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Conditional and marginal models

  Statistical analyses of related data need to take the relations into account to avoid misleading results. Two main types of statistical models are used, conditional and marginal models (1). A conditional model can be fitted within the framework of generalized linear mixed models (GLMM) and a marginal model using generalized estimating equations (GEE). Analyses based on conditional and marginal models give answers to different questions. While a conditional model can be used to estimate the outcome within a subject or cluster after conditioning on covariates, a marginal model can estimate the average outcome for the population accounting group-specific effects. For example, in a longitudinal study comparing patients receiving diet recommendations (treated) with patients receiving exercise recommendations (controls), uncontrolled blood pressure at repeated visits can be modelled using both a conditional and marginal model. Neither of these two models is automatically more correct ...
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The ICMJE recommendations

The most important guideline for writing a manuscript to be submitted to a medical scientific journal is the Manuscript Preparation and Submission recommendations from the International Committee of Medical Journal Editors (ICMJE). It can be found here (1). From a statistical viewpoint, two of the recommendations are exceptionally useful. The first is: "Describe statistical methods with enough detail to enable a knowledgeable reader with access to the original data to judge its appropriateness for the study and to verify the reported results." To be able to judge appropriateness and verify reported results is not only about naming the methods that have been used, it is also about why a particular method has been used, the investigators' intention. It may therefore be necessary to motivate the method choice by explaining the analysis strategy. Vague or otherwise unclear statements such as "independent samples t-test" and "as appropriate" should be avoi...
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Frequentists and Bayesians

Followers of today's two main traditions of statistical inference are known as frequentist and Bayesian. Ronald Fisher attempted during the 1930s to develop a third school called fiducial inference (1), but this was broadly considered controversial and do not play a major role today. The fundamental difference between frequentists and Bayesians is that they define probability in different ways. For a frequentist, a probability is an objective measure, a long-run relative frequency. For example, a fair coin has probability 0.5 of heads because, across a very large number of comparable tosses, half are heads, and a drug lowering blood pressure has an objectively measurable average effect among the patients taking the drug. The central limit theorem, which states that as the sample size increases, the distribution of sample means tends to approximate a normal distribution, is essential in frequentist inference. A Bayesian, on the other hand, sees probability as a subjective measure, a...
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