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PubMed1975

[Statistics--a panacea? (author's transl)].

The fundamentals of statistics and the limitations of statistics are discussed from their practical and philosophical aspects. In summary results based on statistical methods should be reviewed again and again and corrected if necessary and one should not disregard results which have already been obtained by other basic sciences. In a given goal and the related expectations and opinions it is erroneous to rely completely on the results of a statistical and mathematical calculation. A definitive border between secure knowledge and mere opinion cannot be obtained. Only degrees of certainty exist. The problems of the calculation methods for the perinatal mortality of newborns and their practical conclusions and the question of the relationship between income and number of offspring are used to show that statistical and mathematical methods have their value but cannot be a goal in themselves. It is dangerous to persist in the uncritical belief that each number is equivalent to absolute truth as long as it recorded accurately enough. The ancillary science of statistics does not furnish a general comprehension of the subject matter in natural sciences and the humanities.

Birth Rate↗
PubMedDate not supplied

[Statistics as support tool and not as a decision tool].

The use of statistics in medical articles has risen a lot during the last decades, however it is used in a thoughtless manner in many instances. Today, Statistics is the only tool that allows the medical researcher to obtain results and benefits from those studies the relationships of which can not be interpreted from a determinist perspective, because it is a branch of applied mathematics objective of which is to manage and quantify the uncertainty of the available information, to support decision taking. The objective of this article is to review the basic statistical concepts that every doctor should know to be able to perform and/or detect quality research, as well as to underline the most frequent errors committed when interpreting statistical results. We review the general concepts about data synthesis and differentiation of the different types of measurements, hypothesis testing and errors that can be committed doing it, the real meaning of the "p" value, differentiation between statistically significant and clinically relevant results, the importance of confidence intervals as a measure of significance and clinical relevance, the confusion generated between two concepts that are different as standard deviation and standard error, and the criteria that govern the selection of the adequate statistical tests to evaluate relationships between variables.

Confidence Intervals↗
PubMed2003

Non-governmental health statistics on the World Wide Web.

Health statistics are an important decision-making resource for professionals and consumers alike. Professional use of statistics is wide ranging and often includes legislative decision making, medical research, grant writing, and population analysis. Locating needed or even helpful statistics to make these decisions or analyses can seem overwhelming. Organizations advocating for the treatment or assessment for particular diseases and health conditions are a prime resource for needed statistics. This column describes links and provides summaries to organizations that offer statistical resources for many of the leading causes of disability and death; it is the second of two columns devoted to finding health statistics on the Web. A helpful search strategy is presented which includes the utilization of evaluative criteria to seek legitimate and credible information.

Databases, Factual↗
PubMed2003

A review of the limitations of statistical analysis of treatment outcomes used in periodontal research.

Results of clinical trials are reported in terms of statistical significance, but interpretation of statistical significance in relation to clinical benefits is limited. Furthermore, the compromises inherent in the design of clinical trials, and in the statistical analysis techniques themselves cast doubts on the soundness of the inferences drawn from data from clinical trials. The aim of this paper is to review the published research on data management and interpretation in periodontal research, and to attempt to find ways in which the results of clinical trials could be reported in a more meaningful way, in terms of treatment outcomes. It is concluded that due to the limitations of current statistical methods and trial design, that clinical outcome variables should be reported in addition to statistical significance. Furthermore, due to the multiplicity of sites, subjects, and variables used to report on clinical conditions that may not be linear in their progression or regression, care needs to be exercised in interpretation of the results of clinical trials to avoid reporting positive outcome results that arise out of the limitations of current statistical analysis techniques or computational errors rather than clinical changes.

Clinical Trials as Topic↗
PubMedDate not supplied

[Use of statistics in occupational medicine. Analysis of papers presented at the national congresses of the Italian Society of Occupational Medicine and Industrial Hygiene].

The statistical methods used in occupational health studies were evaluated by analyzing the papers published from 1986 to 1990 in the proceedings of the annual meetings of the Italian Society of Occupational Medicine and Industrial Hygiene, in order to calculate the degree of understanding of the readers provided with only one-sided knowledge of statistical subjects and to improve the educational objectives in postgraduate schools of occupational health. Almost 70% of the 1151 articles reviewed contained some kind of statistical analysis: methods more complicated than descriptive statistics were used in about 35%. Student's t test (15%) and chi square (12%) were the most common methods. Other methods were less frequently used, so that it was possible to estimate that the learning of any new method would improve the understanding of about 1-2% of the articles. A wider use of statistical methods in data analysis is recommended; the attainment of a higher level of statistical knowledge should be a priority target in occupational health training.

Education, Medical, Graduate↗
PubMed2003

Input-output statistical independence in divisive normalization models of V1 neurons.

Simoncelli and co-workers have proposed statistically-derived nonlinear divisive normalization models of the primary visual cortex (V1) that are consistent with the hypothesis that sensory systems are adapted to the signals to which they are exposed. In this paper, we present a more rigorous mathematical formulation and analysis of these statistically-derived models in terms of mutual information as a metric for statistical independence. We prove that the ad hoc choice of divisive normalization parameters proposed by Simoncelli and co-workers does not guarantee statistical independence between the output responses, but interestingly such choice does guarantee that each output response is statistically independent of almost all the linear inputs. This holds for the two different models of natural image statistics analysed theoretically, and is consistent with empirical results obtained on a set of natural images.

Animals↗
PubMed2005

[Appropriate application of statistical analysis method in medical science and technology articles].

OBJECTIVE: To introduce the appropriate application of statistical analysis method in medical science and technology articles. METHODS: Explained the correct application of statistical theories in statistical package, statistical analysis methods and test criterion which compose the basic statistical content in an article. RESULTS: If the distribution of numerical variable is normal, mean and standard deviance can be used to describe this variable. In the same way, t test and analysis of variance (ANOVA) can be used to test the difference of mean in each group. If it is not normal, median and range can be used to describe the variable and rank sum test can be used to test the difference of distribution in each group. Categorical variable can be described by rate, proportion and ratio. There are chi-square test, fisher's exact test and rank sum test to test the difference of rates. CONCLUSION: It is the key of choosing rational statistical methods to distinguish the type of design and variable.

Analysis of Variance↗
PubMedDate not supplied

Evidence-based program requirements: evaluation of statistics as a required course.

A retrospective student record review was conducted to determine how achievement in a prerequisite statistics course related to achievement in nursing research courses and the overall program for undergraduate and graduate nursing students. For undergraduate students (n=218 generic, n=111 RN/BS), the statistics grade was associated with 4.3 percent of the variance in research course grades and 6.8 percent of the variance in graduating grade point average (GPA), controlling for entering GPA. For students in accelerated second-degree programs (n=33), there were minimal differences in mean research course grades and graduating GPA between students with and without prior statistics courses. For master's degree students (n=160), higher statistics grades were not associated with graduate research course grades. At best, the amount of prediction associated with statistics course grades was found to be small and not educationally meaningful. The value of statistics as a program requirement for undergraduate or graduate nursing students cannot be supported by these analyses.

Analysis of Variance↗
PubMedDate not supplied

Support with clarity: a proper trend in medical statistics.

BACKGROUND: The aim of this study was to establish effective methods to review and evaluate, and to emphasize in support with clarity as a proper trend in medical statistics. METHODS: The clinical research material used in this study is stemmed from JAMC. STUDY: I, N is 220 subjects, from Pattern of Coronary Arterial Distribution and Its Relation to Coronary Artery Diameter, Z A Kaimkhani, MM Ali, AMA Faruqi JAMC Jan-Mar 2005; 17(1): 40-3. STUDY: II, N is 105 patients, from Sclerotherapy Plus Octreotide Versus Sclerotherapy Alone In The Management Of Gastro-Oesophageal Variceal Hemorrhage. HA Shah, K Mumtaz, W Jafri, S Abid, S Hamid, A Ahmad, Z Abbas. JAMC Jan-Mar 2005; 17(1): 10-4. 2 Systemic review and evaluation with statistical principles is to be used. ASSESSMENT AND DISCUSSION: The reports of 2 clinical researches from JAMC are assessed, and both used for demonstrating the characteristics and pitfalls statistically. CONCLUSION: Before reaching any significant difference in statistics, hopefully, all clinicians will be able to deal with the data to be measured by selecting proper statistical models as the best as we can in order to gain appropriate inference in medical statistics.

Humans↗
PubMed1991

Statistical conclusion validity. Multiple inferences in rehabilitation research.

The problem of multiple statistical inferences and Type I error rates in rehabilitation research is examined. The Bonferroni method is the most commonly advocated procedure to control Type I error in clinical research. The traditional Bonferroni method is often overly conservative and results in a loss of statistical power when more than a small number of comparisons are evaluated. Adjustments to the Bonferroni method designed to control or reduce the incidence of Type I errors and improve the statistical conclusion validity of rehabilitation research are presented. The adjusted or sharpened Bonferroni methods allow the researcher to control the incidence of Type I errors while maintaining statistical power. Adjustments to the Bonferroni method are simple to compute and applicable to a wide variety of statistical tests. The use of appropriate multiple comparison procedures will reduce the number of Type I errors and improve the statistical conclusion validity of rehabilitation research studies.

Bias↗
PubMedDate not supplied

[Vital statistics: mechanisms of improvement].

Within the realm of basic statistics, information on vital facts represents a main tool for the study of the movement and dynamics of a population. Despite their importance, vital statistics in developing countries have had a limited development due to financial, cultural, geographical and coordination factors among the different institutions involved in recording vital statistics. Since 1982, Mexico has experienced a radical change in its system of vital statistics, which remained almost untouched for close to a century. Now (1988), the Mexican system of vital statistics has a completely modified and updated structure. This new structure permits interinstitutional actions which are necessary in order to achieve the consolidation of the system of vital statistics in Mexico.

Humans↗
PubMed1988

A statistical procedure for measuring and evaluating performance in interlaboratory comparison programs.

There are scientific and regulatory needs to measure individual laboratory performance on a series of challenges, for single analytes and for all analytes in a particular discipline. Because these needs must be met with a very limited amount of information, optimal statistics should be used to measure performance. Since punitive action could result from poor performance, there should be precise quantitative goals that can be measured directly with the performance statistic. Finally, it is important to limit the likelihood of falsely penalizing a laboratory, since the large majority of laboratories are not in need of regulatory action. A statistic is described that measures individual performance on quantitative interlaboratory proficiency tests. This statistic is based on actual error relative to the amount of error that is tolerable. It can be accumulated over several challenges (specimens tested as unknowns) to give an estimate of a participant's performance level for that analyte. It can also be accumulated across analytes to give scores for Survey mailings or for accumulated performance. Because the statistic measures error, it contains more information than does the percentage of acceptable results, and therefore has greater power to detect poor performance. The distribution of the statistic is described and tested for validity. Then, a procedure is presented to evaluate laboratories relative to a performance goal. The entire procedure is then tested with recent College of American Pathologists Chemistry Survey data.

Academies and Institutes↗
PubMedDate not supplied

Breast cancer statistics: use and misuse.

Statistics can be manipulated, by using various methods of reporting, to support almost any type of regime in breast cancer. The basis for statistical calculations include: the definition of the population of patients treated, the time used for starting calculations, the exact nature of the treatment used and any adjuvant therapy, the prognostic parameters utilized, the importance of long-term follow-ups, adequate number of cases, therapy standardization and pathological reporting, methods of evaluating survival such as observed or crude, relative and no evidence of disease, methods of calculating observed survival rates such as absolute, actuarial or life-table and Kaplan Meier or product limit, statistical evaluation for planned improvement with acceptable Type I and Type II errors, and the use of arithmetic and logarithmic scales in plotting statistics. Purveyors of innovative methods for the treatment of breast cancer aimed at preserving part or all of the breast as cosmetic alternatives to mastectomy, by limited operations with and without primary radiotherapy, have a most appealing argument to a woman with breast cancer. To determine if such procedures are justifiable alternatives to selective mastectomies and reconstructions, detailed, long-term statistical data on large numbers of cases must be available and end-results comparisons of various therapeutic modalities must be made on the basis of comparable statistical data.

Breast Neoplasms↗
PubMed1985

Statistical analysis of multi-eye data in ophthalmic research.

An apparently common error in statistical analysis of ophthalmic data is to perform statistical tests that do not account for the correlation generally present between observations made for the right and left eyes of a subject. This error has as a consequence an overstatement of the precision of the study, resulting in incorrect P values which indicate a greater measure of statistical significance than the data warrant. As measures to reduce the occurrence of this serious error in statistical analyses, the authors recommend increased emphasis on educational programs for investigators, stimulation of nontechnical articles reviewing statistical methods, and a sharper focus upon statistical analysis in the peer review process.

Eye Diseases↗
PubMed1983

A comparison of test statistics for assessing the effects of concomitant variables in survival analysis.

In data analysis involving the proportional-hazards regression model due to Cox (1972, Journal of the Royal Statistical Society, Series B 34, 187-220), the test criteria commonly used for assessing the partial contribution to survival of subsets of concomitant variables are the classical likelihood ratio (LR) and Wald statistics. This paper presents an investigation of three other test criteria with potentially major computational advantages over the classical tests, especially for stepwise variable selection in moderate to large data sets. The alternative criteria considered are Rao's efficient score statistic and two other score statistics. Under the Cox model, the performance of these tests is examined empirically and compared with the performance of the LR and Wald statistics. Rao's test performs comparably to the LR test in all the cases considered. The performance of the other criteria is competitive in many cases. The use of these statistics is illustrated in a study of coronary artery disease.

Analysis of Variance↗
PubMed1980

Interpretation of research data: selected statistical procedures.

Selected statistical procedures used in the analysis of research data are presented. The relationship of significance testing to research hypotheses is explained in terms of tests of differences and correlation. Also, the differences, assumptions, and advantages and disadvantages of parametric and nonparametric statistics are discussed. With regard to each statistic presented, emphasis is placed on the hypotheses that would be tested, the kinds of data for which the statistic is appropriate, the method of calculation, and how to test for "significance." The selected statistical procedures include the Student's t-test and chi square. An explanation of the concept of correlation is provided, and several correlation coefficients are discussed, including the Pearson r, Spearman rho, Kendall's tau, the point biserial, biserial, phi coefficient, and contingency coefficient. Pharmacists must know basic statistical procedures in order to be able to effectively interpret the results of published research or to appropriately analyze data that have been collected in their own research endeavors.

Methods↗
PubMed1997

Doctors' statistical literacy: a survey at Srinagarind Hospital, Khon Kaen University.

Medical doctors need to keep abreast of new developments in medicine. This is often done by reading medical journals and carrying out research activities that require an understanding of statistical methods. This study was designed to assess the knowledge of statistics among doctors in Thailand. A pretested, self-administered questionnaire with nine multiple-choice questions on basic statistical issues was used. In a survey of university hospital staff, there were 365 doctors, including 156 specialists, 152 residents and 57 final year medical students (externs). The overall response rate was 40.0 per cent. The overall median number of correct answers was 4.0 (95% CI 3.0, 4.0). Specialists had a significantly higher median score, 4.0 than residents, and externs, 3.0's, (p = 0.02). Respondents who had previously attended statistical workshops had a significantly higher median score (5.0) than those who had not (3.0) (p < 0.01). These results indicate that doctors in our hospital have insufficient knowledge of the basic statistical concepts that are commonly used in medical journals. Continuing education in statistics for doctors during residency and post doctoral training must be given serious consideration.

Adult↗
PubMed1991

Statistics in medical journals: developments in the 1980s.

This paper reviews changes in the use of statistics in medical journals during the 1980s. Aspects considered are research design, statistical analysis, the presentation of results, medical journal policy (including statistical refereeing), and the misuse of statistics. Despite some notable successes, the misuse of statistics in medical papers remains common.

Clinical Trials as Topic↗