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Ten Fatal Flaws of NIH Grant Submissions (and how to avoid them). Steffanie A. Strathdee, PhD Thomas L. Patterson, PhD. Disclaimers. These points reflect the opinion of a few seasoned NIH-funded researchers who are also chartered reviewers; but are admittedly somewhat subjective
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Ten Fatal Flaws of NIH Grant Submissions (and how to avoid them) Steffanie A. Strathdee, PhD Thomas L. Patterson, PhD
Disclaimers • These points reflect the opinion of a few seasoned NIH-funded researchers who are also chartered reviewers; but are admittedly somewhat subjective • Most refer to applications aimed at social, behavioral and epidemiologic topics • Points do not include scientific misconduct • Points in italics reflect actual quotes that we have received in our own summary statements , and those we have written in reviews of others’ applications
10) Waiting Until the Last Minute • Goal: Drafts should be circulated to coauthors at least a few weeks before the deadline • Consequences: • No time for feedback • Typos, details can lack consistency • Grant lacks polish, fabric, cross-referencing, and appearance of a ‘single voice’
9) Wrong Funding Mechanism • Goal: • Communicate with funders to determine agency interest and appropriate funding mechanism • For an R01, present preliminary data to demonstrate feasibility (especially for a trial) • If lacking, consider other mechanisms, such as R21, R03 or R34. • Consequences: • Feasibility questioned • Study appears premature • Often considered to be a fatal flaw
8) Human Subjects Concern • Goal: To ensure safety of subjects and staff, addressing ‘4 points’, upholding equipoise in the case of RCTs • Consequences: • Actual or perceived human subjects violation • Infers inexperience and/or disregard for ethical scientific conduct • Can be a fatal flaw • If proposal receives a fundable score, NGA is not awarded until HRPP removes Human Subjects Concern
7) Weak Statistical Plan or Study Power • Goal: • Study design factors in sufficient power in real-world situations (e.g. attrition, missing data, control for confounders) • Exception: pilot study • Power and analysis sections shown for each aim and hypothesis • should link back to conceptual framework and measures • present alternative strategies • Should include up to date statistical techniques and software
7) Weak Statistical Plan or Study Power • Consequences: • Reviewers will question feasibility for meeting aims, (‘believability factor’) • PI appears inexperienced • Often a fatal flaw • Statistical plan appears to have been ‘written by someone else’, or ‘cookie-cutter’, inferring inexperience or laziness • Methods can appear passé
6) Lack of a Back-up Plan • Goal: Present a logical, feasible plan for alternate strategies in case experiment or hypothesis is not borne out as hoped • Success of one aim should not depend on the success of another • Consequences: • Reviewers will consider this a fatal flaw • Aims appear as a ‘house of cards’
5) Gaps in Expertise • Goal: • Every content area and method matched to at least one investigator with relevant expertise • Co-investigator % effort matches what is required to meet the aims • Consultants included (with letters of support) to fill any gaps in expertise • Consequences: • Proposal appears overly ambitious • Fatal flaw for a new PI
4) Proposal Poorly Organized • Goal: • Background/significance should be concise, present both sides of controversies • Write for the layman, not the expert • Half the proposal should be dedicated to methods • Consequences: • Background is too long, no room for methods • Lit review appears one-sided, biased • Background too technical, reviewer is lost in jargon • Methods lacking sufficient detail or appear overly dense and hard to digest
3) Missing /Problematic Hypotheses or Weak/Absent Conceptual Framework • Goal: • Aims should be linked to clear, testable hypotheses for which the outcome is not already well established • Aims and hypotheses should map onto conceptual framework, measures, power and analysis
3) Missing /Problematic Hypotheses or Weak/Absent Conceptual Framework Consequences: • Application appears merely ‘descriptive’ • Hypotheses appear ‘pedestrian in nature’ • Research questions and design appear murky • Study design ‘lacks focus’ • Power and statistical analysis section appears to be ‘cookie cutter’ since they do not tie back to hypotheses/framework.
2) Lack of Significance/Innovation • Goal: Proposal deals with an important, exciting topic re: public health and/or clinical decision-making, or moves the field forward. • Consequences: • Reviewers will be bored, significance rating will significantly hamper overall score • Proposal has a hard time competing with others • A beautifully designed study that has no real significance or innovation will not be funded
“And now, for the #1 fatal flaw of NIH grant submissions…”
1) Overly Ambitious • Goal: • Project is designed to be feasible within the time frame • Aims support one coherent project, not 2 or more • Provide enough detail for reviewers to understand novel methods and measures • Project generates preliminary data to guide future studies
1) Overly Ambitious Consequences: • Threatens the ‘believability factor’ • Budget may not realistically support the aims • Makes PI appear inexperienced; possible fatal flaw • Reviewers may propose cutting an entire aim or 2, or may unscore the proposal after deciding they ‘cannot re-write it for the PI’ • If you are funded, you stand to risk not being able to meet aims, which can risk your reputation