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
        • Options to address confounders:
          • Adjustment Analysis
          • 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
        • Case Control - start with outcome and look at exposure
          • Start with one exposure, look at multiple outcomes. 
        • Cohort - start with exposure, and look at outcome
          • 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). 
            • Prospective --> don't have data yet, start with patients, and follow them to get data. 

    Types of Studies

    • Randomized Control Trials (RCT)
      • Proves causation
      • Pros:   
        • Only study design that establishes causation
      • Cons:
        • Restricted population, VERY expensive, very focused question
    • Observational Studies
      • Proves association
      • Pros: Can study adverse effects, rare diseases
      • Cons: Bias!
      • Two Types:
        • Case Control
        • Cohort Studies
    • Types of Studies.png

     

     

    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
    • DiagnosticTests.png

    • 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
    • DOWNLOAD
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      • Mendeley: free reference manager
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