Design of observational studies●Research Design
2nd May 2018●

For the description of current status– Adequate sampling from the population is essential– Increasing the sample size can improve precision, but it costs a lot– In the case of rare disease (or other health outcome), long term observation(including past records) is necessary
Observational studies with hypothesis testing– Cross-sectional study●Adequate sampling from the population is essential●
If the target research items are many, large sample may be needed– Case-control study

In principle, all cases (with eligibility criteria) are recruited at thetarget institute. The key is how to sample the adequate controls●
Basically, compare the past exposures between cases and controls– Cohort study●
Follow up the exposed and non-exposed groups, comparing thehealth outcome measures between those two groups●

Experimental study design

Fisher's three principles

Any experimental study must be carefully
designed.

Replication: at least 2 or more repeatedmeasurements are needed for each treatment

The experimental study design was originated for
agricultural study at Rothamstead by R.A.
Fisher.

Randomization: The order and area allocation of
the experiment must be randomly assigned.

In medical and health studies, such kind of design
is needed for dose-response relationshipanalysis of toxicicity testing and clinical trial.
Of course, the study must pass the ethics
committee, which requires the appropriate study
design with proper sample size.

George Orwell's 11 rules
1. Use tea from India or Ceylon (Sri Lanka), not China
2. Use a teapot, preferably ceramic
3. Warm the pot over direct heat
4. Tea should be strong - six spoons of leaves per 1 litre
5. Let the leaves move around the pot - no bags or strainers

Local control (blocking): If the experiment is donein a large-scale, the randomization of wholesamples is invalid. Instead, making local
blocks in several ways, and randomization
within the blocks can partly remove bias asinter-block variation.

Perfect cuppa (cnt'd)
A Japanese blogger examined the taste of 130cc tea
and 30 cc Takanashi's pasteurized milk about which
first makes better taste.
According to the subjective judge, “milk first” makes
better taste.
http://blog.livedoor.jp/teatime312/archives/cat_123365.html

6. Take the pot to the boiling kettle
7. Stir or shake the pot
8. Drink out of a tall, mug-shaped tea cup
9. Don't add creamy milk
10. Add milk to the tea, not vice versa
11. No sugar!

Famous experimental designs
One group pretest-posttest design: paired t-test.
Completely randomized design: t-test/ANOVA forquantitive data, chi-square test for proportion
Randomized block design: similar to completelyrandomized design / considering block's effectLatin-square: usually ANOVA
Crossover design: Matched (paired) analysis ofvariance (within-subject difference will be zero ornot, adjusted by the order of treatment)Example of paired t-test
We can use “survey” dataframe of MASS package in R
(EZR), whereas it is the result of cross-sectional study.
In EZR, select [File] and [Read data included inpackage], then select [MASS] and [survey].
The “survey” contains the responses of 237 students at
the University of Adelaide to a number of questions(Venables and Ripley, 1999). Variables include thespan (distance from tip of thumb to tip of little finger of
spread hand) of writing hand as [Wr.Hnd] and that of
non-writing hand as [NW.Hnd].
Select [Statistical Analysis], [continuous variables], then[paired t-test]. Select [NW.Hnd] at left panel and [Wr.Hnd]
at right panel and click [OK]. That's all.

Pararell group design(=Completely randomized design)

ExerciseCompare the results of simple
calculation test before andafter drinking coffee.

Very simple. The subjects who signed informed consent arecompletely randomly (not haphazardly) assigned to one of theseveral treatment (exposure).

In EZR, at first, making data:
select [File], [New data],then enter the data as
right screen-capture.

There are several randomization methods. Fleiss JL (1986)“The design and analysis of clinical experiments”
recommends to use “random permutation tables” instead of“random number tables”.

Conducting paired t-test
can be done in similarmanner as NW.HndWr.Hnd

However, now we can use computer software. If we want toassign 45 subjects into 3 treatments, typematrix(sample(1:45, 45, replace=FALSE),3) in R.
> matrix(sample(1:45, 45, replace=FALSE),3) -> x
> x[1,][1] 34 13 37 17 3 11 4 23 18 39 42 40 36 8 25
> t(apply(x,1,sort))[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
[1,]
34811
13
1718
23
25
3436
37
39
40
42
[2,]
16
7
10
14
15
16
1926
29
31
33
41
43
44
[3,]
259
1220
21222427
28
30
32
35
38
45

[t = -2.862, df = 9, p-value =0.01872] mean significant
difference.

Randomized block(s) design（乱塊法）

How to describe?
Dropouts sometimes occur.
The design is usuallyshown as diagram (right).
In this diagram, thequantitive data of thesubjects can becompared by one-way
ANOVA , proportion bychi-square test.
Unbalanced sample size
may reduce the statisticalpower.

50,000 residents who may potentiallygo to the target hospital
45 samples are supposed
to attend, but 42 in fact.

Treat A
15

Treat B
13

Treat C
1413
1115
completed completed completed
protocolprotocolprotocol

Factorial design
Example of 2x2 factorial design
McMaster et al. (1985): a randomized trial toevaluate breast self-examination teachingmaterials as leaflets or tape/slides.
The treatment groups were designed as four pararellgroups as
1. No leaflets nor tape/slides given (control)
2. Leaflets displayed
3. Tape/slides program
4. Both given
The effect of teaching can be evaluated using
ANOVA: two kinds of materials can be evaluated.

Crossover design（クロスオーバー法）
Subjects will be seriallytreated by 2 kinds ofintervention with properinterval (wash-outperiod to avoid carryover) in different order.
(Example) Hilman BC etal. “IntracutaneousImmune Serum
Globulin Therapy inAllergic Children.”,
JAMA. 1969; 207(5):902-906.

Due to incompleted study, completely randomized design
may lead to unbalanced sample sizes among groups.
If the sample size for each treatment is 15 and the kind oftreatment is 3, randomized block design randomly selectone of 6 possible blocks ({A, B, C}, {A, C, B}, {B, C, A},{B, A, C}, {C, A, B}, {C, B, A}) 15 times. By doing so, if
the study may suspend in the middle, the sample size
difference is at most 1 among groups. Description andanalysis can be similar to complete randomization, but
the analysis considering blocks' effect is also possible.
Another method to keep size balance is “Minimizationdesign”. It minimize the sample size difference at eachtime of sampling.

Latin-square design（ラテン方格法）
When the experiment have one
treatment (A) with p≧2 levels,
2 nuisance variables (B, C)
b1each with p levels, this design
is useful. The name isb2originated from ancient puzzle.

c1

c2

c3

a1

a2

a3

a2

a3

a1

Assume p is 3. Latin square isshown as right. Group 1 getsthe combination of treatment
a1b1c1 for n1 individuals.
Following n2 individuals gettreatment a1b2c3 as group 2.

a3

a1

a2

b3

By doing so, the effects of B andC on the outcome measurecan be controlled in ANOVA.

Types of outcome measure
Superiority trials: The effect of new treatment is significantly
better than control or not
Test the null-hypothesis of "no difference". If p-value is lessthan the significance level (usually 0.01 or 0.05), reject the nullhypothesis
Equivalence trials: The effect of new treatment is similar tocontrol or not
Don't use the hypothesis test. Using enough size of sample,
confidence interval (CI) are used. If the CI is included within theequivalence margin (regarded as clinically equivalent), theeffects by the two treatments are judged as similar.
Non-inferiority trials: Special case of equivalence trials.
Using only half of the confidence interval, sample size will besaved.