Biostadistics

Question 1

Intention to treat analysis

Answer 1

subject results are analyzed according to the group that they were initially assigned to (not according to adherence) is one technique used to analyze outcome data and preserve randomization

Question 2

When the cut-off point of a test is decreased, what happen with specificity and sensitivity?

Answer 2

Decrease in specificity (TN/(TN+FP)) and an increase in sensitivity (TP/(TP+FN)).
To identify more patients (usually in the form of decreasing the cutoff value) but with more false positives occurring.

Question 3

p-value of 0.052 meaning in a study that compares two medications?

Answer 3

There is a 5.2% chance that A is more effective than B is due to chance.

Question 4

If the cutoff value of a diagnostic test is increased (meaning it takes more of a finding to suggest a diagnosis), what should we say about specificity and sensitivity?

Answer 4

The sensitivity decreases while the specificity increases.

Question 5

If the confidence interval of a relative risk contains the value 1

Answer 5

The result is likely not epidemiology significant.

Question 6

Berkson bias

Answer 6

occurs when hospitalized study subjects are more likely to have a greater burden of illness than other possible subjects.

Question 7

Attrition bias

Answer 7

Occurs because patients who are lost to follow-up may be different from those who remain in the study.

Question 8

Selection bias

Answer 8

A sampled population is not representative of the population researchers are trying to study.
Due to inappropriate recruitment or attrition of study participants.

• non-response bias (participants who answer a survey may be less sick than participants who don’t)
• the healthy worker effect (employed subjects may be healthier than others)
• volunteer bias (volunteers may be different from those who do not volunteer)
• late-look bias (patients with severe disease may be less likely to be studied due to death or disability).

Attrition bias (patients who are lost to follow-up may be different from those who remain in the study).

Question 9

Response bias

Answer 9

Occurs when the outcome metric is a subjective patient-reported measure because patients may change their responses in a non-random manner.

Question 10

The factors that increase prevalence

Answer 10

Increase in new cases (increased incidence)
An improvement in the quality of care (prolonged duration of disease)
Improved diagnostic ability (early detection thus more cases).

Question 11

Normal distribution, mean, median and mode.

Answer 11

In a normal distribution, the mean, median, and mode are identical

Question 12

Hawthorne effect.

Answer 12

participants change their behavior when they are aware that they are being studied.

Question 13

The Pygmalion effect

Answer 13

an investigator inadvertently conveys his desired result to the participant, who then alters his behavior accordingly.

Question 14

External validity

Answer 14

is the ability to use results from a study to draw conclusions about populations different from those used in the study.

Question 15

Internal validity

Answer 15

refers to the degree to which a study’s results are accurate and can be used to establish a cause-and-effect relationship. A randomized and controlled trial has the highest level of internal validity.

Question 16

A latency period

Answer 16

The negative effects of a disease or the positive benefits of a treatment take a long time to become clinically apparent.

Question 17

Lead time bias

Answer 17

occurs when the early detection of a disease falsely elevates the survival time of a disease.

Question 18

T-test

Answer 18

Compare 2 groups in a clinical trial. This test is used to determine whether a significant difference exists between 2 means. Therefore, it can only be used for continuous variables

Question 19

Chi-squared

Answer 19

analyze categorical (not continuous) variables

Question 20

Effect modification

Answer 20

occurs when an external variable positively or negatively modifies the effect of a risk factor on a certain disease, effect modification changes the magnitude or direction of an effect.

Question 21

Confounding

Answer 21

occurs when a third factor is associated with both the exposure and the outcome of interest. For example, if smoking is associated with chewing gum, it could seem that chewing gum is associated with lung cancer even though smoking is the confounding variable.

Question 22

The hazard rate

Answer 22

The probability of an event occurring in the next time interval divided by the length of that interval.
If the ratio is less than 1, then the treatment reduces risk, and if it is greater than 1, then it represents an increased risk.

Question 23

Measure in Case control studies

Answer 23

Odds ratio

Question 24

Measure in Cohort studies

Answer 24

Relative risk

Question 25

When odds ratio can be used in cohort studies?

Answer 25

when the outcome or disease of interest is rare (because the odds ratio approximates the relative risk for rare outcomes)

Question 26

A narrower confidence interval is usually due to?

Answer 26

Increased study power.

Question 27

Number needed to treat

Answer 27

1/Absolute risk reduction ARR= control rate - treatment rate

Question 28

Matching

Answer 28

Causes confounding variables to be distributed equally between groups.

Question 29

Case control vs cohort studies start with?

Answer 29

Cohort studies start with the risk factor and assess for the incidence of an outcome of interest in both groups. Case-control studies start with the outcome of interest and assess for the presence of a risk factor in cases and controls.

Question 30

Relative risk and confidence interval

Answer 30

RR <1.0: exposure decreases risk RR = 1.0: no effect RR >1.0: exposure increases risk. A CI indicates if RR is statistically significant: includes RR = 1.0 = not significant; excludes RR = 1.0 = significant.

Question 31

Effect modification

Answer 31

Occurs when an extraneous variable alters the association between a risk factor and a disease, being linked to the disease but not to the risk factor.

Question 32

Relative risk reduction

Answer 32

(RRR) Quantifies how much a treatment lowers the risk of an adverse outcome. It can be calculated via absolute risk reduction (ARR) or relative risk (RR) using the formulas: RRR = ARR / Risk control = (Risk control - Risk treatment) / Risk control RRR = 1 - RR = 1 - (Risk treatment / Risk control).

Question 33

A phase III trial evaluates?

Answer 33

Treatment efficacy and safety by comparing a new treatment to standard treatments or placebo in a large sample of affected patients, typically using randomization and blinding.

Question 34

A phase II trial evaluates?

Answer 34

The efficacy, dosing, and side effects of a new treatment in a small patient group.

Question 35

P and confidence interval

Answer 35

p < 0.05 indicates a 95% confidence interval excluding the null value p < 0.01 indicates a 99% confidence interval excluding the null value.

Question 36

A statistically significant ANOVA

Answer 36

(p-value < 0.05) shows at least one group mean differs, but further tests are needed to identify which means differ. Additional comparisons identify differences in group means without providing p-values, only confidence intervals A CI that includes 0 = no difference in means. CI that excludes 0= a difference.

Question 37

Type II error

Answer 37

Failing to identify a true difference between groups when one truly exists.

Question 38

Type II error and power are affected by four interrelated factors:

Answer 38

power = 1 - β - Sample size - Significance level (α): Lower significance makes detection harder, thus high α leads to increased power and lower β. - Variability: Smaller variability around means improves detection. - Effect size: Larger differences between groups improve detection.

Question 39

Power

Answer 39

Ability to detect an effect between groups when it exists

Question 40

T-test

Answer 40

Compares the means of two groups, requiring a quantitative dependent variable and categorical independent variable. **An independent samples t-test is for independent groups. **Paired t-test is for dependent groups, where observations from one group are matched with another. Examples include pre- and post-intervention assessments and matched groups based on attributes like age or disease severity.

Question 41

The independent and dependent variable

Answer 41

Independent variable: is the cause that is manipulated or changed. Dependent variable: the effect. What is measured and that changes in response to the independent variable. ****The simplest way to distinguish them: "What variable am I changing to see its effect on another?" That is the independent variable; what is affected is the dependent variable.

Question 42

Reporting bias

Answer 42

Study participants over- or under-report exposure history due to perceived social stigmatization.

Question 43

Factorial design studies

Answer 43

Involves randomization and multiple experimental interventions, each with variables studied independently.

Question 44

Cluster analysis

Answer 44

Is the grouping of different data points into similar categories. Cluster analysis usually involves randomization at the level of groups rather than at the level of individuals.

Question 45

Cross-over study

Answer 45

A group of participants is randomized to one treatment for a period of time and the other group is given an alternate treatment for the same period of time. At the end of the time period, the two groups then switch treatments for another set period of time.

Question 46

Parallel study

Answer 46

Randomizes one treatment to one group and a different treatment to the other group, such as treatment drug to one group versus placebo to the other group.