Design and Reporting of Targeted Anticancer Preclinical Studies: A Meta-Analysis of Animal Studies Investigating Sorafenib Antitumor Efficacy
Bibliographic record
Abstract
The validity of preclinical studies of candidate therapeutic agents has been questioned given their limited ability to predict their fate in clinical development, including due to design flaws and reporting bias. In this study, we examined this issue in depth by conducting a meta-analysis of animal studies investigating the efficacy of the clinically approved kinase inhibitor, sorafenib. MEDLINE, Embase, and BIOSIS databases were searched for all animal experiments testing tumor volume response to sorafenib monotherapy in any cancer published until April 20, 2012. We estimated effect sizes from experiments assessing changes in tumor volume and conducted subgroup analyses based on prespecified experimental design elements associated with internal, construct, and external validity. The meta-analysis included 97 experiments involving 1,761 animals. We excluded 94 experiments due to inadequate reporting of data. Design elements aimed at reducing internal validity threats were implemented only sporadically, with 66% reporting animal attrition and none reporting blinded outcome assessment or concealed allocation. Anticancer activity against various malignancies was typically tested in only a small number of model systems. Effect sizes were significantly smaller when sorafenib was tested against either a different active agent or combination arm. Trim and fill suggested a 37% overestimation of effect sizes across all malignancies due to publication bias. We detected a moderate dose-response in one clinically approved indication, hepatocellular carcinoma, but not in another approved malignancy, renal cell carcinoma, or when data were pooled across all malignancies tested. In support of other reports, we found that few preclinical cancer studies addressed important internal, construct, and external validity threats, limiting their clinical generalizability. Our findings reinforce the need to improve guidelines for the design and reporting of preclinical cancer studies. Cancer Res; 76(16); 4627-36. ©2016 AACR.
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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 | 0.175 | 0.262 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.064 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".