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Statistical Tools for Evidence-Based Health Care: Insights from the 5th Annual EBHC Workshop

This document presents key insights from the 5th Annual Workshop on Evidence-Based Health Care held on September 24-25, 2010. It discusses statistical tools essential for answering clinical questions by testing hypotheses, generating p-values, and estimating parameters. Topics include various outcome measures, the importance of choosing appropriate statistical methods, and the need to consider correlated observations. The toolkit aims to facilitate learning and practice in evidence-based health care, providing resources such as glossaries and online calculators.

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Statistical Tools for Evidence-Based Health Care: Insights from the 5th Annual EBHC Workshop

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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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