13 Protocol Design Benchmarks
Protocol design decisions shape everything that follows in a clinical trial—enrollment timelines, operational complexity, data volume, and ultimately the probability of success. Research has documented a clear pattern: protocols have grown steadily more complex over decades, and this complexity correlates with worse performance. Understanding these trends—and the benchmarks that characterize typical protocols—provides context for anyone designing or evaluating clinical trial protocols.
13.1 The Trend Toward Complexity
Protocol complexity has increased since the 1980s. Each decade has brought new drivers of complexity.
The pursuit of blockbuster therapies in the 1980s expanded the number of assessments conducted, investigators engaged, and patients enrolled—particularly in Phase III trials. The 1990s brought cost containment measures that paradoxically increased Phase II complexity as sponsors tried to gather more information before committing to expensive Phase III programs.
The 2000s saw globalization of trials, driven by pressure to reach treatment-naïve populations, find experienced investigators at lower cost, and support simultaneous international submissions. Quality by design principles and enhanced risk evaluation drove growth in safety procedures and data collection.
Between 2010 and 2020, novel designs—adaptive trials, master protocols—were piloted. The proportion of programs targeting rare diseases and narrowly defined patient subpopulations increased dramatically, supported by growth in biomarker and genetic data collection.
Throughout this entire period, nearly all protocol design changes have been additive. Research by the Tufts Center for the Study of Drug Development (Tufts CSDD) consistently finds that benchmarked protocol designs have yet to show a downward trend in any design element. Each new requirement layers atop existing ones.
13.2 Current Benchmarks by Phase
Benchmark data from protocols completed just before the COVID-19 pandemic reveal the scope of modern clinical trials.
Phase I protocols—focused on safety and pharmacokinetics in small populations—generate surprising data volume. Phase II and III protocols expand complexity significantly across most dimensions (see Table 13.1).
| Metric | Phase I | Phase II | Phase III |
|---|---|---|---|
| Endpoints | 16 | 21 | 19 |
| Eligibility Criteria | 32 | 31 | 30 |
| Distinct Procedures | 29 | 34 | 35 |
| Total Procedures | 169 | 260 | 266 |
| Protocol Pages | 87 | 108 | 116 |
| Countries | <2 | ~6 | ~14 |
| Investigative Sites | ~5 | ~35 | ~82 |
| Datapoints Collected | >330,000 | >2 million | >3.5 million |
13.3 Oncology and Rare Disease Protocols
Two categories of trials show distinctive patterns: oncology and rare disease.
Oncology protocols are characterized by longer durations and more intense procedural burdens (summarized in Table 13.2), despite enrolling similar numbers of patients to non-oncology trials.
| Metric | Oncology | Non-Oncology |
|---|---|---|
| Total Procedures | 315 | 243 |
| Planned Visits | 29 | 16 |
| Countries | 13 | 8 |
| Follow-up Duration | 331 days | 77 days |
| Completion Rate | 31% | 80% |
The completion rate stands out: only 31% of enrolled patients complete oncology trials, largely due to disease progression. This attrition affects sample size planning significantly.
Rare disease protocols face distinct challenges (Table 13.3), particularly in recruitment and site activation.
| Metric | Rare Disease | Non-Rare Disease |
|---|---|---|
| Sample Size | 224 | 425 |
| Sites | 27 | 64 |
| Distinct Procedures | 38 | 33 |
| CRF Pages | 244 | 159 |
| Enrollment Duration | 603 days | 444 days |
| Study Initiation | 173 days | 141 days |
The data highlights the difficulty of rare disease trials: finding eligible patients takes significantly longer (603 days vs 444 days) despite much smaller target sample sizes.
13.4 The Complexity-Performance Relationship
Complexity is not merely academic—it directly affects trial performance.
Protocols with higher relative numbers of endpoints, eligibility criteria, and procedures are associated with lower physician referral rates, increased administration burden, diminished volunteer willingness to participate, lower recruitment and retention rates, lower dose adherence, increased data volume, and higher incidence of protocol deviations and amendments.
These outcomes contribute to higher failure rates, longer cycle times, poorer data quality, and greater costs. The Tufts CSDD research shows significant correlations: the number of endpoints correlates with the number of eligibility criteria and with total datapoints collected. Protocols with more investigative sites and countries take longer to complete.
Clinical trial durations reflect this relationship. The average Phase III protocol takes 1,328 days from protocol finalization to database lock. Study initiation (from protocol approval to first patient first visit) averages 166 days. Enrollment (from first patient first visit to last patient first visit) averages 508 days. Treatment duration (from first patient first visit to last patient last visit) averages 1,064 days.
Oncology trials are particularly long: an average of 1,599 days from protocol to database lock, 1.5 times longer than non-oncology protocols. The widest differences appear in durations associated with patient enrollment.
13.5 Core Versus Non-Core Procedures
The data suggests a substantial opportunity for simplification: across Phase II and III protocols, nearly a quarter (24-25%) of all procedures are non-core. This proportion is lower in rare disease trials (14%), reflecting a tighter focus on core assessments, but in non-rare disease protocols, the figure often exceeds 25%. Because every non-core procedure adds cumulative burden to sites and patients without directly supporting the primary approval decision, protocol designers must rigorously evaluate whether each assessment justifies its incremental cost and complexity.
13.6 Drivers and Implications
The persistent trend toward higher complexity is driven by several interlocking forces. At the strategic level, ambitious development programs often target more challenging diseases, requiring sophisticated biomarker stratification to understand differences between patient subgroups. This is compounded by regulatory expectations for comprehensive safety data, which necessitate more frequent procedures and longer follow-up periods to satisfy risk evaluation and mitigation requirements.
Executional and competitive dynamics further accelerate this growth. Global trials require more countries and sites to meet enrollment targets, introducing complex logistical and coordination overhead. Simultaneously, a competitive urge to “collect everything” often leads sponsors to believe that more comprehensive datasets will improve their regulatory and commercial positioning.
The implications for researchers are significant. The strong correlation between executional variables—the number of sites and countries—and longer trial durations suggests that efficiency gains are possible through better design. By prioritizing site quality over quantity and rigorously questioning whether every endpoint and non-core procedure truly adds value, protocol designers can reduce complexity without sacrificing the integrity or enrollment speed of the trial.
13.7 Decentralized Trials and Protocol Complexity
Decentralized clinical trial (DCT) elements—telemedicine, wearables, home visits, direct-to-patient shipment—may reduce enrollment duration and protocol amendments through more flexible execution. However, they also introduce new data sources, vendors, and operational practices that can increase short-term protocol complexity. As trials shift toward wherever patients can most conveniently participate, the balance between executional flexibility and protocol simplicity remains an open question. For detailed treatment of DCT logistics and implementation, see Chapter 18.