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Danny's MD4001 CNS > 31-10-23 - Interpreting evidence 1 > Flashcards

31-10-23 - Interpreting evidence 1 Flashcards

1
Q

Learning outcomes

A
  • Be familiar with the normal distribution and its percentiles as well as the concept of skew.
    • Understand how to calculate odds and risk ratios, relative and absolute risk reductions, number needed to treat (NNT)
    • Understand the meaning of a 95% confidence interval around an estimate and how to use it to interpret research findings.
  • Be familiar with the concept of hypothesis testing and statistical significance including the Null Hypothesis and p-values
  • Be familiar with basic statistical tests such as t-tests and chi-square tests, when their use is appropriate and how to interpret the results of such tests
  • Be aware of the concept of multiple testing and the how to use the Bonferroni correction to reduce ‘false positive’ results (i.e Type I error)
  • Be aware of non-parametric tests for comparing means
  • Be aware of extensions to the t-test for comparing more than two groups: 1- and 2-way ANOVA.
  • Be familiar with the concepts of correlation and regression
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2
Q

Why do we need Statistics to interpret evidence?

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3
Q

Recap types of data (in picture)

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4
Q

Describe how to calculate risk in a control and treatment group

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5
Q

What is the formula for risk?

Comparing risk between groups.

Describe the following formulas:
* Absolute Risk Reduction (ARD)
* Relative risk (risk ratio)
* Number needed to treat (NNT) – number needed to treat for favourable outcome

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6
Q

What is relative risk independent of?

What must we do when using relative risks?

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  • Relative risk is independent of the original prevalence
  • Can be misleading –always state baseline (absolute) risks as well as relative risks
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7
Q

When are odds ratios used?

Odds example part 1 (in picture)

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8
Q

Odds example part 2

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9
Q

Odds example part 3

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10
Q

Describe the formula for odds ratio.

What can this provide association between?

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11
Q

What is baseline risk (in picture)

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12
Q

If odds are equal in case and control group, What does Odds ratio (OR) equal?

What is OR similar to?

When is OR is a good approximation to the RR?

What is OR independent of? What is OR used for?

A
  • If odds are equal in case and control group OR=1
  • Similar to risks but must remember they are not the same
  • If events are rare then OR is a good approximation to the RR
  • Like RR they are independent of baseline risk (prevalence)
  • Used in some types of regression (logistic) and therefore found in the literature frequently
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13
Q

What is a population?

What is a sample?

When are samples used?

What must samples do?

A
  • Population
  • Theoretical concept to describe the group of individuals of interest to the research question e.g. 13 year old girls, diabetics in the UK, men aged 15-25 who attempt suicide
  • Sample
  • In practice we can’t take measurements on every individual. We take a sample –preferably a random sample, that is representative of the population in which we are interested
  • Usually much smaller than the population in which we are interested
  • Must summarise the sample using basic statistics
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14
Q

Describing ‘Central Tendency’. Describe the mean, median, and proportion

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15
Q

Describe the median and interquartile range

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16
Q

How do means and medians compare to each other?

A
  • Mean –uses all data but can be influenced by outliers
  • Median –not influenced by outliers, but doesn’t use all data (less informative)
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17
Q

What is standard deviation a measure of?

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18
Q

Mean + 1SD

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19
Q

Describe normal and skewed distributions

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20
Q

What % of observations are within 1SD and 2SD?

A
  • Around 68% of observations within 1SD of mean
  • Approx. 95% of observations within 2 SD of mean (actually 1.96)
21
Q

Estimating from samples.

What is the mean and prevalence in a sample used for?

How does sample size affect confidence?

A
  • Estimating from samples
  • In practice we usually have a sample of individuals
  • Use the mean of the sample to ESTIMATE the ‘true’ mean of the population
  • Use the prevalence in a sample to ESTIMATE the ‘true’ proportion in the population
  • Prevalence is the proportion of a particular population found to be affected by a medical condition at a specific time
  • E.g sample of 100 patients with asthma used to estimate rate of inhaler use in Scotland
  • We will have more confidence that the sample mean/prevalence is a good estimate of the population mean/prevalence if the sample is large
  • Larger samples –more confidence
22
Q

From sample to population.

How good is a sample mean as an estimate of the population mean?

What is the standard error of mean (SE)?

What does a large and small SE indicate?

A
  • From sample to population
  • How good is a sample mean as an estimate of the population mean?
  • If we took repeated samples, the variability of the sample means could be measured
  • This is called the standard error of the mean (SE)
  • A large SE indicates that there is much variability in sample means; that many lie a long way from the population mean
  • A small SE indicates there is not much variability between the sample means
23
Q

Why is SE always smaller than SD?

What can we also calculate the standard error of?

How does sample size affect SE? What is the formula for SE (in picture)?

A
  • SE is always smaller than SD because there is less variability between sample means than between individual values.
  • Can also calculate the standard error of a proportion, rate, odds ratio etc
  • Larger samples lead to smaller SE
  • Formula for SE (in picture)
24
Q

What is a confidence interval?

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25
Q

What is the formula for a 95% confidence interval?

A
  • 95% Confidence interval = sample mean +/- 1.96*SE
  • We are only 95% confident
  • 5% of the time the confidence interval WILL NOT include the true mean (based on a single sample)
  • 95% is an arbitrary choice
26
Q

Calculating upper and lower limit values (in picture)

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27
Q

What can there be variability between?

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  • There can be variability between people and within people
28
Q

Statistical testing and interpretation of results example part 1

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29
Q

Example part 2; Null hypothesis and research hypothesis

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30
Q

Example part 3: Statistical testing and interpretation of results

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31
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Example part 4: Statistical testing and interpretation of results

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32
Q

Example part 5: Odds ratio and interpretations

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33
Q

Example part 6: Interpretation of results

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34
Q

Statistical tests for comparing groups.

What 2 comparisons do we conduct?

What is the question we ask?

What is the answer we ideally want?

What test do need?

What do we need to consider when choosing a test?

A
  • Statistical tests for comparing groups

1) Comparisons
* Comparing our results with a gold standard
* Comparing one sample with another after an intervention

2) Question
* When is a difference STATISTICALLY SIGNIFICANT?
* i.e When do we reject the Null hypothesis?

3) Answer
* Ideally want a simple Yes/No answer

  • Want a test statistic that will allow us to make a decision
  • Do we have enough evidence to REJECT the null hypothesis
  • Many tests available, skill is in knowing which is appropriate for your outcome
  • Important to understand type of data e.g. Categorical or continuous, ordinal etc
  • Important to think about the distribution of the outcome –normal or non-normal
35
Q

What are 2 statistical tests for comparing groups?

A
  • 2 statistical tests for comparing groups:

1) T-test – allows us to statistically compare means between two groups
* 1 dependent continuous variable (e.g height)
* 1 independent binary categorical variable (e.g. sex)

2) Chi-square-test – allows us to statistically compare frequencies
* 1 dependent categorical variable (e.g. alternative drug types)
* 1 independent categorical variable (e.g. Deprivation category)

36
Q

What are T-tests used to determine?

What are they also called?

What does it give a probability for?

A
  • T-test is used to determine whether two means are significantly different from each other
  • Also referred as Students t-test.
  • Gives a probability (p-value) that such a difference in means (or a greater difference) would be found by chance, IF THE NULL HYPOTHESIS IS TRUE
  • E.g compare the height of men and women, compare mean from your data with published literature, compare blood pressure readings before and after exercise
37
Q

What is a one-sample T-test?

A
  • A one sample t-test is a comparison of a single mean with a hypothesized value
38
Q

T-test and P value example part 1

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39
Q

Part 2: Hypothesis testing

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40
Q

Part 3: P-value

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41
Q

Part 4: Hypothesis testing

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Danny's MD4001 CNS (60 decks)

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