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PubMed2003

Assessing statistical analyses of clinical trials: primer in statistical methods for thoracic surgeons.

Each year there are many randomized controlled trials published for thoracic surgeons. To apply the results of these articles to their patients, surgeons need to have an understanding of the criteria for assessing the validity of these articles. They need to be able to recognize both methodological and statistical flaws a study may have. This article presents a published "validity" tool that can be used for assessment, gives flowcharts to show which statistical tests can be used, and also presents the assessment findings of a review of 52 published randomized controlled trials. Key issues discussed include documentation of validity items in the methods section, overuse of P values, and correct statistical analyses for patients measured at various times. The article concludes with suggestions for future assessment and publication.

Clinical Trials as Topic↗
PubMedDate not supplied

Descriptive statistics, Part II: Most commonly used descriptive statistics.

Descriptive measures can reveal a great deal of information about any variable of interest, whether the data be clinical, administrative, educational, or research data. To make best use of a descriptive statistic, it is important to know what levels of measurement should be used with the statistic, and what information the statistic can provide. To find out about the most typical case, measures of central tendency are appropriate. To discover whether the variable has a normal distribution, measures of shape should be applied. And to discover the variability about the mean of the variable, measures of dispersion should be used. Finally, percentiles and quartiles are useful for describing the placement of a single case in a population.

Analysis of Variance↗
PubMed2001

Evaluating statistical difference, equivalence, and indeterminacy using inferential confidence intervals: an integrated alternative method of conducting null hypothesis statistical tests.

Null hypothesis statistical testing (NHST) has been debated extensively but always successfully defended. The technical merits of NHST are not disputed in this article. The widespread misuse of NHST has created a human factors problem that this article intends to ameliorate. This article describes an integrated, alternative inferential confidence interval approach to testing for statistical difference, equivalence, and indeterminacy that is algebraically equivalent to standard NHST procedures and therefore exacts the same evidential standard. The combined numeric and graphic tests of statistical difference, equivalence, and indeterminacy are designed to avoid common interpretive problems associated with NHST procedures. Multiple comparisons, power, sample size, test reliability, effect size, and cause-effect ratio are discussed. A section on the proper interpretation of confidence intervals is followed by a decision rule summary and caveats.

Confidence Intervals↗
PubMed1985

Computer evaluation of statistical procedures, and a new quality-control statistical procedure.

I describe a program for definitive comparison of different quality-control statistical procedures. A microcomputer simulates quality-control results generated by repetitive analytical runs. It applies various statistical rules to each result, tabulating rule breaks to evaluate rules as routinely applied by the analyst. The process repeats with increasing amounts of random and systematic error. Rate of false rejection and true error detection for currently popular statistical procedures were comparatively evaluated together with a new multirule procedure described here. The nature of the analyst's response to out-of-control signals was also evaluated. A single-rule protocol that is as effective as the multirule protocol of Westgard et al. (Clin Chem 27:493, 1981) is reported.

Chemistry, Clinical↗
PubMed2005

An introduction to medical statistics for health care professionals: basic statistical tests.

This article, the third and final article in the series, aims to give health care professionals (HCPs) a sound and helpful introduction to medical statistics (Thomas, 2004, 2005). A brief summary of the content of the previous articles is given in Table 1. The current article will cover the area of basic statistical tests with the aim of guiding HCPs to the correct test for a particular research question and dataset. The article will not go into great depth of the formal methods of calculation required for all the tests covered but I would suggest that the reader refer to standard textbooks (Jordan et al., 1998; Swinscow, 1998; Altman, 1994; Bland, 2000), the help sections of statistical packages (SPSS or Stata), or consult a statistician. For ease of reference within the article the tests have been grouped by the data type, i.e. numerical or categorical. Further separation within each data type has been carried out depending on the number of groups being compared, whether the groups are independent, the size of the sample, and, in the case of numerical data, the distribution of the variable. For quick reference, two other tables are also presented which summarize which analysis methods should be used in each situation.

Journal Article↗
PubMed1993

Statistical analyses on the success potential of osseointegrated implants: a retrospective single-dimension statistical analysis.

Osseointegrated implants are a form of therapy that is finding increasing application in dental treatment. Long-term success can be ensured if the results of recall examinations are systematically documented. Life table analyses are being used to a growing extent to ensure objective assessment of the success of osseointegrated implants. With the help of the statistical method designed by Kaplan-Meier and Cutler-Ederer in 1958, the survival time of the implant can be calculated and the relationship between various covariables and the service time of the implants can be determined. This study calculates the success potential of 683 osseointegrated implants (IMZ and Brånemark) using the customary input-output statistics and life table analyses to explain the discrepancy between the two statistical methods, based on the results obtained.

Age Factors↗
PubMed2005

Statistical considerations for vaccine immunogenicity trials. Part 2: Noninferiority and other statistical approaches to vaccine evaluation.

Part 2 of this series investigates the statistical considerations of vaccine evaluation in an active-control trial. In particular, the strengths and weaknesses of the noninferiority methodology will be explored and contrasted for T-cell independent (does not elicit a memory response) and T-cell dependent (elicits a memory response) vaccines. At present, the noninferiority model is widely accepted as the primary tool for comparing the immunogenicity of a new or reformulated vaccine to an already existing licensed product. However, conclusions drawn from statistical hypothesis testing are dependent on the bioassay endpoint (e.g., antibody concentration) and the metric analyzed (e.g., geometric mean concentration, proportion fold-response, etc.). Competing vaccines may be highly immunogenic and still be judged inferior to licensed vaccines. T-cell dependent vaccines introduce new issues into the evaluation process regarding the analysis of short- and long-term immune response. Also, the kinetics of vaccine response is increasingly being recognized as an important variable in quantifying peak antibody levels after an immunization. This report will also illustrate a method for using multiple immunogenicity endpoints to measure vaccine effectiveness and protection through the use of statistical models and indicate the strengths and weaknesses of using these techniques.

Animals↗
PubMed1981

Laser eye safety: the implications of ordinary speckle statistics and of speckled-speckle statistics.

The implications of speckle statistics on laser eye-safety considerations are evaluated. The concept of speckled speckle is introduced, and its statistics are shown to correspond to the K0 function. Speckled speckle is defined in terms of the retinal power density when the eye is viewing an optically rough surface that is illuminated by a laser beam diffused through a ground-glass screen-a situation corresponding to subjective speckle modulated by objective speckle. Extensive numerical results are developed relating the ratio of the average power density on the retina over the eye-damage level to the acceptable probability that speckle statistics will cause the damage level to be exceeded. For ordinary speckle and for speckled speckle, for a probability of 10(-6) (10(-9)) of exceeding the damage level, the average power densities must be 0.072 (0.48) and 0.017 (0.0079) of the damage level, respectively.

Accident Prevention↗
PubMed1990

Factors affecting the precision of bone mineral measurements. Part 2: Some statistical notes relating to photon counting statistics and rates of bone loss.

This paper discusses some statistical aspects of absorptiometric bone mineral measurements. In particular, the contribution of photon counting statistics to overall precision is estimated, and methods available for carrying out statistical comparisons of bone loss and determining their precision are reviewed. The use of replicate measurements as a means of improving measurement precision is also discussed.

Absorptiometry, Photon↗
PubMed1989

[Coding of cause of death for mortality statistics--a comparison with results of coding by various statistical offices of West Germany and West Berlin].

1.136 death certificates representing all 1985 Bremen cardiovascular deaths and a 50%-sample of non-cardiovascular deaths in the age group 25-69 years were analyzed for reliability of nosologists' coding according to ICD-coding rules (9th revision). The 1.136 photocopied death certificates were used to assess intra-observer-variation in Bremen and to determine inter-observer-variation among 7 nosologists from 6 different State Statistical Offices and the Federal Statistical Office. Intra-observer-agreement in Bremen was found to be similar to the results presented in a comparable US-study: Bremen: 92.1%; Curb et al. 1983: 94.8%-96.1%; 3-digit-ICD-Code. Inter-observer-agreement was found to be much lower in Germany than in two US-studies: 3 coders agreeing on 3-digit-ICD-Code: Bremen: 67.7% (average, 3 coders out of 7); Curb et al.: 90.2% (3 coders); 3 coders agreeing on 4-digit-ICD-Code: Bremen: 61.5%; NCHS 1980: 90.3%. Agreement-rates were also much lower in Germany than in the USA (Curb et al.) when particular disease groups were analysed: Ischaemic heart disease (ICD 410-414): Bremen: 82.7% (average); USA: 97.2%; cerebrovascular disease (ICD 430-438): Bremen 65.6% (average); USA: 93.2%; neoplasms (ICD 140-239): Bremen: 94.0% (average); USA: 97.8%. We conclude that training, individual characteristics of nosologists, and other factors may cause important artifacts when comparing German mortality statistics on a regional level or during different time intervals.

Berlin↗
PubMed1996

The statistical big bang of 1911: ideology, technological innovation and the production of medical statistics.

This paper examines the relationship between intellectual debate, technologies for analysing information, and the production of statistics in the General Register Office (GRO) in London in the early twentieth century. It argues that controversy between eugenicists and public health officials respecting the cause and effect of class-specific variations in fertility led to the introduction of questions in the 1911 census on marital fertility. The increasing complexity of the census necessitated a shift from manual to mechanised forms of data processing within the GRO. The subsequent increase in processing power allowed the GRO to make important changes to the medical and demographic statistics it published in the annual Reports of the Registrar General. These included substituting administrative sanitary districts for registration districts as units of analysis, consistently transferring deaths in institutions back to place of residence, and abstracting deaths according to the International List of Causes of Death.

Censuses↗
PubMed1984

[Statistics of obligatory accident insurance--a contribution to health statistics in Switzerland].

After our compulsory accident insurance was reorganized, all employees and workers of Switzerland have been insured against accidents and occupational diseases since the beginning of 1984. The federal law on accident insurance prescribes also homogeneous statistics and SUVA has to run this task. The data thus gained are important for the Swiss health statistics and make it possible to set forth on a broader basis the work begun by SUVA under the previous law. The essay reviews object, organization and use of the data as well as the various aspects of data protection.

Accidents, Occupational↗
PubMedDate not supplied

Processing of data generated by 2-dimensional gel electrophoresis for statistical analysis: missing data, normalization, and statistics.

Several high-throughput statistical methods were evaluated for processing data generated by two-dimensional polyacrylamide gel electrophoresis, including how to handle missing data, normalization, and statistical analysis of data obtained from 2-D gels. Quantile normalization combined with a nonparametric permutation test based on minimizing false discover rates gave the highest yield of proteins that changed with genotype and detected the anticipated 50% decrease in Mn-superoxide dismutase (MnSOD) protein levels in mitochondrial extracts obtained from MnSOD-deficient mice.

Animals↗
PubMed1999

Statistical method to evaluate management strategies to decrease variability in operating room utilization: application of linear statistical modeling and Monte Carlo simulation to operating room management.

BACKGROUND: Operating room (OR) managers seeking to maximize labor productivity in their OR suite may attempt to reduce day-today variability in hours of OR time for which there are staff but for which there are no cases ("underutilized time"). The authors developed a method to analyze data from surgical services information systems to evaluate which management interventions can most effectively decrease variability in underutilized time. METHODS: The method uses seven summary statistics of daily workload in a surgical suite: daily allocated hours of OR time, estimated hours of elective cases, actual hours of elective cases, estimated hours of add-on cases, actual hours of add-on cases, hours of turnover time, and hours of underutilized time. Simultaneous linear statistical equations (a structural equation model) specify the relationship among these variables. Estimated coefficients are used in Monte Carlo simulations. RESULTS: The authors applied the analysis they developed to two OR suites: a tertiary care hospital's suite and an ambulatory surgery center. At both suites, the most effective strategy to decrease variability in underutilized OR time was to choose optimally the day on which to do each elective case so as to best fill the allocated hours. Eliminating all (1) errors in predicting how long elective or add-on cases would last, (2) variability in turnover or delays between cases, or (3) day-to-day variation in hours of add-on cases would have a small effect. CONCLUSIONS: This method can be used for decision support to determine how to decrease variability in underutilized OR time.

Humans↗
PubMed1993

Clinical methodologies and incidence of appropriate statistical testing in orthopaedic spine literature. Are statistics misleading?

An analysis of 300 randomly drawn orthopaedic spine articles, published between 1970 and 1990, was performed to assess the quality of biostatistical testing and research design reported in the literature. Of the 300 articles, 269 dealt with topics of an experimental nature, while 31 documented descriptive studies. Statistical deficiencies were identified in 54.0% of the total articles. Conclusions drawn as the result of misleading significance values occurred in 124 experimental studies (46%) while 96 failed to document the form of analysis chosen (35.7%). Statistical testing was not documented in 34 studies (12.6%), while 20 (7.4%) employed analyses considered inappropriate for the specific design structure.

Evaluation Studies as Topic↗
PubMed2002

Advanced statistics: statistical methods for analyzing cluster and cluster-randomized data.

Sometimes interventions in randomized clinical trials are not allocated to individual patients, but rather to patients in groups. This is called cluster allocation, or cluster randomization, and is particularly common in health services research. Similarly, in some types of observational studies, patients (or observations) are found in naturally occurring groups, such as neighborhoods. In either situation, observations within a cluster tend to be more alike than observations selected entirely at random. This violates the assumption of independence that is at the heart of common methods of statistical estimation and hypothesis testing. Failure to account for the dependence between individual observations and the cluster to which they belong can have profound implications on the design and analysis of such studies. Their p-values will be too small, confidence intervals too narrow, and sample size estimates too small, sometimes to a dramatic degree. This problem is similar to that caused by the more familiar "unit of analysis error" seen when observations are repeated on the same subjects, but are treated as independent. The purpose of this paper is to provide an introduction to the problem of clustered data in clinical research. It provides guidance and examples of methods for analyzing clustered data and calculating sample sizes when planning studies. The article concludes with some general comments on statistical software for cluster data and principles for planning, analyzing, and presenting such studies.

Cluster Analysis↗
PubMed1995

A mini-lesson in statistics: what causes treatment groups to be deemed 'not statistically different'?

The theory behind a "not-statistically significant difference" (NS) in group comparison research is crucial for proper study design. Proprietary company-sponsored research whose purpose is to demonstrate null intervention effects is widespread. Therefore, a discussion of null effects in research results was undertaken. Type I and Type II errors are explained, and reasons for "NS" results detailed. Clinical differences, power, statistical tests chosen, and values of alpha and beta are included in the discussion. Examples from studies of the effects of artificial baby milk gift packs are offered. If the aim of research is to demonstrate NS between treatments, or if the conclusion of published research is NS, then the research must demonstrate a design which ensures ethical and appropriate levels of both Type I and Type II errors.

Bias↗