Univariate Analysis and Table 1,Mark Drazner, MD, MSc University of Texas Southwestern Medical CenterJune 11, 2004,Risk Factor Outcome,Exposure Explanatory variable Independent variable,Dependent variable Response Disease,Types of Variables in Your Experiment,Objective,Is the risk factor associated with the outcome? Is there a pattern?,Validity and Generalizability,Internal ValidityI find a pattern, is it real (reproducible)? Chance Bias ConfoundingExternal ValidityDoes it apply to other patients?Generalizability,Tools Against Invalidity,Chance: Statistics Bias: Study design (Was it a fair study?) Confounding: Multivariable analysis,Statistical Analyses,Univariate1 Risk Factor 1 OutcomeMultivariableMultiple risk factors 1 Outcome,Which Statistical Test Do I Use?,It depends on type of data being analyzed Types of data in clinical research Categorical: Nominal vs. Ordinal Dichotomous 2 categories Continuous Survival Paired data Limited types of data means limited number of statistical tests,Paradigm of Hypertensive Heart Disease,“Transition to Failure”,Rame et. al, AJC, 2004,Retrospective Study: Parkland Echocardiography Database (1992 1994),Rame, AJC, 2004,Some Purposes of Table 1,Provide baseline characteristics What patients were studied? Are they applicable to the readers patients (generalizability)? Is the study believable e.g., authors say they studied advanced heart failure, do patients have characteristics of advanced heart failure? What are differences at baseline between 2 groups (e.g., case-control)? Randomized clinical trial: Did randomization work?,How Did We Make Table 1?,Methods Section,“Data are expressed as proportions, means SD, or medians (25th and 75th percentiles). Differences in characteristics between patients who did or did not progress to a depressed EF were compared by Fishers exact test, Students t test with equal or unequal variances, or Wilcoxon rank-sum test, where appropriate.”,Rame, AJC, 2004,What Type of Data are You Analyzing?,Dichotomous Outcome,Either Or,Dichotomous Risk Factors,Epidemiologists Love 2 x 2 Tables,Disease/Outcome,Risk Factor/ Exposure,+ -,+,-,Statistical Tests for Dichotomous Risk Factor + Dichotomous Outcome,Chi-square (approximation) Fishers Exact Test,Chi-Square,Observed effect (what we see) Expected (under null),Expected (under null),Dichotomous Risk Factors,18/28 who develop low EF are AA 104/131 who do not develop low EF are AA P = 0.14,Dichotomous Risk Factors,Only 129 subjects (not 159) = missing data 10 (45%) who develop low EF had CAD 19 (18%) who did not develop low EF had CAD P = 0.01,Learn a Statistical Package!,SAS SPSS STATA,Continuous Risk Factors,Continuous Risk Factor Dichotomous Outcome,Students T test Equal variance Unequal variance Wilcoxon Rank Sum (Mann-Whitney-U),T test vs. Wilcoxon Rank Sum:Are data normally distributed?,How Do You Tell if Data are Normally Distributed?,Look for characteristics of normality Is it bell-shaped (histogram)? Mean = median = mode Symmetry of data (skewness) Length of tails/how common are values near and far from mean (kurtosis) Statistical tests of normality Normal probability plot Wilk-Shapiro test,Normal Distribution,Left skew,Right skew,High kurtosis Lot of tails,Low kurtosis “Boxy”,Table 1,A series of statistical tests Dichotomous risk factors + outcome Chi square Fishers exact test Continuous risk factors and dichotomous outcome T test Wilcoxon rank sum Have not addressed categorical risk factors or outcomes with 2 levels, survival analysis, paired data,Confounder,3rd factor that is associated with exposure/risk factor (height) Independent determinant of outcome (wealth),How to handle in analysis: Stratification Multivariable analysis,Univariate Analysis,Its a start but Need to move to Multivariable Analysis to know if your risk factor is independently associated with outcome (“unconfounded association”),Conclusions,Guard against chance, bias, confounding Learn a statistical package Use the right statistical test, based on what type of data,