Intro to Evidence Based Medicine Flashcards

1
Q

EBM triad domains

A
  • clinical judgement
  • best relevant evidence
  • pt’s preferences & values
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2
Q

types of empirical evidence

A
observations = data grounded in physical reality
quantitative = chemical & biological processes that involve the use of stats
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3
Q

function of stats

A
  1. Summarize a set of data
  2. Measure the degree of association between 2 variables
  3. Control potential confounding variables
  4. Predict future health outcomes
  5. Distinguish in sample data between genuine trends and random variability
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4
Q

descriptive statistics

A

frequency & percentage for categorical variables

ex) pie charts or bar graphs of BMI category

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5
Q

continuous variables

A

mean

standard deviations

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6
Q

measure of effect size

A

quantifies effect of X on Y
effect can be:
-causal
-statistical relationship

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7
Q

purpose of clinical research

A

investigates the causes of disease, factors that prevent disease, and treatments that restore health
-empirical investication

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8
Q

categorical variables

A

BMI

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9
Q

box plot

A

graphical technique which is useful for determining if the data are skewed and for detecting outliers

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10
Q

whiskers in a box plot

A

drawn from each end of the box to a point

-indicates outliers by the IQR

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11
Q

interquartile range

A

distance between the 75th and 25th percentiles in a box plot

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12
Q

top whisker in box plot

A

1.5 IQR above the 75th percentile

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13
Q

bottom whisker in box plot

A

below the 25th percentile

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14
Q

mild outliers

A

more than 1.5 IQR

-represented as circles in a box plot

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15
Q

extreme outliers

A

more than 3.0 IQR

-represented as asterisks in a box plot

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16
Q

effect

A

refers to either a causal or a statistical relationship

17
Q

Risk Reduction Measures

A
  • The more a treatment reduces risk,
  • The greater will be the ARR
  • The greater will be the RRR
  • The smaller will be the NNT
18
Q

confounding variables

A

methods of statistical control

-referred to as Z

19
Q

statistical control

A

quantify the degree of relationship between X and Y after taking into account a third variable (Z) that is related to both

20
Q

regression analysis

A

popular method for statistically controlling confounding variables

21
Q

prognostic statistics

A

Predict future health-related outcomes

-based on various characteristics of that patient

22
Q

inferential statistics

A

Distinguish in sample data between genuine trends and random variability
-help researchers to decide whether what was found to be true of the sample
*
Is likely to be true of the population?
or
Was just due to random sampling variability (chance)?

23
Q

test statistics

A

generate 95% confidence intervals and P values

24
Q

types of inferential statistics (test statistics)

A
  1. Z (generated by the Z-test)
  2. t (generated by the t-test)
  3. F (generated by the F-test)
  4. Χ2 (generated by the chi-square test)
  5. Wald (generated by the Wald test)
    * All are used to compute 95% CIs and P values.
25
Q

Evidence-based medicine is the intersection of 3 sets of considerations. One of these is Clinical Judgment. What are the other two?

A
  1. Patient’s Preferences and Values

3. Best Relevant Evidence

26
Q

Identify type of statistic: The probability of recurrence of breast cancer over the next 10 years for a postmenopausal woman who does not smoke.

A

Prognostic statistic

27
Q

Identify type of statistic: The proportion in a sample of Americans who are hypertensive.

A

Descriptive statistic

28
Q

Identify type of statistic: A 95% confidence interval for the proportion of Americans in the population who are hypertensive.

A

Inferential statistic

29
Q

Identify type of statistic: The correlation between the body mass index (BMI) of anorexics and their preferred BMI.

A

Measure of effect size

30
Q

Identify type of statistic: Using regression to take into account gender in a study of the relationship between forced expiratory volume and age in a sample of children.

A

Statistical control

31
Q

the 3 conditions required to establish causality in a clinical study

A
  1. Covariation
  2. Correct time order
  3. Absence of plausible alternative explanations
32
Q

Covariation

A

To show that one variable (X) is a cause of another (Y), researchers must first show that the 2 variables covary or are correlated
> If Y is a disease, and X is a suspected cause, then the presence of X must be shown to be associated with the presence OR the absence of Y
ex) lung CA and smoking
X (prevention) - no Y ( disease)
ex) flu vaccine and absence of flu

33
Q

how clinical researchers document associations between cause & effect

A

in terms of likelihood, tendencies or what is true on average

ex) People who smoke should be more likely to contract lung cancer.
ex) Patients who receive acupuncture should on average experience less severe headaches.

34
Q

likelihood

A

measured in terms of probability or odds

35
Q

probability

A

number of times an outcome will occur divided by the number of times the outcome could occur
* varies from 0 to 1

36
Q

Odds

A

The probability an outcome will occur divided by the probability the outcome will not occur
* vary from 0 to infinity