# Clinical Epi

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Based on lecture given by Dr. Thiwanka Wijeratne.

Another resource is "Basic Clinical Epidemiology"

## Approach to Critical Appraisal

1. PICOT Statement
2. Randomization Worked? (Look at Table 1)
3. Blinding? (who? what?)
4. Outcome (reliable etc)
5. Type of Analysis (intention to treat? per-protocol?)
6. Generalizable? (Sampling, Eligibility)

## Approach to Analysing Studies

• PICOT --> Identify what the study is studying
• Internal Validity
• Biases
• Selection Bias - A systematic error in creating intervention groups (causing them to differ with respect to prognosis)
• Measurement Bias - Any aspect of the way information is collected in the study that creates a systematic difference between compared population that is not the studied association (i.e. recall bias, lead time bias, etc..)
• Confounder -> A Type of bias -> (i.e. coffee is a confounder when studying smoking and MI, b/c comes with smoking and can influence MI)
• 3 Features:
• Predictor of Outcome
• Associated with Exposure
• Not intermediatry between exposure & outcome
• Regression Techniques
• Stratification
• External Validity
• Population of study and how it applies to real world.
• Sample Size (the larger the size, the more relevant patients)
• Alpha Error - (aka Random Error) - Falsely seeing a difference due to random chance (no difference exists)
• Example: Table 1 (baseline characteristics in randomization).  Usually (1 in 20 times) will find a difference, so if more than 20 variables in Table 1, there is a chance one will be significant.
• Can use Bon Feroni correction.
• DO NOT use P-values, instead use "Standardized Mean Difference" to see if two groups are homogeneous.  (b/c if you have enough patients and variables you will find a difference where one does not exist).
• Beta Error - (aka Underpowered) - Not capturing a true difference due to insufficient sample size and/or study power.
• Structure
• RCT
• Randomization (
• Allocation Concealment (whether investigators and participants know their treatment).
• I.e. Randomization: allocate each person to A or B (random), but did not conceal treatment
• Allocation Concealment:  Give each participant a number, which corresponds to a treatment (unknown).
• Blinding
• Outcome Measurement
• Observational Studies
• Start with one outcome, and look at multiple exposures (i.e. MI)
• Retrospective vs. Prospective
• Accessibilty of information
• Retrospective --> already been done (go back to 2010, get patients, and look prospectively).

## Study Characteristics

• Types of outcomes:
• Continuous
• Binary
• Time to Event
• Are outcomes clinically important? (valid, reliable (reproducible), interpretable, accurate, responsive)
• Type of analysis:
• Intention To Treat (participants stay in groups to which they were allocated)
• Per Protocol (able to change)
• Type of randomization
• Sequence-based is poor form (i.e. assign each one sequentially.  Can bias)
• Must have "Allocation Concealment!" (aka blinding)
• Must define who is blinded --> patients, treating physicians, analysts, paper writers
• Do not use terms like "double blinded", "triple blinded" etc..
•

## Diagnostic Tests

• Sensitivity
• Specificity
• PPV
• NPV
• • Likelihood ratios
• Ratio of:
• Probability of positive test in pts with disease
• Probability of positive test in pts w/o disease.
• LR+
• sn / (1- sp)
• Probability of positive test of pts with disease (a/a+c) OVER probability of positive test in pts without disease (b/b+d)  ---> simplifes to sn / (1-sp)
• LR -
• Probability of negative test in pts with disease (c/c+a) OVER probability of a negative test in pts w/o disease (d/b+d)
• (1-sn)/sp
• ODDS RATIOS
• Utility: find post-test probability from pre-test
• Two ways:
• Calculate:
• Probability --> to pre-test Odds --> (multiply by LR+ or LR- ) -->to post-test Odds --> post-test probability
• Opre = Ppre/(1-Ppre)
• Ppost=Opost(1+Opost)
• Use Nomogram
• "2-5-10" rule
• LR = 2 then probability should increase by ~ 15%
• LR = 5  --> 30%
• LR = 10  --> 45%
• etc...

## Treatment Effect

•  Outcome + Outcome - Exp A B Contr C D
• Experimental Event Rate (EER) --> A/ A+B
• Control Event Rate (CER) --> C/ C+B
• Relative Risk (Risk Ratio)
• Probability of given event in exposed group vs. control.
• EER / CER
• Relative Risk Reduction
• RRR = (CER - EER / CER)
• Absolute Risk Reduction
• ARR = CER - EER
• Number Needed to Treat
• NNT = 1/ARR
• NOTE:
• Case control --> cannot calculate absolute risk.
•  - can only look at Odds Ratio
• Odds Ratio
• OR = (A/B) / (C/D)
• Odds of target outcome within treatment group vs controls (i.e. odds within treated group vs odds in untreated group).

## Systemic Reviews & Meta-Analysis

• Systematic Reviews
• A systematic review is a scientific tool that can be
used to appraise, summarise, and communicate the
results and implications of otherwise unmanageable
quantities of research.
Meta-Analysis
• Extension of systematic review --> actually use pooled numbers to make assessment across many studies.
• Process:
• Research Question
• Search for studies
• Review studies for inclusion
• Assess quality of sutides
• Summarise
• Qualiatively - Tables, Text
• Quantatively
• Forrest Plots
• Puts studies together.
• Look at model used to combine information:
• Fixed Model --> If all studies looked at same patients
• Random Effects Model - If heterogeneous population
• See if all studies have are heterogeneous
• Clinical Heterogeneity --> look clinically and decide if studies are the same (i.e. gastroparesis --> diabetics & crohn's disease)
• Statistical Heterogeneity --> Chi^2
• Q-statistic --> P value statistically significant?)
• I^2 > 40% --> then statistically heterogeneous
• If heterogeneous populations
• Measure it
• Random effects model (takes into account differences between studies)
• Subgroup analysis
• Meta-Regression
• Funnel Plot
• Looks at publication bias
• Should look like a tree (should be triangle) --> but if studies are biased, then will see only one side of the triangle.  (i.e. only positive results are reported).  goes to a point, so the more N in a trial, then narrows to a point.

## Power Analysis

• Important to select an appropriate sample size to ensure the study has enough statistical power to avoid:
• A.)  Type I error  - incorrect rejection of null hypothesis ("false positive")
• B.)  Type II error - incorrectly retaining a false null hypothesis ("false negative")
• Variables:
• N = sample size
• alpha = Type I error chance
• beta = Type II error chance
• 1-beta = Power
• To calculate any above variable, you need the others..
• Two ways to do power analysis
• A priori analysis
• - Most ideal.  Done before the trial, estimating the needed alpha, and 1-beta
• Post-hoc analysis
• - Less ideal.  Once you have N, and alpha, it calculates 1-Beta
• Compromise power analysis
• - When N is too large to be feasible.  Then have to compromise on the alpha or beta.

## Useful Resources

• CONSORT STATEMENT
• Checklist for reporting RCTs
• STROBE STATEMENT
• Checklist for reporting observational studies
• PRISMA STATEMENT
• Preferred reporting items for systematic reviews and meta-analysis
• MOOSE STATEMENT
• For systematic reviews of observational studies