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Statistics! > Interpreting results > Flashcards

Interpreting results Flashcards

1
Q

Define descriptive statistics

A

Used to organise or summarise data collected from a sample population

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2
Q

Define inferential statistics

A

Uses data collected from a sample population to make generalisations about the target population

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3
Q

Define independent variable

A

An entity that is manipulated in the study

- Also known as experimental variable

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4
Q

Define dependent variable

A

An entity that is affected by the change in the value of the independent variable
- Also known as outcome variable

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5
Q

How do you calculate incidence?

A

Incidence = Number of new cases / population size

- Measure of the risk of a disease

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6
Q

How do you calculate point prevalence?

A

Point prevalence = Number of people with disease at a given time / size of population at the same time

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7
Q

Define intention to treat analysis

A

All participants are included in the analysis regardless of whether they completed the study

  • Pragmatic approach
  • Mirrors real life scenario
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8
Q

Define last observation carried forward

A

Last recorded results of individual who drop out are incorporated into the results
- Used in intention to treat analysis

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9
Q

Define worst case scenario analysis

A

Subjects who drop out are recorded as non responders with worst possible outcomes
- Used in intention to treat analysis

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10
Q

Define imputation

A

Missing data is substituted with plausible values to allow data analysis to proceed
- Used in intention to treat analysis

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11
Q

Define sensitivity anaylsis

A

Assumptions are made when the missing values are put in

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12
Q

Define risk

A

The probability of something happening

  • Expressed as p (number between 0-1) or percentage
  • Number of times event occurs / total number of possible events
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13
Q

Define odds

A

Way of expressing chance

  • Expressed as ratio or fraction
  • Odds is the ratio of the number of times an event is likely to occur / number of times it is likely not to occur
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14
Q

What is a contingency table?

A

2x2 table comparing risk and odds across 2 groups

  • Rows consist of exposure
  • Columns consist of outcome event status
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15
Q

Define control event rate (CER)

A

Outcome event rate in control group

- outcome event in controls / outcome event in controls + no outcome event in controls

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16
Q

Define experimental event rate (EER)

A

Outcome event rate in experimental group
- outcome event in experimental subjects / outcome event in experimental subjects / no outcome event in experimental subjects

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17
Q

Define absolute risk reduction (ARR)

A

Control event rate - experimental event rate

  • Also known as absolute benefit increase
  • If negative number, then called absolute risk increase
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18
Q

Define relative risk (RR)

A

Experimental event rate / control event rate

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19
Q

Define relative risk reduction (RRR)

A

Control event rate - experimental event rate / control event rate

  • Also known as relative benefit increase
  • If negative number, then called relative risk increase
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20
Q

Define number needed to treat (NNT)

A

1 / absolute risk reduction
ARR = CER - EER
- Number of subjects who must be treated with intervention compared with control for one additional subject to experience the beneficial outcome
- If negative number, then can be expressed as NNH

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21
Q

Define odds ratio

A

odds of outcome in exposed group / odds of outcome in control group

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22
Q

What does it mean if the relative risk is equal to 1?

A

No risk difference between the two groups

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23
Q

What does it mean if the relative risk is >1?

A

Increased risk of the outcome in the experimental group

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24
Q

What does it mean if the relative risk is <1?

A

Reduced risk of the outcome in the experimental group

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25
Q

Define number needed to harm (NNH)

A

1 / absolute risk increase

- Number of subjects treated for one extra subject to have an adverse outcome compared with the control intervention

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26
Q

How can risk/benefit ratio be calculated?

A

NNH:NNT

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27
Q

What does an odds ratio of 1 mean?

A

Same outcome in both groups i.e no effect

- If expressed as a log odds ratio then 0 means no effect

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28
Q

What does an odds ratio of >1 mean?

A

Indicates likelihood of developing the outcome is greater among those in the experimental arm

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29
Q

What does an odds ratio of <1 mean?

A

Indicates likelihood of developing the outcome is less among the experimental arm

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30
Q

What are the 4 types of measurement scales?

A

Nominal scales
Ordinal scales
Interval scales
Ratio scales

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31
Q

Define nominal scales

A

Organised into categories

  • No inherent order
  • No mathematical relationship to one another
  • Usually qualitative data
    e. g male, female
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32
Q

Define ordinal scales

A

Organised into categories in a meaningful way

  • Difference between points being equal across the scale
  • Not given numerical value
  • Usually qualitative data
    e. g Mild, moderate, severe
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33
Q

Define interval scales

A

Organised in a meaningful way with differences between points bring equal across the scale

  • No true zero
  • Usually quantitative data
    e. g degrees Celsius
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34
Q

Define ratio scales

A

Similar to interval scale but there is a true zero

  • Usually quantitative data
    e. g Kelvin
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35
Q

Nominal and ordinal data is analysed with non-parametric statistics (True/False)

A

True

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36
Q

Interval and ratio data is analysed with parametric statistics (True/False)

A

True

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37
Q

Define Gaussian distribution

A

A normal distribution, perfectly symmetrical, bell shaped curve on a graph
- Mean, median and mode are of equal value and lie in the center of the distribution

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38
Q

Define weighted mean

A

Some variables are more important than others therefore a weight is attached to reflect this
- If all values are equally weighted then the mean will be unaffected

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39
Q

Define variance

A

Indicates dispersion of values around the mean

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40
Q

Define standard deviation

A

Describes the degree of data spread about the mean

  • Measure of precision
  • Square root of variance
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41
Q

What percentage of observations are included in a range covered by 1 standard deviation above and below the mean?
assuming observations follow a normal distribution

A

68%

i.e area under the curve

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42
Q

What percentage of observations are included in a range covered by 2 standard deviations above and below the mean?
assuming observations follow a normal distribution

A

96%

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43
Q

What percentage of observations are included in a range covered by 3 standard deviations above and below the mean?
assuming observations follow a normal distribution

A

99.7%

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44
Q

Define co-efficient of variation

A

Measure of spread which is independent of the unit of measurement
- Compares studies using different units
coefficient of variation = standard deviation / mean x 100

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45
Q

What is the median if there is an even number of data values?

A

An average of the two values that lie on either side of the middle

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46
Q

Define geometric mean

A

Used when distribution is positively skewed

Each value is replaced by its logarithm in a log normal distribution

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47
Q

Define interquartile range

A

Focus on the spread of the middle 50% of data

Not influenced by outliers

48
Q

What percentage of values lie below the 10th percentile?

A

10%

49
Q

What percentage of values lie below the 20th percentile?

A

20%

50
Q

What percentage of values lie below the 50th percentile?

A

50%

51
Q

Define coefficient of skewness

A

Measure of symmetry

52
Q

Define coefficient of kurtosis

A

Measure of peakedness of distribution

53
Q

Define positively skewed distribution

A

The distribution has an extended tail to the right

- Has a positive coefficient of skewness

54
Q

Is the mean larger or smaller than the median in a positively skewed distribution?

A

Mean > Median > Mode

55
Q

Define negatively skewed distribution

A

The distribution has an extended tail to the left

- Has a negative coefficient of skewness

56
Q

Is the mean larger or smaller than the median in a negatively skewed distribution?

A

Mean < Median < Mode

57
Q

Define symmetrical distribution

A

Coefficient of skewness is zero

58
Q

Define standard error of the mean

A

Measure of the precision of the sample
- More observations = smaller the standard error will be
Standard error of a sample of size n = standard deviation / square root of number of observations in the sample

59
Q

Define confidence interval

A

A range of values that express the degree of uncertainty around an estimate
- Width of a CI indicates the precision of the estimate

60
Q

What does a p value of 0.05 mean?

A

Chance of results being due to chance is 5% meaning the null hypothesis can be rejected
- Results are deemed statistically significant

61
Q

Define a type 1 error

A

Null hypothesis is rejected when it is true

  • False positive result recorded
  • Usually attributable to bias, confounding or multiple hypothesis testing
62
Q

Define a type 2 error

A

Null hypothesis is accepted when it is false

  • False negative result recorded
  • Usually happens when the sample size is not large enough or measurement variance is too large
63
Q

Define alternative hypothesis

A

Proposed experimental hypothesis that runs opposite to the null hypothesis

64
Q

Define a two tailed test

A

Exists if the alternative hypothesis does not offer a direction for the difference between the groups

65
Q

Define a one tailed test

A

Alternative hypothesis specifies a direction for difference

Not used often

66
Q

Define Bonferroni correction

A

Safeguards against multiple tests of statistical significance on the same data which may give a false appearance of significance
- If excessive, it will increase the risk of making a type 2 error

67
Q

If the confidence intervals overlap, the result is statistically significant (True/False)

A

False

- If the confidence intervals do not overlap, the result is significant

68
Q

Define unpaired data

A

Data from two groups with different members

69
Q

Define paired data

A

Data from the same individuals at different time points

70
Q

Which statistical test can be used when comparing two or more groups of categorical data?

A

Chi-squared test (unpaired)

McNemar’s test (paired)

71
Q

Which statistical test can be used when comparing two groups of non-normal data?

A

Mann-Whitney U test (unpaired)

72
Q

Which statistical test can be used when comparing one group of non-normal data?

A

Sign test

Wilcoxon’s signed rank test

73
Q

Which statistical test can be used when comparing more than two groups of non-normal data?

A

Kruskal-Wallis ANOVA test (unpaired)

Friedman’s test (paired)

74
Q

Which statistical test can be used when comparing one group of normal data?

A

One sample t test

75
Q

Which statistical test can be used when comparing two groups of normal data?

A

t test (paired or unpaired)

76
Q

Which statistical test can be used when comparing more than two groups of normal data?

A

One way ANOVA (for unpaired)

Repeated measures ANOVA (for paired)

77
Q

Define a non parametric test

A

Used for analysis of non-normally distributed data

78
Q

Name a non parametric test

A 
Sign test
Man-Whitney U test (unpaired)
Wilcoxon matched pairs (paired)
Kruskal-Wallis ANOVA test (unpaired)
Friedman's test (paired)
79
Q

Define a parametric test

A

Used for data that is normally distributed

80
Q

Name a parametric test

A

t test
Paired t test
ANOVA

81
Q

What is Kendall’s correlation?

A

A statistical test used in analysis of categorical data

82
Q

What is Spearman’s correlation?

A

A statistical test used in analysis of mixed categorical & continuous data

83
Q

What is Pearson’s correlation?

A

A statistical test used in analysis of continuous data

84
Q

What is ANOVA?

A

Used to compare three or more groups of continuous or interval scores. Can have one way, two way or repeated measures ANOVA
- Can also have MANOVA (multiple analysis of variance)

85
Q

Define an equivalence study

A

Researcher attempts to show equivalence between drugs

86
Q

Define a non-inferiority study

A

Researcher assesses whether a new drug is no worse within a specified margin than the standard drug

  • Cheaper to run
  • Require smaller sample size
87
Q

Define multivariate statistics

A

Examine the relationship between several variables and make predictions about the data set
- Continuous data can be analysed using correlation and regression techniques

88
Q

What does correlation assess?

A

The strength of the relationship between two quantitative variables and if there is a linear association

89
Q

Define positive correlation

A

Y increases linearly as X increases

90
Q

Define negative correlation

A

Y decreases linearly as X increases

91
Q

Define zero correlation

A

Complete non association between the compared variables

92
Q

What is simple linear regression?

A

Describing the linear relationship between a dependent variable
- Also known as univariate regression
- Stand errors can be estimated and CIs can be determined
Assumes the dependent variable is continuous

93
Q

What is multiple linear regression?

A

The dependent outcome variable is predicted from 2 or more independent variables
- Independent variables can be continuous or categorical
- Also known as multivariable regression
Assumes the dependent variable is continuous

94
Q

Define logistic regression

A

Used when there is a binary outcome of interest.

- Has a theoretical binomial distribution

95
Q

What are the four main components to a systematic review?

A

Research question
Search strategy
Quality assessment
Interpretation of data

96
Q

What are the key steps for a meta analysis?

A

Synthesis using statistical techniques (Combines results of included studies)
Calculation of pooled estimate of effect, together with P value and CIs
Check for heterogeneity
Check for publication bias
Review and interpret findings

97
Q

What does the vertical line on a forest plot represent?

A

Line of no effect

98
Q

What do the boxes on a forest plot represent?

A

Each box corresponds to a study

  • Size of box is proportional to weight of study in meta analysis
  • Position of box relates to estimate of outcome
99
Q

What are the horizontal lines around boxes on a forest plot?

A

Represent CIs
- If touches/crosses vertical line then outcome not statistically significant or sample size was too small for confidence in result

100
Q

What does the diamond on a forest plot represent?

A

Overall outcome of the meta anaylsis

  • Centre of the diamond is the point estimate of pooled result
  • Horizontal width is CIs
101
Q

What is the PRISMA statement?

A

27 item checklist

- Aims to improve the reporting of systematic reviews and meta analysis

102
Q

Define homogeneity

A

Studies have similar & consistent results, any observed differences are due to random variation

103
Q

Define heterogeneity

A

More variation than would be expected by chance alone after allowing for random variation

104
Q

Define clinical heterogeneity

A

Individuals chosen for two studies differ from one another significantly, results are difficult to collate

105
Q

Define statistical heterogeneity

A

Results of different studies differ from one another significantly more than would be expected by chance

106
Q

What are the two methods for calculating the effect size in a meta analysis?

A

Fixed effects model

Random effects model

107
Q

Define fixed effects model

A

Assumes there is no heterogeneity between studies

  • Used when heterogeneity has been ruled out
  • Examples include Mantel-Haenszel procedure
108
Q

Define random effects model

A

Allows for between study variations when the pooled overall effect estimate is produced
- Gives wider CIs

109
Q

What methods are available to check for heterogeneity?

A

Forest plot (if CIs from studies do not overlap with one another)
Cochran’s Q
Galbraith plot - used when number of studies is small

110
Q

What methods are available to check for homogeneity?

A

L’Abbe plot

111
Q

Define sensitivity analysis

A

Assess how sensitive the results of the analysis are to changes in the way it was done

112
Q

What are the different types of reporting bias?

A 
Time-lag bias
Language bias
Citation bias
Funding bias
Outcome variable selection bias
Publication bias
113
Q

Define publication bias

A

Studies with positive findings are more likely to be submitted and published than those with negative findings

114
Q

What methods are available to identify publication bias?

A

Funnel plots
Galbraith plot
Begg’s rank correlation test

115
Q

What does it mean if there is asymmetry in a funnel plot?

A

There is publication bias

- Can be removed with ‘trim & fill method’

Statistics! (4 decks)

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