## The biostatistical minimum

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Every day in our clinical practice, probability and statistics are used for a broad variety of actions, including the explanation of levels of risk to patients, the access to clinical guidelines, the understanding of research publications, and the writing of investigation papers for analysing numerical data and treatments options. Therefore, an unintentional statistical misconduct may originate from many sources. It is often difficult to detect, and little is known regarding the prevalence or underlying causes of research misconduct among biomedical researchers. The improvements in teaching statistics to thoracic surgeons should improve the thoughtful of statistical concepts and should reduce the incidence of fallacies. The Biostatistical basis should comprise the aspect of the Biostatistics that surgeons should be aware of correctly interpreting in their research findings: the understanding of p-values, confidence intervals, Student’s t-tests, Z test, chi-square goodness of fit, ANOVA tables, and basic statistical models (linear or logistic regression). The understanding of Biostatistics is essential to all thoracic surgeons, and it is not unaware since most received some statistics lessons in their training. The Statistic Corner of the Journal of Thoracic Diseases kept the emphasis on Biostatistical methods to applies and when. Thus, various authors wrote about the analyses of several types of outcomes variables, the analyses of study design, the measures of association and impact, and the general strategies for the statistical analyses. Deceptively, these Statistic Corner articles have only scratched the surface. Nonetheless, we hope that had provided a stimulus to enhance the skills to interpret Biostatistics.

## Using the confidence interval confidently

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Biomedical research is seldom done with entire populations but rather with samples drawn from a population. Although we work with samples, our goal is to describe and draw inferences regarding the underlying population. It is possible to use a sample statistic and estimates of error in the sample to get a fair idea of the population parameter, not as a single value, but as a range of values. This range is the confidence interval (CI) which is estimated on the basis of a desired confidence level. Calculation of the CI of a sample statistic takes the general form: CI = Point estimate ± Margin of error, where the margin of error is given by the product of a critical value (z) derived from the standard normal curve and the standard error of point estimate. Calculation of the standard error varies depending on whether the sample statistic of interest is a mean, proportion, odds ratio (OR), and so on. The factors affecting the width of the CI include the desired confidence level, the sample size and the variability in the sample. Although the 95% CI is most often used in biomedical research, a CI can be calculated for any level of confidence. A 99% CI will be wider than 95% CI for the same sample. Conflict between clinical importance and statistical significance is an important issue in biomedical research. Clinical importance is best inferred by looking at the effect size, that is how much is the actual change or difference. However, statistical significance in terms of P only suggests whether there is any difference in probability terms. Use of the CI supplements the P value by providing an estimate of actual clinical effect. Of late, clinical trials are being designed specifically as superiority, non-inferiority or equivalence studies. The conclusions from these alternative trial designs are based on CI values rather than the P value from intergroup comparison.

## Truths, lies, and statistics

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Dissemination of quality research findings are necessary for the widespread improvement of patient care. Without quality research, practice improvement would depend upon individual clinicians own observations and desire to improve.

## Design of interventional studies in thoracic surgery

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Interventional studies differ from observational studies in that one or more specific interventions are evaluated. Randomized controlled trials remain the gold standard for interventional studies and can take different forms. In surgical studies, the three types of randomized controlled trials most commonly encountered are: (I) trials that compare two different medical treatments for patients undergoing surgery; (II) trials that evaluate two different surgical techniques and (III) studies that compare surgery vs. non-operative management. When an intervention is to be evaluated but a randomized controlled trial is not feasible, alternative interventional study designs may be considered.

## Guidelines for standardizing and increasing the transparency in the reporting of biomedical research

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Guidelines for standardizing and increasing the transparency in the reporting of biomedical research

## How to design a randomized clinical trial: tips and tricks for conduct a successful study in thoracic disease domain

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How to design a randomized clinical trial: tips and tricks for conduct a successful study in thoracic disease domain

## How to describe univariate data

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How to describe univariate data

## Basic statistics with Microsoft Excel: a review

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Basic statistics with Microsoft Excel: a review

## Types of biological variables

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Types of biological variables

## A general introduction to adjustment for multiple comparisons

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A general introduction to adjustment for multiple comparisons

## An overview of the use of artificial neural networks in lung cancer research

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An overview of the use of artificial neural networks in lung cancer research

## Tips and tricks of the propensity score methods in the thoracic surgery research

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Tips and tricks of the propensity score methods in the thoracic surgery research

## Nanos gigantium humeris insidentes: the awarded Cox proportional hazards model

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Luca Bertolaccini Alessandro Pardolesi Fabio Davoli  Piergiorgio Solli

## P value interpretations and considerations

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Matthew S. Thiese, Brenden Ronna, Ulrike Ott

## Study designs in thoracic surgery research

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Andrea Viti, Alberto Terzi, Luca Bertolaccini

## Misuse of statistics in surgical literature

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Matthew S. Thiese, Brenden Ronna, Riann B. Robbins

## Statistical modelling for thoracic surgery using a nomogram based on logistic regression

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Run-Zhong Liu, Ze-Rui Zhao, Calvin S. H. Ng

## The game theory in thoracic surgery: from the intuitions of Luca Pacioli to the operating rooms management

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Argante Ciocci, Andrea Viti, Alberto Terzi, Luca Bertolaccini

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Alan D. L. Sihoe

## Longitudinal studies

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Edward Joseph Caruana, Marius Roman, Jules Hernández-Sánchez, Piergiorgio Solli

## Interpretation of lung cancer study outcomes

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Diego Cortinovis, Marida Abbate, Paolo Bidoli, Davide Pelizzoni, Stefania Canova

## Mismatching of population groups in thoracic surgery case control studies

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Marius Roman, Edward Caruana, Jules Hernandez-Sanchez, Piergiorgio Solli

## The Statistical point of view of Quality: the Lean Six Sigma methodology

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Luca Bertolaccini, Andrea Viti, Alberto Terzi

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Antonello Pasini

## The use of correlation functions in thoracic surgery research

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Lucile Gust, Xavier Benoit D’journo

## A practical overview on probability distributions

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Andrea Viti, Alberto Terzi, Luca Bertolaccini

## Multivariate analysis in thoracic research

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Noemí Mengual-Macenlle, Pedro J. Marcos, Rafael Golpe, Diego González-Rivas

## A synopsis of resampling techniques

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Alessandro Brunelli

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Eric Lim

## The chicken-and-egg debate about statistics and research

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Luca Bertolaccini, Andrea Viti, Alberto Terzi