.
Based on lecture given by Dr. Thiwanka Wijeratne.
Another resource is "Basic Clinical Epidemiology"
Approach to Critical Appraisal
 PICOT Statement
 Randomization Worked? (Look at Table 1)
 Blinding? (who? what?)
 Outcome (reliable etc)
 Type of Analysis (intention to treat? perprotocol?)
 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
 3 Features:
 Biases
 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 Pvalues, 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).
 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.
 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.
 Case Control  start with outcome and look at exposure
 RCT
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
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
 Sequencebased 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 / (1sp)
 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)
 (1sn)/sp
 Ratio of:
 ODDS RATIOS
 Utility: find posttest probability from pretest
 Two ways:
 Calculate:
 Probability > to pretest Odds > (multiply by LR+ or LR ) >to posttest Odds > posttest probability
 Opre = Ppre/(1Ppre)
 Ppost=Opost(1+Opost)
 Use Nomogram
 "2510" rule
 LR = 2 then probability should increase by ~ 15%
 LR = 5 > 30%
 LR = 10 > 45%
 etc...
 Calculate:
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
 Case control > cannot calculate absolute risk.
 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 & MetaAnalysis
 Systematic Reviews
 A systematic review is a scientific tool that can beused to appraise, summarise, and communicate theresults and implications of otherwise unmanageablequantities of research.MetaAnalysis

 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
 Qstatistic > 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
 MetaRegression
 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
 1beta = 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 1beta
 Posthoc analysis
  Less ideal. Once you have N, and alpha, it calculates 1Beta
 Compromise power analysis
  When N is too large to be feasible. Then have to compromise on the alpha or beta.
 A priori analysis
Useful Resources
 CONSORT STATEMENT
 Checklist for reporting RCTs
 STROBE STATEMENT
 Checklist for reporting observational studies
 PRISMA STATEMENT
 Preferred reporting items for systematic reviews and metaanalysis
 MOOSE STATEMENT
 For systematic reviews of observational studies
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