Threats to Validity in the Design and Conduct of Preclinical Efficacy Studies: A Systematic Review of Guidelines for In Vivo Animal Experiments
Bibliographic record
Abstract
BACKGROUND: The vast majority of medical interventions introduced into clinical development prove unsafe or ineffective. One prominent explanation for the dismal success rate is flawed preclinical research. We conducted a systematic review of preclinical research guidelines and organized recommendations according to the type of validity threat (internal, construct, or external) or programmatic research activity they primarily address. METHODS AND FINDINGS: We searched MEDLINE, Google Scholar, Google, and the EQUATOR Network website for all preclinical guideline documents published up to April 9, 2013 that addressed the design and conduct of in vivo animal experiments aimed at supporting clinical translation. To be eligible, documents had to provide guidance on the design or execution of preclinical animal experiments and represent the aggregated consensus of four or more investigators. Data from included guidelines were independently extracted by two individuals for discrete recommendations on the design and implementation of preclinical efficacy studies. These recommendations were then organized according to the type of validity threat they addressed. A total of 2,029 citations were identified through our search strategy. From these, we identified 26 guidelines that met our eligibility criteria--most of which were directed at neurological or cerebrovascular drug development. Together, these guidelines offered 55 different recommendations. Some of the most common recommendations included performance of a power calculation to determine sample size, randomized treatment allocation, and characterization of disease phenotype in the animal model prior to experimentation. CONCLUSIONS: By identifying the most recurrent recommendations among preclinical guidelines, we provide a starting point for developing preclinical guidelines in other disease domains. We also provide a basis for the study and evaluation of preclinical research practice. Please see later in the article for the Editors' Summary.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.461 | 0.782 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".