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Study Power?

The power of a study is the probability of detecting a significant difference between treatments or study groups when there really is one.
Low power increases the likelihood of failing to identify a statistically significant difference when a real difference does exist.
High power (80% or more) is desirable .
Power is affected by sample size, etc.

1

Nominal data

In category, non-parametric

2

Ordinal data?

In order, with unequal interval,non-parametric

3

Interval data?

Equal interval
No absolute zero
Cannot compute ratio
parametric

Eg Tm in Celsius or Fahrenheit

4

Ratio data?

Equal interval
with absolute zero or true zero
Can calculate ratio
parametric

Eg. Wt, hight, Kelvin Tm

"NOIR"

5

Measurement of central tendency?

Mean
Median
Mode

6

Mean= Median = Mode, what distribution?

Normal distribution

7

Relationship of mean, median and mode in right (positive) distribution?

Right skewed -Tail on the right
Mean>Median>mode

(Rule of thumb: mean always follows the tail)

8

The relationship of mean, median and mode in left skewed distribution?

Tail is on the left of the distribution
Mean

9

For normal distribution, select statistic method?

Select Parametric statistics test
Eg. Student t-test, chi-square, ANOVA, ANCOVA, regression analysis

10

For non-normal distribution, eg. Bimodal, skewed, etc. test methods selection?

Non-parametric test eg.Fisher's exact test, McNemar test,Mann-Whitney U test, Wilcoxon's rank sum test, Kruskall-wallis test

11

Ways of obtaining random sample?

1. Simple random sampling
2. Systemic random sampling
3. Stratified random sampling
4. Cluster sampling

12

Bias?

Systemic error
Impacts internal validity

13

Chance

Radom

14

Confounder?

Associated with exposure (risk) and outcome
An independent risk factor for the outcome
Not in the causal pathway between the risk factor and disease

15

Power

The chance of finding an effect in your sample if it truly exist in the population.

Power is not a question in a study that shows a significant effects.

If a study results had failed to show a significant difference (p>0.05) between the two groups, one may wonder whether the study had sufficient power.

16

When apply to a population,
Given sensitivity and prevalence,
True positive =?
False negative =?

True Positive = Sensitivity x Prevalence
False negative = (1- Sensitivity) x Prevalence

17

When apply to a population, given Specificity and Prevalence,
True negative =?
False positive =?

True Negative = Specificity x (1- Prevalence)

False positive = (1- Specificity) x (1-Prevalence)

18

Regression toward the mean

In any group selected on a characteristic with substantial day-to-day variation, many will have values closer to the population mean when the measurement is repeated and worst pts will improve.

19

Baseline drift

Which occurs with measurement on certain machines that requires frequent calibration.

20

Hawthorne effect

A tendency among study subjects to change simply because they are being studied or watched.

21

1SD =? %
2SD =? %
3SD =? %

1 SD = 68% (Z score = 1)
2 SD = 95% (Z score = 2)
3 SD = 99% (Z score = 3)

22

When two events are independent, the probability of either will occur?

Is the sum of their probability, minus the probability that both will occur.
P (A or B) = P (A) + P (B) - P (A and B)

23

When two conditions are mutually exclusive, the probability that either one will occur is

The sum of their probability

24

Randomization

Assignment occurs by chance

25

ROC curve - Receiver-operator curve

X axis: 1 - specificity, or the false - positive rate

Y axis: Sensitivity

26

ROC curve is used to determine

Optimal Cut-off point for the respective test.
In general, the point closest to the upper-left corner, where sensitivity is highest and the false-positive rate is lowest, is chosen as the cut-off.

27

In ROC cure, the Area Under the Curve (AUC) is used to?

To calculate the diagnostic accuracy (best sensitivity and specificity) of the test, that is the probability of correctly identifying disease based on the result of the test.
The larger the area under the curve, the better the test.

28

Kappa statistic

Used for reliability studies, eg to assess inter-rater reliability or intra-eater reliability.
Used in assessing the degree to which two or more raters, examine the same data, agree when it comes to assigning the data to categories.

29

Effect modification

Occurs when one factor modifies the effect on outcome of another.