Evidence-Based Medicine

.title[
# Evidence-Based Medicine - Part 2
]
.author[
### Austin Meyer, MD, PhD, MPH, MS, FAAP, etc.
]
.date[
### November 17, 2023
]

---

# Agenda

**Epidemiology 1 - times**
 
1. Must Know - ? minutes
 
2. Study Bias - 10 minutes
 
3. Repeat Some Statistics - ? minutes
 
4. Brief Overview of Stats for Last Time - 5 minutes
 
5. Types of Studies and Associated Stats - 15 minutes
 
6. Limitations of Each Study - 10 minutes

---

# Must Know
--
 
- **Internal Validity**
--
 - Results of a study represent the truth of the study population
--
 
- **External Validity**
--
 - Results of a study apply to populations that aren't studied

---

# Types of Biases - My groupings

---

# Biases of design or unseen variables

- **Selection bias**
  - Non-random partitioning of individuals into groups
--

- **Observer-expectancy**
  - Observer is unblinded and expects a particular outcome
--

- **Effect modification bias**
  - Magnitude of effect varies by third variable
  - __Can__ be eliminated by stratification
--

- **Confounding**
  - Unseen third variable is an underlying cause for correlation of two other variables
  - __Cannot__ be eliminated by stratification

---

# Biases of information (measurement)

--
- **Recall bias**
  - Subjects with disease can recall exposures better than healthy subjects
--

- **Procedure bias**
  - Experimenters vary systematically in the way they do work
  - e.g. Experimenters don't follow the specified procedure
--

- **Instrument bias**
  - Instrument is broken
  - Instruments can also be things like surveys or __resident evaluations__
  - Just means instrument is not reliable

---

# Biases of time and completion

- **Lead-time bias**
  - New test detects disease earlier
  - Survival appears improved with new test
--

- **Attrition bias**
  - Subjects systematically withdraw
  - Could be things like side effects or lack of improvement
--

- **Loss-to-follow up**
  - Subjects randomly do not report for scheduled followup

---

# Repeat Some Statistical Things

---

# Risk Ratio (RR)
**Only use on random samples**

<table>
<tr><th align="left"></th><th align="right">Disease</th><th align="right">No Disease</th><th align="right">Total</th></tr>
<tr><td align="left">Exposed</td><td align="right">30</td><td align="right">70</td><td align="right">100</td></tr>
<tr><td align="left">Not Exposed</td><td align="right">20</td><td align="right">80</td><td align="right">100</td></tr>
<tr><td align="left">Total</td><td align="right">50</td><td align="right">150</td><td align="right">200</td></tr>
</table>
]

.pull-right[
.left[
`\(R_E = \frac{D_E}{D_E + ND_E} = \frac{30}{30 + 70} = 0.3\)` 
`\(R_{NE} = \frac{D_{NE}}{D_{NE} + ND_{NE}} = \frac{20}{20 + 80} = 0.2\)` 
`\(RR = \frac{R_E}{R_{NE}} = \frac{0.3}{0.2} =\)` **1.5**
]
]

---

# Risk Difference (RD)... Absolute Risk Reduction (ARR)

.pull-right[
.left[
`\(R_E = \frac{D_E}{D_E + ND_E} = \frac{30}{30 + 70} = 0.3\)` 
`\(R_{NE} = \frac{D_{NE}}{D_{NE} + ND_{NE}} = \frac{20}{20 + 80} = 0.2\)` 
`\(ARR = RD = R_E - R_{NE} = 0.3 - 0.2 =\)` **0.1**
]
]

---

# Number Needed to Harm or Treat (NNH or NNT)

.pull-right[
.left[
`\(R_E = \frac{D_E}{D_E + ND_E} = \frac{30}{30 + 70} = 0.3\)` 
`\(R_{NE} = \frac{D_{NE}}{D_{NE} + ND_{NE}} = \frac{20}{20 + 80} = 0.2\)` 
`\(RD = R_E - R_{NE} = 0.3 - 0.2 = 0.1\)` 
`\(NNH = \frac{1}{RD} = \frac{1}{ARR} = \frac{1}{0.1} =\)` **10**
]
]

---

# Types of Studies

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# The pyramid of evidence is a hierarchy

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# Experimental Trials

---

# Randomized control trial is in the name

---

# Randomized control - gold standard

- This is widely considered the gold standard for clinical evidence
--
 
- Question: __Primary__ purpose of randomization?
--

- Answer: To eliminate __selection bias__
  - Selection bias (at the time of randomization) is eliminated if randomization is technically correct
--

- Question: Secondary goal of randomization?
--

- Answer: To help with confounding
 - Confounders are not necessarily eliminated even with perfect technical execution
--
 
- Can use relative risk because investigator knows prevalence of disease and prior exposures

---

# Randomized control - limitations

- Can still technically suffer from selection bias (as defined by epidemiologists). Why?
--
 
 - Loss to follow up
--
 
- They may or may not externalize well
--
 
- There are may ethical concerns in pediatrics
--
 
- They are ridiculously expensive

---

# Crossover trial - groups switch

- This post hoc analysis is overly simplified for real life
 
- This understanding is sufficient for boards
 
- Includes initial randomization

- Also, confounders reduced because a patient can serve as their own control

---

# Observational Studies

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# Prospective cohorts - into the future

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# Retrospective cohorts - from the past

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# Cohorts form the next level of evidence
 
--

- Can still use relative risk because investigator knows prevalence of exposure and disease
- Subjects vary by exposure status
- Can calculate incidence
--
 
- __Selection bias__ is the biggest problem
 - Investigator has infinite control over inclusion
--
 
- Other Prospective biases
 - Attrition - people leave the trial intentionally
 - Loss-to-follow up - people just stop showing up
 - Confounding - baseline characteristics or ongoing characteristics are difference
 - Hawthorne - people act differently once observed
--
 
- Retrospective bias
 - Information bias

---

# Case-control trials measure chance of exposure given disease

---

# Case-control forms the next level down from cohorts
--

- Must use odds ratio because investigator does not know prevalence of disease
--
 
- Subjects grouped by cases and controls
 - Measure __odds of exposure__ in case and control groups
--
 
- Significantly improved power and decreased resource requirements compared to cohorts 
 - Due to cases being selected at out set
--
 
- __Selection and Recall biases__ are the biggest problem
 - Selecting appropriate controls is __highly__ non-trivial
 - Sick people remember exposures (e.g. Melanoma patients stew about their sunburns)
--
 
- Also common
 - Information biases
--
 
- __Cannot calculate incidence or prevalence__

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# Cross-sectional trials measure exposure and disease simultaneously

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# Cross-sectional study - next level

- __Quick, cheap, and easy__
 - Typically this is a starting point
--
 
- Can establish prevalence of disease
--
 
- Must use chi-squared or correlation for statistical test
--
 
- Subjects can be grouped by exposure and diease in to the 2x2 contingency
--
 
- __Cannot establish causation__
--
 
- Cannot calculate risk metrics
 
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# The End