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Avoiding Common Mistakes
12:00-12:05 Group check-in
12:05-12:20 Conceptual figures
12:20-12:40 Common pitfalls
12:40-12:45 Formatting
12:45-1:30 Learning teams
Conceptual figures
Common pitfall Possible solution
Too many words in every section Figures, tables, white space; keep power calculations brief; refer to specific earlier section
New concept introduced late No surprises; have outside editor; line up readers
Overambitious Include conceptual figure, follow roadmap; put together budget; timeline for experimental work (put in approach section); specify which papers you will deliver for each aim
Not linked to hypothesis Edit and re-edit, make sure every paragraph is linked to hypothesis
Little publication record Abstracts don’t count
Investigators untested Optimize your skills; match skills to grant
Missing statistician/statistical methods Give proper % effort for statistician
Not fulfilling your promise Start working on next grant the day you get it, make a plan to get papers out 1-2/year
Fishing expedition Get pilot grant, demote to a secondary aim or exploratory last aim, sell your idea, use as alternative approach
Work doesn’t match aims Have someone else read your grant
Collecting data and not using List all data and match with analysis section
Approach not feasible/inadequate power Consult with statistician; multi-PI grant for test and replication; literature review of good effect sizes; figure of power curve
Insufficient preliminary data Depends on mechanism, whether you need to prove something is feasible
Aims interdependent or insufficient Put high risk aim as a secondary aim
Too many abbreviations Use only when necessary; put in a table on page 2; don’t put in abstract
Reproducibility not included Show you can validate surveys.
Formatting• Keep paragraphs short• Use subject headings• Minimize abbreviations (include a table)• Give logical flow to sections
– Consistent flow/numbering to each section• Make it easy for reviewers to pick out:
– Significance– Approach– Innovation– Investigators– Environment
Aim9/09-3/10 4/10-8/10 9/09-3/11 4/11-8/11
1. Refine AF Risk Prediction, Discrimination, Calibration
If FHS AF risk model does not have adequate model fit in other cohorts we will recalibrate.
If the models still fit poorly we will develop a new score pooling data from the 4 CHARGE cohorts (AGES, ARIC, CHS, FHS) and replicate the derived model in RS
Publish paper in high impact medical journal *Web publish downloadable risk scoring algorithm
at participating cohorts websites.
2. Test if biomarkers enhance discrimination, calibration, reclassification
We will pool AGES, ARIC, CHS and FHS data We will analyze whether the test characteristics are
similar in RS.
Publish paper in high impact medical journal Web publish downloadable risk scoring algorithm at
participating cohorts websites.
3. Test if genetic markers improve discrimination, calibration, reclassification
We will pool AGES, ARIC, CHS and FHS data We will analyze whether the test characteristics are
similar in RS.
Publish paper in high impact medical journal *Web publish downloadable risk scoring algorithm
at participating cohorts websites.
4. Develop statistical methods Publish papers in high impact medical journal *Web publish downloadable statistical macros so
that other investigators can apply the reclassification metrics to other events and other data sets.
*http://www.aricnews.net/calculator.php; http://www.framinghamheartstudy.org/risk/index.html