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Statisticians use words deliberately and specifically, but not necessarily in the way they are used colloquially. For example, in general parlance "statistics" can mean numerical information, usually data. In contrast, one large statistics textbook defines the term "statistic" to denote "a characteristic of a "sample", such as the average score", and the word "parameter" to denote "a characteristic of a "population"." However, for statisticians, statistics means more than just numerical information or a characteristic of a sample. Statistics is also a discipline. It is relevant to all areas of scientific enquiry, and spans study design, data collection, developing methods of analysis and analysing data, interpreting results, and making predictions. Other words that statisticians use carefully, and very particularly, form the cornerstones of statistical reasoning, words such as probability, significance, likelihood. Why does this matter? Well, it may well explain why non-statisticians struggle with the ideas and concepts used by statisticians. The reader is confused by the terminology, not the least because the statistician uses familiar words in different ways, a jargon similar to other professional vocabularies. This necessary precision of language becomes evident when considering one of the statistical methods--"the t test"--commonly used by biological scientists today. There are different ways to look at data. Some provide useful alternatives for scientists. For example, non-parametric tests could be considered. Another possibility may be permutation tests. A natural extension to significance testing is estimation and, in particular, the use of confidence intervals, where a correspondence with significance testing can be made. In this article, the authors describe how Student used statistics to measure the quality of the product at the brewery where he worked. (Contains 3 figures.)

For semi-continuous data which are a mixture of true zeros and continuously distributed positive values, the use of two-part mixed models provides a convenient modelling framework. However, deriving population-averaged (marginal) effects from such models is not always straightforward. Su et al. presented a model that provided convenient estimation of marginal effects for the logistic component of the two-part model but the specification of marginal effects for the continuous part of the model presented in that paper was based on an incorrect formulation. We present a corrected formulation and additionally explore the use of the two-part model for inferences on the overall marginal mean, which may be of more practical relevance in our application and more generally. [ABSTRACT FROM AUTHOR]

Background: Most reported outcome measures in rheumatoid arthritis (RA) trials are composite, whose components comprise single measures that are combined into one outcome. The aims of this review were to assess the range of missing data rates in primary composite outcomes and to document the current practice for handling and reporting missing data in published RA trials compared to the Consolidated Standards of Reporting Trials (CONSORT) recommendations.Methods: A systematic search for randomised controlled trials was conducted for RA trials published between 2008 and 2013 in four rheumatology and four high impact general medical journals.Results: A total of 51 trials with a composite primary outcome were identified, of which 38 (75 %) used the binary American College of Rheumatology responder index and 13 (25 %) used the Disease Activity Score for 28 joints (DAS28). Forty-four trials (86 %) reported on an intention-to-treat analysis population, while 7 trials (14 %) analysed according to a modified intention-to-treat population. Missing data rates for the primary composite outcome ranged from 2-53 % and were above 30 % in 9 trials, 20-30 % in 11 trials, 10-20 % in 18 trials and below 10 % in 13 trials. Thirty-eight trials (75 %) used non-responder imputation and 10 (20 %) used last observation carried forward to impute missing composite outcome data at the primary time point. The rate of dropout was on average 61 % times higher in the placebo group compared to the treatment group in the 34 placebo controlled trials (relative rate 1.61, 95 % CI: 1.29, 2.02). Thirty-seven trials (73 %) did not report the use of sensitivity analyses to assess the handling of missing data in the primary analysis as recommended by CONSORT guidelines.Conclusions: This review highlights an improvement in rheumatology trial practice since the revision of CONSORT guidelines, in terms of power calculation and participant's flow diagram. However, there is a need to improve the handling and reporting of missing composite outcome data and their components in RA trials. In particular, sensitivity analyses need to be more widely used in RA trials because imputation is widespread and generally uses single imputation methods, and in this area the missing data rates are commonly differentially higher in the placebo group. [ABSTRACT FROM AUTHOR]

The article presents a reprint of the article "Presenting data: can you follow a recipe?" by Gordon B. Drummond and Brian D. M. Tom, which appeared in the issue of "The Journal of Physiology," "British Journal of Pharmacology," and "Microcirculation." It highlights the need to consult a statistician concerning the sample size and methods of analysis, the description of statistics and the P values. It also notes the description of symmetrical samples and precision of mean values.

The author talks about statistics in research and the practicalities of how data should be interpreted, presented, and summarized. He suggests that researchers should consult statistician about sample sizes and methods of analysis and comments on the use of confidence intervals and mean difference. He uses as an example frog jumping research with control and experimental groups as a method of explaining confidence intervals and populations with symmetrical distribution or skewed distribution.

The authors offer their insights on a study to determine the effect of training on the jumping distance of frogs at the Calaveras competition in California. They propose to solve the issue through the use of statistical analysis based on the assumption that training has no effect on the mean distance jumped by a specific group of frogs. The authors also suggest the possibility that frogs can jump at varying distances because of genetic factors, and the effect of diet.

Background: A common feature of microarray experiments is the occurence of missing gene expression data. These missing values occur for a variety of reasons, in particular, because of the filtering of poor quality spots and the removal of undefined values when a logarithmic transformation is applied to negative background-corrected intensities. The efficiency and power of an analysis performed can be substantially reduced by having an incomplete matrix of gene intensities. Additionally, most statistical methods require a complete intensity matrix. Furthermore, biases may be introduced into analyses through missing information on some genes. Thus methods for appropriately replacing (imputing) missing data and/or weighting poor quality spots are required. Results: We present a likelihood-based method for imputing missing data or weighting poor quality spots that requires a number of biological or technical replicates. This likelihood-based approach assumes that the data for a given spot arising from each channel of a two-dye (two-channel) cDNA microarray comparison experiment independently come from a three-component mixture distribution — the parameters of which are estimated through use of a constrained E-M algorithm. Posterior probabilities of belonging to each component of the mixture distributions are calculated and used to decide whether imputation is required. These posterior probabilities may also be used to construct quality weights that can down-weight poor quality spots in any analysis performed afterwards. The approach is illustrated using data obtained from an experiment to observe gene expression changes with 24 hr paclitaxel (Taxol ®) treatment on a human cervical cancer derived cell line (HeLa). Conclusion: As the quality of microarray experiments affect downstream processes, it is important to have a reliable and automatic method of identifying poor quality spots and arrays. We propose a method of identifying poor quality spots, and suggest a method of repairing the arrays by either imputation or assigning quality weights to the spots. This repaired data set would be less biased and can be analysed using any of the appropriate statistical methods found in the microarray literature. [ABSTRACT FROM AUTHOR]

In a number of special cases, it has been shown that categorization of a continuous explanatory variable for use in a regression model can lead to efficiency losses. Nevertheless, categorization remains a popular remains of dealing with continuous variables, particularly in medical statistics. Two topics of current interest in medical statistics are subgroup effects in clinical trials and gene-environment interactions in genetic studies. In a regression model setting, these topics involve the examination of interaction effects, often between a binary explanatory variable and a continuous explanatory variable. In this article the efficiency losses associated with dichotomization of a continuous explanatory variable for the testing of such interaction effects are calculated and compared with those associated with the testing of main effects. It is shown that considerable additional efficiency loss can arise because of dichotomization in both a main effect and an interaction. The theoretical development is done in the context of exponential family models, thus also generalizing earlier results on efficiency loss for main effects. Some indication is also given of the losses in efficiency associated with more detailed categorization. The further impact of censoring in time-to-event models is investigated and shown to not alter the qualitative conclusions. The practical importance of the findings is illustrated through the analysis of data from a clinical trial in patients with prostate cancer. [ABSTRACT FROM AUTHOR]

A response by Gordon B. Drummond and Brian D. M. Tom to a letter to the editor about their articles concerning best practices in statistical reporting is presented.