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PAS STATS > WEEK 1 > Flashcards

WEEK 1 Flashcards

1
Q

Hierarchy of evidence

A

Evidence based medicine: integration of best research evidence with clinical expertise and patient values
Systematic reviews
Critically appraised topics [evidence synthesis]
Critically appraised individual articles [articles synopses]
—filtered information
Randomised controlled trials
Cohort studies
Case controlled studies. Case series/reports
—unfiltered information
Background information/expert opinion

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2
Q

Case series

A

Tracks subjects with a known exposure
Report on characteristics of a group subjects with a particular condition
Eg if several people with oesophageal cancer drink hot tea does drinking hot tea causes cancers?

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3
Q

Cross-sectional study

A

Snap shot/one time point. Ask people questions about outcome and exposure
Measures prevalence health outcomes or determinants in population at one point in time
Only describe relationships not causality

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4
Q

Case control studies

A

Compare people with condition to without
Retrospective look backward in time to identity exposures
Confounders: another variable associated with outcome and independent variable, is this variable driving the relationship instead. Statistical adjustments
Reverse causality: the outcome is causing them to use the risk factor eg oesophageal cancer causing them to drink hot tea
Useful if you have a rare outcome , recruit knowing outcome

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5
Q

Cohort study

A

Longitudinal study
Take a large group of people
Sharing a common characteristic followed over long period time to study and track outcomes
Identify group before develop disease
Identify causes
Take lots of other important measurements
Need to adjust and account for confounding factors

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6
Q

The RCT

A

Highest quality evidence for a single study but might not be most appropriate study design to use
Measure effectiveness of a new intervention or treatment compare to alternative
Participants randomly assigned to different treatment groups helps reduce selection bias and balances known and unknown factors that could influence outcome

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7
Q

Systematic reviews and meta analysis

A

Systematically identify all RCTs or other studies
Combine the results
Provide comprehensive overview of existing evidence while meta analysis analysis data to get a more precise estimate of effect size, summary statistics
Forest plots

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8
Q

What is data

A

A set of values of qualitative or quantitative variables about one or more persons
Information about a person or group of people such as:
-demographics- eg age, ethnicity
-health information- eg heart rate, temperature
-diagnostic related information- eg blood test results
-disease registry- eg cancer status

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9
Q

What is statistics in practice

A

Collect data:
-surveys/patient notes
-for different individuals
-for different characteristics (called variables)
Collate data:
-database
-each individual constitutes a row in database and each variables constitutes a column
Summarise data:
-averages or location
-spread or variability

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10
Q

Types of data

A

Quantitative or numerical:
-represents quantity
-measures of values or counts and can be expressed as numbers
-how much, how many, how often
Qualitative or categorical:
-represents quality
-measure of type
-what category
-report numbers and percentages

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11
Q

Numerical data- continuous

A

Numerical data- measures of values or counts and can be expressed in numbers
If data can take any numerical values within a possible range then it can be described as continuous
Examples:
-age
-height
-weight

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12
Q

Numerical data- discrete

A

Numerical data- measures of values or counts and can be expressed as numbers
If the data can only take a value of a whole number within a possible range then its discrete
Examples:
-number children in family
-number GP visits per year
-number of teeth

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13
Q

Categorical data- binary or dichotomous

A

Categorical data- describe characteristic, they are mutually exclusive ( can only belong in one group) exhaustive (every person falls into a group)
If there’s only two possible categories its binary
Examples:
-alive/dead
-disease present/absent
-success/fail

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14
Q

Categorical data-ordinal

A

Categorical data- describe characteristic. They’re mutually exclusive, exhaustive
If there’s 3 or more possible categories, and they can be logically ordered or ranked then its ordinal data
Examples:
-academic grade
-clothing size
-cancer stage groups

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15
Q

Categorical data- nominal

A

Categorical data- describe a characteristic. Mutually exclusive and exhaustive
3 or more possible categories and there’s no logical ordering then it can be nominal
Examples:
-eye colour
-ethnicity
-marital status

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16
Q

Summarising continuous data

A

Plot data on graph
Need to be able to describe:
-values for average/location
-measures of variability
-distribution shape
Histogram

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17
Q

Summarising continuous data- average and variability

A

The average value and variability in data is described using:
-mean and standard deviation: useful when no outliers but if there are then the mean will be pulled in the direction of the outliers
-median and IQR: more robust to outliers

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18
Q

Summarising continuous data- averages-mean

A

Idea of quoting a single value which is most “typical” for measurement
Computed by summing the observations and dividing by sample size
Useful for counts/measurements, depending on shape
Outliers can make mean atypical

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19
Q

Summarising continuous data- variability- standard deviation

A

How much does data vary from average
Standard deviation explains how far on average a measurement is from the mean
Small standard deviation= numbers close together
Large standard deviation= numbers are spread out
Standard deviations are affected by outliers

20
Q

Summarising continuous data- averages -median

A

Identified as half way value when the data is put in rank order
Often used for counts and measurements
Not affected by outliers
Not affected when extreme values are unknown

21
Q

Summarising continuous data-percentiles

A

0th percentile=
25th percentile
50th percentile= median
75th percentile
100th percentile
IQR- interquartile range
-75%-25%

22
Q

Summarising continuous data- averages- mode

A

The mode is the most commonly occurring category
-very rarely used
For numerical data may state the number of modes when describing distribution
Eg unimodal or bimodal

23
Q

Summarising continuous data- distributional shape

A

Negatively skewed away from zero leaning, negative direction towards 0
Normal no skew perfectly symmetrical distribution- mode, median and mean all same place
Positively skewed - leans towards zero. Positive direction away 0
Identifying shape of distribution helps us determine what summary measurements should be reported
Normal distribution- mean and standard deviation reported
Skewed distribution- median and IQR reported

24
Q

Baseline tables

A

Descriptive tables
Frequencies and percentages for categorical variables
Means and standard deviations or medians and IQRs as appropriate for continuous data

25
Q

Comparative studies

A

The majority of epidemiological studies compare outcomes between two or more groups
-randomised controlled trials RCT
-comparative cohort study
-case control study

26
Q

Treatment vs exposure effects

A

Treatment effect- usually from RCT
Exposure effect- usually from observational study
The same statistics can be used in both cases
-interpretation differs

27
Q

Common statistics used for treatment and exposure effects

A

Analysis depends on outcome type
Binary outcome:
-relative risk RR
-risk difference RD
-odds ratio OR
Continuous outcome:
-mean difference MD

28
Q

Describing risk and probability

A

In 10000 control patients 1200 had CVD event in 1 month
Probability 1200/10000=0.12
Percentage 12%
Risk is 12 per 100
1 in (100/12) ~8
Odds= 1200/(10000-1200=8800)=0.14

29
Q

Risk ratios/relative risks

A

Relative risk= the probability (risk) of event on treatment/ probability (risk of event) on control
Risk ratio=1 : risk equal in intervention and control arm
Risk ratio >1: risk outcome greater in treatment arm
Risk ration <1: risk outcome less in treatment greater in control arm

30
Q

Interpretation relative risk

A

Express as percentage increase or decrease in risk
(RR-1) x100
Eg 3.1-1 x100=210% increase in risk on treatment versus control
0.6-1x100=-40% increase=40% decrease in risk on treatment versus control
If exposure instead say risk in exposed group versus unexposed group
Express risk as percentage of risk in other arm:
Eg RR=3.1-> risk on treatment is 3.1x100=310% of risk control
Or RR=0.6x100=60% of risk of control
Risk in intervention arm is RR times the risk in control arm:
-risk on treatment is 3.1x risk in control
-risk on treatment is 0.6 x risk in control

31
Q

Risk difference

A

RD= probability of event on treatment- probability of event on control
RD=0 risk of outcome equal in treatment and control
RD>0 risk of outcome greater in treatment
RD<0 risk of outcome less in treatment greater in control

32
Q

Risk difference interpretation

A

Say absolute risk 2.4% lower in treatment than control
Receiving aspirin rather than control leads to absolute reduction of 2.4% in risk of MI
24 out of 1000 patients given aspirin rather placebo would avoid MI within 1 month (risk measured in 1 month)

33
Q

Number needed to treat

A

The number of patients who on average need to be treated to prevent one event that would otherwise occur
NNT is 1/absolute risk difference
If RD=0.024 (ignore sign) NNT=1/0.024=42
Treating 42 people (for a month) will on average prevent one event

34
Q

Relative versus absolute effects

A

Relative measures are commonly reported
Relative measures can look large where event is rare
Absolute measures less susceptible to misinterpretation
NNT is effective way to communicate to lay population

35
Q

Odds- another way of measuring chance

A

Odds: number with event/number without event
If 9 had outcome event and 1 didn’t in trail
-odds are 9/1=9
If 1 had outcome and 9 didn’t odds=1/9=0.11

36
Q

Odds ratio

A

Odds ratio= odds of event on treatment/odds of event on control
Computes the odds in treatment divide by odds in control
Similar to RR for rare events
Can be calculated in case control studies where RR can’t: less intuitive
OR=1 odds equals in intervention and control
OR>1 odds of outcome greater in treatment arm
OR<1 odds of outcome less in treatment arm greater in control
Say if OR=0.78
The treatment reduces odds to 0.78 78% of odds of control
Reduces odds of event by 22%
Odds of event on treatment are 0.78 times odds of control

37
Q

Issue with odds ratio in RCT

A

Odds difficult to interpret
So odds often interpreted as risk
This can lead to an overstatement of effect size

38
Q

Mean difference

A

Comparing a continuous outcome between two groups
Mean in group 1- mean in group 2
Compute mean in treatment group subtract the mean in control group from it

39
Q

Treatments and exposure effect summary

A

Use treatment and exposure effects to compare outcomes between groups
Choice of measure depends on type data in outcome variable

40
Q

Key comparative effect measures

A

Binary outcome:
-relative measures
—risk ratio/relative risk
—odds ratio
-absolute measures
—risk difference. Number needed to treat
Continuous outcome:
-mean difference

41
Q

Cohort study a and d

A

A cohort study follows a group of individuals over time to determine the incidence and risk factors of certain outcomes. Subjects are typically categorised based on exposure to a specific factor or intervention
Advantages: can study multiple outcomes for a single exposure. Provides temporal info enabling inference about causality
Disadvantages: can be time consuming and expensive, susceptible to loss to follow up which can introduce bias

42
Q

Case control study

A

Compares individuals with a specific condition (cases) to those without (controls) to identify potential exposure factors. It’s retrospective in nature assessing exposure in the past
Advantages: efficient for studying rare conditions, less time consuming and costly compared to cohort studies
Disadvantages: prone to recall bias, as it often relies on participants memories. Does not provide incidence rates (only odds ratio)

43
Q

Systematic review

A

Synthesises evidence from multiple studies on a specific question, often using rigorous and predefined criteria. It may or may not include a meta analysis
Advantages: provides a comprehensive understanding of existing research on a topic. Reduces biases through systematic methods
Disadvantages: quality is dependent on the included studies. Potential for publication bias (over presentation of significant results)

44
Q

Randomised controlled trial

A

Assigns participants to different interventions randomly aiming to determine the effect of the intervention. It’s considered a gold standard for clinical research
Advantages: minimises confounding factors due to randomisation. Allows for causal inference
Disadvantages: can be expensive and time consuming, potential ethical issues, especially if denying treatment

45
Q

Case series

A

Describes characteristics, treatments and outcomes for a group of patients with a particular condition or treatment. It lacks a comparison group
Advantages: useful for rare diseases or novel treatments, relatively quick and easy to produce
Disadvantages: cannot establish causality due to lack of a control group. Prone to selection bias

46
Q

Cross-sectional study

A

Assesses individuals at a single point in time to determine the prevalence of an outcome and its associated factors. It provides a “snapshot” of a population
Advantages: relatively quick and often less expensive than longitudinal studies. Good for assessing prevalence
Disadvantages: cannot determine causality due to its cross sectional nature. Susceptible to confounding, as exposure and outcome are assessed simultaneously

PAS STATS (2 decks)

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