Data Collection and Health Outcomes

Question 1

What type of health outcomes are ‘mortality’ and ‘disease incidence’?

Answer 1

Record-based

Question 2

What type of health outcomes are ‘lab results, BMI, blood pressure’?

Answer 2

Biological / clinical outcomes

Question 3

What are examples of clinician / patient-reported outcomes (PROs)?

Answer 3

• Symptom scores

- Health-related quality of life

Question 4

Difference between objective and subjective health outcomes?

Answer 4

Objective:

• Mortality
• Disease incidence
• BMI
• Blood pressure

Middle:

• Cognition
• Physical functioning

Subjective:

• Pain
• Mental health
• Fatigue

Question 5

What is validity in research?

Answer 5

Does the outcome measure what it is supposed to measure?

Question 6

What are the 3 main types of validity?

Answer 6

1. Construct validity
2. Content validity
3. Face validity

Question 7

What is construct validity?

Answer 7

The degree to which a test measures what it claims, or purports, to be measuring

I.e. does it measure what its supposed to measure?

Question 8

What are the 2 subtypes of construct validity?

Answer 8

1. Convergent

2. Discriminant

Question 9

What does convergent validity refer to?

Answer 9

Convergent validity refers to the degree to which two measures of constructs that theoretically should be related, are in fact related.

Question 10

What does discriminant validity refer to?

Answer 10

Discriminant validity tests whether concepts or measurements that are supposed to be unrelated are, in fact, unrelated.

I.e. it doesn’t measure what it shouldn’t

Question 11

Example for convergent and discriminant validity:

The concept of general happiness

Answer 11

Convergent: If a measure of general happiness had convergent validity, then constructs similar to happiness (satisfaction, contentment, cheerfulness, etc.) should relate positively to the measure of general happiness

Discriminant: If this measure has discriminant validity, then constructs that are not supposed to be related positively to general happiness (sadness, depression, despair, etc.) should not relate to the measure of general happiness.

Question 12

What does content validity refer to?

Answer 12

Refers to the extent to which a measure represents all facets of a given construct.

E.g. a depression scale may lack content validity if it only assesses the affective dimension (depressed mood) but not the behavioural dimension (weight loss, insomnia etc)

Question 13

What does face validity refer to?

Answer 13

The extent to which a test is subjectively viewed as covering the concept it purports to measure.

I.e. it appears to measure what its supposed to measure - refers to the transparency or relevance of a test as it appears to test participants

Question 14

What does reliability in research refer to?

Answer 14

Refers to the consistency of a research study or measuring test.

Question 15

What are the 2 methods of testing reliability?

Answer 15

1. Test-retest

2. Inter-rater

Question 16

What is ‘test-retest’ reliability?

Answer 16

Are measurements consistent over time, if nothing has changed?

i.e. giving participants the same test on two separate occasions

N.B. this can be complex with tests regarding mental health

Question 17

What is ‘inter-rater’ reliability?

Answer 17

Do different assessors give the same results?

Example: researchers observe the same behaviour independently (to avoided bias) and compare their data. If the data is similar then it is reliable.

Question 18

What does responsiveness in research refer to?

Answer 18

The outcome should be able to detect real changes when they occur

Is the outcome sensitive enough (within the population)

Question 19

What is the difference between ‘clinician’ and ‘epidemiologist’ data collection goals?

Answer 19

Clinician: collect, organise, analyse data from INDIVIDUALS to form predictions and recommendations

Epidemiologist: collect, organise, analyse data from MANY INDIVIDUALS (populations) to form predictions and recommendations

Question 20

What are the uses of health outcomes?

Answer 20

• Clinical trials: does a new treatment work?
• Identify national and international variation (promote equality)
• To set performance targets and monitor these over time

Question 21

What is continuous data?

Answer 21

Data that can take any value (within a range)

E.g. height, blood pressure, quality of life

Question 22

What is discrete data?

Answer 22

Information that can only take certain ‘fixed’ values

E.g. number of participants, age in years

Question 23

What is nominal data?

Answer 23

Data that is used for naming or labelling variables, without any quantitative value

E.g. blood type, zip code, gender, race, eye colour

Question 24

What is ordinal data?

Answer 24

A categorical, statistical data type where the variables have natural, ordered categories and the distances between the categories is not known.

E.g. socio economic status (low income, middle income, high income), education level (high school, BS, MS, PhD), income level (less than 50K, 50K-100K, over 100K), satisfaction rating (extremely dislike, dislike, neutral, like, extremely like)

Question 25

What is the null hypothesis?

Answer 25

The null hypothesis states that there is no relationship between the two variables being studied (one variable does not affect the other). It states the results are due to chance and are not significant in terms of supporting the idea being investigated.

Question 26

What is a p value?

Answer 26

When you perform a statistical test a p-value helps you determine the significance of your results in relation to the null hypothesis.

You cannot accept the null hypothesis, we can only reject the null or fail to reject it.

Question 27

What does a p value < 0.05 indicate?

Answer 27

A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the null is correct (and the results are random). Therefore, we reject the null hypothesis, and accept the alternative hypothesis.

Question 28

What does a p value > 0.05 indicate?

Answer 28

A p-value higher than 0.05 (> 0.05) is not statistically significant and indicates strong evidence for the null hypothesis. This means we retain the null hypothesis and reject the alternative hypothesis.