1. Avoiding Common Mistakes 12:00-12:05Group check-in 12:05-12:20Conceptual figures 12:20-12:40Common pitfalls 12:40-12:45Formatting 12:45-1:30Learning teams

2. Conceptual figures

4. Common pitfallPossible solution Too many words in every sectionFigures, tables, white space; keep power calculations brief; refer to specific earlier section New concept introduced lateNo surprises; have outside editor; line up readers OverambitiousInclude 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 hypothesisEdit and re-edit, make sure every paragraph is linked to hypothesis Little publication recordAbstracts don’t count Investigators untestedOptimize your skills; match skills to grant Missing statistician/statistical methodsGive proper % effort for statistician Not fulfilling your promiseStart working on next grant the day you get it, make a plan to get papers out 1- 2/year Fishing expeditionGet pilot grant, demote to a secondary aim or exploratory last aim, sell your idea, use as alternative approach Work doesn’t match aimsHave someone else read your grant Collecting data and not usingList all data and match with analysis section Approach not feasible/inadequate powerConsult with statistician; multi-PI grant for test and replication; literature review of good effect sizes; figure of power curve Insufficient preliminary dataDepends on mechanism, whether you need to prove something is feasible Aims interdependent or insufficientPut high risk aim as a secondary aim Too many abbreviationsUse only when necessary; put in a table on page 2; don’t put in abstract Reproducibility not includedShow you can validate surveys.

5. 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

6. Aim 9/09-3/104/10-8/109/09-3/114/11-8/11 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.htmlhttp://www.aricnews.net/calculator.phphttp://www.framinghamheartstudy.org/risk/index.html