in a position to make a critical analysis of the methodology and content, since one cannot always rely on the conclusions drawn by the authors: statistical errors appear very frequently in medical publications. Due to the specific methodological features involved, the assessment of meta-analyses demands special attention. The number of published meta-analyses has risen 40-fold over the last ten years. Important examples for the practical use of statistical methods in insurance medicine include estimating extramortality from published survival analyses and evaluating diagnostic test results. The purpose of this article is to highlight statistical problems and issues of relevance to insurance medicine and to establish the bases for understanding them.

Actuarial Analysis↗

PubMed2001

[Beta risk: an unrecognized risk of statistical error].

Data collected in a medical study should, from a methodological point of view, be considered as a sample taken from a larger population. The purpose of the statistical analysis is to check whether the differences in the experimental results observed in different subgroups are related to chance or not. The risks of error must be known to assess the validity of the conclusions. The first order risk, also called the alpha risk, is the risk of announcing a wrongly positive conclusion, that is to conclude that there is a significant difference that in reality does not exist. By convention, an alpha risk of 5 p. 100 is generally accepted. This means that it is acceptable to announce a statistically positive test when no difference exists in at most 5 p. 100 of the cases. After recording and processing the data, the statistical analysis produces a value called p that is the exact value of the first order risk in the given situation. If p is less than or equal to the alpha risk accepted before the study, it can be concluded that the observed difference is statistically significant at the chosen alpha level and that the p value represents the risk of first order risk in the given situation. If p is greater than the initially accepted alpha, the observed difference is not considered to be significant at the alpha level. But the assertion that two samples are equivalent, also involves a second order risk, also called the beta risk, that must be known. The beta risk is the risk of announcing wrongly negative results, that is to conclude that two samples are equivalent while in reality they are different. The number of elements in each sample necessary to demonstrate a difference becomes greater as the size of the difference becomes smaller. The beta risk increases as the alpha risk decreases, the number of cases becomes smaller, and the difference to detect becomes smaller. If a difference is not statistically significant at the chosen alpha level, the beta risk of an erroneous conclusion of equivalence is generally less than or equal to 20 p. 100. In most cases, the beta risk is not determined before the study but after, being calculated from the alpha risk, the sample size, and the non-significant difference observed. If the beta risk is found to be greater than 20 p. 100, no conclusion can be drawn and the study data are useless. It is therefore preferable to define both the alpha and beta risk and the smallest clinically pertinent difference, and to calculate the necessary sample size, before initiating the study. Let us take a numerical example where two different treatments, A and B, are given to two groups of 100 patients each. Treatment A produced success in 70 cases and treatment B in 80 cases. The chi-squared test yields a p value of 0.10. The observed difference is thus not statistically significant at an alpha level of 5 p. 100. In this case, the calculated beta risk is 54 p. 100. With 200 patients and a beta risk of 20 p. 100, a difference of 20 p. 100 in the success rates between the two groups cannot be detected. If it is accepted that a difference of 10 p. 100 between the success rates is clinically pertinent, to have an acceptable beta risk of 20 p. 100 and detect the difference, the study would have to include 500 patients instead of 200. In conclusion, when a comparative study concludes that there is no significant difference between two groups, one cannot deduct that these two groups are identical unless the beta risk is less than 20 p. 100. If the beta risk is greater than 20 p. 100, or if it is not mentioned, one cannot conclude that the two groups are equivalent.

Bias↗