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Applied Pharmacy Practice > Evidence Based Medicine > Flashcards

Evidence Based Medicine Flashcards

1
Q

Decision making

A
  • Using the best evidence that we can to make decisions for & w/ patients is incredibly important.
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2
Q

Clinical practice

A
  • Virtually e/t done in clinical practice has evidence for it.
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3
Q

Scientific journals

A
  • Just because it is in a scientific journal doesn’t mean that it is of good quality or true.
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4
Q

Clinical trials

A
  • A control group identical to the experimental patients.
  • Have to be identical to prevent another/ other variables from having an effect (Confounding bias).
  • Show cause precedes the effects = shows causation.
  • Fewer biases
  • Gold standard of evidence= Particularly true for meds
  • Clinical trails can also be used for different types of care or health care professionals i.e diff types of surgery.
  • Clinical trials are NOT GOOD for finding the harm of meds. You won’t find v/ rare side effects in clinical trials.
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5
Q

Efficacy

A

Efficacy = Whether a treatment gives the desired results in ideal experimental settings ( e.g w/i a randomised controlled trial).
- Clinical trials & what was found w/i the study
- i.e If s/t reduces the risk of death in heart failure by 30% in a a clinical trial, that the effigy of the drug

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6
Q

Effectiveness

A
  • The degree of beneficial effect in “real world” clinical settings.
  • What is it like in the real world?
  • Drugs/ meds tend to be less effective in the real world WHY? Clinical trials are the ideal situation.
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7
Q

CON of EBM

A
  • A problem w/ EBM is that when you have evidence from clinical trials it can take people a long time for people to change their actual practice.
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8
Q

Bad decisions

A
  • Making bad decisions in EBM can be quite serious & can lead to quite long advocate problems.
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9
Q

Criticism of EBM

A
  • It just looks at science it doesn’t take into account patients.
  • This is not true
  • If you do a proper EBM you have to merge the evidence with/ the values of the person you’re treating w/ the population values.
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10
Q

Values

A
  • Is s/t worth doing in the long run
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11
Q

Definition of EBM

A
  • Evidence - Based Medicine is the integration of best research evidence with/ clinical expertise & patient values- Dave Sackett.
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12
Q

Factors of EBM

A
  • Patient Concerns
  • Best Research evidence
  • Clinical Expertise
  • Evidence-based medicine should sit at the intersection of these 3 elements
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13
Q

The Evidence Hierarchy

A
  • Systematic reviews = Strongest
  • Randomised Controlled Trials
  • Cohort Studies
  • Case- Control Studies
  • Case Series, Case Reports
  • Editorials, Expert Opinion = WEAKEST
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14
Q

Systematic Reviews

A
  • Gold standard ( for whether a drug works or not)
  • Gathering info from different trials & putting them together
  • In cases of drug interactions, you can’t run clinical trials = Ethically this is awkward
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15
Q

Randomised Controlled trials

A
  • Look @ them in the case of evidence for a drug working
  • V/ hard to find adverse effects in RCTs
  • You wouldn’t know that you were looking for it
  • V/ V/ rare i.e 1 in 200,00 people
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16
Q

Cohort studies

A
  • Observational studies
  • Looking @ real world data to look for trends/ evidence for an effect
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17
Q

Case - Control Studies

A
  • S/t this is the v/ 1st evidence of the adverse effects of drug interactions
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18
Q

Case Series, Case Reports

A
  • Look @ case reports in the case of looking @ adverse drug reactions, particularly ones that haven’t been found prior to the drug being marketed.
  • Observational studies looking @ real world data —> Full of confounding bias
  • Can look @ adverse reactions that haven’t been reported yet—> v/ rare
  • Can’t user randomised & controlled tests
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19
Q

Editorials, Expert Opinion

A
  • Expert is no good w/o evidence
  • Essay written by an expert.= Useful to read, but they are not gold standard for whether s/t is true or not
  • When it comes to drug therapy isn’t always the best one
  • Can be wrong
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20
Q

Types of research

A
  • Hypothesis Generation
    -Hypothesis testing
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21
Q

What does Hypothesis Generation include?

A
  • Qualitative research
  • Ecological
  • Survey
  • Observational studies (Case-control studies & Cohort studies)
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22
Q

Qualitative research

A
  • Not representative
  • Usually based around interviewing people or having a focus group
  • Focus group= just tells you what people think & s/t people involved keep their preferences a secret.
  • Purely about looking for more info that can help you make decision about what sort of thing you might want to look into.
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23
Q

Ecological

A
  • Studies looking @ things happening at a Nationwide level
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24
Q

Survey

A
  • You can’t test the hypothesis
  • No control group
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25
Q

Observational studies

A
  • Look @ / Take real world data & try to find things out from them.
  • Case- control
  • Cohort study = Collect data in future & Retrospectively = use data from the past. Follow cohorts prospectively ( in the future) through time. Prospectively = you collect the data in the future.
    S/ cohort studies can be like RCTs, you can set up a group like a control group & an experimental group. Though may not have the same random allocation of individuals in the 2 groups —-> enables you to test the hypothesis.
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26
Q

Hypothesis Testing

A
  • Quantitative Research
  • Intervention Studies = RCT’S
  • RCT’S = Randomised Controlled trails
  • Cohort studies
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27
Q

Quantitative Research

A
  • Things dealing with/ numbers , which you can do statistical tests on.
  • can do s/ observational studies, but if your data is rubbish (regardless of stats) you can’t prove anything/ results don’t mean anything.
28
Q

Intervention studies (RCTs)

A
  • W/ RCTs you can find out whether s/t works or doesn’t.
29
Q

Case studies or Case series

A
  • Describe experience of one or more people w/ disease or a drug.
  • often first stat alerting to new disease/ condition/adverse effect or syndrome.
    SPECIFIC LIMITATIONS:
  • No denominator (no comparison group), Don’t know how many people have been exposed to the drug from 1 or 2 cases.
  • No comparison= Only happening in a small number.
  • Study population selected = Only those prescribed the drug.
  • Sampling variation
30
Q

Confounding

A
  • Does the exposure cause the effect.
  • Exposure = to a drug, disease or particles from Road traffic.
  • Mixing of the effects of the exposure with/ that of a 3rd factor.
  • The distortion of the apparent effect (because of other factors that you are not aware of) of an exposure brought about by the association w/ other factors that can influence the outcome.
31
Q

Confounding

A
  • Limit of observational studies
    -RCTs control for confounding.
  • i.e Exposure = taking paracetamol
    Effect = it removes your headache
32
Q

Case control studies (observational studies)

A
  • Observational study
  • ALWAYS work backwards in time = Look back @ old data
  • Find diseased (adverse effect) & non-diseased individuals(no adverse effect).
  • Discover /Find exposure in groups
    -i.e you think that a drug is associated with/ blood cancer, so you find those w/ blood cancer & look back through their med records- to see what they have been exposed to . If the drug you concerned w/ is present, you could think that the drug & blood cancer are linked.
33
Q

Case & controls

A
  • Good for finding rare events.
  • Cases = Have the disease or outcome.
  • Controls= Do not have the disease or outcome.
  • Case control studies identify subjects based on outcome status ( when you start the study you already know whether they have it or not).
34
Q

How to select cases?

A
  • Define your disease/outcome, that you’re looking for.
  • Select incident cases ( new cases) = cases w/ disease or adverse drug reaction.
  • Select controls from same underlying population as cases.
  • Matched controls to increase similarity. I.e in height, weight, diet.
  • Common to have 3-4 controls for each case to increase “power”= Control group is bigger than the case group = increases statistical power of the study.
35
Q

MMR & development disorders
MMR= Measles Mumps & Rubella
Smeeth Lancet 2004

A
  • Concern that autism was linked to MMR vaccine.
  • 1294 development disorders CASES = w/ autism 78% had MMR
  • Follow backwards to see exposure
  • 4469 W/ no disorder, matched on age, sex, & GP practice CONTROLS= w/o autism , 82.1% had MMR
  • shows that Kids w/ MMR vaccine are less likely to have autism.
36
Q

Benefits

A
  • Good for rare diseases w/ a long latency (long time response/ effect to happen after you have been exposed).
  • Allow for multiple exposures- you might find more than one influence.
37
Q

Limitations of case-control

A
  • Observational study = subject to confounding (might find s/t else going on or an association but it’s on a 3rd factor).
  • Recall bias = people forget things, memory is diff when asked to recall events.
38
Q

Cohort studies ( observational study)

A

-Find people free of disease.
- Find Cohorts of people exposed & unexposed.
- Follow the cohorts through time.
- Compare the risks of incident outcomes in each cohort (adverse drug reactions or the benefit that you’re looking for).
- Opposite of case-control, because you find the outcome 1st & then look backwards for cohort studies and find people who have been exposed to the drug and those w/o the disease & follow these people through time. Then you compare the risk of incident outcomes.

39
Q

Cohort studies

A
  • similar to RCT’S you have control group and experimental group but NO RANDOMISATION.
  • Can do retrospective = look at the past
  • Can do prospective = Look at the future
  • Look @ exposed and unexposed and follow through time and loom at the differences.
40
Q

When are cohort studies useful?

A
  • To find a temporal relationship.
  • Can be used for multiple outcomes
  • Cohort studies identify subjects based on their exposure status ( To a drug or a pollutant).
41
Q

Limitations of cohort studies

A
  • Observational – Can’t prove cause and effect – just an association. Other factors may have caused the change.
  • Inefficient for rare diseases( use a case-controlled study) or effects with long latency.
41
Q

Intervention studies

A
  • Control Group Data collection.
  • Intervention Group Data collection. ( intervention like medication).
  • Collect data as you go along.
  • you are interfering in the real world.
42
Q

Randomised Controlled Trials= Intervention study

A

When to use?
- Uncertainty about a treatment or an area of medicine (exposure)
- When exposure can be modified ( w/ tablets or injections, 1 group gets it, 1 group doesn’t).
Why
- Remove confounding (differences in the characteristics of
exposed and unexposed individuals) so you can prove causality.
- Equipoise (can’t assign people to a treatment KNOWN to be
worse) – needs uncertainty. If you already know that the treatment you are going to use is worse than the pre-existing treatment you can’t do the clinical trial= Unethical.

43
Q

Population Sample

A
  • Randomly allocate to either intervention or control and then you get your outcomes.
44
Q

Comparison Groups

A
  • No therapy
  • Similar therapy or drug
  • Placebo ( So people don’t know what groups they are in/ secret)
  • Pre-existing standard ( goal standard treatment)
  • Depends on the question and EQUIPOISE
  • How serious the illness is i.e for HIV a placebo =unethical.
45
Q

Randomisation

A

Carried out to balance characteristics (i.e. gender) of control and treatment group.
*Small studies are less likely to achieve balance. You get imbalance.
*1ST THING YOU SHOULD LOOK @ IN CLINICAL TRIALS=Check baseline characteristics ( Tells you whether there is a bias in the control & treatment group & what the 2 populations in the groups are).

46
Q

Baseline characteristics

A
  • Can include
  • Age
  • Male/ female ratio
    Extra info= women tend to be under represented in clinical trials.
47
Q

RCT limitations

A
  • Generalisability of the study population ( Collected from particular groups of people).
  • RCT populations are v/ particular as the study has v/ specific criteria.
  • Generalisability of the study environment.
  • You will be seen in a hospital environment routinely if you are apart of a clinical study.
  • Limited questions & v/ specific
  • Limited Clinical applicability = linked to the type of questions being asked.
48
Q

Intention to treat analysis

A
  • Patients allocated to control and intervention @ start of the study).
  • Analysis carried out on the group (control or intervention) they were initially allocated to…
  • Regardless of whether they dropped out, took the
    medicines, started on another drug etc.
  • Assesses effectiveness – as mirrors REAL LIFE as similar problems may occur.
49
Q

what is risk?

A
  • risk= Probability of s/t happening
  • Harm= Outcome of that risk.
  • risk = number of events of interest / total number of observations
  • risk = Number of people who had the outcome/ Total number of people in that specific group
  • example
    24 people climbing trees, and 6 fall
  • risk of a fall
    = 6 falls/24 who could have fallen
    = 6/24 = ¼ = 0.25 = 25% have fallen off out of the potential 100% who could have fallen.
50
Q

What is odds?

A
  • odds = number of events of interest/number without the event
  • Odds= Number of people who had the outcome/ Number of people who did not have the outcome
  • Add the numerator and denominator together you get the total population.
  • example
    24 people climbing trees, and 6 fall
    -odds of a fall
    = 6 falls/18 did not fall
    = 6/18 = 1/3 = 0.33 (not usually as %)
51
Q

Risk ratio

A
  • Risk ratio=Relative risk
  • Risk on treatment/risk on control
  • Where risk ratio = 1, this implies no difference in
    effect
51
Q

Expressing risk & odds ratio

A

RISK
- What was the risk in %
- How much was the risk reduced by %
ODDS
- How much was the odds reduced by %
- comparison of control and treatment

51
Q

Odds ratio

A
  • Odds ratio = odds on treatment/odds on control
  • Where odds ratio = 1, this implies no difference in effect.
51
Q

Absolute Risk difference

A
  • risk on treatment – risk on control
    example for Blum et al
    119/164 – 130/164 = 0.726 – 0.793
    = -0.067
    usually expressed as a %, -6.7%
    If - = reduced
    If + = increased
  • treatment reduced the risk of being dyspeptic by about 7
    percentage points
  • Where risk difference = 0, this implies no difference in effect
52
Q

Qualitative/Narrative Review

A

These are not good ways of reviewing evidence.
- Summary of all data
- More up to date than a textbook(?) tend to be 5-10 years out of date.
- Subjective – reflecting bias of author

53
Q

Systematic Reviews and Meta-analysis

A
  • Bring together similar RCTs & set a criteria
  • Systematic collection of good quality RCTs
  • Uses pre-defined explicit methodology (reduces bias) of which databases your going to search.
  • Seeks to minimise bias
  • May combine statistics (meta-analysis)
  • Not merely a review – since criteria for inclusion of RCTs explicit (a scientific process)
54
Q

Systematic reviews

A
  • Not all have numbers you can meta analyse.
55
Q

Meta-analyses

A
  • Have to have a predefined question.
56
Q

Pre-defined Question (essential)

A
  • “Does aspirin increase the chance of survival to 6 months after an acute stroke?= Tells us what the drug is, the condition & the outcome of the condition
  • The questions should be specific
57
Q

extra info

A
  • can’t get an average by adding the studies together as all the studies are diff.
  • Effect measure = Risk ratio & Relative risk
58
Q

Forest plots

A
  • Line of no effect in the centre
  • If data arms do not touch the line of no effect = statistically significant ( could show that the drug works).
  • Black box = Effect size for the study.
  • The bigger the box the more patients in the study.
  • Arms either side = confidence intervals = can be 95% confident that the true effect size of the study is w/i those arms.
  • On right side = drug works
  • on left side =drug doesn’t work
  • Small studies = give wider confidence intervals ( To reduce uncertainty about the results w/ the effect size you need lots of patients.
  • Diamond = Combination of all studies = Summary effect measure.
  • Meta- analysis puts all of the studies together & creates a summary effect measure.
59
Q

When can we do a meta-analysis?

A

-When more than one study has estimated an effect ( Can’t do meta-analysis w/o having an effect size).
- When there are no differences in the study characteristics that
are likely to substantially affect outcome (Studies have to be similar)
- When the outcome has been measured in similar ways
- When the data are available (take care with interpretation when only some data are available), S/studies are not published & there is publication bias and bias in academics.

60
Q

Disease Oriented Outcomes (DOOs)

A
  • physiological or surrogate outcomes e.g. changes in blood pressure, blood lipid levels, etc.
  • Measure things in your body that may be correlated to other health aspects but are not actually the thing that you are concerned about.
  • Not as good as POOs.
61
Q

Patient Oriented Outcomes (POOs)

A
  • outcomes that matter directly to patients: e.g. morbidity, mortality, symptom improvement, cost reduction and/or quality of life.
  • Measures things that matter to patients.

Applied Pharmacy Practice (19 decks)

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