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Biostatistics in Nursing Science
Alexandra L. Hanlon, PhD
University of Pennsylvania
October 14, 2009
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Topics
• Introduction
• Areas of Contribution
• Study Design
• Working with Doctoral Students
• Requesting Biostatistics Support
• Computer Labs
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Alexandra L. Hanlon, PhDOffice of Nursing Research
Claire M. Fagin HallRoom 479L
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Areas of Contribution
• Facilitate Research– Pre-award support– Post-award analyses– Unfunded data analyses– Abstract manuscript preparation*– Consultation
*ask for second authorship if I run analyses, write stats, and results portion of paper
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Areas of Contribution, continued
• Teaching– Guest lectures (power, significance, multi-level
modeling)– Independent study– Research Residency– Mentorship– Dissertation Advising
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Pre-Award Support
• Study design
• Study subjects
• Types of variables– Scales of measurement– Types of data– Methods of data collection– Validity and reliability
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Pre-Award Support, continued
• Follow-up period
• Statistical methods
• Sample size calculations/power analysis
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The Well-Designed Study
• Double blind
• Random assignment
• Control, Placebo
• Minimizes extraneous variables
• Adequately powered for statistical significance
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Blinding• Single-blind: The patient or investigator does
not know which intervention s/he is receiving• Double blind: Both patient and investigator do
not know the intervention assignment• Triple blind: The statistician is also masked to
the intervention assignment.• The evaluator may be a different person, and
blinding of this person is crucial
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Random Assignment
• What is it?
• Why do we do it?
• How do we do it?
• Block or stratified randomization
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Intent-to-Treat Principle
• The ITT analysis of an experimental study uses the intervention groups as originally randomized, irrespective of the actual intervention received
• The resulting analysis may lead to a conservative estimate of the intervention effect
• Any other analysis can lead to bias of unknown magnitude and direction
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Description of Statistical Methods
• Terminology and level of detail
• Appropriate methodology
• Assumptions– Transformations
• Collapsing variables
• Estimation and confidence intervals
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Description of Statistical Methods, continued
• Adjustment for confounding
• Cluster randomized trials
• Multiple comparisons
• Intention-to-treat principle
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Multiplicity
• Multiple endpoints, multiple comparisons, subgroup analyses
• To guard against dangers of type I errors:– Adjust p-values for multiple
testing
– Use cross-validation to confirm results/confirm with new studies
– Use sophisticated comprehensive analyses
Number of independent
testsType I error*
1 .05
2 .10
5 .23
10 .40
*detecting significance by chance
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The Process
Methods
Power
Design
Aims
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The Basics
• Comparing two groups– Continuous DV: two-sample t-test– Categorical DV: chi-square test/Fisher’s Exact
test
• Comparing multiple groups– Continuous DV: one-way ANOVA– Categorical DV: chi-square test/Fisher’s Exact
test
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The Basics, continued
• Comparing groups with adjustment for other factors– Continuous DV: ANCOVA or General Linear
Modeling
– Dichotomous DV: Logistic regression
– Count DV: Poisson regression
– Survival time DV: Cox regression
• Account for correlated data (repeated measures, clustering by clinics, etc)
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Sample Size Calculations
• When do you need one?
• Consistent with study aims and statistical analysis
• Account for attrition
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Sample Size Calculations, continued
• Why is it an important consideration?– Underpowered studies discard useful
treatments/interventions
– Overpowered studies waste resources
• Continuous outcomes require smaller sample sizes than dichotomous outcomes
• Calculate the sample size required to detect a smaller effect than has been reported in pilot studies, because the non-significant pilot studies were likely never published
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Statistical versus Clinical Significance
• An over-powered study may yield a significant effect that is quite small– Statistical significance, clinical insignificance
• For example, you observe a p-value of 0.001 with a large sample size, say n=1000, but a difference in blood pressure of 1 mmHg
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Selecting an Outcome Measure
• Continuous measures are the most powerful measures
• Ordinal measures and survival time measures are next
• Dichotomous measures are the least powerful
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Survey Instruments
• Use validated instruments
Examples:– General Health: SF-36 and SF-12– Depression: CES-D or BDI– Pain: Brief Pain Inventory (BPI)– Cancer Specific Quality of Life: FACT-G– Anxiety: Beck Anxiety Inventory–Primary
Care (BAI–PC)
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Dissertation Research:Additional Study Needed?
• Statistics is a wide area of research• Statistical methods have advanced rapidly
in recent years• Early in your research considerations,
discuss with your advisor/statistician whether additional coursework or independent study is necessary to support your level of statistical needs
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Dissertation Research: Areas of Assistance
• Power Analysis
• Database design
• Review statistics plan for appropriate methodology
• Interpretation of software output
• Appropriate presentation of results
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Dissertation Advising: What to Expect
• Schedule a meeting to walk me through your dissertation topic– Have solid research question(s) – Have mature draft aims– Possibly bring data, blank surveys, etc
• Be prepared to describe the level of measurement for study variables (income, continuous or categorized?)
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Dissertation Advising: What to Expect
• Be open to alternative suggestions that might strengthen your study
• Take the time to understand the concepts behind the statistics
• Ask questions, study the literature, and meet with me as often as it takes to understand the concepts
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Limitations
• Research Position – 10% max teaching load• Four hours/week for student meetings• Should research require >3 meetings, consider
independent study, inclusion of statistician on dissertation committee, offer funding
• Four guest lectures per academic year• Directing two students at any given time in
research residency/independent study/active committee work
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Dissertation Advising: The Simple Rules
• The analysis is your responsibility• Seek advise early on• No question is silly
– Why are we using a particular procedure?
– How does it work to answer my research question?
– What results do I need to extract from the computer output?
• Back up your data!
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Request for Biostatistics Support
• Link for Request Form:
https://share.nursing.upenn.edu/sites/onr/stats/Lists/Request%20for%20Biostatistics%20Support/AllItems.aspx
• Intranet Departments ONR Request for Biostatistics Support
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Data Transfer
• Contact me via email to set up folder for file sharing
• A folder will be created in your name on H:\Secured Folders\Research Statistics
• Please DO NOT email data for privacy/security reasons
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Data Transfer, continued
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Computer Lab, Room 210
• 24 PC workstations, each equipped with a CD Burner, Floppy Drive, easily accessible USB ports
• Microsoft Office (Word, PowerPoint, Excel, Access, Front Page)
• SPSS Version 16 is available on workstations 1-6• Various Nursing Specific Applications• 1 Media Station with Scanner• 2 Black & White Laser Printers
http://www.nursing.upenn.edu/otis/services/computerlab/default.asp
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PhD Computer Lab, Room 316
• 10 PC workstations, each equipped with a CD Burner, Floppy Drive, easily accessible USB ports
• All computers have MS Office 2007 (Word, Excel, PowerPoint, Access)
• Statistics software: SAS, SPSS, STATA• Anticipated: PASS• One scanner• One black & white laser printer
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PhD Computer Lab, Room 316
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THANK YOU!Alexandra L. Hanlon, PhD
Claire M. Fagin HallRoom 479L