Biostatistics

Question 1

What are the 2 types of variables and their subcategories?

Answer 1

Discrete, Continuous
Discrete: nominal and ordinal
Continuous: interval and ratio

Question 2

What types of variables are discrete? Continuous?

Answer 2

Nominal and ordinal
Interval and ratio

Question 3

Give an example of a nominal variable? Ordinal? (Counting variables)

Answer 3

Nominal: blood type, mortality, disease state (yes or no)
Ordinal: NYHA class 1-4, Ramsey scale

Question 4

What are some examples of continuous variables (measuring)?

Answer 4

Interval: ranked in specific order with change in magnitude, no real zero —> temperature
Ratio: Like interval but with a real zero —> weight, time, SCr, pulse

Question 5

How do we look at groups of numbers?

Answer 5

Look at measures of central tendencies: mean (average), median (50th percentile, or middle), mode (most frequently occurring)

Question 6

For what type of variables is best to use the mean as the measure of central tendency?

Answer 6

Use ONLY for continuous data (interval and ratio) but it is sensitive to OUTLIERS

Question 7

What type of variables is best used to find a median as measure of central tendency?

Answer 7

Used for ordinal or continuous data, good for SKEWED data as it is INSENSITIVE to outliers

Question 8

What types of variables are best to find the mode as a measure of central tendency?

Answer 8

Used for both discrete or continuous data. *could have more than one mode

Question 9

What is best measure for each variable if had to pick one?

Answer 9

Nominal: mode
Ordinal: median
Interval/Ratio (nominally distributed): mean
Interval/Ratio (skewed): median

Question 10

What is Alpha in biostats?

Answer 10

Typically set at:
0.05 (significant p-value, 1 minus confidence interval)
Alpha = Probability of false positive test

Question 11

What is a type I error vs a type II error?

Answer 11

Type 1 error FALSE POSITIVE finding: when a stat sig diff was found but NO true diff in population
Type 2 error FALSE NEGATIVE finding: when no stat sig diff was found but there IS a true diff in population

Question 12

What is beta in biostats?

Answer 12

Beta is typically set at 0.1 or 0.2 = probability of a negative test

Question 13

What is power in biostats?

Answer 13

Power = (1-beta) = The probability to detect a difference when a difference exists, often set at 80 or 90%

Question 14

What is a confidence interval and when is it significant?

Answer 14

Typically 95% (if p=0.5 is significant)
CI that includes “1” for HR, OR, RR is NOT significant
CI that includes “0” for mean difference is NOT significant

Question 15

How do you calculate relative risk ratio (RRR)?

Answer 15

RRR = % of outcomes in treatment — % of outcomes in control / % Outcomes in control

Question 16

How do you calculate absolute risk ratio (ARR)?

Answer 16

% of outcomes in treatment / % of outcomes in control

Question 17

How do you calculate hazards ratio (HR)?

Answer 17

Requires statistical program to calculate

Question 18

How do you calculate number needed to treat (NNT)? And what does it mean?

Answer 18

1/ARR = for every # of patients treated, 1 additional outcome will be avoided or prevented
Or 1/ % of outcomes in treatment / % of outcomes in control
* can only be calculated if sig difference exists

Question 19

How do you calculate number needed to harm (NNH)? And what does it mean?

Answer 19

1 / AAR = For every # of patients treated, 1 additional patient will have outcome
Or 1 / (% of outcomes the treatment / % of outcomes the control)

Question 20

How do you calculate relative risk (RR)?

Answer 20

RR = [a / (a + b)] / [c / (c + d)]

Group A = a = treatment group and event happened, b = treatment group and event DID NOT happen

Group B = c = control group and even happened, d = control group and even DID NOT happen

*in prospective cohort or RCTs

Question 21

How do you calculate Odds ratio (OR)?

Answer 21

OR = ad / bc

Group A = a = treatment group and event happened, b = treatment group and event DID NOT happen

Group B = c = control group and even happened, d = control group and even DID NOT happen

• in cross-sectional or case-control studies

Question 22

What can the Confidence Interval (CI) tell us?

Answer 22

CI = provides info about a range in which the true value lies with a certain degree of probability, as well as about the direction and strength of the demonstrated effect
- a narrower CI indicates a more precise estimate, while a wider CI indicates a less precise estimate
- If the point of parity (1 or 0) is in the confidence interval on certain data types = there is no stat sig
I.e If CI includes 1 for HR, OR, RR = no stat sig or if CI includes 0 for mean difference (MD) = no stat sig

Question 23

For which data types does it mean no statistical significance if CI contains “1”?

Answer 23

HR, RR, OR

Question 24

For what data type does it mean no statistical significance if CI contains “0”?

Answer 24

Mean difference (MD)

Question 25

For what p-values is the null hypothesis rejected and is considered statistically significant?

Answer 25

When p < alpha (usually 0.5)

Question 26

What is a null hypothesis?

Answer 26

There is NO difference between groups for outcome X

Question 27

What is the order of causal relationship in types of study design starting with the least relationship?

Answer 27

1. Case Report
2. Case Series
3. Case-control study
4. Cohort study
5. RCT

Question 28

What are the types of RCTs?

Answer 28

Designs: Parallel, Cross-over, Factorial
*RCT’s minimize bias via randomization

Question 29

What are the types of observational trials?

Answer 29

Case-control, prospective cohort, retrospective cohort

Question 30

What is a factorial design RCT?

Answer 30

Evaluates two interventions compared to a control
As long as no interaction, advantage is to possible do two experiments at one time

Question 31

What is a cross-over design in RTCs?

Answer 31

Subjects are randomized to sequences of treatments (A then B or B then A)
Uses the patient as own control
Often a “wash-out” period is used to avoid a “carry over” effect
Only appropriate for conditions that are expected to return to baseline levels at the beginning of the second period

Question 32

What are the 4 types of observational studies?

Answer 32

Cohort: consists of group of individuals from a well-defined population followed over time to observe what happens to which groups and when

Prospective: group of subjects/cohorts identified and evaluated as it occurs, Time-sensitive

Retrospective: identified in the past and then evaluated for event, Medication use evaluations, time and cost efficient, cannot completely remove bias or confounders (need special programs)

Case-control: group os subjects with disease or condition are identified, suitable control group identified without the condition, frequency of exposure or risk factor compared in cases and controls, decreases confounding factors that might explain differences between groups

Question 33

In order to calculate NNH/NNT, what do you assess first?

Answer 33

First you must look at P values to see if significant. If p >0.05 not significant and cannot calculate NNH/NNT
If data is given in percent, must convert to decimal 35 = .35 etc
Formula is 1/ARR or 1/ (% of outcome in treatment — % of outcome in control) *always round to greater whole number
If answer is positive = NNH, if answer is negative = NNT