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Paper B > Stats > Flashcards

Stats Flashcards

1
Q

Likelihood ratio of positive test result

A

sensitivity / (1-specificity)

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2
Q

Median

A

middle item in a data set which has been arranged in numerical order

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3
Q

mode

A

most frequent item in a data set

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4
Q

mean

A

add all items in data set together and divide by the number of items

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5
Q

Relative risk reduction

A

ARR / CER

ARR: absolute risk reduction (the difference between the two rates in control and treatment group)
CER: event rate in the control group

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6
Q

What are funnel plots primarily used for?

A

Assess for potential publication bias in meta-analyses
Graph the size of the effects found in individual studies against a measure of the study’s precision or size

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7
Q

Chi-squared test (4)

A

Used to assess differences in categorical variables
Non-parametric test
Applies assumption that the sample is large
Compares the observed frequencies against those that would have been expected if there was no difference and then produces a value which can be used to assess if the difference is significant (p<0.05)

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8
Q

Pearson’s correlation coefficient

A
  • Measures linear correlation between 2 variables
  • sign of the correlation coefficient tells us the direction of the linear relationship: negative then trend line slopes down, positive then trend line slopes us
  • the size/magnitude of the correlation coefficient tells us the strength of a linear relationship: >0.90 = strong, 0.65-0.9 = moderate, <0.65 = weak
  • parametric test
  • if the data is non-parametric or if both variables are not ratio variables then Spearman’s should be used
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9
Q

The 3 types of t-test

A
  • one sample t-test
  • independent t-test
  • paired t-test
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10
Q

one sample t-test

A
  • used to see if there is a difference between a sample mean and the hypothesised population mean
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11
Q

independent t-test

A
  • used when you want to compare means from independent groups
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12
Q

paired t-test

A
  • used when comparing the means of two groups that are considered to be paired (matched, or dependent)
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13
Q

ANOVA

A
  • statistical test to demonstrate statistically significant differences between the means of several groups
  • similar to a student’s t-test apart from that ANOVA allows the comparison of more than just 2 means
  • assumes that the variable is normally distributed
  • works by comparing the variance of the means
  • distinguishes between within group variance and between group variance
  • the null hypothesis assumes that the variance of all the means is the same as between group variance
  • the test is based on the ratio of these two variances, known as the F statistic
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14
Q

Relative risk

A

RR = EER / CER

EER: treatment group risk
CER: control group risk

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15
Q

NNT - number needed to treat

A
  • used in assessing the effectiveness of a healthcare intervention
  • represents the average number of patients who need to be treated to prevent one additional bad outcome or produce one additional good outcome
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16
Q

RISK

A
  • a proportion
  • probability with which an outcome will occur
  • usually expressed as a decimal between 0-1
  • often expressed as a number of individuals per 1000
  • if risk is 0.1, in a sample of 100 people, the number of events observed will on average be 10
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17
Q

ODDS

A
  • odds is a ratio
  • the ratio of the probability that a particular event will occur to the probability that it will not occur
  • can be any number 0-infinity
  • commonly expressed as a ratio of 2 integers, eg odds of 0.01 would be 1:100
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18
Q

absolute risk

A

basic risk
in many studies it will just be the incidence rate
in experiments, will be the number of events in that group divided by the number of people in the group

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19
Q

risk difference / absolute risk reduction

A

the difference between the absolute risk of an event in the intervention group and the absolute risk in the control group

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20
Q

relative risk

A

the ratio of risk in the intervention group to the risk int he control group

1 = estimated effects are the same for both interventions

used in cohort, cross-sectional and randomised control trials

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21
Q

Positive predictive value (PPV)

A

the probability that subjects with a positive screening test truly have the disease

PPV = true positives / (true positives + false positives)

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22
Q

sensitivity

A

how well a test can identify true positives from all actual positives

sensitivity = number of true positives / (true positives + false negatives)

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23
Q

specificity

A

how accurately a test identified those without a condition/disease

specificity = number of true negatives / (true negatives + false positives)

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24
Q

accuracy

A

how close measurements are to ‘true values’

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25
Q

negative predictive value (NPV)

A

likelihood that subjects with a negative screening test truly do not have the disease

NPV = number of true negatives / (true negatives + false negatives)

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26
Q

how to calculate NNT

A

NNT = 1 / (CER-EER)
or
NNT = 1 / absolute risk reduction

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27
Q

arithmetic mean

A

adding up all the values and dividing by the number of values

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28
Q

harmonic mean

A

calculated by dividing the number of observations by the sum of the reciprocal of the value
used when there is a time factor involved eg speed

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29
Q

generalised mean / power mean

A

involves raising each value to a specific power, adding together, taking average and then taking the root of that average

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30
Q

range

A

difference between largest and smallest values

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31
Q

interquartile range

A

aka the mid spread
difference between the 3rd and 1st quartiles

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32
Q

ratio / continuous data

A

like interval but have true zero points
eg kelvin scale temp

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33
Q

interval data

A

measurement where the difference between 2 values is meaningful
eg temperature, pH

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34
Q

ordinal data

A

observed values can be put into set categories which themselves can be ordered
eg social class

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35
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
eg birthplace, eye colour

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36
Q

quantitative data

A

numeric values
can be further classified into discrete and continuous types

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37
Q

qualitative data

A

not numerical, usually names
AKA categorical or nominal variables

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38
Q

endemic

A

consistent presence and/or usual prevalence of a disease in a population within a geographical area

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39
Q

epidemic

A

refers to an increase, often sudden, in the number of cases of a disease above what is normally expected in that population in that area

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40
Q

pandemic

A

an epidemic that has spread over several countries or continents, usually affecting a large number of people

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41
Q

standard error of the mean

A

standard deviation / square root (number of patients)

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42
Q

GRADE system

A

Grading of Recommendations Assessment, Development and Evaluation

rates the quality of evidence in systematic reviews and guidelines

classified as high, moderate, low or very low

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43
Q

internal validity

A

the confidence that we can place in the cause and effect relationship in a study.

the confidence that we have that the change in the independent variable caused the observed change in the dependent variable (rather than due to poor control of extraneous variables)

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44
Q

external validity

A

the degree to which the conclusions in the study would hold for other persons in other places and at other times

ie. its ability to generalise

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45
Q

face validity

A

the general impression of a test
if it appears to test what it is meant to

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46
Q

content validity

A

the extent to which a test or measure assesses the full content of a subject or area.

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47
Q

criterion validity

A

concerns the comparison of tests
you may wish to compare a new test to see if it works as well as an old, accepted method
the correlation coefficient is used to test such comparisons

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48
Q

criterion validity (concurrent)

A

the predictor and criterion data are collected at or about the same time

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49
Q

criterion validity (predictive)

A

the predictor scores are collected first, and criterion data are collected at later point
want to know if the test predicts future outcomes

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50
Q

construct validity

A

the extent to which a test measures the construct it aims to

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51
Q

construct validity (convergent)

A

has convergent validity if it has a high correlation with another test that measures the same construct

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52
Q

construct validity (divergent)

A

demonstrated through a low correlation with a test that measures a different construct

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53
Q

cost effectiveness analysis (CEA)

A

compares a number of interventions by relating costs to a single clinical measure of effectiveness

cost effectiveness ratio = total cost / units of effectiveness

combines costs and effects - usually reported as an incremental cost-effectiveness ratio (ICER)

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54
Q

cost benefit analysis (CBA)

A

technique in which all the costs and benefits of an intervention are measured in terms of money

used to establish which of the alternatives has the greatest net benefit

requires that all the consequences of an intervention, such as life years saves, symptom relieve etc are all allocated a monetary value

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55
Q

cost-utility analysis (CUA)

A

special form of CEA in which health benefits / outcomes are measured in broader, more generic ways enabling comparisons between treatments for different diseases and conditions

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56
Q

cost minimisation analysis (CMA)

A

economic evaluation in which consequences of competing interventions are the same and in which only inputs (costs) are takin into consideration

the aim is to decide the least costly way of achieving the same outcome

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57
Q

test-retest reliability

A

assessed the stability of a measure over time by administering the same test to the same individual on two different occasions

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58
Q

split-half reliability

A

assesses the internal consistency of a test by dividing it into 2 halves and comparing the results of each half

ensures consistency within the test items, but does not address stability of the tool over time

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59
Q

parallel-forms reliability

A

involved administering two equivalent forms of a test to the same group and comparing results.
valuable for avoiding practice effects

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60
Q

internal consistence reliability

A

measures how consistently items within a test measure the same construct, often using statistical methods like Cronbach’s alpha.
does not assess stability over time

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61
Q

inter-rater reliability

A

assesses the consistency of scores when different raters or observers administer the test
critical in situations where multiple clinicians assess the same patient, but not relevant to determining whether tool yields stable results for the same individual across repeated administrations

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62
Q

forrest plot weighting

A

indicated influence an individual study has on pooled result

generally, bigger sample size AND narrower confidence interval, the higher the weight

shown by larger box

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63
Q

heterogeneity in forrest plots

A

refers to variability between studies and can affect the ability to combine the data of the individual studies

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64
Q

clinical heterogeneity

A

variability caused by differences in clinical variables, eg patient population, interventions etc

clinicians determine clinical heterogeneity - subjective

65
Q

statistical heterogeneity

A

the variability in effect estimates between the studies and can be quantified by various statistics

forrest plots only present the statistical heterogeneity

66
Q

denominator for simple variance

A

n-1

67
Q

Berkson’s bias

A

occurs when the selection of participants for a study is influenced by their likelihood to seek healthcare

may not be representative of the general population, and can lead to an overestimation of the association between diseases

68
Q

observer bias

A

aka information of measurement bias

systematic differences in the way data is collected for different groups

could be due to the observer’s knowledge about participant’s exposure status influencing how they measure outcome variables

69
Q

hawthorne effect

A

changes in behaviour that occur when individuals know they are being observed

70
Q

verification bias

A

aka referral or test review bias

happens when subjects with positive results are more likely to have their test results confirmed than those with negative results

may influence the accuracy of diagnostic tests and overall study results, it isnt an example of selection bias since it doesn’t affect who gets selected into a study

71
Q

detection bias

A

arises from differential methods of detection amongst groups leading to an apparent difference in outcome rates between these groups.

often seen in studies where one group receives more frequent screening or follow-up than another group, thereby increasing changes of detecting the disease earlier or more frequently but doesn’t pertain to selection into a study which defines selection bias

72
Q

ethnography

A

qualitative research that seeks to understand and describe the culture or social phenoma from the perspective of the subject group

researches immerse themselves in the setting, observing and participating in daily activities - deep understanding of behaviours/beliefs/experiences in that particular cultural context

73
Q

bracketing

A

method used in qualitative research to mitigate the potential deleterious effects of preconceptions that may taint the research process

involves identifying and holding in abeyance preconceived beliefs and opinions about the phenomenon under study

74
Q

grounded theory

A

research methodology that involves the collection and analysis of data with the aim of developing theories grounded in real-world observations

seeks to explain phenomena by generating new theories

75
Q

phenomenology

A

aims to explore how individuals perceive their experiences
about understanding human behaviour from the individuals own subjective viewpoint

76
Q

ROC curv

A

Receiver Operating Characteristic

illustrates diagnostic ability of a binary classifier system as its discrimination threshold is varied

plotted as sensitivity vs 1-specificity

helps in evaluating the performance of diagnostic tests and making informed decisions about cut-off points to maximise sensitivity and specificity

77
Q

how to calculate standard error of the mean

A

SEM = standard deviation / square room (number of patients)

78
Q

alpha level

A

the probability of rejecting a null hypothesis when it is true

it represents the threshold at which we decide to reject the null hypothesis

commonly set at 0.05

79
Q

Type I errors

A

the null hypothesis is rejected when it is true

aka false positive

80
Q

Type II error

A

the null hypothesis is accepted when it is false

aka false negative

81
Q

P-values

A

the probability of rejecting the null when it is true…
a high p-value indicated a high chance the an observed difference is due to chance and vice versa

if p-value is less than the pre-decided cut off, then you reject the null hypothesis

82
Q

Randomisation

A

method used in the design phase of a study to reduce confounding factors

83
Q

Cumulative incidence

A

The average risk of getting a disease over a certain period of time.

CI = the number of newly detected cases that develop during follow up / the number of disease free subjects available at the start of follow up

84
Q

incidence rate

A

IR = I / PR

I: number of new cases in the cohort
PT: person-time - total time disease free individuals in the cohort are observed over the study period

85
Q

prevalence

A

prevalence = incidence x duration of condition

86
Q

point prevalence

A

number of cases in a defined population / number of people in a defined population at the same time

87
Q

period prevalence

A

= number of identified cases during a specified period of time / total number of people in that population

88
Q

area under the curve

A

the higher the AUC, the better the overall performance of the test (the higher the accuracy)

89
Q

SQUIRE

A

Standards for Quality Improvement Reporting Excellence

19 item checklist
ensure all aspects of QI are thoroughly and transparently conveyed

90
Q

MOOSE

A

Meta-analysis of Observational Studies in Epidemiology

for reporting meta-analyses of observational studies

91
Q

STARD

A

Standards for reporting of diagnostic accuracy studies

reporting studies about diagnostic accuracy

92
Q

PRISMA

A

Preferred reporting items for systematic reviews and meta-analyses

93
Q

CONSORT

A

Consolidated standards of reporting trials

guidelines for reporting RCTs

94
Q

PICO system

A

P - patient
I - intervention
C - comparison
O - outcome

95
Q

Cochrane Library

A

collection of 6 databases:
CDSR
DARE
CENTRAL
CMR
HTA
NHS EED

96
Q

Embase

A

european database
broader range then Medline

97
Q

PsychINFO

A

database of abstract of literature in the field of psychology

produced by American Psychological Association

98
Q

CINAHL

A

Cumulative Index to Nursing and Allied Health Literature

references to journal articles from hundreds of nursing journals from UK, USA and other countries

99
Q

OpenGrey

A

dedicated to grey literature
outside of traditional channels

100
Q

Boolean Logic

A

AND, OR, NOT can be used to combine search terms
must be entered in uppercase letters

101
Q

drug trial phases

A

1 - small number healthy people. safety, side effects and dose range
2 - larger group (100-300), effectiveness and further safety
3 - large groups (1000-3000), effectiveness, SE, compare to commonly used treatments or placebos
4 - after granted a license, eg safety in pregnancy, finding other potential uses for the drug

102
Q

How many lie within +/-1SD

A

68.2%

103
Q

How many lie within +/-2SD

A

95/4%

104
Q

How many lie within +/-3SD

A

99.7%

105
Q

What is the Kappa statistic

A

aka Cohen’s kappa coefficient

gives quantitative measure of the magnitude of agreement between observers

can be any value between -1 and 1

0: agreement observed no better than change
1: complete agreement
-1: complete disagreement

106
Q

primary evidence

A

aka empirical research
sources that contain original data and analysis from research studies

107
Q

secondary evidence

A

sources that interpret and analyse primary sources.
these sources are one or more steps removed from the event

108
Q

how to calculate odds ratio

A

OR = (a/b)/(c/d)

a: exposure yes, outcome yes
b: exposure yes, outcome no
c: exposure no, outcome yes
d: exposure no, outcome no

109
Q

Fixed effect model

A

used to measure the impact of variables that vary over time

110
Q

voluntary sampling

A

made of people who self-select
eg invited to participate in a poll
same chosen by the participants and not the survey administrator

111
Q

Convenience sampling

A

made up of people who are easy to reach
eg approach at hospital cafe

112
Q

Snowball sampling

A

one case identifies another of its kind
often done in marginalised groups eg IVDU or sex workers

113
Q

Quota sampling

A

population divided into groups and then elements are selected
done to ensure that the sample reflects that characteristics of the population
eg proportionate representation of males and females

114
Q

Types of random / probability sampling

A

Simple random sampling
Systematic sampling
Cluster sampling
Stratified sampling
Multistage sampling

115
Q

Types of non-random / non-probability sampling

A

Voluntary sampling
Convenience sampling
Snowball sampling
Quota sampling

116
Q

Simple random sampling

A

a sample in which every member of the population has an equal chance of being chosen
eg each member of population given unique ID number then randomly selected - often via number generator

117
Q

Systematic sampling

A

every nth member of population gets selected for the sample
easier than simple random sampling, but more prone to bias if there is a pattern in the population that is consistent with the sampling frequency

118
Q

Cluster sampling

A

Involves dividing a population into separate groups (clusters), and a random sample of clusters is then selected and each element included in the final sample

119
Q

Stratified sampling

A

An entire population is first divided into groups (strata) and then a random sample taken from each

this ensures can obtain equal numbers of individuals eg male and female

120
Q

Multi-stage sampling

A

more complex method of sampling that involved several steps
two or more sampling methods are combined
allows you to narrow down a large population

121
Q

likelihood ratio for negative test result

A

(1-sensitivity)/specificity

122
Q

Delphi method

A

method for achieving convergence of opinion concerning real-world knowledge solicited from experts within certain topic area

123
Q

Background questions

A

general questions about coniditions/illnesses/pathophysiology etc

124
Q

Foreground questions

A

About issues of care - query specialised and distinct knowledge needed for specific and relevant clinical decision-making

125
Q

Box and whisker plot - interquartile range

A

‘mid spread’
the difference between the 3rd and 1st quartiles

126
Q

line in the box on box and whisker plots

A

median - Q2

127
Q

left skewed

A

more on the left of box and whisker plot
negative skewness

128
Q

right skewed

A

more on the right of the box and whisker plot
positive skewness

129
Q

how to calculate prevalence

A

pre-test odd x likelihood ratio

130
Q

how to calculate post test probability

A

post-test odds / (1 + post-test odds)

131
Q

loss to follow up bias

A

when follow up cases are lost continuously - lost cases may have something in common resulting in an unrepresentative sample

132
Q

disease spectrum bias / case-mix bias

A

when a treatment is studied in more severe forms of a disease
such results may then not apply to mild forms of the disease

133
Q

sampling bias

A

the subjects are not representative of the population - may be due to volunteer bias

134
Q

participation bias / non-response bias

A

those who participate may have shared characteristics resulting in an unrepresentative sample

135
Q

incidence-prevalence bias (survival bias, Neyman bias)

A

occurs in case-control studies and is attributed to selective survival among the prevalent cases (ie. mild, clinically resolved or fatal cases excluded from the case group)

136
Q

exclusion bias

A

occurs when certain patients are excluded for example if they are considered ineligible

137
Q

publication or dissemination bias

A

many studies may not be published
may be due to fact that paper with positive results, and large sample sizes are more likely to get published

138
Q

citation bias

A

articles of high citation are easy to reach and have higher chance to be entered into a given study

139
Q

berkson’s bias aka admission rate bias

A

a type of selection bias
can arise when the sample is taken not form the general population but from a subpopulation
eg when cases and controls both sampled from a hospital rather than from the community

140
Q

detection bias

A

when exposure can influence diagnosis
eg women on OCP more frequent smears so more likely to have cervical cancer diagnosis

141
Q

recall bias

A

in retrospective studies where participants are asked to remember their past exposure to risk factors, it is likely that cases will have thought more about what factors in their past may have caused a disease than controls will have, therefore controls less likely to remember an exposure

142
Q

lead time bias

A

lead time is the period between early detection of disease and the time of its usual clinical presentation.
the lead time must be subtracted from the overall survival time of screened patients to avoid lead time bias.
otherwise early detection merely increases the duration of the patients’ awareness of their disease without reducing their morbidity or mortality

143
Q

interviewer/observer bias

A

interviewer or observer knowledge about in-question hypothesis and disease and/or exposure can take effect on collection and registry of data

144
Q

verification and work-up bias

A

the results of a diagnostic test affect whether the gold standard procedure is used to verify the test result
more likely to occur when a preliminary diagnostic test is negative because many gold standard tests can be invasive, expensive and carry a higher risk

145
Q

hawthorn effect

A

when participants alter their usual behaviour due to their awareness that they are being studied

146
Q

ecological fallacy

A

when conclusions about individuals are based only on analyses of group data

147
Q

expectation bias (pygmalion effect)

A

only a problem in non-blinded trials
observers may subconsciously measure or report data in a way that favours the expected study outcome

148
Q

late-look bias

A

gathering information at an inappropriate time eg studying a fatal disease many years later when some of the patients may have died already

149
Q

tests to check that distribution is normally distributed

A

the kolmogorov-smirnov test
jarque-bera test
wilk-shapiro test
p-plot
q-plot

150
Q

purposive sampling

A

participants selected on purpose because the researcher already knows that they have characteristics of interest

151
Q

triangulation

A

compares the results from either 2 or more different methods of data collection, or 2 or more data sources

152
Q

respondent validation / aka member checking

A

includes techniques in which the investigators account is compared with those of the research subjects to establish the level of correspondence between the two sets

153
Q

bracketing

A

methodological device of phenomenological inquiry that requires deliberate putting aside ones own belief about the phenomenon under investigation or what one already knows about the subject prior to and throughout the phenomenological investigation

154
Q

reflexivity

A

sensitivity to the ways in which the researcher and the research process have shaped the collected data, including the role of prior assumptions and experience, which can influence even the most avowedly inductive inquiries

155
Q

content analysis

A

interviews (individual and group) are transcribed to produce texts that can be used to generate coding categories and test theories
can involve enumerating procedures such as counting work frequencies, sometimes aided by computer software

156
Q

constant comparison

A

based on grounded theory
allows researchers to identify the themes that are important in a systematic way, providing an audit trail as they proceed
used by the researcher to develop concepts from the data by coding and analysing at the same time

157
Q

calculate pre-test odds

A

pre test probability / (1 - pre test probability)

158
Q

calculate post test odds

A

pre test odds x (likelihood ratio positive result)

159
Q
A

Paper B (7 decks)

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