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Pharm 155 > Week 5 > Flashcards

Week 5 Flashcards

1
Q

EBM levels of evidence pyraamid

A 
meta analysis
systemic review
RCT
cohort study
case control study
case series and case reports
animal studies/laboratory studies
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2
Q

What is a RCT used for

A

treatment questions and diagnosis questions

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3
Q

Cohort studies

  • type Q
  • when use
A
  • answer questions of prognosis and etiology/harm

- use when no RCT

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4
Q

Case control studies

  • what Q
  • when use
A
  • answer questions of prognosis, etiology/harm

- used when no cohort studies

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5
Q

case series and case reports

  • what Q
  • when use
A
  • answer questions of prognosis or etiology/harm

- when no case control studies

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6
Q

4 classifications of research

A

Nature of research
time frame of research
investigator approach
type of data

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7
Q

what are the 2 natures of research

A

descriptive and explanatory

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8
Q

what are the 3 time frames of research

A

prospective, retrospective, cross sectional

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9
Q

what are the 2 types of investigator approach

A

observational and experimental

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10
Q

what are the types of data

A

qualitative and quantitative

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11
Q

Observational investigator approach

A

investigator not influence what subjects exposed to

-track natural course/progression

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12
Q

Experimental investigator approach

A

-investigator controls exposures that may influence outcome of interest

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13
Q

4 components of true experimental design

A

1) manipulation
- investigator controls what happens during study

2) control
- presence of group that does not receive intervention being studied
- accounts for outside factors that may affect study

3) random assignment
- study subjects randomly allocated to intervention or control

4) random selection
- study subjects randomly chosen from total eligible population

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14
Q

are experimental studies randomized wrt selection of control/intervention subjects from population

A

yes

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15
Q

are observational studies randomized wrt selection of control/intervention subjects from population

A

no

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16
Q

why is non-random allocation to exposure/control a problem in observational studies

ie. what is the problem with non-randomized studies

A

-variable may be due to selection
+ex. physician chose more sick people for exposure rather than healthier
-characteristics heavily influence outcome

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17
Q

Characteristics of observational studies (3)

A

exposure –> outcome

less rigid than controlled studies

ASSOCIATIONS NOT CAUSE AND EFFECT

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18
Q

why observational studies?

A

not possible have RCT to support every intervention
-still accept some interventions in spite of no RCT
+parachute example

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19
Q

necessity of observational studies (3)

A
  • extent of disease (distribution/epidemiology)
  • etiology of diseases (risk factors)
  • evaluate interventions (such as medications) in large populations to detect rare outcomes
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20
Q

Exposure event rate

A

EER - proportion of subjects in exposure group experiencing the event

EER = a/(a+b) ; a= outcome, b=no outcome where a and b are in the exposure group

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21
Q

Control event rate (CER)

A

CER - proportion of subjects in control group experiencing event

CER = c/(c+d) ; c= outcome and d = no outcome where c and d occur in control group

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22
Q

Relative risk

A

risk of developing disease or adverse event in participants EXPOSED to specific variable compared to those not

RER = EER/CER

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23
Q

RR (relative risk) = 1

A

no association

24
Q

RR (relative risk) < 1

A

negative association

-exposure/intervention decreases likelyhood of outcome

25
Q

RR (relative risk) > 1

A

positive association

-exposure/intervention increases likelihood of outcome

26
Q

When dealing with observational what wording must be used to identify a relationship in variables

A

Associated

-(NOT “CAUSED”)

27
Q

three types of observational studies

A

cohort studies

case control studies

cross-sectional studies

28
Q

Cohort

A

-group of people who share one or more characteristic (treatment, disease, residence etc)

29
Q

Cohort studies

  • who is the population
  • how population allocated
A
  • the population is a cohort

-there is NO RANDOM ALLOCATION
+self selected

30
Q

How collect data for cohort studies

A

1) retrospective
2) prospective
+either or

-

31
Q

what is the risk of prospectively collecting data

A

-may induce additional bias

+people know they are being studied, change behaviour

32
Q

Characteristics of cohort study

A
  • can measure incidence (new diagnosis) and prevalence (new and old diagnosis) among population
  • can assess ASSOCIATION between exposure and outcomes
33
Q

Measuring the effect size of an intervention involves what calculations (3)

A

relative risk reduction (RRR)

Absolute risk reduction (ARR)

Number needed to treat (NNT)

34
Q

Relative Risk Reduction

A

RRR = (CER-EER)/CER

ex interpretation:
handwashing is associated with a relative reduction of childbed fever mortality of 56% compared to no handwashing

35
Q

Absolute Risk Reduction (ARR)

A

ARR = CER - EER

ex interpretation
-handwashing is associated with an absolute reduction of childbed fever mortality by 9%

36
Q

Number Need to Treat (NNT)

A

Applies meaning to ARR
NNT = 1/ARR
* if outcome is unwanted, NNH, number need to harm

ex. 1 MD washing hands saves 0.09 life (ARR)
xMD wash saves 1 life
11MD save one life

37
Q

largest study ever recorded

  • type
  • name/what about
A
  • prospective cohort study (1948)
    -Framingham Heart Study (FHS)
    +ID risk factors for heart disease
38
Q

FHS ID’d risk factors

A

Age, Physcal activity, body weight, diabetes mellitus, hypertension, cholesterol

smoking indivs dev CHD at larger rate

39
Q

does association mean causation

A

no

-SO STUPID

40
Q

can we establish causation without being able to manipulate exposure

A
  • If we cannot manipulate exposure (randomization to exposure or control), several rules can establish causation
  • causality requires assess multiple pieces of evidence, NOT JUST STATISTICAL ASSOCIATION
41
Q

Bradford Hill

A

-guideline for establishing causation

  • Temporal relation (ex. smoke first then get disease)
  • Biological plausibility
  • consistency
  • strength of the association (RR, OR)
  • Dose-response relationship
  • study design
  • judging the evidence
42
Q

Can you establish causation from observational studies

A

-YES, with guidelines for causation such as bradford hill

43
Q

Case-Control study

-5 key points

A

-start collecting CASES and assess exposure among them

-Collect CONTROLS from the same (or similar) “original” population
+must be very similar to the cases

-MUST BE RETROSPECTIVE

-cannot assess incidence or prevalence
+we dont know the OG population

-Can still assess association

44
Q

What do you do when the outcome trying to be monitored in a study is rare

A

start small with a case control study

-ex. regional cancer centre

45
Q

Measuring association in a case-control study

A

ODDS RATIO
-what are the odds of cancer amoung smoking patients

odds = (probability of success)/(Pfailure)

ex.

  • roll 6 on dice
  • odds= 1/5
46
Q

Odds Ratio

A

NUMERATOR
Poutcome among intervention)/Pno outcome among intervention

DENOMINATOR
Poutcome among control/Pno outcome among control

OR=num/denom

47
Q

Interpreting odds ratio

A

OR = 0.63

non-smokers have 0.63 lower odds of developing cancer

OR

NOT smoking reduces the odds of cancer by 37%

48
Q

How is OR an approximate estimation of RR

A

when a disease is rare a

49
Q

OR vs RR

  • what does OR estimate
  • overestimate or underestimate
  • when is degree of estimate greater
A
  • OR is an estimation of RR
  • OR always overestimates RR
  • this overestimation is greater when RR>1

-

50
Q

how do you interpred the graph given %incidence (x) and odds ratio (y)

A

ALWAYS compare the ‘overestimation’ through the analysis of relative risk reduction

example
OR is 0.73 and RR is 0.75

The OR is considered an UNDERESTIMATION because:
given OR=0.73, RRR= 27%
given RR=0.75, RRR= 25%

SO, based on RRR, OR is an overestimate

51
Q

Case Control study: choosing controls

  • does it matter how many controls you chose
  • avoid
A
  • NO, it does not matter how many controls you choose

- avoid biased controls such as people hospitalized due to accidents

52
Q

Case-control study: bad control examples

A
  • using pediatrics for smoking case-control studies

- having more smokers than non-smokers in controls than cancer cases grp etc

53
Q 
Bias types (4)
-what do they do
A

selection bias
information bias
data analysis bias
survival bias

distort relationship between exposure and outcome

54
Q 
Confidence interval (95%)
-when is there statistical significance
A

if the confidence interval does not cross 1
ex. 1.27 - 1.69

NOT STATISTICALLY SIG EXAMPLE
-0.78 - 1.23

55
Q

Confounding variables

A

things that influence the outcome that are not related to the exposure
-such as the biases

ex. post-menopausal women and estrogen
- doctors prescribed the estrogen to healthier women
- selection bias - confounding variable
- result is due to selection bias NOT exposure

56
Q

LOOK AT SECOND TO LAST SLIDE TABLE

A

EEEEEEE UNGA BUNGA

Pharm 155 (2 decks)

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