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Sem12019 > PHRM3031 interventions RCTS I/II > Flashcards

PHRM3031 interventions RCTS I/II Flashcards

1
Q

Study Types

A

Experimental - RCT

Observational - cohort (longtudinal), case-control, cross-sectional, ecological (population-based)

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2
Q

Study Types & their questions

RCT

A

interventions

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3
Q

Study Types & their questions

Cohort

A

Aetiology
Prognosis
Diagnosis
Risks

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4
Q

Study Types & their questions

Case Control

A

Prognosis
Diagnosis
Risks

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5
Q

Study Types & their questions

Cross Section

A

Aetiology
Diagnosis
Frequency

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6
Q

Study Types & their questions

Case Reports

A

Next Steps

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7
Q

Which Study is best for:

Intervention/Therapy

A

RCT

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8
Q

Which Study is best for:

Diagnosis

A

Cohort>Case-Control>cross-sectional

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9
Q

Which Study is best for:

Risk, prognosis

A

cohort>case-control

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10
Q

Which Study is best for:

aetiology

A

cohort>cross-sectional

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11
Q

Which Study is best for:

frequency

A

cross-sectional

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12
Q

Which Study is best for:

observational

A

qualitative

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13
Q

Cross Sectional Study

A
  • the study population is ascertained at one point in time - a snap shot
  • participants are asked about their current disease status (outcome) and their current and past treatments (exposure)
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14
Q

Case Control Study

A

-compares disease outcomes in cases and controls
Cases: patients with a particular disease outcome
Controls: have similar attributes (age, sex, ses) to the cases but do not have the disease

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15
Q

Cohort Study

A

Cohort: a group of individuals with a common characteristic
Cohort Study: cohrts are followed up (observed) over time
Aim –> is exposure to a risk factor associated with new disease outcome in the future

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16
Q

Prospective Study

A

observe outcomes or events that occur after identification of subjects
-RCTs, cohort or longitudinal studies

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17
Q

Retrospective Study

A

review records from the past or obtain information about past events
-case-controls studies

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18
Q

Randomised Controlled Trial (RCT)

A
  • a sample of patients are selected from the population with a particular condition
  • patients are randomised into the treatment group or the control group
  • the two groups are treated in exactly the same ways in everything apart from the treatment
  • clinical measures are made over time and any differences are attributed to the intervention
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19
Q

Critical Appraisal -definition

A
  • structured summary
  • are the results of the study valid?
  • what are the clinically relevant results?
  • will the results help care for my patient?
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20
Q

Critical Appraisal - Design

A

Parallel - common, patients are randomised to either group A or group B at the start of the study and the patient receives the assigned medication for the duration of the trial
Cross-over - fairly rare, patients are randomised to either group A or group B at the start the study and midway through the study, are swapped to the other group
Factorial - rare, sometimes used when interactions between various drugs are to be investigated

21
Q

Assessing Bias: Seven Questions

A
  1. were patients randomised?
  2. was group allocation concealed?
  3. were groups similar at baseline (start of trial)?
  4. were participants, health professional and study personnel blinded to group allocation?
  5. were all participants who entered the trial properly accounted for at it conclusion and how complete was follow-up?
  6. Aside fro the experimental intervention, were the groups treated equally?
  7. were patients analysed in the groups to which they were first allocated?
22
Q

Randomisation: defintion

A
  • process by which patients in a clinical trial are allocated to a group
  • assignment is according to chance rather than any systematic approach
  • randomisation tables, often computer generated
  • patients have an equal chance of being assigned to either group (treatment or control)
  • does not mean that the treatments are evenly allocated to all patients
23
Q

why randomise?

A
  • aims to ensure that the control and intervention groups are as similar as possible
  • known prognostic factors (such as age, weight, gender)
  • unknown factors (cofounder eg. adherence, genetics, socioeconomic status)
  • -> reduces the chance of representation of any one characteristic with the study groups
24
Q

randomisation levels

A

simple: at level of patient
cluster - intact clusters of individuals
stratified - participants are grouped into ‘blocks’ and the allocated to treatment groups; often uses stratification

25
Q

baseline characteristics

A

the treatment and the control group should be similar for all prognostic characteristics except for treatement

26
Q

blinding

A

-concealing the treatment status from the patient, investigators or analysts
-used to guard against bias on the part of investigators or subjects when assessing or reporting the outcome of treatment
Four Types:
-single
-double
-triple
-open label (non blinded)

27
Q

single blind

A
  • the investigator, but not the patient, knows the identity of the assigned treatment
  • can be subject to investigator bias
28
Q

double blind

A

neither the investigator nor the patient knows the identity of the assigned treatment

29
Q

the ultimate triple blind

A

no one knowns what is going on

-most rigorous but least commonly performed blinding

30
Q

non blinded studies

A
  • open label design
  • both the investigator and the patients know the identity of the assigned treatment
  • used when blinding is not possible (eg surgery, radiotherapy, diet)
  • subject to bias by patients and investigators and so there is a greater chance of seeing a positive result
31
Q

power of a trial

A

power is the ability to detect a difference between two groups when a difference actually exists

  • type I (alpha) error: is the detection of a difference when a difference does not exists (false positive)
  • Type II (beta) error: is the inability to detect a difference when a difference actually exists (false negative)
32
Q

data analysis

A

may be either:

  1. according to the treatment to which patients were randomised
  2. according to the treatment actually received
33
Q

analysis by randomisation

A

treatment the patient ws randomised to –> ‘intention to treat’ analysis
evaluates medicine effectiveness: how the medicine work when people take it in an (almost) ordinary setting

34
Q

analysis by actual treatment

A

analysis is according to the treatment actually receive

evaluates medicine efficacy: doe the medicine work when it taken exactly as prescribed?

35
Q

size of effect

A

best estimate is the difference in means (or medians if reported) between the intervention and control group

36
Q

clinical vs statistical significance

A

clinical significance: the size of the effect is real (i.e) statistically significant) and that it is of a magnitude sufficient to change practice or to be considered clinically important
statistical significance: the size of the effect and the 95% CI in relation to the null hypothesis (usually <5% error rate)
trials can be statistically significant but not clinically significant

37
Q

P values

A

-measures of the probability that a result is purely due to chance
a low p-value suggests that chance is an unlikely cause of the observed difference between groups
if the p-value is love (p<0.05) then the probability that the result was due to chance is also low (<5%) –> a statistically significant effect

38
Q

Confidence Intervals (CIs)

A

-generally more informative than p-values
-estimates of the range of values that are likely to include the real value
usually quotes as 95% CIs
-if the 95% CI for the difference between a treatment and control group is small and does not overlap the ‘no effect’ point(1 for a ratio an 0 for a difference) we are confident that the result is real

39
Q

Absolute Risk (INE)

A

AR (NE) absolute risk (incidence) of events in control (not exposed) group (%)

40
Q

Absolute Risk (IE)

A

AR (E) absolute risk (incidence, I) of events in treatment (exposed, E) group (%)

41
Q

Relative Risk (RR)

  • definition
  • formula
A
  • tell us how many times more (or less) likely an event will occur in the treatment (exposed) group relative to the control (non-exposed) group
  • RR=ARe/ARne
  • RR=1 means no difference between the two groups
  • RR<1 means the treatment reduces the risk of the event
  • RR>1 means the treatment increases the risk of the events
42
Q

Relative Risk Difference (RRD)
RRI - increase
RRR - reduction
(formula)

A

RRD = (ARne-ARe)/ARne

or (1-RR)

43
Q

Absolute Risk Difference (ARD)

  • definition
  • formula
A

-tells us the absolute difference in the rates of events between the two groups (treatment difference)
ARD=ARe-ARne
-ARD=0 means that there is no difference between groups (i.e treatment had no effect)

44
Q

Number needed to treat (NNT)

  • definition
  • formula
A

-is the number of patients that need to be treated (compared to the control group) for a period of time to prevent one extra event of interest
NNT=(1/ARD)x100
Clinical outcome

45
Q

Number needed to harm (NNH)

  • definition
  • formula
A

-is the number of patients that need to be treated (compared to the control group) for a period of time to cause one extra event of interest
NNH=(1/ARD)x100
safety outcome

46
Q 
Summary Ratings
Green
Yellow
Red
Black
A

Green - therapies rated green are best you can get- there is clear evidence of patient-important benefits and these benefits clearly out-weigh any associated harm
Yellow - these therapies require more study. the data is inconclusive or not substantial enough to be able to give a clear rating yet. these would benefit from a large, randomised controlled trial to give better sense of effect and harms
Red - benefits and harms may be equal or equivocal, while there may be some benefits the benefits dont outweigh the harms
Black - harms without benefits, these therapies have very clear harms to patients without any recognisable benefit

47
Q

Time to event analysis

A

-survival analysis
-event= dichotomous clinical outcomes that can occur only once
-outcomes may be negative (death), positive (still alive) or neutral (start glucose monitoring etc)
-use the term ‘survival’ regardless of the type of outcome and whether it is related to survival
time periods can . be continuous (months) or categorical (0-6 months)

48
Q

2 methods of analysing survival curves

A
  1. kaplan-meier
    - compares two groups using log-rank test (expected vs observed)
  2. cox-proportional hazards
    - cuts period o observation into small intervals, in each interval, compare rate of events (taking into account numbers of events in the intervals and number of person at risk in the interval) –> a relative risk
    - combines all the estimates of relative risk –> hazard ratio
    - can consider effects of variables (eg. gender, age, smoking)

Sem12019 (12 decks)

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