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# Experimental design part 4 groups

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Part 4 of 5 of experimental design presentations. This one is focused on randomisation, assigning groups, independence and ways to stratify your research cohort. Decent opportunities to present about ethics.

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### Experimental design part 4 groups

1. 1. Identifying independence and assigning samples to groups I declare independence! Not quite Conro! Experimental design: part4
2. 2. At the end of the experiment you will perform statistical analyses on your data And you will plot graphs showing your results But what constitutes a data point? The key considerations is independence here How related are the samples to one another? Where are the sources of variability Before we can decide how many samples to collect, we need to decide what those samples are
3. 3. Let’s start with human studies The biggest source of variability is between individuals Most of the time you would consider each individual person as a separate data point If you take multiple measurements of the same thing in the same person then those data would make you more confident about the value you got for that individual but wouldn’t tell you more about the population
4. 4. You will use the mean or median of the multiple measurements in your final statistical analysis But ultimately 1 person = 1 data point
5. 5. For example: if you had a pair of twins in your study, you might expect them to respond to your treatment in similar ways. It might be inappropriate to consider them as indepedent data points The challenge in relation to independence comes when there are connections between the participants Same idea with people who live together, go to the same school, work together or whatever!
6. 6. There aren’t one size fits all rules here. You have to be the one that decides Consider what the harshest critic of your work would say You have to ask, what connections are relevant to your study? Whatever you decide, you will have to justify the decision
7. 7. Let’s move on to animal studies Mice, rats, cows etc are social animals, they are housed together Your treatment, whatever it is, may cause other animals that share an enclosure to respond Squeak Squeak Squeak Squeak
8. 8. If the differences in response between cages are not the same as the differences in response for animals within the same cage Then your experimental unit should be the cage Note that the within cage effects happen a lot, it is safer to assume that it will for your study!
9. 9. In this set up if all the brown mice got treatment 1, and the white mice treatment 2 The sample size would be 4, for the 4 cages, rather than 8 for the 8 mice getting treated Isn’t that a waste of animals?
10. 10. Isn’t that a waste of animals? Yes, it could be! It could also be a waste of time, money and resources. This is why you should think about these things before your experiment! It’s ethically unforgivable for poor experimental design to waste experimental units
11. 11. One more example: let’s look at cell culture-type experiments A standard sort of experimental set up might involve taking one flask of cells and splitting it into multiple wells of a single multi-well dish
12. 12. Treatments would then be applied to the different wells in the dish Although you would get 6 readings per coloured treatment, each reading isn’t independent They’ve been cultured together, treated together, exposed to the same environmental conditions etc The six values will be conflated to 1 per treatment in your final analysis
13. 13. The six replicates here would all contribute to the one data point So why would you do 6 at all? If you made a mistake in any one well, or got some other spurious result, it would have a smaller impact on the overall findings In this sort of set up, people often talk about “biological” or “experimental” repeats” and “technical repeats”
14. 14. The biological repeat refers to the whole experiment: 1 flask to one set of data Technical repeat are the data from the individual wells or equivalent (mice of the same cage)
15. 15. What one researcher defines as a “biological” repeat may not be the same as another person doing the same experiment So, it is important that you can justify your choice and when you write up your work you make it clear what you mean For example: one stock flask of cells split into 3 sub flasks and each sub- flask gives one 24 well plate For some experiments you might justify saying this is 3 biological repeats, but for others it would just count as 1. The answer depends on where the variability is.
16. 16. Let’s talk about one more situation where identifying potential for problems of replicates not actually being independent Specifically we are going to look at taking measurements through time If you are considering an experiment where you take the same samples and treat twice you need to be sure that the first treatment doesn’t affect the second measurement
17. 17. A + Let’s look at the same experiment we discussed before: Reading 1 Time (washout period) B + Reading 2 You really don’t want there to be an order effect Ideally your pilot data will let you know how long to leave between treatments However, if you can’t remove the effect of the first treatment, then you might not be able to use paired analysis
18. 18. Randomisation and assigning groups I see an issue with your group assignments Leeburt’s group Conro’s group What? You told me to be random And all the young women ended up in your study group? yeah, pure coincidence
19. 19. You should be able to defend your group choices to your harshest critic Let’s look at ways you can go about assigning experimental units to different treatment groups
20. 20. Fully Randomised Probably the most obvious way is to completely randomise which group you assign your experimental units to A random number generator should be used rather than manually trying this, as humans will always introduce patterns subconsciously
21. 21. Fully Randomised Why wouldn’t you fully randomise? Although full randomisation removes researcher bias it doesn’t allow you any chance to control for confounding variables
22. 22. Stratified, then randomised A common approach is to split your population into groups and then randomly assign sub-groups afterward
23. 23. Stratified, then randomised For example, we might consider gender to be a confounding variable and so we split the group first based on gender Doing this will help ensure that the gender balance is roughly equal Then assign groups from the sub populations
24. 24. Stratified, then randomised It might be appropriate to stratify on age as well as gender If you had 2 study populations from this cohort. Then each would have 1 x older women, 3 or 4 older men, 7 younger men and 2 or 3 younger women
25. 25. Stratified, then randomised How much stratification you do depends on the overall study population size and what you think matters to the interpretation Stratification should be prioritised to control for the confounder that will have the biggest effect
26. 26. Non-equal groups Usually you will be able to use stronger statistical tests when your group sizes are the same However, there may be practical or ethical reasons why you might not want to have uneven groups So, try to balance in terms of numbers whenever you can Can you think of any reason why? Treatment Group Control Group
27. 27. Non-equal groups Treatment Group Control Group If the treatment might cause harmful side-effects Yes, unequal groups may not be ideal in terms of stats but they can be the correct thing to do ethically Less animals / humans will suffer harm and you will still be able to answer your question
28. 28. Position and order effects It’s not just who is in which group that matters For example, surgeons will get better at a surgical technique the more they perform it Or might be better at the beginning of their shift compared with the end You also need to consider the experimenter involved in the research
29. 29. Position and order effects You may also need to control for which scientist is doing the research. This is particularly true wherever interaction with the participants is involved such as in collecting patient details Or if there is a subjective measurement involved
30. 30. Position and order effects Position and order effects are also relevant in lab work Setting up your plate like this might make it easier to set up But you won’t know if the effect is due to the treatment or whether it’s he location or order matter
31. 31. Position and order effects Experiment 1 Experiment 2 Experiment 3 In addition, to randomisation within experiments, the set up should be different between each experiment
32. 32. Part 4 Recap. Make sure your samples are truly independent Randomize everything you can, every time you can. Position, order, experimenter. Use groupings as a way to remove the effect of confounding variables from your interpretation
33. 33. Videos Don’t worry Liam, the next video will make the process much easier! Umar John Liam Conro