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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

215-898-4581alhanlon@nursing.upenn.edu

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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

215-898-4581alhanlon@nursing.upenn.edu