EMB: Hypothesis Testing

Question 1

RR:

Answer 1

(population 1 (i.e. diseased) / total) / (population 2 / total)

Question 2

RRI:

Answer 2

1 - (RR)

Question 3

Absolute risk increase:

Answer 3

(population 2/total) - (population 1/total)

Question 4

Number Needed to Harm

Answer 4

1 / (absolute risk increase)

Question 5

Independent Variable:

Dependent Variable

Answer 5

I: Exposure (predictor, explanatory, risk factor)
D: Outcome (response)

Question 6

Important Study Parameters:

Answer 6

1. Types of intervention
2. What outcome is being assessed
3. What is the study population

Question 7

Null Hypothesis

Answer 7

No difference between groups. RR=1

Question 8

Alternative Hypothesis

Answer 8

Exposure is associated with disease.

Can be 1 or 2 sided:
RR>1 or RR doesn’t equal 1

Question 9

Parametric methods

Answer 9

Require some assumptions

Compare different means
T-tests and ANOVA

Question 10

Nonparametric methods

Answer 10

no assumptinos

categorical data

Question 11

Chi-square test

Answer 11

compares PROPORTION between two (or more) groups. Categorical data (yes/no).

(male vs. female)

Question 12

t-test

Answer 12

compares MEAN values between TWO groups

Continuous variable (all different age),

Question 13

ANOVA

Answer 13

analyzes data that includes MULTIPLE variables

compare MEAN values

Question 14

z-score

Answer 14

measure of standard deviation where data is transformed to standard normal distribution

Question 15

Hypothesis test

Answer 15

compare test statistic to critical value (-1.96 to 1.96) if above or below, statistically significant.

This correlates with a P-Value

Question 16

P-value

Answer 16

probabilty that an association as strong or stronger might have arisen by chance alone (IF NULL HYPOTHESIS WERE TRUE)

NOT probability THAT null hypothesis were true.

Question 17

What must you consider when assessing Sample Size

Answer 17

• Type of Study
• Magnitude of difference (what will be clinically important by setting alpha and beta)
  
  • *As difference desired is LARGER (very prevalent), you want smaller population
• *As ALPHA and POWER is smaller the sample becomes larger
• Random Error
• Power

Question 18

Type I Error and Alpha value

Answer 18

Reject Null Hypothesis, but shouldn’t have

Alpha: probability of making a type I error (.05), smaller risk of being wrong because don’t want to change something if you’re wrong.

Question 19

Type II Error

Beta value

Answer 19

Don’t reject Null Hypothesis, but should have. Often due to inadequate power (small population).

Beta: probability of making a type II error. (0.20)

Question 20

Power

Answer 20

Ability of a study to detect a true difference between groups (1 -beta)
b= .2 then power =.8 detecting a difference

Question 21

Point Estimate

Answer 21

• indicator of magnitude of effect

- must be supplemented with measure of random error AKA confidence interval

Question 22

Confidence interval

Answer 22

Measure of Precision. 95% of the time this effect will happen. Lower Bound (Null Hypothesis1) ( your Point estimate) Higher Bound

Question 23

Intention-to-Treat

Answer 23

“analyze what you randomize.” Include ALL participants in analysis, regardless of follow-up (increases generalizability)

Question 24

Stratified Analysis

Answer 24

Examine exposure-outcome relationship in subgroups (male/female)

Question 25

Adjusted Analysis

Answer 25

Control for effect of extraneous variables on the exposure-outcome relationship (control for confounding)

Question 26

Confounding

Answer 26

Estimate of the exposure effect is distorted because it is mixed with effect of extraneous factor