Applied statistics and interpreting results Flashcards

1
Q

Types of data used/found in clinical trials

A

Binary- two types of responses (yes/no)

Continuous- blood pressure, cholesterol

Time-to-event- death, pregnancy

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2
Q

What is categorical data

A

Nominal (categories unordered)
Ordinal (categories ordered)

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3
Q

Describe nominal data

A

Gives averages and means
Discrete- finite values possible
Continuous- all values are possible (decimals)

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4
Q

Parametric vs non-parametric testing

A

Parametric= assume normal distribution, give significant result more often

Non-parametric= compare rank order, not influenced by outliers

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5
Q

What is a null hypothesis

A

No difference exists in treatment A and B
Nothing happens

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6
Q

What is the alternate hypothesis

A

Difference exists between treatment A and B

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7
Q

Describe type 1 error alpha

A

False positive
Falsely rejecting Ho and detecting a significant difference when there is no difference

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8
Q

Describe type 2 error beta

A

False negative
Falsely accept Ho and not notice a difference when there is a difference

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9
Q

What is power (1-beta)

A

Probability of correctly rejecting Ho and detecting difference when difference exists.
Higher the power, the better.

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10
Q

How to measure the effect of binary date

A

Risk difference (absolute risk difference)
Relative risk
Odds ratio
Number needed to treat
Number needed to harm

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11
Q

What is absolute risk

A

Absolute risk difference - difference between risk of event in intervention and control group

Absolute risk reduction- treatment is effective and reduces unwanted event

Absolute risk increase- treatment does not work, increased risk of event

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12
Q

What is NNT

A

Number needed to treat

Number of patients needed to be treated to produce one additional successful outcome

Compare efficacy of treatments

NNT= 1/ARR

Best for comparing treatment with placebo

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13
Q

What is NNH

A

Number needed to harm

Number of patients needed to be treated to produce one ADR

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14
Q

What is relative risk

A

AKA risk ration, risk of event occuring
1.0 is middle number
Lower= death
Higher= cure

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15
Q

What is odds ratio

A

Probability of event occuring over risk of event not occuring

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16
Q

What is selection bias

A

Choosing people to under/overrepresent a study
Not true representation of population

17
Q

What is attrition bias

A

Selective dropout of participants who differ from others.
E.g. those experiencing ADR

18
Q

What is P value

A

Probability by chance

Stat sig= P<0.05
How often chance gives favourable results

19
Q

What is the confidence interval

A

Estimate of the precision of results
95% CI = range of values within which we are 95% confident the true population estimate lies

Narrower CI indicates precise and reliable results

20
Q

What is a significant result in relation to CI

A

Sig if CI does not cross null value

> 1 for RR
0 for ARR

21
Q

Statistical significance vs clinical significance

A

Stat= size of effect and 95% CI in relation to Ho (no diff between treatment and control)

Clin= size of effect and 95% CI in relation to a minimum effect considered to be important

22
Q

What is intention to treat in RCT

A

Final analysis is more robust if all patients are included (even those who didn’t complete)