The retrospective analysis, stepwise triage approach, and prospective validation program.
Every protocol is evaluated across the temporal dimensions of its design — producing 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 land at the moments when meaningful biological change is expected — rather than at intervals chosen for operational convenience.
Whether the biology that unfolds over different horizons each gets a chance to show up in the data, rather than only the parts easiest to capture.
Whether measurements are dense enough to understand how individual patients are actually responding — not just group averages that can hide the signal.
Whether the protocol is built to observe how the biology is moving over time, rather than inferring it after the fact from a few scattered snapshots.
The same framework applies 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.
Whether a trial captures how fast things change — not just where they are at a given moment — is the strongest associative factor in 8 of 10 indications tested.
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.
TDS measures temporal design quality—the timing and sequencing of data collection—which cannot tell you whether a molecule works. What it speaks to is whether a design is positioned to detect an effect if one is present. Trial Readiness validates the framework against outcomes where design quality is informative. Registry-derived outcome classifications include both design-driven failures and efficacy failures (drug or molecule efficacy).
When validation is restricted to design-driven and operational failures (where temporal design has explanatory relevance), 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.
The retrospective analysis shows a clear and consistent association between temporal design quality and trial outcomes. The next phase tests prospectively whether protocols revised on TDS guidance carry that advantage forward — the Validation Partner program is running that now, with Phase 2 sponsors implementing TDS recommendations.
An inter-rater reliability study with independent expert reviewers is underway, corroborating TDS scoring against blinded expert assessment under a pre-specified analysis plan. The retrospective foundation supports use today; this work strengthens it further.
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, or on-treatment sampling — the highest-impact changes, for teams with room to add them.
Many of the most valuable improvements fall in Tier 1 — gains available at near-zero incremental cost, using data a trial is already collecting.
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.