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