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ENSEMBLE · CLINICAL TRIALS

Predict who drops out.
Before enrollment begins.

Clinical trial attrition is not random. The behavioral profile of a patient who will disengage at week 8 is detectable before the first screening visit. Ensemble reads it.

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Four failure points.
All modeled before the trial begins.

Placebo Response Inflation

Identifiable from behavioral profile before randomization.

Pre-Rerandomization Dropout

Detectable at baseline. Flagged before the window closes.

Adherence Failure

Predicted before it is observable. Intervened before it is irreversible.

Screening Bias

Population contamination risk surfaced before the data is dirty.

See detailed breakdown ↓

THE PROBLEM

Four failure points. All modeled before the trial begins.

01

Placebo Response Inflation

Patients likely to respond to placebo are identifiable from their behavioral profile before randomization. Ensemble surfaces this segment pre-enrollment, enabling eligibility criteria and sample size adjustments before protocol lock.

For CNS trials, this is the failure that contaminates the most expensive datasets.

02

Pre-Rerandomization Dropout

Patients who will disengage before the rerandomization window carry a detectable signal at baseline. Ensemble flags them at enrollment, assigns proactive retention protocols, and monitors risk in real time.

Before the window closes, not after the patient has already disengaged.

03

Adherence Failure

Disengagement from complex regimens becomes visible to investigators too late for meaningful intervention. Ensemble predicts it from psychosocial and behavioral conditioning before it is observable.

Enabling site-level action at the point where it can still change the outcome.

04

Screening Bias

Symptom exaggeration and undisclosed treatment history at enrollment are not reliably caught by structured intake. Ensemble models the psychosocial signals that correlate with screening inflation.

Population contamination risk surfaced before randomization, not after the data is dirty.

ENSEMBLE PLATFORM

Simulate before you commit.

Each prediction is calibrated to your specific protocol design and patient population, not generic literature averages.

ENSEMBLE Digital Twin Active
Phase III MDD / Protocol B
PT SEGMENT · HIGH BURDEN Caregiver · Urban · Low-income
RISK SCORE → 0.91
RISK CLIFF → Visit 5
BURDEN SCORE → High
INTERVENTION → Queued
RECOMMENDED INTERVENTION Deploy Visit 5 Prep Protocol. Pre-visit coordinator call plus a consent refresher. +14% PREDICTED LIFT
Retention Forecast · Segment vs Population Risk cliff at Visit 5, where the twin diverges from baseline

INTERVENTION RECOMMENDATIONS

HIGH
Deploy Visit 5 Prep Protocol. Pre-visit coordinator call plus a consent refresher. Targets caregiver scheduling conflict.
+14% lift
MED
Simplify V6–V9 Visit Cadence. Reduce in-person requirements in weeks 12–18. High predicted impact for caregiver segment.
+9% lift
MONITOR
Flag for Site Coordinator Review. Assign a proactive check-in at V4. Burden score elevated, no prior dropout history.
+6% lift
Behavioral risk score by segment Predicted retention vs population baseline Dropout risk cliff, specific to this protocol Ranked interventions with predicted lift

DEPLOYMENT

Three phases. One intelligence layer.

01
PHASE 1

Recruitment Optimization

Before enrollment begins, predict which patient segments are most likely to adhere to your specific protocol. Segment-level retention curves identify where recruitment effort translates to completers. Adjust messaging strategy, channel allocation, and site targeting before screening begins.

Fewer replacement patients. Lower screen failure rates.

02
PHASE 2

Protocol Optimization

Given your protocol and target population, predict who drops out, when, and why. Model visit burden trade-offs against segment-level tolerance before protocol lock. Output: a retention forecast by patient segment with dropout risk concentrated by timepoint and behavioral driver.

Redesign the protocol around patients who will actually complete it.

03
PHASE 3

In-Trial Monitoring

Real-time dropout risk scoring for enrolled patients. Reads existing trial system signals such as ePRO completion rates, visit attendance, and scheduling changes. Surfaces patients at elevated risk and specifies intervention type, channel, and timing, as an intelligence layer on top of existing infrastructure.

Intervention before disengagement. Not rescue after.

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