Clinical Sciences: Statistics, studies and other shizz Flashcards

1
Q

Mean

A

The average of a series of observed values

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2
Q

Median

A

The middle value if series of observed values are placed in order

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3
Q

Mode

A

The value that occurs most frequently within a dataset

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4
Q

Range

A

The difference between the largest and smallest observed value

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5
Q

Randomised controlled trial

A

Participants randomly allocated to intervention or control group (e.g. standard treatment or placebo)

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6
Q

Cohort study

A

Observational and prospective. Two (or more) are selected according to their exposure to a particular agent (e.g. medicine, toxin) and followed up to see how many develop a disease or other outcome.

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7
Q

Cohort study outcome measure

A

RELATIVE RISK

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8
Q

Case-control study

A

Observational and retrospective. Patients with a particular condition (cases) are identified and matched with controls. Data is then collected on past exposure to a possible causal agent for the condition.

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9
Q

Case-control study outcome measure

A

The usual outcome measure is the odds ratio.

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10
Q

Cross-sectional survey

A

Provide a ‘snapshot’, sometimes called prevalence studies

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11
Q

Sensitivity formula

A

TP / (TP + FN )

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12
Q

Positive predictive value formula

A

TP / (TP + FP)

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13
Q

Negative predictive value formula

A

TN / (TN + FN)

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14
Q

Likelihood ratio for a positive test result

A

sensitivity / (1 - specificity)

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15
Q

Likelihood ratio for a negative test result

A

(1 - sensitivity) / specificity

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16
Q

Sensitivity definition

A

Proportion of patients with the condition that have a positive test result

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17
Q

Specificity definition

A

Proportion of patients without the condition who have a negative test result

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18
Q

Positive predictive value

A

The chance that the patient has the condition if the diagnostic test is positive

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19
Q

Negative predictive value

A

The chance that the patient does not have the condition if the diagnostic test is negative

20
Q

Likelihood ratio for a positive test result

A

How much the odds of the disease increase when a test is positive

21
Q

Likelihood ratio for a negative test result

A

How much the odds of the disease decrease when a test is negative

22
Q

Confidence interval

A

a range of values within which the true effect of intervention is likely to lie

23
Q

Standard error of the mean

A

The standard error of the mean (SEM) is a measure of the spread expected for the mean of the observations - i.e. how ‘accurate’ the calculated sample mean is from the true population mean

24
Q

How to calculate SEM

A

SD / square root (n)

25
Q

Standard deviation

A

measure of how much dispersion exists from the mean

26
Q

Clinical trial Phase 1

A

Determines pharmacokinetics and pharmacodynamics and side-effects prior to larger studies

27
Q

Clinical trial phase 2

A

Assess efficacy + dosage

28
Q

Clinical trial phase 3

A

Assess effectiveness

29
Q

Clinical trial phase 4

A

Postmarketing surveillance

30
Q

What does parametric mean?

A

something which can be measured, usually normally distributed

31
Q

Name 2 parametric tests

A

Student’s t-test - paired or unpaired*
Pearson’s product-moment coefficient - correlation

32
Q

Name 4 Non-parametric tests

A

Mann-Witney U test
Wilcoxon signed rank test
Chi-squared test
Spearman rank, Kendall Rank

33
Q

Mann-Whitney U test - what does it do?

A

compares ordinal, interval, or ratio scales of unpaired data

34
Q

Wilcoxon signed-rank test - what does it do?

A

compares two sets of observations on a single sample, e.g. a ‘before’ and ‘after’ test on the same population following an intervention

35
Q

chi-squared test - what does it do?

A

used to compare proportions or percentages e.g. compares the percentage of patients who improved following two different interventions

36
Q

Spearman / Kendall test- what does it do?

A

Correlation

37
Q

p value

A

the probability of obtaining a result by chance at least as extreme as the one that was actually observed, assuming that the null hypothesis is true

38
Q

type I error

A

the null hypothesis is rejected when it is true (false positive)

39
Q

type II error

A

he null hypothesis is accepted when it is false (false negative)

40
Q

The power of a study

A

The power of a study is the probability of (correctly) rejecting the null hypothesis when it is false, i.e. the probability of detecting a statistically significant difference

41
Q

nominal data

A

Observed values can be put into set categories which have no particular order or hierarchy. You can count but not order or measure nominal data (for example birthplace)

42
Q

Ordinal data

A

Observed values can be put into set categories which themselves can be ordered (for example NYHA classification of heart failure symptoms)

43
Q

Discrete data

A

Observed values are confined to a certain values, usually a finite number of whole numbers (for example the number of asthma exacerbations in a year)

44
Q

Continuous data

A

Data can take any value with certain range (for example weight)

45
Q

Binomial data

A

Data may take one of two values (for example gender)

46
Q

Interval data

A

A measurement where the difference between two values is meaningful, such that equal differences between values correspond to real differences between the quantities that the scale measures (for example temperature)

47
Q

Specificity formula

A

TN / (TN + FP)