Prediction Markets in Clinical Trials: Integrity Risks

Author: Dr Nikitas Nanidisi

, Director, Global Medical Affairs
Published on: 10/09/2026

Estimated Reading Time: 8 min

Excelya Medical AffairS Department

Introduction

Prediction markets in clinical trials allow users to trade on study outcomes and regulatory decisions. They have recently expanded into many aspects of our daily life, allowing everyday users to bet for or against the outcome of different events; anything from sports and weather patterns to political race outcomes is currently traded online. In July 2026, the prediction market provider Kalshi partnered with the AI company AppliedXL, launching a pilot into predicting the outcome of clinical trials and FDA regulatory decisions. The supportive argument is that there is a need to cover the fragmented and delayed transparency, citing an FDA report that 30% of clinical trials failed to properly publish their results.

However, prediction markets in clinical trials could have a direct effect on clinical research, as the introduction of a financial incentive tied to trial outcomes creates new risks for participants, sponsors, clinical research organisations (CROs), and the integrity of the evidence generated.

Jump to section

1The Impact of FDA 2026 Guidance

2Why Bayesian Methods Matter

3Trial Strategy & R Programming

4How Excelya Can Support you

5FAQ

Prediction Markets & Clinical Trial Integrity

How Prediction Markets Could Affect Trial Participants

Clinical trial participants may be particularly vulnerable to the effects of publicly traded predictions.

Exposure to Perceived Probabilities

Patients or their caregivers enter trials with varying levels of medical knowledge and often with limited other treatment options. They may be willingly or inadvertently exposed to the perceived probabilities of the success of their treatment through news coverage and social media. This information can substantially impact their behaviour and treatment response, negatively or positively.

Psychological Effects on Treatment Response

Low probability

The nocebo effect

A participant who sees a prediction of low probability of success may become less confident in the investigational treatment, especially since some therapies, like cancer drugs, can produce an initial worsening of symptoms before a subsequent response. This psychological burden can result in an actual reduced therapeutic response, called the nocebo effect.

High probability

The placebo effect

In contrast, a high market probability could create unwarranted confidence and contribute to a beneficial treatment response, or a placebo effect.

Behaviour, Compliance and Retention

Moreover, publicly available probabilities of success could influence the participant’s compliance with the treatment or the trial’s evaluations, reporting of symptoms, or even their willingness to remain in the study.

Key Trial Consideration

These risks are particularly relevant in trials that have objectives around participant-reported outcomes, adherence, or retention.

RISK

Clinical Trial Prediction Markets

Risks to Clinical Trial Integrity and Confidential Information

Prediction markets may introduce new sources of bias, create financial incentives around non-public information, and expose clinical trials to risks that extend beyond conventional insider trading.

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Injected Bias

External expectations may influence trial conduct

Clinical trials are designed and regulated to minimise sources of bias. Investigators and study teams are expected to conduct research without allowing external expectations to influence data collection or their interpretation. So far, the exact probability of a trial’s success is usually only calculated after its completion, when all data have been collected and processed. Prediction markets introduce a constant and fluctuating success probability that is available to the trial team.

Confidential Information

Financial incentives may encourage misuse or disclosure

Apart from this injected bias, there lies a concern about the potential misuse of the trial’s confidential information. Individuals with access to non-public information about the trial’s status may have a financial incentive to trade or disclose it.

Deliberate Interference

The risk extends beyond conventional insider trading

However, the problem is broader than insider trading. Clinical research involves a large network of individuals and organisations. Investigators, nurses, pharmacists, laboratory personnel, trial monitors, and specialist vendors, like couriers, may encounter relevant information at different points in the study lifecycle. Therefore, there could be a risk of deliberate interference.

Hypothetical Scenario

Consider a hypothetical situation in which an individual with access to a temperature-sensitive investigational product deliberately allows it to remain outside controlled conditions because they have a financial interest in an unfavourable trial outcome. Such conduct could cause serious harm to the participants or the discontinuation of development of a potentially life-altering therapy.

Proposed restrictions may reduce, but not eliminate, these risks

Employment verification and restricting markets to non-paediatric late-stage trials may limit some of these risks but not entirely remove them. Kalshi’s argument about allowing betting only on trials with completed enrolment does not address the fact that participants could become aware of market predictions during treatment and follow-up.

Strategic Implications

What Prediction Markets Mean for CROs and Trial Sponsors

Stricter and new controls around issues that have previously not been encountered in the scientific research space would now need to be set and extended well beyond the core study team.

CRO Exposure

CROs face a heightened risk from the effects of prediction markets because they operate across many functions that generate, handle, and transfer clinical trial information. Stricter and new controls around issues that have previously not been encountered in the scientific research space would now need to be set and extended well beyond the core study team.

Sponsor Oversight

Clinical trial sponsors and investors, who may face complex financial implications from these predictions, would also need to strengthen their oversight around access to confidential information, conflicts of interest, access to interim or unmasked data, and the involvement of third-party personnel.

Risk Mitigation

Some risk mitigation measures that could be commonly developed or enhanced could include the thorough review of conflict-of-interest policies, awareness training for these markets and their implications, and offering support to participants who need to understand them. A very important action could also be to adapt centralised monitoring to identify any unusual changes in withdrawals, protocol deviations, site behaviour, or data access patterns

Confidentiality
Conflicts of Interest
Data Access
Centralised Monitoring

Prediction Markets & Clinical Evidence

Why Prediction-Market Probabilities Are Not Clinical Evidence

Clinical trials involve a multitude of specialists for each aspect, and can be so complicated that even the most experienced clinical research professionals have difficulty fully understanding them. Therefore, the general public’s, or even a handful of gambling users’, sentiment on the outcome of a trial cannot be a scientific assessment. It does not measure a treatment’s efficacy or an individual patient’s likelihood of benefit, nor does it complement the evidence generated by the trial. Often, even the most promising new therapies fail to show significant effect in helping the population they aimed to do so.

Evidence-based assessment

Scientific rigour

Prediction market oversight

Working Together

As we navigate the uncharted territory of prediction markets in clinical trials, it is imperative that we identify and monitor the new dangers created by their financial incentives. Excelya will remain dedicated to protecting the well-being of participants, maintaining trial integrity and scientific rigour, and preserving confidence in clinical research.

About the Author

Dr Nikitas Nanidis

Director, Global Medical Affairs at Excelya

Director of Global Medical Affairs and Safety Physician with over 11 years of experience in clinical research and medical affairs. His expertise spans medical monitoring, pharmacovigilance, clinical study documentation, literature review, regulatory support, and medical strategy, across Oncology, CNS, Cardiology, and Rheumatology.

Portrait of Nikitas Nanidis, Global Medical affair departement at Excelya

Frequently Asked Questions

What are prediction markets in clinical trials?

They are markets in which participants trade on whether a clinical trial or related regulatory event will reach a defined outcome. The resulting probability reflects market activity and sentiment, not a clinical analysis of efficacy or individual benefit.

How could prediction markets affect clinical trial participants?

Public probabilities could influence expectations, confidence, adherence, symptom reporting, or willingness to remain in a study. These concerns may be especially relevant where participant-reported outcomes, adherence, or retention are important trial objectives.

Why could prediction markets create risks to clinical trial integrity?

A continuously changing public probability may introduce an external expectation into the trial environment. Financial incentives may also increase concerns around confidential information, conflicts of interest, inappropriate trading, disclosure, or deliberate interference.

What do prediction markets mean for CROs and sponsors?

CROs and sponsors may need to review controls for confidential or unmasked data, conflicts of interest, third-party access, staff awareness, participant support, and centralized monitoring of unusual withdrawals, protocol deviations, site behavior, or data-access patterns.

Are prediction-market probabilities clinical evidence?

No. A market probability does not measure treatment efficacy, establish a participant’s likelihood of benefit, or replace evidence generated through a properly designed, conducted, analyzed, and reported clinical trial.