Platform trials in action beyond oncology trials

One of the complex innovative designs is the study with 'Master protocol' that has been getting popularity in recent years. Master protocol simply refers to the study of more than one drug with a single protocol. In clinical trials with Master protocol, the protocol document stands alone, without reference to specific drugs. Protocol amendments (or study-specific protocol) are used to provide details of each drug. Master protocols establish a trial network with infrastructure in place to streamline trial logistics and improve data quality, and facilitate data sharing and new data collection. Master protocols develop a common protocol for the network that incorporates innovative statistical approaches to study design and data analysis.

Platform trial is one type of Master protocol and refers to establishing the trial infrastructure and master protocol as a perpetuating effort, with drugs entering and leaving the platform.

Two prominent references for master protocols and platform trials are the following: 

 "Master Protocols to Study Multiple Therapies, Multiple Diseases, or Both" in 2017 by Woodcock and LaVange defines Master protocols include three type of trials: Umbrella, basket, and platform trials. 

FDA procedural guidance in 2022 "Master Protocols: Efficient Clinical Trial Design Strategies to Expedite Development of Oncology Drugs and Biologics Guidance for Industry" provided the definition for Master protocols:

a master protocol is defined as a protocol designed with multiple substudies, which may have different objectives and involve coordinated efforts to evaluate one or more investigational drugs in one or more disease subtypes within the overall trial structure.

A master protocol may be used to conduct the trial or trials for exploratory purposes or to support a marketing application and can be structured to evaluate, in parallel, different drugs compared with their respective controls or to a single common control. The sponsor can design the master protocol with a fixed or adaptive design with the intent to modify the protocol to incorporate or terminate individual substudies within the master protocol. 

Master protocols can include basket trial, umbrella trial, and platform trial and these trials can be schematically displayed as the following: 

Basket trial

Umbrella Trial

Platform Trial:

In  a paper by Berry et al, "The Platform Trial An Efficient Strategy for Evaluating Multiple Treatments", the platform trial was compared to the traditional trial: 

While platform trial has its challenges, we see the implementation trials in action, beyond the oncology trials. Here are examples of platform trials in areas other than the oncology trials. 

HEALEY ALS Platform Trial

The HEALEY ALS Platform Trial is a perpetual multi-center, multi-regimen clinical trial evaluating the safety and efficacy of investigational products for the treatment of ALS. This trial is designed as a perpetual platform trial. This means that there is a single Master Protocol dictating the conduct of the trial.

In this trial, multiple investigational products for ALS will be tested simultaneously or sequentially. Each investigational product will be tested in a regimen. Each regimen consists of a placebo-controlled trial, meaning that the active investigational product and matching placebo will be tested in each regimen.

The study website is here: https://www.massgeneral.org/neurology/als/research/platform-trial

The study master protocol can be accessed here

Several testing drugs have been completed or graduated from the platform, please see the news releases

PrecISE (Precision Interventions for Severe and/or Exacerbation-Prone Asthma) Network Study

PrecISE is an adaptive platform trial under master protocol with common biomarker screening where drugs enter when available and discontinue based on futility analysis. According to clinicaltrials.gov, 5 novel interventions are currently listed as the testing drug for study: Medium Chain Triglycerides (MCT), Clazakizumab, Broncho-Vaxom, Imatinib Mesylate, Cavosonstat; each active will be tested against its own control.

the study design was described in the paper by Israel et al "PrecISE: Precision Medicine in Severe Asthma: An adaptive platform trial with biomarker ascertainment"

REMAP-CAP: A Randomised, Embedded, Multi-factorial, Adaptive Platform Trial for Community-Acquired Pneumonia

REMAP-CAP platform trial was originally designed for identifying the treatments for community-acquired pneumonia. After the Covid pandemic in 2020, REMAP-CAP has quickly implemented the Pandemic Appendix to the Core Protocol so that the platform can respond rapidly in the event of widespread disease resulting from the novel 2019 coronavirus (COVID-19).

ACTIV (NIH) Accelerating Covid-19 Therapeutic Interventions and Vaccines

Working in an unprecedented time frame, the Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) public–private partnership developed and launched 9 master protocols between 14 April 2020 and 31 May 2021 to allow for the coordinated and efficient evaluation of multiple investigational therapeutic agents for COVID-19. The ACTIV master protocols were designed with a portfolio approach to serve the following patient populations with COVID-19: mild to moderately ill outpatients, moderately ill inpatients, and critically ill inpatients.

The study design was described in the paper by LaVange et al "Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV): Designing Master Protocols for Evaluation of Candidate COVID-19 Therapeutics"

RECOVER Clinical Trials - for Long Covid

Long COVID is defined as "a multifaceted disease that can affect nearly every organ system" and can manifest as new or worsening chronic health problems, including but not limited to heart disease, diabetes, kidney disease, hematologic issues, and mental and neurologic conditions. The signs, symptoms, and conditions continue or arise anew 4 weeks or more after the initial symptomatic or asymptomatic infection and may be relapsing and remitting.

NIH RECOVER trial is also using platform protocols/clinical trials to investigate treatments for long covid and master protocol is posted on the study website: https://trials.recovercovid.org/design. There will be multiple master protocols: RECOVER-VITAL, RECOVER-NEURO, RECOVER-AUTONOMIC (coming soon), RECOVER-SLEEP (coming soon) to investigate different

It is ironic that the RECOVER trial is started after NIH spent 2.5 years and $1 billion on long Covid research and failed to test meaningful long Covid treatments. See the article "Underwhelming’: NIH trials fail to test meaningful long Covid treatments — after 2.5 years and $1 billion "

Clinicaltrialsarena.com has an featured article "Platform trials: an opportunity for rare dystrophies or gene therapies?". The article discussed the possibility of platform trials in rare diseases and gene therapies. PaVeGT platform trial was listed: 

PaVe-GT: Paving the Way for Rare Disease Gene Therapies

PaVe-GT will develop and test AAV-9, with a different gene for each indication, using a single master protocol. The NCATS-led Platform Vector Gene Therapy (PaVe-GT) pilot project seeks to increase the efficiency of clinical trial startup by using the same gene delivery system and manufacturing methods for multiple rare disease gene therapies. We will make program results and regulatory documents publicly available, with the intention of benefiting future gene therapy clinical trials for very rare diseases.

 In a paper by Collignon (2022) "An Economic Perspective on Platform Trials—The Gift and the Curse", the following conclusion was made about the platform trial: 

"...sharing a common control group in a platform trial can be viewed as a gift and a curse, and choosing whether to implement a platform trial or a more standard development program is complex since it is contingent on numerous factors that are disease and context dependent. To make such a decision, a clear framework needs to be implemented. Decision-making in the pharmaceutical industry is becoming increasingly more quantitative, and in practice, both development approaches would be compared according to a series of standard metrics, including

(1) duration of the clinical program;

(2) cost and expected reward of the clinical program;

(3) flexibility of the clinical program (eg, reporting of the analyses corresponding to the different treatments while maintaining data and trial integrity, ability to add sites, and so forth); (4) probability of success for each treatment, for all treatments, or for at least 1 treatment, assuming all or some of the treatments have a certain efficacy;

(5) probability of (multiple) false-positive findings (eg, achieving statistical significance) for each treatment or for at least 1 treatment, assuming all or some of the treatments are comparator-like; and

(6) the extent to which a positive readout informs the success of potential subsequent trials and the ability to de-risk the next phase of development.

Using these metrics, governance boards would then make their decisions on whether to progress developing a treatment according to a range of diverse considerations, such as portfolio opportunities and investment priorities."

Mediation Analysis vs. Landmark Analysis for Clinical Trial Data

Mediation analysis and Landmark analysis are two valuable statistical tools in different contexts, providing insights into different aspects of data analysis. Both methods can be utilized to investigate if a surrogate endpoint or short-term measure can predict the long-term clinical endpoint or to investigate if short-term measures can be mediators for the long-term clinical endpoint. Both mediation analysis and landmark analysis are useful tools for post-hoc, exploratory analyses, but not the primary analysis method for the primary efficacy endpoint. 

Mediation analysis is a statistical method used to explore and understand the mechanism or process through which an independent variable influences a dependent variable. It helps to determine whether the effect of an independent variable on a dependent variable is mediated (i.e., transmitted through) one or more intermediate variables, often referred to as mediators.

In mediation analysis, the main focus is on understanding the causal chain of relationships between variables. It involves three key components:

• Independent variable (X): The variable that is hypothesized to influence the dependent variable directly or indirectly through one or more mediators.
• Mediator (M): The variable(s) that mediates the relationship between the independent variable and the dependent variable. It explains how and why the independent variable affects the dependent variable.
• Dependent variable (Y): The variable that is influenced by the independent variable either directly or indirectly through the mediators.

The analysis aims to estimate the direct effect of the independent variable on the dependent variable and the indirect effect mediated through the mediator(s). Several statistical techniques can be used for mediation analysis, such as regression-based methods (e.g., ordinary least squares regression) or more advanced methods like structural equation modeling.

In our published paper "Contemporary risk scores predict clinicalworsening in pulmonary arterial hypertension - Ananalysis of FREEDOM-EV",  mediation analysis was used to test the hypothesis that improvements in risk score (a surrogate endpoint) contributed to reduced likelihood for clinical worsening (a long-term clinical endpoint). 

 

In a paper by Blette et al, "Is low-risk status a surrogate outcome in pulmonary arterialhypertension? An analysis of three randomised trials ", the mediation analysis was used to investigate surrogacy. The author stated:

"we performed a mediation analysis considering each candidate surrogate as an intermediate outcome. To avoid inconsistent mediation (ie, when direct and indirect effects cancel each other out, the direct effect is even larger than the total effect, or other situations that can result in a negative proportion mediated), we first empirically tested four criteria to justify the mediation analysis. Next, Cox proportional hazards models were fit for the clinical worsening and survival outcomes conditional on each candidate surrogate separately, as well as treatment, corresponding baseline risk score, and a fixed effect variable with three levels for trial membership (to allow for similarity within each trial). Results from these models were combined with parallel models that did not condition on the candidate surrogates, but otherwise conditioned on the same set of variables to perform the difference method for mediation, estimating the total effect and direct effects of treatment on clinical worsening and survival, as well as the indirect effects through each candidate surrogate. The proportion of the effect mediated through each surrogate risk score was estimated, along with 95% CIs via a bootstrap procedure."

In a previous post, the mediation analysis was discussed. "Mediation analysis and SAS CAUSALMED procedure"

Landmark analysis, also known as landmark survival analysis, is a statistical method commonly used in survival analysis to investigate the impact of time-dependent variables on the occurrence of an event of interest. It allows for the assessment of time-varying effects in longitudinal studies or clinical trials where the values of variables may change over time.

In landmark analysis, the follow-up time is divided into predefined intervals or "landmarks," and the analysis is performed separately for each landmark. The key steps in landmark analysis are as follows:

• Define landmarks: Choose specific time points of interest during the follow-up period.
• Create landmark cohorts: At each landmark, divide the study population into subgroups based on the status of the time-dependent variable(s) of interest.
• Analyze survival outcomes: Estimate survival probabilities or hazard rates for each subgroup defined by the landmark cohorts.
• Compare survival outcomes: Compare the survival outcomes between the different subgroups to assess the impact of the time-dependent variable(s) on the event occurrence.

Landmark analysis allows researchers to capture time-varying effects and observe how the relationship between variables changes over the course of a study. It is particularly useful when analyzing data with time-dependent covariates, treatment interventions, or changes in exposure levels over time.

In a paper by McLaughlin et al "Pulmonary Arterial Hypertension-Related Morbidity Is Prognostic for Mortality", the landmark analysis was used to assess the impact of morbidity events on the risk of subsequent mortality.

In a paper by Eisenstein et al "Clopidogrel use and long-term clinical outcomes after drug-eluting stent implantation" , landmark analyses were performed to explore the association of extended clopidogrel use and long-term clinical outcomes of patients receiving drug eluting stents (DES) and bare-metal stents (BMS) for treatment of coronary artery disease.

The tutorial paper "Landmark Analysis at the 25-Year Landmark Point" by Dr Dafni is a good reference for Landmark analysis. 

Mediation analysis and Landmark analysis differ in their goals, focus, and types of data they analyze:

Goals: Mediation analysis aims to understand the mechanism or process through which an independent variable influences a dependent variable, exploring direct and indirect effects. Landmark analysis, on the other hand, focuses on assessing time-varying effects and understanding how the occurrence of an event is influenced by time-dependent variables.

Focus: Mediation analysis emphasizes identifying mediators that explain the relationship between an independent variable and a dependent variable. Landmark analysis focuses on investigating the impact of time-dependent variables on survival outcomes or event occurrences.

Data: Mediation analysis typically requires cross-sectional or longitudinal data with variables measured at different time points. It is applicable to both continuous and categorical variables. Landmark analysis is commonly used in survival analysis, analyzing time-to-event data, and requires longitudinal data with time-dependent variables.

Both mediation analysis and landmark analysis are valuable statistical tools in different contexts, providing insights into different aspects of data analysis. Here is a table to compare the mediation analysis and the landmark analysis generated by ChatGPT, but I added the last row for implementing the mediation and landmark analyses. 

Aspect	Mediation Analysis	Landmark Analysis
Statistical Method	Investigates relationships and effects between variables	Analyzes time-varying effects and event occurrences
Time Dependency	Considers temporal aspect of data	Accounts for time-varying effects and changing values over time
Causal Inference	Aims to understand causal chain of relationships	Examines impact of time-dependent variables on event outcomes
Focus	Identifying mediators that explain relationships	Assessing time-dependent variables and event occurrences
Variables	Independent, mediator, and dependent variables	Time-dependent variables influencing event occurrences
Data Type	Cross-sectional or longitudinal with measured variables	Longitudinal data with time-dependent variables
Analytical Steps	Estimating direct and indirect effects using regression	Dividing follow-up into landmarks, comparing survival outcomes
Research Questions	Mechanisms and processes of variable influence	Impact of time-dependent variables on events or survival
Implementation	In SAS, Procedure CASUALMED allows you to estimate direct and indirect effects using different mediation models. It supports various regression-based mediation approaches, including Sobel, bootstrapping, and Bayesian estimation. In R, to conduct mediation analysis, the most commonly used package is "mediation." This package provides a comprehensive set of functions to estimate direct and indirect effects in mediation models. It supports various mediation methods, including the causal steps approach, bootstrapping, and structural equation modeling (SEM).	In SAS, Procedures LIFETEST, PHREG, and LIFEREG can all be used. You can divide the follow-up time into landmark intervals and analyze survival outcomes for different subgroups defined by the landmarks In R, you can utilize the "survival" package, which is widely used for survival analysis. The "survival" package provides functions to handle time-to-event data, perform survival analysis, and estimate survival probabilities. You can divide the follow-up time into landmarks and analyze survival outcomes for different landmark cohorts.

Personally, I prefer the mediation analysis to the landmark analysis. In the landmark analysis, the subjects who did not reach the landmark timepoint were excluded from the analysis and the analyses are performed on a subset of the overall population - sort of principle stratum. The endpoint measure at the landmark timepoint and whether or not the subjects reach the landmark timepoint itself is meaningful. Excluding it from the analysis is against the intention-to-treatment principle and may cause biases.