1 Need for a good control

  • A good control group is crucial.

  • To assess the effect of an intervention, we need to compare a test and control group.

  • This is often not possible in a pretest/post-test design: e.g. effect before and after administering a drug without the use of a placebo group.

  • Groups in an observational study are often not comparable: advanced statistical methods are required to draw causal conclusions.

  • Double blinding

  • We have to be aware of confounding!

  • Randomized studies: random assignment of subjects in the study to the different treatment arms \(\rightarrow\) comparable groups.


2 Randomization

  • Randomization completely at random (no systematic allocation).

2.1 Simple Randomization

  • Can lead to differences in the number of experimental units in each treatment arm

  • in 5% of the cases we might observe an imbalance of
    • of at least 60:40 in a study with 100 subjects, and
    • of at least 531:469 in a study with 1000 subjects.
  • This imbalance is not problematic, but causes a loss in precision.


2.2 Balanced Randomization

  • Equal numbers of each treatment are assigned to a block of 2 or 4 patients.
      1. AB, (2) BA
      1. AABB, (2) ABAB, (3) ABBA, (4) BABA, (5) BAAB, (6) BBAA
  • Balanced Randomization ensures \(\pm\) the same number of people in the control and the treatment arm of the experiment.

  • Does not make that we have an equal number of males with and without the treatment, etc.

  • In small studies, it is possible that the groups are unbalanced in other characteristics (e.g. gender, race, age …)

  • This is not problematic because it occurs at random, but, again it causes a loss in precision.


2.3 Stratified randomization**

  • The imbalance according to for instance gender can be avoided using stratified Randomization: balanced randomization per stratum
Stratified Randomizatie

Stratified Randomizatie


3 Blocking

3.1 Gene expression example

  • dm: diabetic medium, nd: non diabetic medium, co: control
  • 4 bio-reps, 2 techreps/biorep

  • dm: diabetic medium, nd: non diabetic medium, co: control
  • 4 bio-reps, 2 techreps/biorep, 2 plates A & B
  • Treatment and plate almost entirely confounded

3.2 Nature methods: Points of significance - Blocking


4 Sample size

  • The sample size and the design are crucial.

  • The larger the sample size, the more precise the results.

5 Wrap-up

  • Sample size is very important.

  • To assess the effect of a treatment, we should compare comparable and representative groups of subjects with and without the treatment (a good control!).

  • In observational studies, the researcher cannot choose the treatment. It was the patient or their MD who had chosen it

  • In experimental studies, the researcher assigns the treatment.

  • Confounding can be avoided via randomization.

  • We can also correct for confounding in the statistical analysis for the confounders that have been registered.


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