Collaborating with Quantitative Scientists

1. Collaborating with Quantitative Scientists Kevin Weinfurt, PhD Steve Grambow, PhD Professional Development Series May 18, 2005

2. 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?

3. Overview • Quantitative scientists at the medical center • Foundational technical issues for collaboration • Collaborations • Grant • Manuscript

4. Overview • Quantitative scientists at the medical center • Foundational technical issues for collaboration • Collaborations • Grant • Manuscript

5. When Might You Need a Quantitative Scientist? • Grant writing • Study planning • Study execution / monitoring • Developing and writing research paper • “Rescue work” on a manuscript

6. 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

7. 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

8. 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

9. 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

10. 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 . . .

11. Quantitative Scientists Have Little or No Time to Help You ☺

12. 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

13. 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 yearsMaster’s degree • Access to statisticians • Inform your superiors of work that cannot be done due to lack of statistical support

14. Overview • Quantitative scientists at the medical center • Foundational technical issues for collaboration • Collaborations • Grant • Manuscript

15. 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

16. 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 (TreatmentMediatorOutcome)

17. 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

18. 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)

19. 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

20. Overview • Quantitative scientists at the medical center • Foundational technical issues for collaboration • Collaborations • Grant • Manuscript

21. 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

22. 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

23. 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

24. 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

25. Quantitative Scientists Have Little or No Time to Help You ☺

26. 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

27. Overview • Quantitative scientists at the medical center • Foundational technical issues for collaboration • Collaborations • Grant • Manuscript

28. 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

29. 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

30. Discussion