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“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
“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


Statistical tools answer questions
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


Glossaries and online calculators
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


Clinical questions
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


Evaluating or choosing statistical tools hinges on the question of interest
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


Outcome measures
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


Outcome measures1
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


Outcomes
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


Outcomes measured in other ways require other statistical tools

5th Annual EBHC Workshop 9-24-2010


Boilerplate
Boilerplate tools

“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


Statistical tools fit the features of the question
Statistical tools fit the features of the question tools

  • P Population

  • I Intervention, prognostic factor, or exposure

  • C Comparison group

  • O Primary outcome

  • (Study design)

5th Annual EBHC Workshop 9-24-2010


Statistical tools fit the features of the question1
Statistical tools fit the features of the question tools

Comparison group

defined by

Intervention or Exposure

Outcome

Population Covariates

Age, Sex

Disease Severity

Comorbid conditions

5th Annual EBHC Workshop 9-24-2010


Features of statistical model
Features of statistical model tools

  • Statistical interaction or

    “effect modification”

  • Correlated observations of the outcome

  • Multiple comparisons

5th Annual EBHC Workshop 9-24-2010


Interaction between marital status and c1 enrollment regarding incidence of infant death
Interaction between marital status and toolsC1 enrollment regarding incidence of infant death

5th Annual EBHC Workshop 9-24-2010


Certain study designs obtain tools(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


Statistical tools that appropriately handle correlated observations
Statistical tools that appropriately toolshandle 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


Multiple comparisons
Multiple comparisons tools

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



The right statistical tool for the question
The right statistical tool for the question. tools

“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


“… 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


“…The of group*time interaction to assess whether change in HbA1c over time differed between the treatment groups. …”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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