Collaborating with Quantitative Scientists

Kevin Weinfurt, PhD

Steve Grambow, PhD

Professional Development Series May 18, 2005

Poll

• How many people have submitted a grant?– How many worked with a quantitative

scientist on a proposal?

• How many people collaborated with a quantitative scientist on an empirical manuscript?

• What challenges did you encounter?

Overview

• Quantitative scientists at the medical center

• Foundational technical issues for collaboration

• Collaborations– Grant–Manuscript

Overview

• Quantitative scientists at the medical center

• Foundational technical issues for collaboration

• Collaborations– Grant–Manuscript

When Might You Need a Quantitative Scientist?

• Grant writing

• Study planning

• Study execution / monitoring

• Developing and writing research paper

• “Rescue work” on a manuscript

Who are Quantitative Scientists?

• Statistics– Emphasis on theory

• Biostatistics– Emphasis on applications in medical research

• Doctor of Public Health, Biostatistics concentration

• Bioinformatics• Statistical genetics / biomathematics• Psychometrics

– Analysis of measurement properties

• Econometrics

Expertise and Car Mechanics

• Variability in education, experience, and interest

• Not every expert is suited for every problem– Type of problem– Complexity of problem

• You might know statistical conventions in your particular area better than the consulting QS

Expertise and Car Mechanics

• Variability in education, experience, and interest

• Not every expert is suited for every problem– Type of problem– Complexity of problem

• You might know statistical conventions in your particular area better than the consulting QS

Expertise and Car Mechanics

• Variability in education, experience, and interest

• Not every expert is suited for every problem– Type of problem– Complexity of problem

• You might know statistical conventions in your particular area better than the consulting QS

Why Do QSs Do What They Do?

• Intellectual recognition (authorship)

• Financial support– “5% gets you a meeting”– “No less than 10%…”

• A good feeling inside

• But . . .

Quantitative Scientists Have Little or No Time to Help

You

☺

Coping with Limited Statistical Resources

• Collegial attitude– Respect for what they know and do– Appreciation for what they should not

do• “Data Monkey”

– Acknowledgment of contributions

• Know your data and research questions

Coping with Limited Statistical Resources (cont.)

• Cultivate basic technical knowledge– Read printed and online material (see

Biostatistics Resource Guide)– Duke’s Clinical Research Training

Program• $16k total for two yearsMaster’s degree• Access to statisticians

• Inform your superiors of work that cannot be done due to lack of statistical support

Overview

• Quantitative scientists at the medical center

• Foundational technical issues for collaboration

• Collaborations– Grant–Manuscript

Hypotheses and Research Questions

• Have Some!• The Risks of Fishing Trips

– Iterative nature is emotionally frustrating for the QS

– Unprincipled explanations of possible statistical artifacts (a.k.a. “flukes”)

• An important scientific question is important because of the question, not the answer– Encourages better science than fishing for

interesting answers to ill-formed or unasked questions

General Categories of Variables in a Treatment-Outcomes Study

• Outcomes– Direct or indirect (surrogate) measures?

• Baseline covariates– To improve statistical power– To identify meaningful subgroups for secondary

analyses

• Process variables– Help us understand how the intervention did or did

not work– Compliance, intensity of treatment exposure– Mediating variables (TreatmentMediatorOutcome)

Sample Size Estimation: Statistical Considerations

• Type I error rate (α; usually .05)• Type II error rate (β; 1 – β = Power)• Variability in the outcome (e.g., standard

deviation)• Size of effect you would like to detect– Minimum clinically relevant effect size

• Not the same as an effect found by someone else

– What is the smallest policy-relevant difference?• Example: Difference in adherence rates > 15%

• Sample size

Sample Size Estimation: Logistic Considerations

• Need to identify outcome(s) that determine sample size– Primary versus Secondary outcomes

• Budget• Ability to recruit from target population• Accrual period • Anticipated refusal rate• Anticipated dropout rate (longitudinal

only)

What Do You Contribute to the Modeling Process?

• Defining candidate variables• Providing hints about the functional form of

relationships– Linear? Curvilinear? Step function?

• How variable is hypothesized to work in the model– Additive (just adds to predictive power)– Statistical adjustment of other variables’ effects– Moderation of other variables’ effects (statistical interaction)

• Clinical utility of a model – Availability of variables, etc.

• Clinical/practical significance of results

Overview

• Quantitative scientists at the medical center

• Foundational technical issues for collaboration

• Collaborations– Grant–Manuscript

Initial Steps

• Contact QS when you decide to develop an application– If QS is very junior, consider adding

experienced QS for small % effort to supervise/consult

• Reminder: Grant preparation requires substantial unsupported time for the QS

• Review research questions, hypotheses, variables, design, and data collection plan

• Agree on a writing plan

The Data Analysis Section

• Generally 1-2 pages• Section needs to be written for two audiences

– Trained biostastician• Appropriate and up-to-date methods• Appreciation for alternative methods if assumptions are

not met

– Non-statistician• The Aims/Hypotheses are being addressed (somehow)• Sounds thoughtful (e.g., alternative approaches

described)• Consistency between Measures section and variables

mentioned in Data Analysis

Organization of the Data Analysis Section

• Data management• Consider organizing by Specific Aim or . . .

– General modeling strategy– Describe how each Aim/Hypothesis is addressed within

this strategy

• Consider Secondary/Exploratory analyses• Refer back to Measures section often

– Using variable categories in Measures helps to organize the Data Analysis section

• Where appropriate, note that you will monitor methodological developments and incorporate as appropriate into final analysis plan

Roles and Responsibilities

• What can you do?– Create the structure and put down Aims,

hypotheses, measures, etc.– Refer back to categories of variables to understand

how each variable will be used in the analysis– Provide information necessary for sample size

estimation

• What can biostatistician do?– Select and describe appropriate statistical model for

addressing hypotheses / research questions– Provide overall guidance

Quantitative Scientists Have Little or No Time to Help

You

☺

Roles and Responsibilities

• What can you do?– Create the structure and put down Aims,

hypotheses, measures, etc.– Refer back to categories of variables to understand

how each variable will be used in the analysis– Provide information necessary for sample size

estimation

• What can biostatistician do?– Select and describe appropriate statistical model for

addressing hypotheses / research questions– Provide overall guidance

Overview

• Quantitative scientists at the medical center

• Foundational technical issues for collaboration

• Collaborations– Grant–Manuscript

Collaborating on a Research Manuscript

• Dataset creation (see Biostatistical Resource Guide)

• Discuss authorship up front– Paying the QS or making them an author?

• Give QS an example of an article in your field• Writing process

– Analysis plan (helpful to include table shells / figure mock-ups)

– Prelim discussion about results– May need to revise analyses

What should I understand?

• Acquiring greater quantitative sophistication is always beneficial

• There will always be a “jump-off point” (even for the QS)

• Requires some trust in the QS

• You are responsible for the study data and their presentation

Discussion