About the Initiative
- Francis S. Collins and Lawrence A. Tabak discuss initiatives that the US National Institutes of Health is exploring to restore the self-correcting nature of preclinical research. “Policy: NIH plans to enhance reproducibility” (Nature, 27 January 2014, Volume 505, Issue 7485)
- Letter to community from Dr. Larry Tabak, NIH's Principal Deputy Director
To improve the quality of NIDA-sponsored research, NIDA issued NOT-DA-14-007 “Improving Reporting of Research Methods and Results in Translational Addiction Research Involving Animals is a NIDA Commitment” By doing so, NIDA joins NINDS (NOT-NS-11-023), NIMH (NOT-MH-14-004), and NICHD (PAR-13-195) in efforts to enhance the reporting of research methods and results.
NIDA's mission is to advance science on drug use and addiction and to apply that knowledge to improve individual and public health. To make meaningful and powerful research progress, foundational data upon which new advances will hinge must be reliable and reproducible. This is especially important in order to enable translation of preclinical findings into human applications intended to facilitate the development of new therapies. Toward this end, NIDA is committed to the support of translational studies involving animals, which are marked by transparency in reporting on the design, conduct and analysis of experiments. NIDA encourages the investigators proposing translational studies involving animals to address a core set of research parameters/reporting standards, as listed below.
Points to Consider for NIDA Grant Applications Involving Preclinical Animal Research
NIDA believes that it is important for investigators and reviewers to consider the following points in study design and to address those that are appropriate. Keep in mind that many of the issues will be addressed in the Research Strategy section, others in the Vertebrate Animals section, and possibly others elsewhere in the grant application.
- Rationale for the selected models, including species, strain, housing, diet, sex, age and weight
- Rationale for endpoints selected
- Adequacy of the controls
- Route, dosing and timing of treatment
- Sample size estimates, including power calculation
- Statistical methods to be used in analysis and interpretation of results, including justification for assuming normalized distribution if parametric statistics to be used
- Methods of blinding (allocation concealment and blinded assessment of outcome)
- Inclusion and exclusion criteria
- Strategies for randomization and/or stratification
- Procedures for dealing with missing data due to attrition or exclusion
- Reporting of all results (negative and positive)
- Reporting of independent validation/replication, if available
- Robustness and reproducibility of the observed results, including whether replicates involved different lots of animals, drugs, reagents, experimenters
- Dose-response results
- Verification that interventional drug or biologic reached and engaged the target
Applicants are encouraged to include the statements about sample size estimates, blinding, and randomization procedures, even if those procedures were not used.
Training Modules to Enhance Rigor and Reproducibility in Neuroscience Research
Addressing the need for systematic, formal training for graduate students and postdoctoral fellows, NIDA joins NIGMS (RFA-GM-15-006) and supported the Society of Neuroscience (SfN) project "Promoting Awareness and Knowledge to Enhance Scientific Rigor in Neuroscience" (the Grant Number 1R25DA041326-01).
SfN has partnered with leading neuroscientists to offer the series of webinar-based training modules and the workshops that address scientific, technical, and interpersonal skills necessary to tackle issues of scientific rigor in neuroscience:
- Webinar 1: Improving Experimental Rigor and Enhancing Data Reproducibility in Neuroscience
- Webinar 2: Minimizing Bias in Experimental Design and Execution
- Webinar 3: Best Practices in Post-Experimental Data Analysis
- Webinar 4: Best Practices in Data Management and Reporting
- Webinar 5: Experimental Design to Minimize Systemic Biases: Lessons from Rodent Behavioral Assays and Electrophysiology Studies
- Webinar 6: Statistical Applications in Neuroscience
- Workshop 1: Tackling Challenges in Scientific Rigor: The (Sometimes) Messy Reality of Science