Week 3: Validity and reliability

Reliability

Estimates evaluate the stability of measures, internal consistency of measurement instruments, and interrater reliability of instrument scores

Validity

the extent to which the interpretations of the results of a test are warranted, which depends on the particular use of the test is intended to serve.

Reliability estimates

used to evaluate:
1. the stability of measures administered at different times to the same individuals or using the same standard.
2. The equivalence of sets of items from the same test or of different observers scoring a behaviour or event using the same instrument.

Reliability coefficients

0.00 to 1.00
- higher levels indicate higher reliability

Stability

(test-retest reliability)
- administering a test at 2 different points in time to the same individuals and determining the correlation or strength of association.

Internal Consistency

gives an estimate of the equivalence of sets of items from the same test.
- Cronbach's alpha is most widely used to measure internal consistency

Interrater Reliability

Establishes the equivalence of ratings obtained with an instrument when used by different observers.
- Cohens Kappa is used

Exposure Measurement (4 doses)

Available dose:
- cumulative vs current

Administrated dose:
- the amount that comes in contact

Absorbed dose:
- the amount that enters the body

Active dose:
- that actually affects the target organ

Ratio

* relationship between 2 numbers
* numerator: **NOT NECESSARILY INCLUDED** in denominator
eg. (binary) sex ratio

Proportion

* relationship between 2 numbers
* numerator: **HAS TO BE INCLUDED** in the denominator
* proportion always ranges between 0-1

Calculating the odds

In a population of 100, 25 are diabetic. What are the odds of being diabetic?
- probability of an event occurring relative to not occurring
- 25/75 = 0.33

Calculating the rate

speed of occurrence of an event over time

numerator: # of events observed for a given time

denominator: population in which the events occur 2 in 100 people?2/100 = 0.02

Measuring Prevalence

prevalence rate:
the proportion of the population that has a given disease or other attribute at a specified time
2 types:
1. point prevalence rate
2. Period prevalence rate

Point Prevalence rate

PR:
# with disease at specific time/ population at same time

Incidence rate

* the proportion of the population at risk that develops a given disease or other attribute during a specific time period.

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* IR:# new events during specified time period/ population at risk

Incidence vs prevalence

__**Incidence:**__

* measures frequency of disease onset
* @@what is new@@

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__**Prevalence**__

* measures population disease status
* %%what exists%%

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all may be expressed in any power of 10

* per 100, 1,000, 10,000

Relative risk

tells us how many times as likely it is that someone who is ‘exposed’ to something will experience a particular health outcome compared to someone who is not exposed

* Tells us about the __strength of an association__
* Can be calculated using any measure of disease occurrence:
**Prevalence
Incidence rate**

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Calculation of relative risk

Random error

error due to chance

systematic error

error due to unrecognizable source
*can have both random and systematic error

precision vs accuracy

A measurement scale/ tool with a high precision is reliable with high accuracy is valid

High precision is _____
HIgh accuracy is _____

reliable
valid

Insufficient Precision

could be:
- The measurement tool is not precise enough (a ruler in cm is not precise when meaningful differences are in millimeter)
- Two (independent) interviewers rate the same person differently using the same scale (inadequate training?)
- The same interviewer rates the same person differently

sources of measurement error

interviewer or observer
- record abstracting (random error)
- biased overestimation or underestimation

Participants
- recall
- random or systematic

Classification of participants

2 types:
1. Non-differential (the same in all study groups)
- Usually weakens associations – i.e. brings effect estimates (RR, OR, AR) closer to the null value (but not always)

2. Differential (different in different study groups)
- Effect estimates may change in any direction, depending on the particular error

Non-differential Misclassification of 10%

10% of all exposed cases and exposed controls are misclassified as unexposed & vice versa (10% of unexposed cases and unexposed controls are misclassified as exposed)

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^^brings OR,RR, AR closer to the null^^

Differential Misclassification

20% of unexposed cases (but not controls) are misclassified as exposed

Reducing measurement error

- Little (or nothing) can be done to fix information bias once it has occurred.
- Information bias must be avoided through careful study design and conduct
Information bias cannot be “controlled” in the analysis.

Case control vs cohort

Case control: case vs control
Cohort: exposed vs unexposed