# Estimation of clinical trial success rates and related parameters Source: https://onco.cc/key-papers/paper-wong-biostatistics/ OnCo record `paper-wong-biostatistics` (Key paper). Data CC BY-NC 4.0, attribute "Data from OnCo (onco.cc)"; commercial use needs a licence. ## TL;DR Paper cited by one bottleneck page and 22 idea pages, indexed on Europe PMC as PubMed record 29394327 and published in Biostatistics (Oxford, England); the citing pages link this DOI, which is how the record was matched. ## Summary Previous estimates of drug development success rates rely on relatively small samples from databases curated by the pharmaceutical industry and are subject to potential selection biases. Using a sample of 406 038 entries of clinical trial data for over 21 143 compounds from January 1, 2000 to October 31, 2015, we estimate aggregate clinical trial success rates and durations. We also compute disaggregated estimates across several trial features including disease type, clinical phase, industry or academic sponsor, biomarker presence, lead indication status, and time. In several cases, our results differ significantly in detail from widely cited statistics. For example, oncology has a 3.4% success rate in our sample vs. 5.1% in prior studies. However, after declining to 1.7% in 2012, this rate has improved to 2.5% and 8.3% in 2014 and 2015, respectively. In addition, trials that use biomarkers in patient-selection have higher overall success probabilities than trials without biomarkers. Indexed on Europe PMC as PubMed record 29394327 (DOI 10.1093/biostatistics/kxx069). Matched by DOI alone: one bottleneck page and 22 idea pages cite this DOI among their external links (the pages are listed under Related), and this page was written so that the citation resolves inside OnCo. No figure has been checked by an editor. ## Fields - Kind: Key paper - Last checked: 2026-09-22 - Tags: europepmc-ingest - Journal: Biostatistics (Oxford, England) - Year: 2019 - DOI: 10.1093/biostatistics/kxx069 - Authors: Wong CH, Siah KW, Lo AW - What it means: One bottleneck page and 22 idea pages on OnCo cite this paper by its DOI; this record gives the citation a page of its own so a reader can follow it without leaving OnCo. Read the abstract above alongside the citing pages listed under Related; the record was created automatically from the Europe PMC entry and its figures have not been checked by hand. - Caveats: Matched to the citing OnCo records by DOI alone; the summary reproduces the Europe PMC abstract and no figure has been verified against the full paper. ## Sources - Biostatistics 2019: https://doi.org/10.1093/biostatistics/kxx069 - PubMed: https://pubmed.ncbi.nlm.nih.gov/29394327/ - Europe PMC: https://europepmc.org/article/MED/29394327 ## Connected records - bottlenecks: [Lab models that fail to predict what happens in patients](https://onco.cc/bottlenecks/b-preclinical-models/) - ideas: [A home for the animal and organoid experiments that failed](https://onco.cc/ideas/idea-bio1-negative-preclinical-repository/), [A virtual cancer cell that predicts what a drug will do before you test it](https://onco.cc/ideas/idea-bio1-virtual-cell-perturbation/), [An open organoid bank for cancers too rare to have models](https://onco.cc/ideas/idea-bio1-rare-cancer-organoid-bank/), [Build laboratory models of the organs cancer spreads to](https://onco.cc/ideas/idea-bio1-metastatic-niche-models/), [Every resistance mechanism found in a patient must be rebuilt in the laboratory](https://onco.cc/ideas/idea-bio1-reverse-translation-resistance-models/), [Grow models from tumour cells in the blood when a biopsy is impossible](https://onco.cc/ideas/idea-bio1-ctc-derived-explants/), [Grow tumour organoids together with the patient's own immune cells](https://onco.cc/ideas/idea-bio1-organoid-immune-coculture/), [Hold organoid drug tests to the same standard as a diagnostic test](https://onco.cc/ideas/idea-bio1-organoid-assay-clinical-validation/), [Humanised mice with an immune system matched to the tumour donor](https://onco.cc/ideas/idea-bio1-immune-matched-humanised-mice/), [In silico trials to choose the dose before the first patient](https://onco.cc/ideas/idea-bio1-in-silico-trials-dose/), [Keep a freshly removed tumour alive on a pump and test drugs in it](https://onco.cc/ideas/idea-bio1-ex-vivo-perfused-tumour/), [Linked human organ chips to predict side effects before people are dosed](https://onco.cc/ideas/idea-bio1-multi-organ-chip-tox/), [Make bespoke mouse cancer models in weeks with in vivo gene editing](https://onco.cc/ideas/idea-bio1-somatic-crispr-gemms/), [Multi-centre randomised animal trials before committing to a human trial](https://onco.cc/ideas/idea-bio1-multicentre-mouse-trials/), [Run the mouse or organoid trial at the same time as the human trial](https://onco.cc/ideas/idea-bio1-co-clinical-avatar-trials/), [Score every model system on how well it predicted real trial results](https://onco.cc/ideas/idea-bio1-model-predictivity-benchmark/), [Self-driving laboratories that run the cancer biology hypothesis loop autonomously](https://onco.cc/ideas/idea-moon-self-driving-cancer-labs/), [Test cancer drugs in old and unhealthy animals, not just young fit ones](https://onco.cc/ideas/idea-bio1-aged-comorbid-models/), [Test drugs on freshly cut slices of the patient's own tumour](https://onco.cc/ideas/idea-bio1-tumour-slice-cultures/), [Tumour-on-a-chip with blood flow to test whether big drugs actually get in](https://onco.cc/ideas/idea-bio1-tumour-on-chip-penetration/), [Use patient organoids to check a cell therapy will work before infusing it](https://onco.cc/ideas/idea-bio1-organoid-cell-therapy-potency/), [Zebrafish avatars for a drug answer within a week](https://onco.cc/ideas/idea-bio1-zebrafish-avatars/) --- JSON: https://onco.cc/api/v1/entities/paper-wong-biostatistics.json