EBM Flashcards
1
Q
Evidence based medicine definition:
A
- The conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients
2
Q
EBM effect on Diagnosis:
A
- Will the results of this test help me to improve the accuracy of my diagnosis
3
Q
EBM: prognosis
A
- How long will a patient with this disease survive?
4
Q
EBM: aetiology
A
- What are the risk factors to this disease?
5
Q
EBM: treatment
A
- Is this treatment better than the existing treatment or no treatment (placebo)
6
Q
Main Types of study:
A
- Observational
- Interventional
7
Q
Observational study types:
A
- Cross-sectional study
- Case-control study
- Cohort study
8
Q
Interventional study types:
A
- Randomised control trial (RCT)
- Experiment (NA)
9
Q
Rank epidemiological studies for strongest evidence of causality:
A
- RCT
- Cohort
- Case control
- cross-sectional
10
Q
Cross-sectional study: (3)
- What does it measure?
- What does it show/prove?
- Metaphor???
A
- Measures the prevalence of disease in a population at a particular time
- Shows the true burden of a disease in a population
- ‘Clinical iceburg’
11
Q
Prevalence:
A
= No. with disease at a particular time / total population at that particular time
12
Q
Confounding factors:
A
- A confounder is a third factor that provides an alternative explanation for an association of two other factors
13
Q
Case-control study:
- Definition
- Aims to:
A
- Patients who have developed a disease are identified and their past exposure to aetiological factors is compared with a control group without the disease.
- Aims to identify frequency and amount of exposure to identify what caused the disease
14
Q
Example of a case-control study:
A
- Case: bowel cancer
- Controls: no bowel cancer
- Exposure: red meat consumption
15
Q
Prospective cohort study:
A
- A group of similar people (cohort) and studies them over time
- At the start of the study nobody has the disease of interest, but some are expected to develop it due to certain variables that would divide the cohort
- The two groups are then later compared using a variety of methods
16
Q
Example of a prospective cohort study:
A
- Female nurses who smoke and female nurses who don’t smoke
- They are compared for a particular outcome, development of lung cancer
17
Q
Randomised control trial:
A
- The randomised control trial (RCT) is a trial in which subjects are randomly assigned to one of two groups: one (the experimental group) receiving the intervention that is being tested, and the other (control) receiving an alternative (conventional) treatment
18
Q
What is a standard deviation?:
A
- The variation in the sample, utilised as an estimate for variation in population
19
Q
What does standard error measure? :
A
- It measures the precision of the sample mean as an estimate of the population mean (SD)
20
Q
Standard error equation:
A
SE = SD/square root(n)
21
Q
95% confidence interval:
A
- Sample means/proportions are normally distributed
- So 95% of data lies between:
Sample statistic - (1.96 x SE) and sample statistic + (1.96 x SE)
22
Q
What does the 95% confidence interval mean?:
A
- A 95% CI is a range of values you can be 95% sure contain true sample statistic
- i.e. We can be 95% confident that the interval does contain the true value of the population statistic
- The narrower the CI, the greater the precision of the sample statistic
- CI not a measurement of accuracy
23
Q
Precision in stats:
A
- How close two or more measurements are to eachother
24
Q
Accuracy in stats:
A
- How close a measurement is to the true value
25
Difference between calculating a reference range and a confidence interval:
- Mean - (1.96 x Z)
to mean + (1.96 x Z)
- Reference range: Z = SD
- Confidence interval: Z = SE
26
- Null hypothesis
| - Falsification:
- A hypothesis that states no association between an exposure and an outcome
- finding evidence against the null hypothesis to prove an association
27
P values:
- Use
- What does it tell us?
- Practical use
- Used to investigate the hypothesis
- The P value tells us the strength of the evidence against the null hypothesis
- The smaller the P value, the stronger the evidence against the null hypothesis
28
Statistically significant but not clinically significant:
- If a small effect but a large sample size
| - Impact may be small
29
Clinically significant but not statistically significant:
- If a large effect but a small sample size
| - Promising, but larger studies are required
30
PICO acronym:
- Patient Intervention Comparison Outcome
| - In [Patient] what is the effect of [Intervention] or exposure compared with [Comparison] on [Outcome]
31
Measures of effects of RCT's
| Clue: Ri
- Risk ratio
| - Risk difference
32
(RCT) Risk ratio:
- RR= Risk in treated / Risk in controls
| - Tells us the amount by which exposure to [smoking] multiplies the risk of [lung cancer]
33
Risk:
- The probability that an individual in a defined population will develop a disease
34
[RCT]: Risk difference
- RD = Risk in exposed - risk in unexposed
| - Measures the extra risk of lung cancer in smokers compared to non-smokers (per 1000)
35
Historical (retrospective) cohort study:
- Case group contains people who have a disease/outcome and control group who do not.
- Study investigates back in time to identify frequency of disease in those exposed to certain factors
36
Methods used to compare cohorts in a prospective cohort (4)
- In-person/phone interviews
- Imaging tests
- Internet/mail questionnaires
- lab tests/physical exams
37
Advantages of a prospective cohort study: (3)
- No ethical issues of a RCT
- Easy to calculate incidence and prevalence rate
- Multiple disease outcomes can be studied at the same time
38
Disadvantages of prospective cohort studies: (3)
- Selection bias and confounding variables
- Expense and time consumption
- Usually requires a very large sample size
39
Example of a historical (retrospective cohort study):
- 100 people with AIDS are asked about their lifestyle choices and medical history to study the origins of the disease
- A control group of 100 people without AIDS are also studied to compare the two groups
40
Example of a prospective study:
- 100 people with high risk factors for AIDS are followed for 20 years to see if they develop the disease
- A control group of 100 people with low risk factors are also followed for comparison
41
Measure of effect in a case-control study:
- Odds ratio (OR): how much more common the exposure is in cases vs controls
OR = Odds of exposure in diseased / ODDS of exposure in non-diseased
42
Difference between case-control and retrospective cohort:
- Case control: concerned with the frequency and amount of exposure in subjects with a specific disease
- Retrospective cohort: concerned with the frequency of disease in exposed and non-exposed individuals (specific exposure)
43
Ecological studies:
- Examine the correlation between average exposure in populations and overall frequency of disease in the population
44
Ecological fallacy:
- The assumption that the average characteristics of the populations are applicable to individuals within the population
45
Linear regression:
- Describes the relationship between variables using the equation of a straight line
- Allows estimations of value of y (outcome) per unit change in x (exposure)
46
Criteria for confounding: (3)
- A E
- R F
- C P
To be a confounder, the third variable must:
(i) be associated with exposure
(ii) be a risk factor for disease, and
(iii) must not be on the casual pathway between the exposure and the disease
47
Bias in RCTs and how to deal with it:
- Confounding:
- Selection bias:
- Performance/detection bias:
- Confounding: randomisation
- Selection bias: allocation sequence is concealed from clinicians/researchers who recruit participants
- Performance/detection bias:
48
Bias in cohort studies:
- Confounding:
- Selection bias:
- Non-differential misclassification:
- Confounding: adjust for confounders in analysis
- Selection bias: minimise losses to follow up
- Non-differential misclassification:
exposures and outcomes to be measured accurately and consistently
49
Bias in case control studies:
- Confounding
- Selection bias
- Recall/interviewer bias
- Confounding: adjust for confounders in analysis
- Selection bias: controls are representative sample from at-risk population
- Recall/interviewer bias: Questions must be asked in the same way so as to not influence response (standardised questionnaire)
50
Bradford hill criteria: temporal sequence
- The cause (exposure) precedes the effect (disease outcome) is an absolute criterion for causality. Can we exclude reverse causality?
51
BHC: Strength of the association
- The stronger the risk ratio the more likely the relationship is causal and not explained by confounding
52
BHC: Consistency of the association
- Is the observed association consistent when examined under different circumstances?
- Do the geographical and temporal data show what would be predicted by the hypothesis
53
BHC: Biological gradient
- Results are more convincing if risk increases with exposure
54
BHC: specificity
- An exposure disease association should ideally be specific and not associated with multiple outcomes
55
BHC: coherence
- Is there an explanation for the observed Association (biologically plausible mechanism?)
56
BHC: reversibility
- Prevention of exposure should reduce or prevent disease.
56
BHC: reversibility
- Prevention of exposure should reduce or prevent disease.
57
What is public health?:
- Defined as "the science and art of preventing disease, prolonging life and promoting health through the organised efforts of society"
58
What does public health target?:
| (3) levels of disease
- All health including:
- Asymptomatic/prodromal
- not yet presented to medical services
- being managed by medical services
59
3 domains of public health:
- Health improvement
- Health protection
- Healthcare public health
60
Public health: health improvement: (3)
- Wider factors that affect health and wellbeing
- Healthy lifestyle and choices
- Inequalities
61
Public health: Health protection
- Infectious disease control
- Emergency response
- Environmental hazards
62
Public health: healthcare
- Disease prevention
- Service improvement
- Evidence based practise
- Equity of provision
63
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Examples of tools for improving population health: (4)
- S P
- I
- L
_ P of H B
```
- Screening programmes
- Immunisation
- Legislations
- Promotion of healthy behaviour
64
Primary disease prevention:
- Aims to prevent the onset of disease
| - May alter an environmental factor or change behaviour
65
Secondary disease prevention:
- Aims to halt progress of disease
- Focus on early detection or diagnosis followed by prompt and effective treatment
- May be aimed at symptomatic people
66
Tertiary disease prevention:
- Focus on treatment and rehabilitation of people with established disease
- Aims to minimise complications and disability
67
Incidence rate:
- Incidence rate=
| no. new cases of disease / (Total no. disease free at outset x time interval)
68
Prevalence:
No. with disease at a particular time / total population at that time
