Basic Statistics

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Basic Statistics
Principles Practice of Evidence-Based Medicine
Mini-lecture series
EBM

• Roberto Cardarelli, DO, MPH

Center for Evidence-Based Medicine
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Why we have statistics?

• Descriptive Statistics
• identify patterns
• leads to hypothesis generating
• Inferential Statistics
• distinguish true differences from
• random variation
• allows hypothesis testing

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Descriptive studies
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Describing Data with Numbers

• Measures of Central Tendency
• MEAN -- average
• MEDIAN -- middle value
• MODE -- most frequently observed
  value(s)
• Measures of Dispersion
• RANGE - highest to lowest values
• STANDARD DEVIATION - how closely do values
  cluster around the mean value
• SKEWNESS - symmetry of curve

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The Normal Distribution

• Mean median mode
• Skew is zero
• 68 of values fall between 1 SD
• 95 of values fall between 2 SDs

Mean, Median, Mode
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2?
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Inferential Statistics

• Used to determine the likelihood that a
  conclusion based on data from a sample is true

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Terms

• p value the probability that an observed
  difference could have occurred by chance.
• The significance is determined by alpha
• Confidence interval the range of values we can
  be reasonably certain includes the true value.

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Types of Errors
Truth
Conclusion
Power 1-?
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Statistical Tests

• Two types of variables
• Dichotomous (sex, race, etc.)
• Continuous (age, BP, etc.)
• chi-square two dichotomous variables
• example deaths from bike accidents depending on
  helmet use between two time intervals.
• t-test one continuous and one dichotomous
• example difference in mean birth weights between
  males and females
• correlation coefficient two continuous
  variables
• examples average daily fat intake and incidence
  of breast cancer.
• meta-analysis a statistical method used to
  combine results of several studies into a single
  study.

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Statistics and EBM

• We want terms that makes sense to clinicians.
• Easy to apply to patients
• Easy to explain to patients
• Differentiate what is true and not true
• We want clinically important information

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Articles of Diagnosiswell learn

• Sensitivity - the proportion of people with
  disease who have a positive test.
• Specificity - the proportion of people free of a
  disease who have a negative test.
• Likelihood Ratio - the likelihood that a given
  test result would be expected in a patient with
  the target disorder compared to the likelihood
  that the same result would be expected in a
  patient without that disorder.
• Negative Predictive Value (-PV) - the proportion
  of people with a negative test who are free of
  disease.
• Positive Predictive Value (PV) - the proportion
  of people with a positive test who have disease.

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Articles of Prognosiswell learn

• Survival rates
• Example 1 or 5-year survival rates.
• Median survivals
• Example length of follow-up by which 50 have
  died.
• Survival curves
• Example At each point in time, the proportion of
  the study sample who do not have the outcome
  (death).

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Example Survival curve
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Articles on Therapywell learn

• Absolute Risk Reduction (ARR) - The difference in
  the event rate between control group (CER) and
  treated group (EER.
• ARR CER - EER.
• Relative Risk Reduction (RRR) - The percent
  reduction in events in the treated group event
  rate (EER) compared to the control group event
  rate (CER)
• RRR (CER - EER) / CER 100
• Number Needed to Treat (NNT) - The number of
  patients who need to be treated to prevent one
  bad outcome. It is the inverse of the ARR.
• NNT 1/ARR.

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Articles on Harmwell learn

• Odds ratio - describes the odds of an
  experimental patient suffering an adverse event
  relative to a control patient.
• Risk Ratio - The ratio of risk in the treated
  group (EER) to the risk in the control group.
• (CER)RR EER/CER.
• RR is used in randomized trials and cohort
  studies.
• Number Needed to Harm (NNH) is the number of
  patients who need to be exposed to cause one
  additional harmful event.

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Next class

• Start the good stuff
• Diagnosis and screening
• DOES ANYONE WANT TO BRING IN AN ARTICLE ON
  DIAGNOSIS?
• 1st 30 minutes Lecture
• 2nd 30 minutes Appraise the article