“ebhc statistical toolkit” david m. thompson dept. of biostatistics and epidemiology college of...
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“EBHC Statistical Toolkit”
David M. ThompsonDept. of Biostatistics and Epidemiology
College of Public Health, OUHSC
Learning to Practice and Teach Evidence-Based Health Care
Fifth Annual WorkshopSeptember 24-25, 2010
5th Annual EBHC Workshop 9-24-2010
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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
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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
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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
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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
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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
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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
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OutcomesEBHC 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
FormulaRisk
reductionRisk
increaseBenefit
increase
Relative |EER - CER|/CER
Rel. risk reduction
Rel. risk increase
Rel. benefit increase
Absolute |EER - CER| Harmful or beneficial events per person
“Number neededto …”
1/ |EER - CER|
Persons per harmful or beneficial event
NNT NNH NNT
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Outcomes measured in other ways require other statistical tools
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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
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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
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Statistical tools fit the features of the question
5th Annual EBHC Workshop 9-24-2010
OutcomeComparison group defined by Intervention or Exposure
Population CovariatesAge, SexDisease SeverityComorbid conditions
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Features of statistical model
• Statistical interaction or
“effect modification”
• Correlated observations of the outcome
• Multiple comparisons
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Interaction between marital status and C1 enrollment regarding incidence of infant death
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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
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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
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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
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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. …”
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“… 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. …”
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“…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