Protocol Complexity and Its Correlation with Screen Failure Rates
Published January 2026 — A review of 72 protocols across the network reveals that studies with more than 12 eligibility criteria experience screen failure rates 2.3x higher than those with streamlined criteria, with the effect most pronounced in CNS and cardiovascular trials.
The Hidden Cost of Protocol Complexity
Screen failure — the disqualification of a patient after screening but before randomization — represents one of the most significant sources of waste in clinical trial execution. Each screen failure consumes site resources, erodes investigator confidence, delays enrollment timelines, and imposes direct costs estimated at $3,500 to $7,200 per failed screen depending on therapeutic area and screening procedure complexity.
While screen failures have multiple causes — including patient noncompliance, intercurrent illness, and laboratory variability — the single most modifiable driver is protocol design complexity. Specifically, the number and nature of eligibility criteria establish the probability that any given patient will successfully navigate the screening funnel.
This research brief presents a systematic analysis of 72 clinical trial protocols executed across the Clinitiative network between 2022 and 2025, examining the quantitative relationship between eligibility criteria count, criteria type, and observed screen failure rates. The findings reveal a clear, dose-dependent relationship between protocol complexity and screen failure — and identify specific protocol design interventions that can meaningfully reduce failure rates without compromising scientific rigor.
Key Findings
The analysis of 72 protocols encompassing 14,600 screened patients and 9,180 enrolled patients reveals a strong, consistent relationship between eligibility complexity and screening outcomes.
Protocols with more than 12 eligibility criteria experienced screen failure rates 2.3 times higher than protocols with 12 or fewer criteria. The median screen failure rate was 47% for complex protocols compared to 21% for streamlined protocols.
The financial impact of elevated screen failures in complex protocols averaged $2.1 million per study in direct screening costs, site resource consumption, and timeline extension costs attributable to the need to screen additional patients to meet enrollment targets.
Across the full dataset, 68% of all screen failures were attributable to just three criteria categories: laboratory value thresholds, concomitant medication washout requirements, and disease severity scoring cutoffs — all of which are potentially modifiable through protocol design optimization.
Studies with screen failure rates above 40% required an average of 4.7 additional months to complete enrollment compared to their original projections, driven by the need to screen 1.8x more patients than initially planned to achieve enrollment targets.
Methodology
The analysis employed a retrospective cohort design examining all multi-site clinical trials with completed enrollment executed across the Clinitiative network between January 2022 and September 2025.
Protocol Classification
Each of the 72 protocols was independently reviewed and classified along multiple dimensions. Eligibility criteria were enumerated and categorized into inclusion criteria (requirements for entry) and exclusion criteria (conditions that disqualify participation). Each criterion was further classified by type: demographic (age, sex, BMI), diagnostic (confirmed diagnosis, disease stage), laboratory (blood chemistry, hematology, biomarker thresholds), medication-related (washout periods, prohibited concomitant therapies), procedural (willingness to undergo biopsy, imaging requirements), and functional (performance status, cognitive assessments). The total criterion count ranged from 6 to 28 across the 72 protocols, with a median of 14.
Screen Failure Adjudication
For each screen failure event, the primary reason for disqualification was extracted from site-level screening logs and categorized according to the eligibility criterion that triggered the failure. When multiple criteria contributed to a single screen failure, the first failing criterion in the screening sequence was designated as the primary cause. A secondary analysis examined multi-criterion failures separately to understand compounding effects. Screen failure rate was calculated as the number of screen failures divided by the total number of patients who signed informed consent and initiated screening procedures.
Statistical Approach
The relationship between criteria count and screen failure rate was modeled using logistic regression with random effects to account for site-level clustering within studies. Covariates included therapeutic area, trial phase, geographic region, site type, and patient demographic characteristics. A threshold analysis was performed to identify inflection points in the criteria count-failure rate relationship. Subgroup analyses were pre-specified for each therapeutic area, for inclusion vs. exclusion criteria separately, and for each criterion type category.
Therapeutic Area Deep Dives
While the criteria count-failure rate relationship was consistent across therapeutic areas, the magnitude and primary drivers varied substantially. CNS and cardiovascular trials exhibited the most pronounced effects.
CNS trials demonstrated the highest overall screen failure rates in the dataset, with a median of 52% across all 14 protocols. The primary drivers were cognitive assessment score thresholds (responsible for 31% of failures in Alzheimer’s trials) and concomitant psychotropic medication exclusions (responsible for 24% of failures in depression and anxiety trials). CNS protocols averaged 17.2 eligibility criteria — the highest of any therapeutic area — reflecting the diagnostic complexity and comorbidity burden of neurological patient populations. Protocols that adopted broader cognitive score ranges (within scientifically justified limits) reduced screen failure rates by 15% without measurable impact on treatment effect detection.
Cardiovascular trials showed the steepest dose-response relationship between criteria count and screen failure rate. Each additional criterion above 10 was associated with a 4.8 percentage point increase in screen failure rate — nearly double the 2.6 percentage point increase observed across all therapeutic areas. The dominant failure drivers were laboratory thresholds for renal function (eGFR cutoffs responsible for 22% of failures), HbA1c exclusion ranges in populations with high diabetes comorbidity (18% of failures), and electrocardiographic criteria that were overly restrictive relative to the study population’s baseline cardiac status.
Oncology protocols had a median of 15.8 eligibility criteria and a median screen failure rate of 38%. The relationship between criteria count and failure rate was significant but less steep than in CNS and cardiovascular trials, partly because oncology patients and investigators are accustomed to complex eligibility requirements. Biomarker-based selection criteria (e.g., specific genetic mutations, PD-L1 expression levels) were responsible for 34% of screen failures in targeted therapy trials — a rate that is largely non-modifiable given the scientific rationale for biomarker selection.
Immunology trials showed moderate screen failure rates (median 29%) with the most common failure driver being prior biologic therapy washout requirements. Protocols requiring 5 or more half-life washout periods for prior biologics had 35% higher screen failure rates than those allowing shorter washouts or run-in designs. This finding suggests that washout period optimization — in consultation with pharmacology teams — represents a high-impact protocol design intervention for immunology studies.
Rare disease protocols had the fewest eligibility criteria (median 9.5) and the lowest screen failure rates (median 18%). However, each individual screen failure has an outsized impact on enrollment timelines in rare disease studies due to the limited patient population. Genetic confirmation requirements were the primary failure driver (42% of failures), reflecting the inherent challenge of confirming diagnosis in populations where genetic testing may not have been previously performed.
Metabolic trials in the dataset (primarily NASH and Type 2 diabetes) showed a strong relationship between HbA1c range restrictions and screen failure rates. Protocols with HbA1c inclusion ranges narrower than 2.0 percentage points had 41% screen failure rates, while those with ranges of 2.5 percentage points or wider had 24% failure rates. Liver biopsy requirements in NASH trials were the single highest-cost screen failure driver in the dataset, at an estimated $4,800 per failed biopsy screen.
Protocol Simplification Recommendations
Based on the findings of this analysis, we identify five categories of protocol design interventions that can reduce screen failure rates without compromising the scientific integrity or regulatory acceptability of the study.
Laboratory Threshold Optimization
Laboratory value thresholds were the single largest contributor to screen failures across the dataset, responsible for 28% of all failures. Many thresholds are set based on conservative safety margins that, upon review, exceed what is scientifically necessary for patient safety or data interpretability. We recommend that protocol development teams conduct a criterion-by-criterion review of laboratory thresholds, comparing each cutoff against published safety data and the specific pharmacological profile of the investigational product. In our dataset, protocols that underwent this optimization process reduced laboratory-related screen failures by 34% while maintaining equivalent safety profiles.
Concomitant Medication Rationalization
Prohibited concomitant medication lists accounted for 22% of screen failures, with many prohibitions based on theoretical drug-drug interactions that lack clinical significance at therapeutic doses. Protocols should distinguish between medications that are genuinely contraindicated (based on confirmed pharmacokinetic or pharmacodynamic interactions) and those that are excluded as a precautionary measure. Converting precautionary exclusions into monitored allowances — with appropriate safety monitoring — can reduce medication-related screen failures by up to 40% based on our analysis of protocols that adopted this approach.
Disease Severity Range Expansion
Disease severity scoring cutoffs were responsible for 18% of screen failures. In many cases, the severity range defined in the protocol is narrower than what is clinically meaningful for treatment response assessment. Broadening severity ranges — particularly at the lower bound of inclusion — can increase the eligible population by 25-40% without diluting treatment effects, provided the statistical analysis plan accounts for severity as a stratification variable or covariate.
Washout Period Optimization
Washout requirements for prior therapies contributed to 12% of screen failures and were disproportionately impactful in immunology and oncology trials. Extended washout periods not only cause direct screen failures (patients unwilling to discontinue effective therapy) but also introduce a selection bias toward patients with less severe disease who are more willing to undergo treatment interruption. Pharmacokinetic modeling to determine the minimum washout period consistent with the study’s scientific objectives — rather than defaulting to 5 half-lives — can reduce washout-related failures while maintaining data integrity.
Pre-Screening Implementation
While not a protocol simplification per se, implementing structured pre-screening against the most common failure criteria before initiating formal screening procedures can reduce the resource waste associated with screen failures by 50-60%. Pre-screening allows sites to identify patients who are highly likely to fail on readily assessable criteria (age, BMI, basic laboratory values, current medications) before committing to the full screening battery. In our dataset, sites that implemented pre-screening protocols reduced their per-patient screening costs by 38% and improved investigator and coordinator satisfaction with the enrollment process.
Financial Impact of Screen Failures
The financial consequences of elevated screen failure rates extend well beyond the direct per-patient screening costs. Our analysis quantifies the full economic impact across four cost categories.
Direct screening costs — including laboratory tests, imaging, biopsies, and clinical assessments — averaged $4,600 per screened patient across the dataset. For a protocol with a 47% screen failure rate (the median for protocols with more than 12 criteria), a study targeting 300 enrolled patients must screen approximately 566 patients, incurring $1.22 million in screening costs for the 266 patients who fail. By comparison, a streamlined protocol with a 21% screen failure rate would require screening only 380 patients to enroll 300, with screening costs of $368,000 for the 80 failures — a savings of $854,000.
Site resource costs represent the second-largest impact. Each screen failure consumes an estimated 6.2 coordinator hours for scheduling, consent, sample collection, data entry, and follow-up communication. At an average loaded coordinator cost of $55 per hour, the coordinator burden of excess screen failures in complex protocols costs an additional $340 per failure, or approximately $90,000 per study.
Timeline extension costs are the most significant but least visible impact. The 4.7-month average enrollment extension associated with high screen failure rates carries opportunity costs estimated at $600,000 to $8 million per month depending on therapeutic area, trial phase, and competitive market dynamics. For a Phase III oncology trial with a monthly delay cost of $3.2 million, a 4.7-month extension attributable to screen failures represents $15 million in lost time-to-market value.
Finally, investigator morale and retention costs — while difficult to quantify — are consistently cited by site leaders as a consequence of high screen failure rates. Sites that experience persistent screening frustration are more likely to deprioritize the study, redirect referrals to competing trials, and decline participation in future studies from the same sponsor.
Conclusions
The evidence from 72 protocols and over 14,600 screened patients is unambiguous: protocol complexity, as measured by eligibility criteria count and type, is the single most modifiable determinant of screen failure rates. The 2.3x higher screen failure rate observed in protocols with more than 12 criteria represents a quantifiable and largely avoidable burden on trial timelines, budgets, and site resources.
Protocol simplification is not about reducing scientific rigor — it is about eliminating criteria that do not contribute meaningfully to patient safety or data interpretability. The five intervention categories identified in this brief — laboratory threshold optimization, concomitant medication rationalization, severity range expansion, washout period optimization, and pre-screening implementation — provide a structured framework for protocol teams to systematically evaluate each criterion against its demonstrated impact on screen failure rates.
Sponsors that invest in protocol design optimization before study initiation will realize compounding benefits: faster enrollment, lower costs, better site relationships, and — ultimately — more representative patient populations that improve the generalizability of trial results.
Want to Learn More?
Contact our team to discuss protocol optimization strategies and how to reduce screen failure rates in your clinical trials.