Study Design and Statistics for Stratified Medicine

Question 1

What is statistics?

Answer 1

Methods to:

• understand variability between different units under study(e.g. people, genes, drugs)
• formally weigh up quantitative evidence

Question 2

What do descriptive statistics do?

Answer 2

Summarise a set of observations

Question 3

What do Inferential statistics methodsmake inferences about?

Answer 3

Some characteristic of apopulationbased on information contained in asample

Question 4

1. A simple linear regression..

2. What can a hypothesis test be used for?

Answer 4

1. Assumes that a linear relationship between X and Y exists.

Y is the dependent variable (or response)

X is the independent variable (or predictor)

Y ~ β0 + β1X

For every one unit change in Xwe expect Y to change by β1 units.

2. β1 only estimates the true relationship between X and Y.

A hypothesis test can be used to decide whether the true value of β1 is significantly different from zero. If so, X and Y are statistically associated

Question 5

What does a multiple regression model?

Why is this harder to represent visually?

Answer 5

The joint effect of multiple different independent variables (Xs) on a dependent variable (Y)

A one unit increase in X1 (calorie intake) leads to an expected increase of β1 units of Y (BMI)

A one unit increase in X2 (physical activity) leads to an expected increase of β2 units of Y – β2 could be negative…

Question 6

What can hypothesis tests determine in a multiple regression?

Answer 6

Determine whether each β is significantly different from zero, i.e. whether each of the Xs is associated with Y

Accounting for the other variables in the model!

Question 7

What does a descriptive study describe?

Answer 7

A cohort without making comparisons of exposure and outcome

Question 8

What does an analytical study involve?

Answer 8

Comparisons made. Difference depends upon when exposure or outcome is made.

Question 9

If an investigator does not assign exposures in a study, what kind of study is this? What are different types of studies relating to this and how can they differ?

Answer 9

An observational study. This involves analytical study and descriptive study.

If this involves a comparison group it is an analytical study.

Question 10

What are the 3 different forms of analytical study?

Answer 10

Cohort study

Case control

Cross study

Question 11

What are the two types of experimental study

How do they differ?

Answer 11

Randomised controlled trial

Non-randomised controlled trial

Randomised controlled trial involves random allocation

Question 12

1. What does a randomised controlled trial involve?
2. What are advantages of this study design?
3. What is a limitation

Answer 12

Experimental: investigator randomly assigns exposures(e.g. drug vs no drug)

2. Randomisation helps to eliminate bias

Blinding also minimises information bias

Gold standard in clinical research; high internal validity

3. Expensive, not always appropriate; external validity?

Question 13

1. What does a cross sectional study ascertain?
2. What is an advantage?
3. What is a limitation?

Answer 13

Exposures and outcomes at the same time

e. g. capture rheumatoid arthritis status and weight
2. Can associate presence or absence of disease (RA) with presence or absence of exposure (weight)
3. Can’t infer causal direction (RA → weight or weight → RA)

Important example: population-based biobank data

Question 14

1. What does a Cohort study involve?
2. What are two advantages of this
3. What is a limitation

Answer 14

1. Proceeds “forwards in time”
   Select groups based on exposure and monitor for outcomes
2. Good for estimating risk of incident disease (new diagnosis)

Direction of causality can be inferred from design(e.g. weight → RA)

3. Can be expensive and not effective for rare diseases

Question 15

1. What does a Case-control study involve?
2. What is an advantage?
3. What are the limitations?

Answer 15

1. Works “backwards in time” – harder to correctly interpret
   Identify groups based on outcome and look back for exposures
2. Good for rare diseases
3. Big problem: control group must be similar to cases in all important respects except for not having the outcome in question

Other differences could be picked up as incorrect associations

Also: recall bias = better recollection of exposures among cases

Question 16

What research method has the highest risk of bias and confounding variables?

What do these variables affect?

Answer 16

Observational research

Internal validity of study

Question 17

What are the three main classes of bias and confounding variables?

Answer 17

Selection bias
Information bias
Confounding

Question 18

What does selection bias refer to/ask?

What are three examples?

Answer 18

Are the groups similar in all important respects?

Examples:

Membership bias
Jogging → heart disease risk?Joggers also differ in diet, smoking, …

Non-respondent bias
Smokers less likely to return questionnaires

Neyman bias
Hospital-based sample of myocardial infarction cases won’t

Question 19

What does information bias refer to/ask?

What are three examples?

Answer 19

Was information gathered in the same way?

Examples:

Diagnostic suspicion bias
More intensive search for disease among people exposed to “suspected” cause

Family history bias
Controls less likely to know about family history of a disease

Recall bias
Abortion → cancer?Abortion underreported in contr

Question 20

Investigating confounding variables focuses upon what?

What is a example?

Answer 20

Is an extraneous factor blurring the effect?

The effect being observed is not due to the exposure, but a third factor with which it is associated

Example:

Salpingitis appears to be associated with using an intrauterine device (IUD)

But actually salpingitis is associated with having >1 sexual partner

Women with >1 sexual partner more likely to have an IUD

Question 21

How can confounding variables be dealt with prior to study conduction?

Answer 21

Matching:

For each case, find a control that matches on potential confounders
e.g. find control with same smoker status

Question 22

How can confounding variables be dealt with after study conduction?

Answer 22

Stratification-
Split data into strata and analyse separately
e.g. analyse smokers and non-smokers separately

Multivariate techniques -
Include potential confounders in a joint model with the exposure

e.g. use multiple regression and include a term for smoker status

Question 23

What must a biomarker study design capture?

Answer 23

sufficient information to examine the hypothesised effect

Sufficient numbers of:

biomarker + (positive) and biomarker negative participants

outcome + (positive) and outcome (negative) participants

Question 24

Why is there higher potential for biases and confounding in a biomarker study?

Answer 24

Focusing on treatment effect study: exposure/outcome and biomarker

E.g. Biomarker study: exposure/biomarker/outcome

Question 25

What is needed for a biomarker study?

Answer 25

All need replication via repeated demonstration in independent studies

Question 26

What does a prognostic biomarker need to demonstrate?

Answer 26

Need to demonstrate that biomarker + group has better (or worse) outcome than biomarker – group regardless of treatment

Question 27

What does a predictive biomarker need to demonstrate?

Answer 27

A significant treatment by biomarker interaction

Question 28

What terms are used for a multiple regression to the model a treatment biomarker interaction design for a predictive biomarker?

Answer 28

How outcome changes due to treatment in all patients (regardless of biomarker status)

How outcome changes due to presence of biomarker(regardless of treatment)

Additional impact of treatment in biomarker positive patients

Question 29

What is biomarker sensitivity/ true positive rate?

Answer 29

is the ability of a test to correctly identify those with the specified characteristic (e.g. disease, drug response)

“How well does biomarker do at picking out the positive cases?”

Question 30

What is biomarker specificity/ true negative rate?

Answer 30

is the ability of a test to correctly identify those without the specified characteristic
“How well does biomarker do at only picking positive cases?”

Question 31

Sensitivity and specificity are intrinsic characteristics of the test, but do not take into account what?

Answer 31

The proportion of actual positives and negatives in the population being tested

Question 32

What is the Positive Predictive Value (PPV)?

Answer 32

Positive Predictive Value (PPV)

The fraction of the group that test positive that are actually positive

Question 33

What is the Negative Predictive Value (NPV)?

Answer 33

The fraction of the group that test negative that are actually negative

Question 34

What do Receiver Operating Characteristic (ROC) curves provide?

What does the area under the curve (AUC) provide and is useful for?

Answer 34

a graphical representation of the trade-off by displaying the range of possible cut points of a diagnostic test with their associated sensitivity (or TPR) against (1 – specificity) (or FPR).

An indication of the utility of the predictor(0.5 = random chance, 1 = perfect separation)

Is useful in assigning the best cutoffs for clinical use

Provide a means of comparing two or more predictive tests