A scoping review shows that several nonvalidated budget planning tools for randomized trials are available

Decision Analytic Modeling for Global Clinical Trial Planning: A Case for HIV-Positive Patients at High Risk for Mycobacterium tuberculosis Sepsis in Uganda

Sepsis is a significant cause of mortality among people living with human immunodeficiency virus (HIV) in sub-Saharan Africa. In the planning period prior to the start of a large multi-country clinical trial studying the efficacy of the immediate empiric addition of anti-tuberculosis therapy to standard-of-care antibiotics for sepsis in people living with HIV, we used decision analysis to assess the costs and potential health outcome impacts of the clinical trial design based on preliminary data and epidemiological parameter estimates. The purpose of this analysis was to highlight this approach as a case example where decision analysis can estimate the cost effectiveness of a proposed clinical trial design. In this case, we estimated the impact of immediate empiric anti-tuberculosis (TB) therapy versus the diagnosis-dependent standard of care using three different TB diagnostics: urine TB-LAM, sputum Xpert-MTB/RIF, and the combination of LAM/Xpert. We constructed decision analytic models comparing the two treatment strategies for each of the three diagnostic approaches. Immediate empiric-therapy demonstrated favorable cost-effectiveness compared with all three diagnosis-dependent standard of care models. In our methodological case exemplar, the proposed randomized clinical trial intervention demonstrated the most favorable outcome within this decision simulation framework. Applying the principles of decision analysis and economic evaluation can have significant impacts on study design and clinical trial planning.

Practical guidance for planning resources required to support publicly-funded adaptive clinical trials

Adaptive designs are a class of methods for improving efficiency and patient benefit of clinical trials. Although their use has increased in recent years, research suggests they are not used in many situations where they have potential to bring benefit. One barrier to their more widespread use is a lack of understanding about how the choice to use an adaptive design, rather than a traditional design, affects resources (staff and non-staff) required to set-up, conduct and report a trial. The Costing Adaptive Trials project investigated this issue using quantitative and qualitative research amongst UK Clinical Trials Units. Here, we present guidance that is informed by our research, on considering the appropriate resourcing of adaptive trials. We outline a five-step process to estimate the resources required and provide an accompanying costing tool. The process involves understanding the tasks required to undertake a trial, and how the adaptive design affects them. We identify barriers in the publicly funded landscape and provide recommendations to trial funders that would address them. Although our guidance and recommendations are most relevant to UK non-commercial trials, many aspects are relevant more widely.

Cost-efficient clinical studies with continuous time survival outcomes

Time-to-event outcomes are common in clinical studies. For example, the time to a first major adverse cardiovascular event (MACE, defined as CVD death, nonfatal myocardial infarction, or stroke) is a commonly used outcome in cardiovascular outcome trials. Owing to the lengthy time frame and other factors, the high costs of conducting such studies has been identified as one of the major obstacles in conducting clinical trials in the United States. However, typical approaches for designing clinical trials with time-to-event outcomes do not consider study costs. For a given effect size (eg, hazard ratio), the power to detect differences between two groups is typically a function of the total number of events observed in the study. Therefore, the same level of power will be achieved based on various combinations of the total number of participants, length of enrollment and total follow-up times, and group allocation probability. Herein, we provide a general framework for designing cost-efficient studies comparing treatments with respect to continuous time-to-event outcomes. Among the various designs that achieve the desired level of power to detect a given effect size for a fixed type-I error level, the optimal cost-efficient design is the design that minimizes the expected total study cost. The method is general and can be used for Cox proportional hazards models or Aalen additive models, and under various recruitment and censoring assumptions. The proposed approach for designing cost-efficient studies is illustrated for a Weibull time-to-event outcome with uniform recruitment and exponentially distributed censoring time. The case of an additive hazards model is also described. A Shiny web application implementation of the proposed methods is presented.

Budgeting of non-commercial clinical trials: development of a budget tool by a public funding agency

BACKGROUND

Investigator-led multicentre randomised trials are essential to generate evidence on the optimal use of medical interventions. These non-commercial trials are often hampered by underfunding, which may lead to difficulties in gathering a team with the necessary expertise, a delayed trial start, slow recruitment and even early trial discontinuation. As a new public funder of pragmatic clinical trials, the KCE Trials programme was committed to correctly pay all trial activities in order to assure timely delivery of high-quality trial results. As no appropriate trial budget tool was readily publicly available that took into account the costs for the sponsor as well as the costs for participating sites, we developed a tool to make the budgeting of a clinical trial efficient, transparent and fair across applicants.

METHODS

All trial-related activities of the sponsor and sites were categorised, and cost drivers were identified. All elements were included in a spreadsheet tool allowing the sponsor team to calculate in detail the various activities of a clinical trial and to appreciate the budget impact of specific cost drivers, e.g. a delay in recruitment. Hourly fees by role were adapted from published data. Fixed amounts per activity were developed when appropriate.

RESULTS

This publicly available tool has already been used for 17 trials funded since the start of the KCE Trials programme in 2016, and it continues to be used and improved. This budget tool is used together with additional risk-reducing measures such as a multistep selection process with advance payments, a recruitment feasibility check by sponsor and funder, a close monitoring of study progress and a milestone-based payment schedule with the last payment made when the manuscript is submitted.

CONCLUSIONS

The budget tool helps the KCE Trials programme to answer relevant research questions in a timely way, within budget and with high quality, a necessary condition to achieve impact of this programme for patients, clinical practice and healthcare payers.