3. Some concepts on experimental design

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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.

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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.

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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.

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2.3 Stratified randomization

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

Stratified Randomization

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3 Sample size

• The sample size and the design are crucial.

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

4 Bad design 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

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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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