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“EBHC Statistical Toolkit”

“EBHC Statistical Toolkit”. David M. Thompson Dept. of Biostatistics and Epidemiology College of Public Health, OUHSC Learning to Practice and Teach Evidence-Based Health Care Fifth Annual Workshop September 24-25, 2010. Statistical tools answer questions. by testing hypotheses

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“EBHC Statistical Toolkit”

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  1. “EBHC Statistical Toolkit” David M. Thompson Dept. of Biostatistics and Epidemiology College of Public Health, OUHSC Learning to Practice and Teach Evidence-Based Health Care Fifth Annual Workshop September 24-25, 2010 5th Annual EBHC Workshop 9-24-2010

  2. Statistical tools answer questions by testing hypotheses and generating p-values by estimating parameters and generating confidence intervals on those estimates 5th Annual EBHC Workshop 9-24-2010

  3. Glossaries and online calculators • 5th 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 Research 5th Annual EBHC Workshop 9-24-2010

  4. Clinical Questions • Epidemiology • Impact of symptoms and disease on patient or others • Etiology • Screening • Diagnosis • Treatment/Management • Prognosis 5th Annual EBHC Workshop 9-24-2010

  5. Evaluating (or choosing) statistical tools hinges on the question of interest • P Population • I Intervention, prognostic factor, or exposure • C Comparison group • O Primary outcome • (Study design) 5th Annual EBHC Workshop 9-24-2010

  6. Outcome measures • Categorical • Binary • disease vs. no disease • Multilevel and unordered • Multilevel and ordered • Disease stage I,II,II,IV • Opinion: disagree, neutral, agree 5th Annual EBHC Workshop 9-24-2010

  7. Outcome measures • Numeric • Discrete • Counts of events of disease or adverse events • Number of apoptotic cells • Continuous • HbA1c • Natural log of C reactive protein • Time to event • Progression free survival • Overall survival 5th Annual EBHC Workshop 9-24-2010

  8. Outcomes EBHC 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 9-24-2010

  9. Outcomes measured in other ways require other statistical tools 5th Annual EBHC Workshop 9-24-2010

  10. Boilerplate “Continuous 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, Fisher’s exact tests.” 5th Annual EBHC Workshop 9-24-2010

  11. Statistical tools fit the features of the question • P Population • I Intervention, prognostic factor, or exposure • C Comparison group • O Primary outcome • (Study design) 5th Annual EBHC Workshop 9-24-2010

  12. Statistical tools fit the features of the question Comparison group defined by Intervention or Exposure Outcome Population Covariates Age, Sex Disease Severity Comorbid conditions 5th Annual EBHC Workshop 9-24-2010

  13. Features of statistical model • Statistical interaction or “effect modification” • Correlated observations of the outcome • Multiple comparisons 5th Annual EBHC Workshop 9-24-2010

  14. Interaction between marital status and C1 enrollment regarding incidence of infant death 5th Annual EBHC Workshop 9-24-2010

  15. Certain study designs obtain(and take advantage of) nonindependent (or correlated ) observations of the outcome. Observations can be correlated • temporally • spatially • hierarchically 5th Annual EBHC Workshop 9-24-2010

  16. Statistical tools that appropriatelyhandle correlated observations • Repeated measures analysis of variance • Linear mixed models • for numeric outcomes • Generalized linear models • for outcomes that are binary, categorical, ordinal, or counts • conditional and marginal models 5th Annual EBHC Workshop 9-24-2010

  17. Multiple comparisons The probability of detecting and reporting differences that don’t truly exist accumulates in a study that examines several hypothesis tests. 5th Annual EBHC Workshop 9-24-2010

  18. 5th Annual EBHC Workshop 9-24-2010

  19. The right statistical tool for the question. “Between-group differences in HbA1c were assessed using a mixed regression model that accounted for the study’s repeated and, therefore, correlated measurements on each subject. …” 5th Annual EBHC Workshop 9-24-2010

  20. “… Hypothesis testing focused on the model’s estimate of group*time interaction to assess whether change in HbA1c over time differed between the treatment groups. …” 5th Annual EBHC Workshop 9-24-2010

  21. “…The 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 9-24-2010

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