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