1 Basic Biostatistic Application in Research of Anesthesia Chan Wei-Hung MD Department of Anesthesiology National Taiwan University.

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Basic Biostatistic Application in Research of Anesthesia

Chan Wei-Hung MD

Department of Anesthesiology National Taiwan University

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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 (Clinical Trial)

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.

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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!

Cohort study: patients with an exposure are analyzed along with patients without the exposure.

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Case Control Study

Parturient

C-section NSD

WithEpidural

WithEpidural

WithoutEpidural

WithoutEpidural

outcome

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Cohort Study

Parturient

With Epidural Without Epidural

C/S NSD C/S NSD

Exposure

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Clinical Trial

Parturient

Epidural

Random Grouping

Analgesics Normal Saline

C/S C/S NSDNSD

Random, blind, well-controlled

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Attention for Observational Study

Since the cause-effect relationship can not be established in this kind of study, if you want to do such a study, please notice that:

The sample size should be big. Documentation should be complete.

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Random Assignment

Simple random sampling with a random numbers chart

Number of patients can be balanced within a block of frame of patients (restricted randomization).

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Complete Random Assignment

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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.

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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

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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 stronger

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Determination of Sample Size

In a t-test

N = 2 [(Zα- Zβ) * SD

Mean 1 – Mean 2]

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SD: 正常值 ( 對照組 ) 的標準差Mean 1 – Mean 2: 預估偵測到的差別值Zα: 預估的 α 所得的 Z 值 (p=0.05 時 , Zα=1.96)Zβ: 預估的 β 所得的 Z 值 (β=0.90-1.280;

β=0.80-0.825; β=0.70-0.525)

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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 ?

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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 each group

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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)

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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 (chance of not to make a false-negative error)

Epidural Analgesia Enhances Functional Exercise Capacity and Health-related Quality of Life After Colonic Surgery

Anesthesiology 2002, 97: 540-549

Anesthesiology 2002, 97: 565-573

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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? T-test ANOVA

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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 Repeat-measures ANOVA

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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”.

There is a tendency to use this method in anesthesia research (esp. PCA studies).

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An Example for Survival Analysis

Patients received meperidine or hydromorphone in the POR.

The time to start IVPCA is compared.

(Kaplan-Meier Survival Analysis)

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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!)

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Central Belief

Biostatistics is not a hindrance but an aid for data analysis.

As long as you have an idea for study, biostatistics should not be the excuse that you cannot finish the study.

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THE END

GOOD LUCK

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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.

The differences between groups are of interest.

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Wilcoxon Signed Rank Test

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

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

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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.

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Sensitivity and Specificity

Disease Positive Disease Negative

Test Positive A B

Test Negative C D

Sensitivity =

Specificity =

A/(A+C)

False-negative = 1- sensitivity

False-positive = 1 - specificity

D/(B+D)

Sensitivity Specificity False-negative

False-positive

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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