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Randomisation and alternation in clinical trials

  In early clinical trials patients were allocated to treatment and control groups by alternation, i.e. patients were assigned to the treatment group and control group in a strict order. The advantage with this method was that it produced groups with equal size. The two groups would also be fairly compared if the order of the patients included in the trial was random. However, the group allocation for a patient was predictable, which could potentially introduce selection bias into the trial's outcome through differential invitations to participate. The only way to avoid this issue was, of course, to make the group assignment concealed, which necessitated a non-systematic group assignment sequence. D'Arcy Hart (1) describes the development with two trials from the 1940s. The first one investigating the effects of patulin (a mycotoxin) on the common cold in 1943-1944 with strict alternation. The second one, having a randomised group assignment, investigating the effects of strept...
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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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Pretesting normality

One common misunderstanding demonstrated in medical publications is the testing of normal distribution as part of a decision process: If Shapiro-Wilk Test or Kolmogorov-Smirnov Test indicates that a variable has a statistically significant departure from normal distribution, group mean differences are tested using the Mann-Whitney Test instead of Student's t-test. This decision rule may sound rock solid, but it may be a serious mistake. First, the practice treats absence of evidence as evidence of absence. A statistically nonsignificant normality test is simply not evidence of a normal distribution. Second, for a two-sample t-test or a linear statistical model, the relevant distributional issue concerns the difference or model residual conditional on covariates, not whether each observed treatment group's raw outcome values are exactly normal. Third, with small sample sizes, where the normality assumption may be important, the normality test has low power to detect non-normalit...
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