Basic Biostatistic Application in Research of Anesthesia

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Basic Biostatistic Application in

Research of Anesthesia

Chan Wei-Hung MD

Department of Anesthesiology National Taiwan University

How to Conduct a Study?

Experimental study: best for cause-effect relationship determination

Observational study: only associations are made; not cause-effect relationship

 Retrospective  Prospective

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Experimental Study

Patients are assigned into different groups, receiving different intervention in each group. Random, blind, well-controlled (control over other confounding factors) design is key to success.

Power of measurement and cause-and-effect determination are also vital to success.

Observational Study

Descriptive study (case report/series): no comparison is made

Case-control study: patients with an outcome (case) are analyzed along with patients without the outcome (control). ESPECIALLY PRONE TO SAMPLE SELECTION BIAS!

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Case Control Study

Parturient

C-section NSD

With

Epidural _EpiduralWith Without

Epidural _EpiduralWithout

Cohort Study

Parturient

With Epidural Without Epidural

C/S NSD _{C/S NSD}

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Clinical Trial

Parturient

Epidural

Random Grouping

Analgesics Normal Saline

C/S NSD C/S NSD

Attention for Observational Study

 The sample size should be big.

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Random Assignment

Simple random sampling with a random numbers chart

Number of patients can be balanced within a

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Restricted Randomization

Group A: 20 patients Group B: 20 patients Frame size: 10 patients

No. of A and B are balanced within every 10 patients.

p, α and β Error

p value: the probability that one will wrongly conclude that there is a difference between groups.

Type I error: also called α error, false-positive error.  p value

Type II error: also called β error, false-negative error

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Type II Error (β Error)

False-negative error ( p>0.05 in the presence of difference)

When p>0.05, it is difficult to determine

between lack of true difference or inability to detect the difference.

Most common problems: insufficient sample size, bias in selection, confounding factors

Statistical Power

The ability to detect an effect when it is present. Equal to 1 – false negative error (1-β)

A statistical power around 80% (β<0.2) for a reasonable effect

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How to Increase the Power?

1. Increase the size number

2. Reduce variation between measurements 3. The effect of intervention should be

Determination of Sample Size

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Example in Size Number Determination

Onset of two muscle relaxants will be

compared. You wish to detect a difference of 10 sec. The standard variation of the

onset time is about 5 sec (according to the literature). You desire a p=0.05 and a

statistical power of 80%. The sample size of each group would be how many ?

Example in Size Number Determination

2 x [(1.96+0.825)x5/10]2 =3.87; about 4 in

each group

If you want to detect a difference of 5 sec: 2 x [(1.96+0.825)x5/5]2 =15.5; about 16 in

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Noncentrality Parameter (φ)

You can also determine the sample size by computing φ and look up the table.

Φ=δ/σ

(the difference of effects / standard deviation of population)

Critical Reviews of the Results

When you want to say there is an effect of intervention  give us the p value

(chance of false-positive error)

When you want to say there is no effect of intervention  give us the power

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Determining the Test (I)

What kind of variables are they?

1. Numerical variable 2. Ordinal variable

3. Categorical variable (Nominal)

How many groups are there?

Determining the Test (II)

Are they “normal distribution”?

Parametric vs. nonparametric methods.

T-test  Mann-Whitney U test ANOVA  Kruskal-Wallis test

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Determining the Test (III)

Measurements are taken from the same patient for more than one time (before and after treatment); you should use

 Paired t-test

Determining the Test (IV)

Common data are analyzed when they are completed (all the measurements are

finished); but there are some studies that data input are still ongoing (5-year analysis for two treatment for lung cancer); basically for this kind of “unfinished studies”.

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An Example for Survival Analysis

Patients received meperidine or hydrom orphone in the POR.

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Trick for Study Design

Thorough examination of past similar studies (sample size, statistical

methods, items of measurements --- you can apply them to save you from brain drainage and avoid fatal errors!)

Central Belief

Biostatistics is not a hindrance but an ai d for data analysis.

As long as you have an idea for study, b iostatistics should not be the excuse tha t you cannot finish the study.

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THE END

Paired t-test

When the two groups of data are obtained from the same subject

(repeated measurements from a subject under different conditions), paired t-test should be used.

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Wilcoxon Signed Rank Test

In a repeated measurement, the differen ces are usually not “normally distributed ”.

A Wilcoxon signed rank test should be used in the case.

Analysis of Variance (ANOVA)

Comparison of variation conditions of different groups.

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Screening Test Evaluation

The effectiveness of diagnostic or prognostic tests is assessed.

Sensitivity and specificity are explored in such studies.

Sensitivity and Specificity

Disease Positive Disease Negative

Test Positive A B Test Negative C D Sensitivity = Specificity = A/(A+C) False-negative = 1- sensitivity D/(B+D)

Sensitivity Specificity

False-negative positive

False-Palm print grade>0 1.00 0.57 0 26

Mallampati >1 0.41 0.80 13 12 Mallampati >2 0.50 0.98 21 1 TMD <6 cm 0.14 0.9 19 6 Head extension<35° 0.50 0.70 11 18 BMI > 27 0.23 0.97 17 2 DM > 10 yrs 0.91 0.67 2 20 DM type 0.45 0.51 12 30

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