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Clinical Research: Basic Statistics and Appraising the Literature

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Epidemiology and Biostatistics Epidemiology: Study design and interpretation Biostatistics: Methods for analysis

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Importance of Understanding Basic Statistics in Medicine Research –Design Studies –Plan Analyses –Data Interpretation Clinical Medicine –Understanding the Literature –Evidence-based practice

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Learning the Language Sampling Variable types –Determine analysis method(s) Continuous Categorical (nominal, ordinal) Independent vs. Correlated Data Parametric vs. Non-parametric

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Sampling: Is the study group representative? CAD case:Control Study n=328/group Non-diabetic Middle-aged Italian Men Colomba F et al. ATVB 2005; 25: 1032

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Sampling: Is the study group representative? Dallas Heart Study Probability-based sample Over-sampling Minorities

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Statistical Testing: Principles Question: Is blood pressure associated with stroke? Study 1Study 2 Stroke No Stroke Average= 136 mm/Hg 132 mm/Hg Average= 136 mm/Hg 132 mm/Hg

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Statistical Testing: Principles Question: Is blood pressure associated with stroke? Study 1Study 2 Stroke No Stroke 132 mm/Hg Average= 136 mm/Hg Average= 136 mm/Hg

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Statistical Testing Observed effect (what we see) – Expected (under null) Variability of the data Test Statistic = Use test statistic to generate a p-value

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Learning the Language Sampling Variable types –Determine analysis method(s) Continuous Categorical (nominal, ordinal) Independent vs. Correlated Data Parametric vs. Non-parametric

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Categorical Data Data where the results are in categories of some qualitative trait (yes/no) –Can be nominal or ordinal

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Nominal v. Ordinal Nominal data (no order to the categories) –Smoking status (smoker, non- smoker) –Hair color (blonde, red, black) –Race (black, white, hispanic, other) Ordinal data (order to categories) –Med school year (1 st, 2 nd, 3 rd, 4 th ) –Heart failure class (NYHA 1, 2, 3, or 4)

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Continuous Data Data that are quantitative and measured (can perform arithmetic on) (can be divided into smaller values) –Blood pressure –Age –Cholesterol levels

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Variable Types: Ordinal, Numerical and Categorical Svensson AM, et al. Eur Heart J 2005; 26: 1255

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Learning the Language Sampling Variable types –Determine anlaysis method(s) Continuous Categorical (nominal, ordinal) Independent vs. Correlated Data Parametric vs. Non-parametric

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Data from Independent Samples Park L et al. Nat Med 4:1025 3 g IP day -1 15 g IP day -1 20 g IP day -1 40 g IP day -1 Diabetic ApoE null mice Control ApoE null mice Control ApoE null mice

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Baseline 24 Hours Control GIK 0 0.5 1 1.5 2 2.5 0 0.5 1 1.5 2 2.5 Data from Repeated Measures: Correlated Data Addo T, et al. Am J Cardiol 2004; 94: 1288

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Learning the Language Sampling Variable types –Determine anlaysis method(s) Continuous Categorical (nominal, ordinal) Independent vs. Correlated Data Parametric vs. Non-parametric

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Parametric (Gaussian) Distribution

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Skewed Data

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Statistical Tests: What Type of Data? NominalOrdinalParametricNon-Para ContinousCorrelatedPaired t-test Wilcoxon Sign Rank Independt-testWilcoxon Rank Sum CategoricalCorrelatedMcNemar Test IndependFisher’s Exact Chi-square trend test

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Power and Sample Size

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Power: What is it Power = (1- ): –The probability of rejecting the null hypothesis when it is false –English: the probability of detecting a true association between an exposure and an outcome when there is one

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Sample Size and Power: The assumptions Sample size: –To determine sample size, enter three parameters: Power : (80 or 90%) Effect size –Control value and variance, or event rate –dependent on parameter of interest –best to have pilot data Significance level ( ) : (0.05) –1-tailed or 2-tailed testing (Confounders) –Non-compliance, Cross-overs (Drop Ins/Outs), Lost to follow up

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Standards for Effect Size Small –20% Medium – 50% Large – 80% –only rough guidelines Small, medium and large are subject dependent

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Adequacy of Sample: Size Matters Total # of eventsSample Size if risk 10% Power for 25% RRR Adequacy of size 0-50(under 500)<10%Utterly inadequate 50-150(1000)10-30%Probably inadequate 150-350(3000)30-70%Possibly adequate, possibly not 350-650(6000)70-90%Probably adequate Over 650(10,000)>90%Definitely Adequate

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Effect of trial size on results: 24 trials of -blockade vs. Placebo Total deaths Mean Sample Size p<0.5 against Trend against Trend favorable p<0.5 favorable 0-50(255)0550 50-150(861)0191 150-350(2925)0012 350-650N/A---- Over 650N/A---- TOTAL(866)06153

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Ways to Reduce Required Sample Size Higher Event Rate –High risk populations –Composite Endpoints Larger Effect Size Lower power Larger –1-tailed or 2 Change analysis type –Time dependent

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Sample size planning How much money do you have? How much time to you have? How many patients/subjects can you expect to reasonably get? “What sample size and study design can I afford?”

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The words to use to describe this The study was designed to have >80% power to detect an effect size of >20% with a 2-tailed significance level of 0.05, with a planned sample size of 400 participants in each group.

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Suggested Reading Reference texts –Dawson-Saunders B, Trapp RG. Basic and Clinical Biostatistics, Appleton and Lange, Norwalk, CT, 2 nd Edition, 1994. –Sackett DL. Clinical Epidemiology: a basic science for clinical medicine. Little Brown, Boston, MA, 2 nd Edition, 1991. Selected papers: –Bias Sackett DL. Bias in analytic research. J Chron Dis 1979; 32:51-63 –Power Moher D, Dulberg CS, Wells GA. Statistical power, sample size, and their reporting in randomized controlled trials. JAMA 1994; 272: 122-4. –Subgroup analyses Assmann SF, Pocock SJ, Enos LE, Kasten LE. Subgroup analysis and other (mis)use of baseline data in clinical trials. Lancet 2000; 355: 1064-1069. Yusuf S, Wittes J, Probstfield J, Tyroler HA. Analysis and interpretation of treatment effects in subgroups of patients in randomized clinical trials. JAMA 1991; 266: 93-98.

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