Precision medicine demands a new evidence strategy

A practical framework for aligning evidence across development, regulatory approval, reimbursement and commercialization.

Precision medicine is reshaping far more than drug development. It is transforming how therapies are evaluated, reimbursed, prescribed and adopted. As a result, sponsors need a broader, more integrated evidence strategy, one that supports decisions across development, regulatory review, reimbursement and commercialization.

For sponsors advancing targeted, biomarker-driven or rare disease programs, clinical success is no longer sufficient for commercial success. The central question is no longer simply whether a therapy works in a broad population. Regulators, payers, health technology assessment (HTA) bodies, providers and patients each require different evidence to support critical decisions.

Stakeholders increasingly want to know:

• Which patients are most likely to benefit?
• Which biomarkers define response?
• How will those patients be identified in clinical practice?
• What diagnostic workflows are required?
• And what evidence will support decisions across regulatory review, reimbursement, provider adoption and patient access?

Companion diagnostics, biomarker testing, patient identification and long-term outcomes increasingly determine whether innovative therapies reach patients. Answering these questions requires an evidence strategy that is as precise as the therapy itself. More and more, the greatest risk to precision medicine programs is not clinical uncertainty, it is evidence misalignment across stakeholders.

Precision medicine raises the evidence bar

As precision medicine evolves, so do expectations for what constitutes decision-grade evidence. Precision medicine has created new possibilities for treating patients based on molecular, genomic, clinical and other defining characteristics. But it also raises expectations for the evidence sponsors must generate. Decision-makers increasingly expect evidence that supports multiple stakeholders, multiple decisions and the full product life cycle—not just regulatory approval.

Targeted therapies often focus on smaller, more defined patient populations. Biomarkers, companion diagnostics and testing pathways can influence trial feasibility, patient identification, clinical adoption and market access. Traditional clinical trial evidence remains foundational, but it may not fully address questions about real-world care patterns, long-term outcomes, treatment sequencing or value in routine practice. Approval is now only one milestone in a much longer evidence journey that ultimately determines reimbursement, provider adoption and patient access.

The result is that evidence planning can no longer be treated as a linear activity that begins with clinical development and expands after approval. Sponsors need integrated evidence strategies that connect clinical, biomarker, companion diagnostic, real-world, health economic and patient-centered evidence from the earliest stages of development—not after gaps appear. By aligning evidence generation to the needs of regulators, payers, providers and patients from the outset, sponsors can reduce downstream evidence gaps and better support successful commercialization.

Where decision risk emerges

Decision risk rarely appears all at once, but rather accumulates across the development life cycle. Evidence gaps surface at critical decision points, when sponsors are preparing for regulatory engagement, reimbursement discussions, provider education or launch planning.

Common challenges include:

• Evidence generation activities that are planned sequentially rather than strategically across the product life cycle.
• Companion diagnostic development and reimbursement planned separately from therapeutic development.
• Clinical, regulatory, real-world evidence (RWE), health economics and outcomes research (HEOR), medical affairs, and market access teams that operate in silos.
• Limited understanding of real-world biomarker testing pathways.
• Biomarker or diagnostic considerations that are introduced too late to shape trial design, patient identification or adoption planning.
• Rare or biomarker-defined populations that limit sample sizes, making it more difficult to generate robust evidence on comparative effectiveness, long-term outcomes and treatment patterns.
• Clinical trial endpoints that answer regulatory questions but leave commercial stakeholders unconvinced.
• Existing data assets—including registries, electronic health records (EHR), claims, genomic data and natural history studies—that are underused.
• Evidence packages that are designed for regulatory approval but do not fully address payer, provider or patient questions about value, access and use in practice.

For sponsors, the existence of evidence gaps is just one risk they face. That risk multiplies when these gaps are discovered too late and the options to address them are more limited, costly or less aligned to stakeholder expectations.

The questions to ask earlier

Building a more precise evidence strategy starts with asking better questions. Before pivotal development decisions are locked in, sponsors can pressure-test their evidence using these critical questions:

• What evidence gaps could delay regulatory review, reimbursement decisions or provider adoption?
• Do we understand the natural history, care pathway and testing patterns of the target population?
• Will our companion diagnostic support broad patient identification?
• How will biomarker testing vary across health systems and markets?
• Which existing secondary datasets could be transformed into fit-for-purpose RWE?
• How will biomarker testing, patient identification, and diagnostic workflows affect trial feasibility and market uptake?
• What evidence will be required to demonstrate value beyond clinical efficacy?
• What evidence will payers require that regulators do not?

The answers to these questions can enable sponsors to shift evidence planning from reactive gap-filling to proactive decision support.

Integrating evidence planning across the life cycle

RWE shouldn’t be treated solely as a post-approval activity. In precision medicine, evidence planning needs to begin early enough to shape development decisions, biomarker strategies, diagnostic planning, payer engagement and adoption pathways.

An integrated precision evidence strategy enables sponsors to determine where existing secondary datasets can support decision-making, where prospective data collection is needed and how clinical, biomarker, diagnostic, HEOR, patient-centered and real-world evidence should work together across the product life cycle.

This approach helps teams align key decision points, stakeholder needs and evidence standards. It also reduces duplicated effort, improves cross-functional coordination and ensures that evidence generated for one purpose does not leave critical questions unanswered for another.

Using real-world data in rare and biomarker-defined populations

Rare and biomarker-defined populations can make traditional evidence generation more difficult. Eligible patients may be geographically dispersed, undiagnosed, untested or treated across fragmented care settings. Sample sizes may be small, randomized controlled trials may be difficult to execute and long-term follow-up may extend beyond trial timelines.

In precision medicine, real-world data (RWD) is no longer simply a source of supporting evidence. It is a strategic asset that helps sponsors understand patient populations, optimize development decisions and continue evidence generation long after pivotal trials conclude. For example, natural history studies may characterize disease progression, outcomes and unmet need. Registries and longitudinal datasets can provide insight into patient journeys and treatment outcomes over time. Claims, EHR, genomic and other secondary data sources may help identify relevant populations, describe testing and referral patterns, and provide context when prospective evidence is limited.

However, RWD is not a shortcut around rigorous evidence generation. Its value depends on whether the data are relevant, complete and methodologically appropriate for the decision at hand. In small populations, the goal is not to use RWD simply because it’s available, but to identify fit-for-purpose data that can answer a specific research question with enough quality, completeness and relevance to support decision-making.

Improving patient identification and cohort development

For many precision therapies, patient identification is the greatest barrier to commercial success. A therapy may be highly targeted, but the patients most likely to benefit may remain unidentified without appropriate testing, referral and diagnostic workflows.

RWD enables sponsors to understand where eligible patients are, how they move through care and where delays or drop-offs occur. These insights illuminate diagnostic delays, biomarker testing patterns, referral pathways, treatment sequencing, and differences across health systems or geographies.

This understanding informs enrollment strategy, site selection, endpoint planning and subgroup interpretation, while also supporting the development of real-world cohorts that better reflect the intended treatment population.

Strong cohort design is especially important when sponsors are considering external control arms or real-world comparator strategies. The credibility of those approaches depends on whether the data source, cohort definition, endpoint measurement, and analytic methods are aligned to the research question and designed to minimize bias.

Considering external controls and real-world comparators

External control arms may be useful when randomized trials are difficult, impractical or ethically challenging. In precision medicine, these situations may arise when patient populations are very small, when the disease is severe, or when there is high unmet need and limited standard-of-care evidence. Rather than treating external controls as a contingency when randomized trials are constrained, sponsors should evaluate early where they fit within an integrated evidence plan.

RWD can provide the context needed to interpret trial outcomes against natural history, historical treatment patterns or current standards of care. However, external controls are not appropriate for every program. Their credibility depends on proactive planning, data quality, endpoint alignment, cohort comparability and analytic methods designed to reduce bias.

Sponsors must evaluate whether the available data are fit-for-purpose, key variables are captured consistently, endpoints can be measured reliably and the comparator cohort is sufficiently aligned to the trial population. These considerations are most effectively addressed during evidence strategy development, not after pivotal trial decisions have been made.

When planned early and executed rigorously, real-world comparators contribute to a more complete evidence package, supporting regulatory, payer and clinical decision-making. In this context, external controls become more than an alternative study design, they become a strategic evidence asset. Added too late, they may be limited by data gaps, misaligned endpoints or insufficient methodological planning.

Evidence generation does not end at launch

Precision medicine decisions do not end at launch. Payers, providers, patients and caregivers often need evidence that extends beyond the clinical trial window and may ask sponsors the following questions.

• How durable is the response?
• How do outcomes vary across subgroups?
• What are the long-term safety considerations?
• How does the therapy perform in routine practice?
• What is the impact on quality of life, treatment burden, health care utilization and cost?

Longitudinal evidence helps answer these questions. Registries, long-term follow-up studies, observational studies and linked datasets can track disease progression, treatment sequencing, durability of response, safety and patient-centered outcomes over time.

This type of evidence supports post-approval commitments, coverage and reimbursement discussions, value communication, payer engagement, medical education, label and indication expansion opportunities, competitive differentiation, and life cycle planning. It also enables sponsors to refine future development strategies by demonstrating how therapies are actually used and experienced in clinical practice, helping sustain long-term value.

Generating evidence for stakeholders

Every stakeholder makes different decisions and therefore requires different evidence.

• Regulators focus on safety, efficacy, biomarker rationale, endpoint validity and benefit-risk.
• Payers and health technology assessment bodies require comparative effectiveness, durability, cost impact, quality of life and real-world value.
• Health care providers need evidence on patient selection, testing workflows, treatment sequencing and safety management.
• Patients and caregivers need clear information about expected benefits, risks, testing, treatment burden and quality of life.

Evidence that supports one audience is not always sufficient for another. A regulatory evidence package may establish safety and efficacy but leave unanswered questions about access, reimbursement or real-world use. A payer-focused analysis may demonstrate value but require additional clinical or patient-centered context to support provider adoption.

An integrated evidence strategy anticipates these needs early and aligns evidence generation to the decisions that matter across the full product life cycle.

A precision evidence strategy framework

Leading sponsors can strengthen precision medicine programs by applying a structured evidence framework:

1. Define critical development, regulatory and access decisions.
2. Understand the disease landscape, patient journey and testing ecosystem.
3. Identify fit-for-purpose data sources and existing evidence assets.
4. Design integrated clinical and RWE programs.
5. Generate decision-grade evidence across stakeholder needs.
6. Translate evidence into stakeholder-specific value narratives.

Sponsors can use this framework to move from fragmented evidence activities to an integrated plan that connects science, strategy and stakeholder decision-making from the earliest stages.

The role of an evidence-generation partner

Coordination across disciplines, data sources and decision points is a must for precision medicine. The role of an evidence-generation partner is not simply to execute studies, but to help sponsors anticipate future evidence requirements before they become development risks. That means identifying where evidence gaps are likely to emerge; determining which data sources are fit-for-purpose; and aligning clinical, biomarker, companion diagnostic, real-world, patient-centered and health economic evidence to the decisions regulators, payers, providers and patients need to make.

A partner with expertise across clinical development, RWE, biomarkers, companion diagnostics, HEOR, patient-centered evidence and market access can help sponsors design evidence strategies that are both scientifically rigorous and commercially relevant.

With the PPD™ clinical research business of Thermo Fisher Scientific, this support includes evaluating fit-for-purpose RWD sources; designing natural history studies; developing registries and longitudinal evidence programs; building external control strategies; aligning evidence plans across functions; and translating evidence into value narratives for regulatory, payer, provider and patient audiences—all with the goal of generating the right evidence, for the right stakeholders, at the right time.

Is your evidence strategy precise enough?

Precision medicine has changed the rules of evidence generation. Success is no longer determined solely by demonstrating efficacy. It depends on anticipating the evidence required to support every critical decision from regulatory review through reimbursement, provider adoption and patient access.

Sponsors that continue to plan evidence sequentially risk uncovering critical gaps when they are most costly to address. Those that align evidence generation across development, regulatory review, reimbursement and commercialization are better positioned to accelerate access, strengthen adoption and maximize the value of their precision therapies.

In precision medicine, the question is no longer whether you have enough evidence. It is whether you are generating the right evidence, for the right stakeholders, at the right time to support the decisions that determine commercial success.