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EBHC Statistical ToolkitDavid M. ThompsonDept. of Biostatistics and EpidemiologyCollege of Public Health, OUHSC

Learning to Practice and Teach Evidence-Based Health CareFifth Annual WorkshopSeptember 24-25, 201015th Annual EBHC Workshop 9-24-2010Statistical tools answer questionsby testing hypothesesand generating p-values

by estimating parametersand generating confidence intervalson those estimates

5th Annual EBHC Workshop 9-24-20102Glossaries and online calculators5th Annual Workshop - Learning to Practice and Teach EBHC

OUHSC Bird Library - Evidence Based Healthcare

Duke - UNC Chapel Hill Intro to EBP

EBM calculators at Can. Inst. of Health Research5th Annual EBHC Workshop 9-24-20103Clinical QuestionsEpidemiologyImpact of symptoms and disease on patient or othersEtiologyScreeningDiagnosisTreatment/ManagementPrognosis45th Annual EBHC Workshop 9-24-2010Evaluating (or choosing) statistical tools hinges on the question of interestPPopulationIIntervention, prognostic factor, or exposureC Comparison groupOPrimary outcome(Study design)55th Annual EBHC Workshop 9-24-2010Outcome measuresCategoricalBinary disease vs. no diseaseMultilevel and unorderedMultilevel and ordered Disease stage I,II,II,IVOpinion: disagree, neutral, agree5th Annual EBHC Workshop 9-24-20106Outcome measuresNumericDiscrete Counts of events of disease or adverse eventsNumber of apoptotic cellsContinuousHbA1cNatural log of C reactive proteinTime to eventProgression free survivalOverall survival5th Annual EBHC Workshop 9-24-20107OutcomesEBHC glossaries focus on treatment effects in studies of an Intervention, Exposure, or Prognostic factor

that presume the outcome is a countable event.(http://ktclearinghouse.ca/cebm/glossary/)

5th Annual EBHC Workshop 9-24-20108FormulaRisk reductionRisk increaseBenefit increaseRelative|EER - CER|/CERRel. risk reductionRel. risk increaseRel. benefit increaseAbsolute|EER - CER|Harmful or beneficial events per personNumber neededto 1/ |EER - CER|Persons per harmful or beneficial eventNNTNNHNNTOutcomes measured in other ways require other statistical tools

5th Annual EBHC Workshop 9-24-20109BoilerplateContinuous variables were analyzed using t-tests or, when appropriate, their nonparametric analogs. Associations between categorical variables were assessed using Chi-square tests or, when expected values were small, Fishers exact tests.5th Annual EBHC Workshop 9-24-201010Statistical tools fit the features of the questionPPopulationIIntervention, prognostic factor, or exposureC Comparison groupOPrimary outcome(Study design)

5th Annual EBHC Workshop 9-24-201011Statistical tools fit the features of the question5th Annual EBHC Workshop 9-24-201012OutcomeComparison group defined by Intervention or ExposurePopulation CovariatesAge, SexDisease SeverityComorbid conditionsFeatures of statistical modelStatistical interaction or effect modification

Correlated observations of the outcome

Multiple comparisons5th Annual EBHC Workshop 9-24-201013Interaction between marital status and C1 enrollment regarding incidence of infant death5th Annual EBHC Workshop 9-24-201014

Certain study designs obtain(and take advantage of) nonindependent (or correlated ) observations of the outcome.Observations can be correlatedtemporallyspatiallyhierarchically5th Annual EBHC Workshop 9-24-201015Statistical tools that appropriatelyhandle correlated observationsRepeated measures analysis of varianceLinear mixed modelsfor numeric outcomesGeneralized linear modelsfor outcomes that are binary, categorical, ordinal, or countsconditional and marginal models

5th Annual EBHC Workshop 9-24-201016Multiple comparisonsThe probability of detecting and reporting differences that dont truly exist accumulates in a study that examines several hypothesis tests.5th Annual EBHC Workshop 9-24-2010175th Annual EBHC Workshop 9-24-201018

The right statistical tool for the question.Between-group differences in HbA1c were assessed using a mixed regression model that accounted for the studys repeated and, therefore, correlated measurements on each subject. 5th Annual EBHC Workshop 9-24-201019 Hypothesis testing focused on the models estimate of group*time interaction to assess whether change in HbA1c over time differed between the treatment groups. 5th Annual EBHC Workshop 9-24-201020The model also produced stratum-specific estimates of the change in HbA1c levels over time (in mg/dL/year) along with 95% confidence intervals.5th Annual EBHC Workshop 9-24-201021