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Academic Foundation Programme > Statistics > Flashcards

Statistics Flashcards

1
Q

What is Significance Level?

A

The probability of rejecting the null hypothesis given that it is true (a Type I Error)

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2
Q

What is “Power”?

What can influence “Power”?

A

Power refers to the probability that a test will reject a false null hypothesis

Factors influencing power: Sample size, Standard deviation, Effect size, Alpha, Beta

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3
Q

Effect size

A

Effect size is a quantitative measure of the strength of a phenomenon

Examples of effect sizes are:

  • correlation between two variables
  • the regression coefficient in a regression
  • the difference between the two means
  • the risk with which something happens
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4
Q

Regression coefficient

A

The constant that represents the rate of change of one variable as a function of changes in the other

It is a in y=ax+b

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5
Q

Hazard ratio

A

The ratio of the
probability of a harmful event in the
experimental arm to the probability in the comparator arm

It is a measure at a specific time point, whereas relative risk is cumulative over a period of time.

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6
Q

Odds ratio

A

Represents the odds that an outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure.

OR= ratio of positive outcome (exposed/non exposed) divided by ratio of no outcome (exposed/non exposed)

OR=1 Exposure does not affect odds of outcome
OR>1 Exposure associated with higher odds of outcome
OR<1 Exposure associated with lower odds of outcome

Used for case-control studies
Applies only to sample tested, not overall population
Not a measure of probability, unlike relative risk
In rare diseases, odds ratio approximates to relative risk

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7
Q

Relative risk

A

Probability of an event when exposed divided by probability of event when not exposed

Probability of event in exposed/all exposed divided by probability of event not exposed/all not exposed

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8
Q

P-value

A

Probability of obtaining a result equal to or “more extreme” than what was actually observed, when the null hypothesis is true

Differs from alpha which is the level

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9
Q

Absolute risk reduction

A

The difference in risk of an outcome with and without intervention

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10
Q

Relative risk reduction

A

Absolute risk reduction divided by control event rate

  • Takes into account control rate of event
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11
Q

Selection bias

A

Individuals being more likely to be selected for study than others

Berksonian bias is a type of selection bias when both the disease and exposure affect participant selection e.g. case control study

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12
Q

Bias

A

Systematic deviation of results or inferences from truth

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13
Q

Spectrum bias

A

Failure or diagnostic test to account for variation in the population e.g. evaluating diagnostic tests on biased patient samples, leading to an overestimate of the sensitivity and specificity of the test.

In an ideal world, every variation would be included proportionally within the study and stratified for according to probability of an outcome.

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14
Q

Omitted variable bias

A

Bias that appears in estimates of parameters in a regression analysis when the assumed specification omits an independent variable that should be in the model.

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15
Q

Detection bias

A

Systematic differences between groups in how outcomes are determined

N.B selection bias: participant with influential characteristic more likely to be recruited/selected for

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16
Q

Funding bias

A

May lead to selection of outcomes, test samples, or test procedures that favor a study’s financial sponsor

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17
Q

Reporting bias

A

Selective revealing or suppression of information

-more likely in self-reporting surveys for habits perceived as positive or negative

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18
Q

Exclusion bias

A

Systematic exclusion of certain individuals from the study.

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19
Q

Attrition bias

A

Arises due to a loss of participants e.g. loss to follow up during a study - less than 5% is of little concern, over 20% poses serious threats to validity

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20
Q

Recall bias

A

Arises due to differences in the accuracy or completeness of participant recollections of past events

Differs from reporting bias as to do with memory rather than perception

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21
Q

Response bias

A

Systematic difference between survey answers and actual participant experiences

Includes recall bias and reporting bias

22
Q

Observer bias

A

Systematic difference between a true value and the value actually observed due to observer

23
Q

Confidence interval

A

“Is a range that the true value lies within”

Actually means that if the experiment was repeated an unlimited number of times, the results would lie within CI 95% of the time, if the confidence level is 95%.

24
Q

Performance bias

A

Systematic differences between groups in the care that is provided, or in exposure to factors other than the interventions of interest

25
Q

Absolute risk

A

Number of events over total population

26
Q

Number needed to treat

A

Number of people who have to be exposed to treat 1

= 1/Absolute risk reduction
= 1/AR control - AR treatment

27
Q

Relative risk

A

Rate of events with treatment divided by rate of events in controls

Differs from absolute risk which is rate of events with treatment divided by total population

28
Q

Confounder

A

Factor that is not the intervention which may influence outcome

29
Q

Incidence

A

Number of new cases occurring within a period of time

30
Q

Prevalence

A

Actual number of cases alive, with the disease either during a period of time (period prevalence) or at a particular date in time (point prevalence)

31
Q

Intention to treat analysis

A

Patients analysed in groups in which they were randomly allocated, regardless of the treatment they ultimately recieved

“Once randomised, always randomised”

32
Q

Per protocol analysis

A

Only those who completed study protocol are analysed

Subject to attrition bias if high dropout rate

33
Q

Negative predictive value

A

If the test is negative, chances that the patient does not have the disease

34
Q

Positive predictive value

A

If the test is positive, chances that the patient has the disease

35
Q

Sensitivity

A

If the patient has the disease, probability that the test is positive

36
Q

Specificity

A

If the patient does not have the disease, probability that the test is negative

37
Q

Internal validity

A

The extent to which you are able to say that the tested variable caused the result

  • must account for confounders
38
Q

External validity

A

Ability to apply results of the study to the clinical context

39
Q

ROC curve

A

Assess a tests ability to discriminate between two outcomes

40
Q

Chi-squared test

A

A statistical test for heterogeneity

Assesses whether variability between results are compatible with chance alone

Exposure and outcome must be categorical

Assumes large population

41
Q

Fisher’s exact test

A

Statistical significance test used when exposure and outcome are categorical. Can be used for all population sizes, but tends to be selected for small samples

42
Q

T-test

A

Statistical significance test used to compare two categorical exposures with a continuous outcome

Assumes Gaussian distribution

43
Q

Mann-Whitney/ Wilcoxon Rank Sum

A

Statistical significance test used to compare two categorical exposures with a continuous outcome

Assumes non Gaussian distribution

44
Q

ANOVA

A

Statistical significance test used to compare more than two categorical exposures with a continuous outcome

Assumes non Gaussian distribution

45
Q

Pearson test

A

Statistical significance test used to compare a continuous set of exposures with a continuous outcome

Assumes Gaussian distribution

46
Q

Spearman test

A

Statistical significance test used to compare a continuous set of exposures with a continuous outcome

Assumes non Gaussian distribution

47
Q

Linear regression

A

Statistical test used to compare a continuous set of exposures with a continuous outcome

48
Q

Logistic regression

A

Statistical test used to compare a continuous set of exposures with a binary outcome

49
Q

Cox proportional hazard test

A

Statistical test used wen exposure is categorical or continuous and outcome is time dependent

Allows multi-variable adjustments to account for confounders (unlike log rank test)

50
Q

What is a Type I error?

What is a Type 2 error?

A

Type I error: Rejection of true null hypothesis (False positive)

Type II error: Acceptance of false null hypothesis (False negative)

Academic Foundation Programme (9 decks)

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