For decades, clinical research has relied heavily on randomized controlled trials involving large patient populations to establish the safety and effectiveness of investigational therapies. Advances in genomic sequencing, molecular diagnostics, bioinformatics, and artificial intelligence have demonstrated that patients with similar clinical presentations may have different biological mechanisms underlying their diseases. Precision medicine addresses these differences by identifying therapies based on individual molecular and genetic characteristics.

For example, patients diagnosed with non-small cell lung cancer may receive different targeted treatments depending on the presence of specific genomic alterations, including EGFR mutations or ALK rearrangements. How can investigators establish statistically reliable and clinically meaningful evidence when the patients most likely to benefit from a treatment represent only a small fraction of the broader disease population?

When treatment depends on biomarkers

Biomarkers are measurable biological characteristics that may indicate disease processes, predict treatment responses, or provide information about therapeutic effectiveness. Examples include EGFR mutations in lung cancer, HER2 alterations in breast cancer, and BRCA-associated molecular characteristics relevant to certain targeted cancer treatments.

However, restricting trial enrollment to biomarker-positive patients reduces the eligible population. Consider a hypothetical clinical trial investigating a targeted therapy for a genomic alteration found in 5% of patients with a disease. Of 10,000 patients screened, 500 would be biomarker-positive and 200 eligible after additional criteria. This is an illustrative example only; actual eligibility depends on the disease, biomarker, protocol, and screened population.

This creates several difficulties, including prolonged recruitment periods, higher screening costs, increased dependence on specialized laboratories, and the need for collaboration across multiple research institutions. Smaller populations may also limit investigators’ ability to evaluate treatment effects across demographic groups and detect uncommon adverse events.

Generating reliable evidence with fewer patients

Statistical power is one of the most important considerations in clinical trial design. It represents the probability that a study will detect a specified treatment effect when that effect truly exists. Conventional confirmatory clinical trials frequently target 80% or 90% statistical power while controlling the probability of a false-positive conclusion. However, achieving adequate power becomes more challenging when the eligible population is small.

For a simplified two arm clinical trial evaluating a continuous outcome, the approximate sample size required per treatment group can be expressed as:

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Where:

This relationship illustrates that larger expected treatment effects may require fewer participants, while smaller effects or greater outcome variability require larger samples.

Biomarker enrichment can potentially improve statistical efficiency by identifying patients with stronger treatment responses. Nevertheless, smaller sample sizes may produce wider confidence intervals, unstable effect estimates, limited safety information, and reduced ability to perform subgroup analyses. Statistically significant findings may not always translate into clinically meaningful improvements. Researchers must establish whether observed benefits are sufficiently precise, reproducible, and clinically relevant. The challenge of precision medicine is not simply conducting smaller clinical trials; it is generating stronger evidence from smaller populations.

Recruitment and access

Precision medicine introduces additional barriers because eligibility frequently depends on molecular characteristics that are not routinely assessed in all healthcare settings. Patients with rare genomic alterations may be dispersed across different healthcare systems, states, and countries, requiring multicenter and multinational recruitment strategies.

Access to molecular diagnostic testing varies according to healthcare infrastructure, insurance coverage, clinical expertise, and socioeconomic circumstances. Patients who lack access to genomic testing may never be identified as potential trial candidates. Researchers should critically evaluate whether each eligibility requirement is scientifically justified and necessary for participant safety.

Electronic health records, genomic registries, molecular tumor boards, and validated AI-assisted screening systems may improve identification of eligible participants. However, these tools require appropriate data governance, privacy protections, and oversight to prevent bias or inaccurate patient classification.

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Trial designs that can improve efficiency

Basket trials evaluate a targeted treatment across different diseases that share a particular molecular alteration. They may increase recruitment opportunities across diagnostic categories, although treatment effects can vary by disease. Umbrella trials investigate multiple targeted therapies within one disease category, assigning patients to treatment groups according to molecular characteristics.

Adaptive designs allow prespecified changes to aspects of a study based on accumulating evidence. Potential modifications include treatment allocation, sample size reassessment, or early discontinuation of ineffective interventions. Bayesian methods combine prior information with emerging clinical trial data to update estimates of treatment effects. They can provide flexible decision-making frameworks, provided that assumptions and operating characteristics are carefully evaluated.

Platform trials and other master protocols may further improve efficiency by evaluating multiple interventions within a coordinated research infrastructure. Importantly, these designs do not eliminate the need for appropriate controls, bias prevention, and rigorous statistical planning.

Real-world evidence and artificial intelligence

Real-world evidence is becoming increasingly relevant to precision medicine because traditional randomized trials may be difficult to conduct in very small populations. Real-world data may originate from electronic health records, disease registries, insurance claims, genomic databases, and routine clinical care.

In selected circumstances, external control groups derived from historical or contemporaneous patient data may help contextualize findings from single-arm clinical trials. However, such comparisons require careful consideration of selection bias, confounding, missing data, and differences in clinical practice.

For example, comparing patients receiving a new targeted therapy with historical patients who did not undergo comparable genomic testing may create misleading estimates of treatment effectiveness. Real-world evidence should complement rather than automatically replace randomized evidence. Its scientific value depends on the quality of the data, appropriateness of the research design, and transparency of analytical methods.

Potential applications include identifying eligible patients, predicting recruitment performance, optimizing site selection, supporting biomarker discovery, and detecting anomalies in clinical research data. However, AI-generated recommendations must be validated before use in consequential research decisions. Poor-quality training data, inadequate representation of patient populations, and algorithmic bias may compromise accuracy and generalizability. Artificial intelligence should therefore be viewed as a tool that supports clinical and scientific judgment rather than replacing qualified researchers.

Quality, regulation and ethics

Innovative clinical trial designs cannot compensate for weak study execution. In precision medicine, operational errors may have a disproportionate impact because each participant can represent a meaningful proportion of the study population. Misclassification of biomarker status, inconsistent endpoint assessment, protocol deviations, missing data, and inadequate safety reporting can undermine otherwise well-designed studies.

Clinical operations teams should emphasize rigorous biomarker testing procedures, investigator training, accurate source documentation, timely data management, and risk-based monitoring. The International Council for Harmonisation’s Good Clinical Practice framework emphasizes quality by design, proportionate risk management, and reliable clinical trial results.

Precision medicine also introduces regulatory considerations involving biomarker validation, companion diagnostics, genomic privacy, informed consent, and evidence sufficiency. Ethical challenges arise when access to genomic testing determines eligibility for investigational treatments. Patients from underserved populations may experience barriers to testing, specialist referral, and clinical trial participation. Sponsors and investigators should develop inclusive recruitment strategies while maintaining scientifically justified eligibility requirements.

The future of precision medicine

The successful implementation of these strategies requires collaboration among biostatisticians, epidemiologists, genomic scientists, clinical investigators, regulatory professionals, and clinical operations teams.

Nevertheless, these approaches must be supported by rigorous scientific methodology, regulatory compliance, strong clinical operations, and effective quality management. Ultimately, the goal of precision medicine is not merely to deliver the right treatment to the right patient, but to establish reliable evidence demonstrating that the treatment meaningfully improves patient outcomes.

About the Author

Dr. Gabriel N. Dikong is a senior clinical research specialist, healthcare executive and U.S. Air Force veteran. He is the Founder and Chairman of CMFIT Global Group Corp. With over 14 years of experience across clinical research, pharmaceuticals, biotechnology, medical devices and public health, his work focuses on clinical operations, regulatory compliance, patient safety and healthcare innovation. He holds a Doctor of Public Health along with advanced degrees in biostatistics, epidemiology and biomedical informatics.

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