Error, Misclassification, and Bias

Question 1

What are the two types of error in epi inference?

Answer 1

Systematic bias and random error.

Question 2

What is systematic bias?

Answer 2

Distortion of results of a study in a CONSISTENT manner.

Question 3

What is random error?

Answer 3

Variability in data from one observation to the next. Dealt with via statistical methods.

Variance estimated in CI around estimate and presents random error.

Question 4

How can we define validity?

Answer 4

Relative absence of bias or systematic error. Estimates are more valid as validity goes up and bias does down.

Question 5

What is precision?

Answer 5

Define as relative lack of random error. More precise as sample size goes up.

Question 6

What are some combinations of precision and validity?

Answer 6

Good precision (close estimates) with good validity (closely around bullseye-which is the truth)

Good precision and poor validity (close estimates but not close to target)

Poor precision and good validity (far away from each other but close to target)

Question 7

What are the types of systematic bias?

Answer 7

3 types:

Confounding
Information Bias
Selection Bias

Question 8

What is information bias?

Answer 8

Misclassification or measurement error.

Recall bias, for example.

Question 9

What is selection bias?

Answer 9

Who is recruited. How they are recruited.

Who is retained and how.

Question 10

What is bias towards the null versus away from the null?

Answer 10

Bias towards the null: estimate for RR and OR is closer to 1 compared to the true value. Observed is smaller than true magnitude.

Bias away from null: estimate from RR and OR is away from 1. Estimate is LARGER than true value.

Question 11

What is sensitivity analysis?

Answer 11

Quantitative assessment to examine source of bias and amount of resulting uncertainty in studies.

Question 12

How do we assess systematic bias versus how do we assess random error?

Answer 12

Random error usually assessed through statistical analysis.

Systematic error assessed through sensitivity analysis.

Question 13

What is the general approach to sensitivity analysis?

Answer 13

4 steps:

• Hypothetical values used to create alternate estimates.
• Hypothetical values are given across a range to determine the range of possible inaccuracy around estimate.
• Range accounts for possible bias. What kind of estimate the readers should expect given a range around estimate we present as valid.
• CI estimates random error.

Question 14

How we account for confounding in design?

Answer 14

RCT, stratification, restriction.

Question 15

How do we account for confounding in analysis?

Answer 15

Standardization, stratification, and multivariate methods.

Question 16

What are the three main kinds of confounders?

Answer 16

Known and measured confounders, known and unmeasured confounders, and unknown confounders.

Question 17

What type of confounder does sensitivity analysis deal with?

Answer 17

Known but unmeasured confounders.

Question 18

What two things do we need for sensitivity analysis?

Answer 18

1) Estimated guesses for associations between outcomes and confounder among exposed and unexposed.
2) Prevalence of unmeasured confounder among people who are exposed versus unexposed.

Question 19

What does information bias refer to?

Answer 19

Results from measurement error.

Question 20

What are some types of information bias?

Answer 20

Detection bias
Instrument bias
Recall bias
Social desirability bias
How we code it might be information bias

Question 21

How can information bias occur?

Answer 21

How we collect data, how PPTs report data, and how we analyze data

Question 22

What is differential bias?

Answer 22

Differential misclassification occurs when the probability of being misclassified differs between groups in a study

Question 23

What is non-differential bias?

Answer 23

Non-differential misclassification occurs when the probability of individuals being misclassified is equal across all groups in the study.
Toward null ONLY.

Question 24

What is sensitivity?

Answer 24

The proportion of people with the disease who are classified as having disease:

TRUE positives/ALL positives (true pos + false neg)

Question 25

What is specificity?

Answer 25

The proportion of people without the disease classified as not having disease

True negs/Total negs (true negative +false positive)

Question 26

What do we need to address information bias?

Answer 26

Need sensitivity and specificity of exposure measurement among persons with outcome and also without outcome!