Research Concepts

Question 1

Retrospective Studies

Answer 1

• Looking back at data already collected.
• Tend to miss data.

Question 2

Prospective Studies.

Answer 2

Collect data as study goes on

Question 3

Experimental Study

Answer 3

You are manipulating something to get information.

Question 4

Observational Study

Answer 4

• Simply looking at data, not actually manipulating anything to get information.
• Cannot make claims about causation.

Question 5

What are Case Control studies, what do they look at, and what measure of statistics is used.

Answer 5

• Compare case (has disease) with control (doesn’t have disease).
• Cannot tell prevalence as you are selecting cases so prevalence artificially high.
• Tend to be experimental studies.
• Looking at “What is the Frequency of exposure? in those with disease and those without disease.
• Use Odds Ratio.

Question 6

Cohort Study

Answer 6

• Take cohort as it comes, you aren’t selecting particular cases with disease.
• Reflects true prevalence.
• Tend to be observational studies.
• Looking at “Is the outcome occuring?” in those exposed and those not exposed.
• Use Relative Risk.

Question 7

Null hypothesis (Ho)

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Answer 7

• Mutually exclusive to the working hypothesis.
• E.g. Null = The person is not guilty vs
  Working = The person is guilty.
• Study wants to show evidence to falsify the null hypothesis so the null is rejected. If the null is rejected, what is left is that the working hypothesis is true.

Question 8

Confidence interval

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Question 9

Estimation

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Question 10

Inference

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Question 11

Intention to Treat

Answer 11

• Analysis includes all participants, no matter if they completed trial or not.
• Use if wanting to know if clinician should use treatment.

Question 12

Per protocol Design

Answer 12

• Analysis only includes participants who complete trial.
• Use if wanting to know mechanism of drug (i.e. need people who have completed treatment).

Question 13

Descriptive statistics

Answer 13

• Describe the sample data.
• Reduce many numbers down –> fewer numbers.
• E.g. mean, median, standard deviation.
• Sample statistics.
• Use english letters (e.g. M, SD)

Question 14

Inferential statistics

Answer 14

• Use sample statistics to predict population parameters.
• Use greek letters (e.g. mu, delta).

Question 15

Independent variable

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Answer 15

The proposed cause

Question 16

Dependent variable

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Question 17

Variability

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Answer 17

We assume the sample is randomly chosen from population BUT this doesn’t happen, therefore, sample statistics will not exactly match population parameters –> sampling variability.
Need to take this variablility into account = inferential statistics.

Question 18

Parameter Estimates

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Answer 18

Use sample statistic to estimate population parameter.
E.g. sample mean is an estimate of population mean.
Confidence interval is one method.

Question 19

Fishers p

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Answer 19

p (D | Ho)

probability of the data under the null

Question 20

Fisher’s p value

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Answer 20

• Index of the probability of observing the data if we assume the null hypothesis is true.
• Long run probability of observing data at least that extremeif we reapete the experiment over and over again
• Something rare has happened that is unexpected –> this is ‘significant’
• a small p value = something very unlikley has happened, the null hypothesis is…

Question 21

Neyman-Pearce p value

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Question 22

Neyman-Pearson p value:
Decisions

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Answer 22

Correctly reject the null hypothesis (

Question 23

Type 1 error

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Answer 23

• Incorrectly reject the null hypothesis –> accept working hypothesis.
• The probability is the alpha-level (usually 0.05).
• False positive (e.g tell a male he is pregnant or e.g use a drug which is ineffective or has adverse effects).
• Due to P value.

Question 24

Type 2 error

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Answer 24

• Incorrectly retain the null hypothesis –> working hypothesis is rejected.
• The probability is the beta level (often 0.2).
• False negative (e.g tell a pregnant women she isn’t pregnant or e.g. don’t use a drug which is effective for a particular disease).
• Due to Statistical power i.e. sample size.

Question 25

Confidence intervals

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Answer 25

• We can be 95% confident that the true parameter sits between these two values.
• If we repeated the procedure (maths calculation to get confidence interval) many times over again, the confidence interval would capture the true value. 95% of the time.
• Never the probability of the parameter having a certain value.

Question 26

Relative risk (risk ratio)

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Answer 26

ratio of the probability of outcome in one group to the probability of the outcome in another group
RR = (TE / (TE + TN)) / (CE / (CE + CN))
TE = treatment with events.
TN = treatment, non-events.
CE = control with events.
CN = control, non-events

RR = 1 =

Question 27

What are the Canadian Task force Level of evidence?

Answer 27

I: At least 1 RCT with proper randomisation.
II.1: Well designed cohort of case-control study.
II.2: Time series comparison or dramatic results from uncontrolled studies.
III: Expert opinion.

Question 28

What are the levels of evidence from Sackett?

Answer 28

I: Large RCTs with clear cut results.
II: Small RCTs with unclear results.
III: Cohort and case-control studies.
IV: Historical cohort or case-control studies.
V: Case series, studies with no controls.

Question 29

What are the levels of evidence for Prognostic studies?

Answer 29

I: High quality prospective cohort study with adequate power or systemic review of these studies.
II: Lesser quality prospective cohort, retrospective cohort study, untreated controls from RCT, or systematic review of these studies.
III: Case-control study or systematic review of these studies.
IV: Case series.
V: Expert opinion, case report or clinical example, or evidence based on physiology, bench research or “first principles”.

Question 30

What are the levels of evidence for therapeutic studies?

Answer 30

1A: Systematic review (with homogeneity) of RCTs.
1B: Individual RCT (with narrow confidence intervals).
1C: All or none study.
2A: Systematic review (with homogeneity) of cohort studies.
2B: Individual cohort study (including low quality RCT e.g. < 80% follow-up).
2C: “Outcomes” research, ecological studies.
3A: Systematic review (with homogeneity) of case-control studies.
3B: Individual case-control study.
4: Case series (and poor quality cohort or case-control study).
5: Expert opinion without explicit critical appraisal or based on physiology bench research or “first principles”.

Question 31

What are the Grade Practice Recommendations?
-Grade A

Answer 31

• Strong recommendation.
• Level I evidence or consistent findings from multiple studies of levels II, III or IV.
• Clinicians should follow a strong recommendation unless a clear and compelling rationale for an alternative approach is present.

Question 32

What are the Grade Practice Recommendations?
-Grade B

Answer 32

• Recommendation.
• Levels II, III or IV evidence and findings are generally consistent.
• Generally clinicians should follow a recommendation but should remain alert to new information and sensitive to patient preferences.

Question 33

What are the Grade Practice Recommendations?
-Grade C

Answer 33

• Option.
• Levels II, III, or IV evidence, but findings are inconsistent.
• Clinicians should be flexible in their decision-making regarding appropriate practice, although they may set bounds on alternatives; patient preference should have a substantial influencing role.

Question 34

What are the Grade Practice Recommendations?
-Grade D

Answer 34

• Option.
• Level V evidence: little or no systematic empirical evidence.
• Clinicians should consider all options in their decision making and be alert to new published evidence that clarifies the balance of benefit vs harm; patient preference should have a substantial influencing role.