Common Pitfalls In Statistical Analysis Research Articles

Common pitfalls in statistical analysis: Understanding the properties of diagnostic tests - Part 1.

In this article in our series on common pitfalls in statistical analysis, we look at some of the attributes of diagnostic tests (i.e., tests which are used to determine whether an individual does or does not have disease). The next article in this series will focus on further issues related to diagnostic tests.

Common pitfalls in statistical analysis: Linear regression analysis.

In a previous article in this series, we explained correlation analysis which describes the strength of relationship between two continuous variables. In this article, we deal with linear regression analysis which predicts the value of one continuous variable from another. We also discuss the assumptions and pitfalls associated with this analysis.

Common pitfalls in statistical analysis: Measures of agreement.

Agreement between measurements refers to the degree of concordance between two (or more) sets of measurements. Statistical methods to test agreement are used to assess inter-rater variability or to decide whether one technique for measuring a variable can substitute another. In this article, we look at statistical measures of agreement for different types of data and discuss the differences between these and those for assessing correlation.

Common pitfalls in statistical analysis: The use of correlation techniques.

Correlation is a statistical technique which shows whether and how strongly two continuous variables are related. In this article, which is the eighth part in a series on ‘Common pitfalls in Statistical Analysis’, we look at the interpretation of the correlation coefficient and examine various situations in which the use of technique of correlation may be inappropriate.

Common pitfalls in statistical analysis: Intention-to-treat versus per-protocol analysis.

During the conduct of clinical trials, it is not uncommon to have protocol violations or inability to assess outcomes. This article in our series on common pitfalls in statistical analysis explains the complexities of analyzing results from such trials and highlights the importance of “intention-to-treat” analysis.

Common pitfalls in statistical analysis: Absolute risk reduction, relative risk reduction, and number needed to treat.

In the previous article in this series on common pitfalls in statistical analysis, we looked at the difference between risk and odds. Risk, which refers to the probability of occurrence of an event or outcome, can be defined in absolute or relative terms. Understanding what these measures represent is essential for the accurate interpretation of study results.

Common pitfalls in statistical analysis: The perils of multiple testing.

Multiple testing refers to situations where a dataset is subjected to statistical testing multiple times - either at multiple time-points or through multiple subgroups or for multiple end-points. This amplifies the probability of a false-positive finding. In this article, we look at the consequences of multiple testing and explore various methods to deal with this issue.

Common pitfalls in statistical analysis: Odds versus risk.

In biomedical research, we are often interested in quantifying the relationship between an exposure and an outcome. “Odds” and “Risk” are the most common terms which are used as measures of association between variables. In this article, which is the fourth in the series of common pitfalls in statistical analysis, we explain the meaning of risk and odds and the difference between the two.

Common pitfalls in statistical analysis: "No evidence of effect" versus "evidence of no effect".

This article is the first in a series exploring common pitfalls in statistical analysis in biomedical research. The power of a clinical trial is the ability to find a difference between treatments, where such a difference exists. At the end of the study, the lack of difference between treatments does not mean that the treatments can be considered equivalent. The distinction between “no evidence of effect” and “evidence of no effect” needs to be understood.

Common pitfalls in statistical analysis: Clinical versus statistical significance.

In clinical research, study results, which are statistically significant are often interpreted as being clinically important. While statistical significance indicates the reliability of the study results, clinical significance reflects its impact on clinical practice. The third article in this series exploring pitfalls in statistical analysis clarifies the importance of differentiating between statistical significance and clinical significance.

Common pitfalls in statistical analysis: "P" values, statistical significance and confidence intervals.

In the second part of a series on pitfalls in statistical analysis, we look at various ways in which a statistically significant study result can be expressed. We debunk some of the myths regarding the ‘P’ value, explain the importance of ‘confidence intervals’ and clarify the importance of including both values in a paper