The retrospective analysis, stepwise triage approach, and prospective validation program.
Every protocol receives a score across four components. Together they form the Temporal Design Score — a single read on how well a study's measurement architecture matches the temporal structure of the disease it's studying.
Whether the protocol's visit schedule and assay timing are placed at the moments when meaningful biological change is expected to occur.
Whether short-window, mid-range, and long-horizon biology are each captured — rather than only the timescales most convenient to measure.
Whether each patient's measurement density is sufficient to model their individual course — not just compute group-level averages.
Whether the protocol measures rate-of-change directly, rather than inferring it from sparse cross-sectional snapshots. The strongest associative factor in 8 of 10 indications tested.
The same four components apply at every stage of development, including preclinical study design.
Most Phase 2 protocols are operationally sound and still measure the wrong thing at the wrong moment. The biology moves on one clock; the visit schedule runs on another. When those clocks don't line up, a trial can read as a drug failure when it was really a measurement failure. The composites below are anonymized patterns drawn from publicly registered trials that did not meet their endpoints — the kinds of gaps a Trial Readiness assessment surfaces during triage, before a single patient is enrolled.
A checkpoint-inhibitor protocol assessed tumor response on a conventional imaging cadence. The relevant immune activation occurred weeks earlier and was never measured — so early responders and true non-responders looked identical at the only timepoint that counted.
Visits fell at weeks 4, 8 and 12 “because that's when patients come in.” The effect being studied stabilized on a slower arc, so the final measurement landed in a transitional window that flattered the placebo arm and muddied the signal.
The protocol captured a single severity score at baseline and at end-of-study. With only two points, rate of change — the variable most associated with durable benefit — could not be estimated at all.
Only a late-stage anatomic measure was collected. With no early molecular or functional readouts in between, there was no way to tell a non-responding patient from one whose mechanism was working but whose anatomy hadn't caught up.
The biological process turned over faster than the gap between visits. By the time the next sample was drawn, the informative window had already opened and closed — so the curve was reconstructed from points too far apart to be reliable.
The mechanistic biomarker and the clinical endpoint were each sampled on schedules that never overlapped. The two could not be linked in time, so a clean biomarker signal could not be tied to — or used to interpret — the clinical result.
Every one of these reads as procedurally fine on paper. None is a dosing error or a statistical mistake — they're timing decisions that only look wrong once you map the schedule against the biology. That mapping is what a Trial Readiness assessment does before enrollment, when the schedule is still cheap to change.
A retrospective analysis of 275,000 interventional trials from ClinicalTrials.gov found a statistically significant association between estimated temporal design quality and trial outcomes (p = 2.7 × 10−101, Cohen's d = 0.19 all-comers, d = 0.40 oncology). The association replicates across 5 global regions and 10 therapeutic areas.
Trials with sufficient per-patient data density to model individual trajectories succeeded at 58% vs. 27% for those relying on group-level analysis.
Diagnostic Velocity — whether a trial captures how fast things change, not just where they are — ranks as the strongest TDS component in 8 of 10 indications.
TDS is associated with Scientific and Design outcomes but not Operational or Commercial failures — the pattern expected if it captures temporal design adequacy rather than overall sponsor sophistication.
The TDS–outcome association holds across North America, Europe, Asia-Pacific, and multi-regional trials with no regional exceptions.
Trial Readiness validates the temporal design framework against outcomes where design quality is predictive. Registry-derived outcome classifications include both design-driven failures and efficacy failures (drug or molecule efficacy). TDS measures temporal design quality—the timing and sequencing of data collection—which cannot predict whether a molecule works.
When validation is restricted to design-driven and operational failures (where temporal design is theoretically predictive), effect sizes strengthen significantly, confirming that the framework's signal is robust to outcome heterogeneity. This filtering demonstrates that reported effect sizes are conservative, and the true TDS signal in design-driven contexts is stronger than all-comers estimates suggest.
A retrospective association between estimated temporal design quality and trial outcomes across 275,000 interventional trials, with effect sizes ranging from d = 0.19 (all indications) to d = 0.40 (oncology). The association replicates across 5 regions, 10 therapeutic areas, and all sponsor types.
Causation. We have not yet demonstrated that improving a protocol's TDS score changes its outcome. Retrospective association — even a strong one — does not prove that temporal optimization prevents failure. Our prospective Validation Partner program is designed to address this question directly.
Expert validation of 100 scored trials through independent reviewers. Prospective validation partnerships with Phase 2 sponsors implementing TDS recommendations.
Our recommendations are organized into three operational tiers so sponsors implement only what fits their constraints.
Use specimens already collected. Add statistical analyses to existing data. Recompute existing measurements differently.
One additional tube at an existing draw, or running an extra panel on an existing biopsy.
New timepoints, additional imaging, on-treatment biopsies. Highest TDS impact per recommendation.
Tier 1 recommendations alone improved TDS by 2–3 points per trial at near-zero incremental cost. Full stepwise triage is included in Tier 2 and Tier 3 engagements.
Converting a site-based protocol to hybrid or remote changes its temporal architecture. Early mechanistic timepoints — Day 1, 3, 7 blood draws — are often the first casualties when protocols go decentralized. TDS quantifies this trade-off and identifies mitigation strategies.
Our assessments score both site-based and decentralized versions side by side, with DCT feasibility flags for every recommendation. Request early access →
1 BIO, Informa Pharma Intelligence, QLS Advisors. Clinical Development Success Rates 2011–2020. Phase II success rate: ~30.7%. bio.org
2 Wong CH, Siah KW, Lo AW. Estimation of clinical trial success rates. Biostatistics. 2019;20(2):273-286. Oncology POS: 3.4%. doi.org
3 Schuhmacher A, et al. Benchmarking R&D success rates. Drug Discovery Today. 2025;30(2):104291. sciencedirect.com
TDS retrospective analysis: Scientari LLC, 275,000 interventional trials from ClinicalTrials.gov. Association is retrospective and correlational.