Cost effectiveness of caplacizumab in acquired thrombotic thrombocytopenic purpura.

Acquired thrombotic thrombocytopenic purpura (TTP) is a life-threatening disease characterized by thrombotic microangiopathy leading to end-organ damage. The standard of care (SOC) treatment is therapeutic plasma exchange (TPE) alongside immunomodulation with steroids, with increasing use of rituximab ± other immunomodulatory agents. The addition of caplacizumab, a nanobody targeting von Willebrand factor, was shown to accelerate platelet count recovery and reduce TPE treatments and hospital length of stay in TTP patients treated in 2 major randomized clinical trials. The addition of caplacizumab to SOC also led to increased bleeding from transient reductions in von Willebrand factor and increased relapse rates. Using data from the 2 clinical trials of caplacizumab, we performed the first-ever cost-effectiveness analysis in TTP. Over a 5-year period, the projected incremental cost-effectiveness ratio (ICER) in our Markov model was $1 482 260, significantly above the accepted 2019 US willingness-to-pay threshold of $195 300. One-way sensitivity analyses showed the utility of the well state and the cost of caplacizumab to have the largest effects on ICER, with a reduction in caplacizumab cost demonstrating the single greatest impact on lowering the ICER. In a probabilistic sensitivity analysis, SOC was favored over caplacizumab in 100% of 10 000 iterations. Our data indicate that the addition of caplacizumab to SOC in treatment of acquired TTP is not cost effective because of the high cost of the medication and its failure to improve relapse rates. The potential impact of caplacizumab on health system cost using longer term follow-up data merits further study.

Advances in clinical trial design: Weaving tomorrow's TB treatments.

Christian Lienhardt and co-authors discuss the conclusions of the PLOS Medicine Collection on advances in clinical trial design for development of new tuberculosis treatments.

Economic Evaluations in National Cancer Institute-Sponsored Network Cancer Clinical Trials.

OBJECTIVES: Amid a rapid increase in cancer care costs, we examined the extent to which economic evaluations (EEs) were conducted for new treatments evaluated in clinical trials at SWOG, a large National Cancer Institute-sponsored cancer research network. METHODS: We investigated phase III cancer treatment clinical trials activated from 1980 onward with primary articles reporting the protocol-designated endpoints published in scientific journals by 2017. Using PubMed, Web of Science, and EconLit, we searched for EEs using trial name, cancer type, information on the comparison arms, and refined keywords for EE designs. We reported the overall proportion of trials with associated EEs and trends of this proportion over time. We synthesized and analyzed information on funding sources, health outcomes, and sources of quality-of-life and cost data from the EEs. RESULTS: Among 182 examined trials, 15 EEs were associated with 13 (7.1%) trials. Among the EEs, almost half (7 of 15) were either unfunded or did not report funding information, whereas nearly half (7 of 15) were funded by pharmaceutical companies and 2 (2 of 15, 13.3%) were supported by federal funding. All EEs reported a healthcare payer perspective. The proportion of trials with an associated EE increased from 1980 to 1989 and 2000 to 2009, but never exceeded 11%. Sources for cost and quality-of-life data for the EEs primarily came from outside the clinical trials. CONCLUSIONS: Few economic studies of treatments evaluated in National Cancer Institute-sponsored clinical trials have been conducted. Policymakers, payers, and patients lack economic evidence to consider newly evaluated cancer treatments, despite an urgent need to control healthcare costs.

Empowering phase II clinical trials to reduce phase III failures.

The large number of failures in phase III clinical trials, which occur at a rate of approximately 45%, is studied herein relative to possible countermeasures. First, the phenomenon of failures is numerically described. Second, the main reasons for failures are reported, together with some generic improvements suggested in the related literature. This study shows how statistics explain, but do not justify, the high failure rate observed. The rate of failures due to a lack of efficacy that are not expected, is considered to be at least 10%. Expanding phase II is the simplest and most intuitive way to reduce phase III failures since it can reduce phase III false negative findings and launches of phase III trials when the treatment is positive but suboptimal. Moreover, phase II enlargement is discussed using an economic profile. As resources for research are often limited, enlarging phase II should be evaluated on a case-by-case basis. Alternative strategies, such as biomarker-based enrichments and adaptive designs, may aid in reducing failures. However, these strategies also have very low application rates with little likelihood of rapid growth.

Pilot study of the ability to probabilistically link clinical trial patients to administrative data and determine long-term outcomes.

BACKGROUND: Clinical trials are important but extremely costly. Utilization of routinely collected administrative data may simplify and enhance clinical trial data collection. PURPOSE: The aim of this study was to test the feasibility of use of administrative databases in Ontario, Canada, for long-term clinical trial follow-up, specifically (a) to determine whether limited patient identifiers held by the Canadian Cancer Trials Group can be used to probabilistically link with individuals in the Institute for Clinical Evaluative Sciences databases and if so, (b) the level of concordance between the two data sets. METHODS: This retrospective study was conducted through collaboration of established health service (Institute for Clinical Evaluative Sciences) and clinical trial (Canadian Cancer Trials Group) research groups in the province of Ontario, Canada, where healthcare is predominantly funded by the government. Adults with pre-treated metastatic colorectal cancer previously enrolled in the Canadian Cancer Trials Group CO.17 and CO.20 randomized phase III trials were included, limited to those in Ontario. The main outcomes were rate of successful probabilistic linkage and concordance of survival data, stated a priori. RESULTS: Probabilistic linkage was successful in 266/293 (90.8%) participants. In those patients for whom linkage was successful, the Canadian Cancer Trials Group (trial) and the Institute for Clinical Evaluative Sciences (administrative) data sets were concordant with regard to the occurrence of death during the period of clinical trial follow-up in 206/209 (98.6%). Death was recorded in the Institute for Clinical Evaluative Sciences, but not the Canadian Cancer Trials Group, for 57 cases, where the event occurred after the clinical trial cut-off dates. The recorded date of death matched closely between both databases. During the period of clinical trial conduct, administrative databases contained details of hospitalizations and emergency room visits not captured in the clinical trial electronic database. CONCLUSION: Prospective use of administrative data could enhance clinical trial data collection, both for long-term follow-up and resource utilization for economic analyses and do so less expensively than current primary data collection. Recording a unique identifier (e.g. health insurance number) in trial databases would allow deterministic linkage for all participants.

Non-commercial vs. commercial clinical trials: a retrospective study of the applications submitted to a research ethics committee.

There are many difficulties in undertaking independent clinical research without support from the pharmaceutical industry. In this retrospective observational study, some design characteristics, the clinical trial public register and the publication rate of noncommercial clinical trials were compared to those of commercial clinical trials. A total of 809 applications of drug-evaluation clinical trials were submitted from May 2004 to May 2009 to the research ethics committee of a tertiary hospital, and 16.3% of trials were noncommercial. They were mainly phase IV, multicentre national, and unmasked controlled trials, compared to the commercial trials that were mainly phase II or III, multicentre international, and double-blind masked trials. The commercial trials were registered and published more often than noncommercial trials. More funding for noncommercial research is still needed. The results of the research, commercial or noncommercial, should be disseminated in order not to compromise either its scientific or its social value.

The Pharmacological Costs of Second-Line Treatments for Recurrent Ovarian Cancer.

INTRODUCTION: In ovarian cancer, it is uncertain which chemotherapy regimen is more clinically effective and cost-effective for the treatment of recurrence; therefore, it might be interesting to make a balance between the cost of the drugs administered and the difference in progression-free survival (PFS) and overall survival (OS). METHODS: The present evaluation was restricted to pivotal phase 3 randomized controlled trials. We calculated the pharmacological costs necessary to get the benefit in PFS and OS. The costs of drugs are at the pharmacy of our hospital and are expressed in Euros (&OV0556;). We have subsequently applied the European Society for Medical Oncology Magnitude of Clinical Benefit Scale. RESULTS: Our study evaluated 3 phase 3 randomized controlled trials, including 2004 patients. The most relevant increase of costs was associated with the combination chemotherapy including trabectedin, with the highest costs for month of PFS gained (15,836 &OV0556;) and for month of OS gained (7198 &OV0556;), but it substantially differs considering the data of partially platinum-sensitive populations (platinum-free interval of 6-12 months), with 3959 &OV0556; for month of OS gained. CONCLUSIONS: The addition of trabectedin to pegylated liposomal doxorubicin for the treatment of recurrent ovarian cancer can lead to an increase of pharmacological costs. Differently, considering OS in patients with platinum-free interval of 6 to 12 months, there is a halving of pharmacological costs with the addition of trabectedin to pegylated liposomal doxorubicin. These costs are in line with the spending suggested as sustainable (thresholds of <$61,500 per life-year gained).

Bayesian probability of success for clinical trials using historical data.

Developing sophisticated statistical methods for go/no-go decisions is crucial for clinical trials, as planning phase III or phase IV trials is costly and time consuming. In this paper, we develop a novel Bayesian methodology for determining the probability of success of a treatment regimen on the basis of the current data of a given trial. We introduce a new criterion for calculating the probability of success that allows for inclusion of covariates as well as allowing for historical data based on the treatment regimen, and patient characteristics. A new class of prior distributions and covariate distributions is developed to achieve this goal. The methodology is quite general and can be used with univariate or multivariate continuous or discrete data, and it generalizes Chuang-Stein's work. This methodology will be invaluable for informing the scientist on the likelihood of success of the compound, while including the information of covariates for patient characteristics in the trial population for planning future pre-market or post-market trials.

Quantifying the magnitude and cost of collecting extraneous protocol data.

Although most research professionals believe that protocol designs contain a growing number of unnecessary and redundant procedures generating unused data, incurring high cost, and jeopardizing study success, there are no published studies systematically examining this issue. Between November 2011 and May 2012, Tufts Center for the Study of Drug Development conducted a study among a working group of 15 pharmaceutical companies in which a total of 25,103 individual protocol procedures were evaluated and classified using clinical study reports and analysis plans. The results show that the typical later-stage protocol had an average of 7 objectives and 13 end points of which 53.8% are supplementary. One (24.7%) of every 4 procedures performed per phase-III protocol and 17.7% of all phase-II procedures per protocol were classified as "Noncore" in that they supported supplemental secondary, tertiary, and exploratory end points. For phase-III protocols, 23.6% of all procedures supported regulatory compliance requirements and 15.9% supported those for phase-II protocols. The study also found that on average, $1.7 million (18.5% of the total) is spent in direct costs to administer Noncore procedures per phase-III protocol and $0.3 million (13.1% of the total) in direct costs are spent on Noncore procedures for each phase-II protocol. Based on the results of this study, the total direct cost to perform Noncore procedures for all active annual phase-II and phase-III protocols is conservatively estimated at $3.7 billion annually, not including the indirect costs associated with collecting and managing Noncore procedure data and the ethical costs of exposing study volunteers to unnecessary risks associated with conducting extraneous procedures.

Violations of the independent increment assumption when using generalized estimating equation in longitudinal group sequential trials.

In phase 3 clinical trials, ethical and financial concerns motivate sequential analyses in which the data are analyzed prior to completion of the entire planned study. Existing group sequential software accounts for the effects of these interim analyses on the sampling density by assuming that the contribution of subsequent increments is independent of the contribution from previous data. This independent increment assumption is satisfied in many common circumstances, including when using the efficient estimator. However, certain circumstances may dictate using an inefficient estimator, and the independent increment assumption may then be violated. Consequences of assuming independent increments in a setting where the assumption does not hold have not been previously explored. One important setting in which independent increments may not hold is the setting of longitudinal clinical trials. This paper considers dependent increments that arise because of heteroscedastic and correlated data in the context of longitudinal clinical trials that use a generalized estimating equation (GEE) approach. Both heteroscedasticity over time and correlation of observations within subjects may lead to departures from the independent increment assumption when using GEE. We characterize situations leading to greater departures in this paper. Despite violations of the independent increment assumption, simulation results suggest that operating characteristics of sequential designs are largely maintained for typically observed patterns of accrual, correlation, and heteroscedasticity even when using analyses that use standard software that depends on an independent increment structure. More extreme scenarios may require greater care to avoid departures from the nominal type I error rate and power.

Managing competing demands in the implementation of response-adaptive randomization in a large multicenter phase III acute stroke trial.

It is well known that competing demands exist between the control of important covariate imbalance and protection of treatment allocation randomness in confirmative clinical trials. When implementing a response-adaptive randomization algorithm in confirmative clinical trials designed under a frequentist framework, additional competing demands emerge between the shift of the treatment allocation ratio and the preservation of the power. Based on a large multicenter phase III stroke trial, we present a patient randomization scheme that manages these competing demands by applying a newly developed minimal sufficient balancing design for baseline covariates and a cap on the treatment allocation ratio shift in order to protect the allocation randomness and the power. Statistical properties of this randomization plan are studied by computer simulation. Trial operation characteristics, such as patient enrollment rate and primary outcome response delay, are also incorporated into the randomization plan.

Analyzing overall survival in randomized controlled trials with crossover and implications for economic evaluation.

BACKGROUND: Offering patients in oncology trials the opportunity to cross over to active treatment at disease progression is a common strategy to address ethical issues associated with placebo controls but may lead to statistical challenges in the analysis of overall survival and cost-effectiveness because crossover leads to information loss and dilution of comparative clinical efficacy. OBJECTIVES: We provide an overview of how to address crossover, implications for risk-effect estimates of survival (hazard ratios) and cost-effectiveness, and how this influences decisions of reimbursement agencies. Two case studies using data from two phase III sunitinib oncology trials are used as illustration. METHODS: We reviewed the literature on statistical methods for adjusting for crossover and recent health technology assessment decisions in oncology. RESULTS: We show that for a trial with a high proportion of crossover from the control arm to the investigational arm, the choice of the statistical method greatly affects treatment-effect estimates and cost-effectiveness because the range of relative mortality risk for active treatment versus control is broad. With relatively frequent crossover, one should consider either the inverse probability of censoring weighting or the rank-preserving structural failure time model to minimize potential bias, with choice dependent on crossover characteristics, trial size, and available data. A large proportion of crossover favors the rank-preserving structural failure time model, while large sample size and abundant information about confounding factors favors the inverse probability of censoring weighting model. When crossover is very infrequent, methods yield similar results. CONCLUSIONS: Failure to correct for crossover may lead to suboptimal decisions by pricing and reimbursement authorities, thereby limiting an effective drug's potential.

Disclosure of funding sources and conflicts of interest in phase III surgical trials: survey of ten general surgery journals.

BACKGROUND: Discussions regarding disclosure of funding sources and conflicts of interest (COI) in published peer-reviewed journal articles are becoming increasingly more common and intense. The aim of the present study was to examine whether randomized controlled trials (RCTs) published in leading surgery journals report funding sources and COI. METHODS: All articles reporting randomized controlled phase III trials published January 2005 through December 2010 were chosen for review from ten international journals. We evaluated the number of disclosed funding sources and COI, and the factors associated with such disclosures. RESULTS: From a review of 657 RCT from the ten journals, we discovered that presence or absence of a funding source and COI was disclosed by 47 % (309) and 25.1 % (165), respectively. Most articles in "International Committee of Medical Journal Editors (ICMJE)-affiliated journals" did not disclose COI. Disclosure of funding was associated with a journal impact factor >3 (51.7 vs 41.6 %; p < 0.01), statistician/epidemiologist involvement (64.2 vs 43.7 %; p < 0.001), publication after 2008 (52.9 vs 41.1 %; p < 0.01), and the journal being ICMJE-affiliated (49.3 vs 40 %; p < 0.05). Conflict of interest disclosure was associated with publication after 2008 (38.7 vs 11.3 %; p < 0.001), and with the journal not being affiliated with ICMJE (36.9 vs 21.3 %; p < 0.001). CONCLUSIONS: Of the published studies we investigated, over half did not disclose funding sources (i.e., whether or not there was a funding source), and almost three quarters did not disclose whether COI existed. Our findings suggest the need to adopt best current practices regarding disclosure of competing interests to fulfill responsibilities to readers and, ultimately, to patients.

Treatment costs associated with interventional cancer clinical trials conducted at a single UK institution over 2 years (2009-2010).

BACKGROUND: The conduct of clinical trials should be an integral part of routine patient care. Treating patients in trials incurs additional costs over and above standard of care (SOC), but the extent of the cost burden is not known. We undertook a retrospective cost attribution analysis to quantitate the treatment costs associated with cancer clinical trial protocols conducted over a 2 year period. METHODS: All patients entered into oncology (non-haematology) clinical trials involving investigational medicinal products in 2009 and 2010 in a single UK institution were identified. The trial protocols on which they were treated were analysed to identify the treatment costs for the experimental arm(s) of the trial and the equivalent SOC had the patient not been entered in the trial. The treatment cost difference was calculated by subtracting the experimental treatment cost from SOC cost. For randomised trials, an average treatment cost was estimated by taking into account the number of arms and randomisation ratio. An estimate of the annual treatment costs was calculated. RESULTS: A total of 357 adult oncology patients were treated on 53 different trial protocols: 40 phase III, 2 randomised II/III and 11 phase II design. A total of 27 trials were academic, non-commercial sponsored trials and 26 were commercial sponsored trials. When compared with SOC, the average treatment cost per patient was an excess of £431 for a non-commercial trial (range £6393 excess to £6005 saving) and a saving of £9294 for a commercial trial (range £0 to £71,480). There was an overall treatment cost saving of £388,719 in 2009 and £496,556 in 2010, largely attributable to pharmaceutical company provision of free drug supplies. CONCLUSION: On an average, non-commercial trial protocols were associated with a small per patient excess treatment cost, whereas commercial trials were associated with a substantially higher cost saving. Taking into account the total number of patients recruited annually, treatment of patients on clinical trial protocols was associated with considerable cost savings across both the non-commercial and commercial portfolio.

Comparison of methods to estimate health state utilities for ovarian cancer using quality of life data: a Gynecologic Oncology Group study.

BACKGROUND: Cost-effectiveness/cost-utility analyses are increasingly needed to inform decisions about care. Algorithms have been developed using the Functional Assessment of Cancer Therapy (FACT) quality of life instrument to estimate utility weights for cost analyses. This study was designed to compare these algorithms in the setting of ovarian cancer. METHODS: GOG-0152 was a 550-patient randomized phase III trial of interval cytoreduction, and GOG-0172 was a 415-patient randomized phase III trial comparing intravenous versus intraperitoneal therapy among women with advanced ovarian cancer. QOL data were collected via the FACT at four time points in each study. Two published mapping algorithms (Cheung and Dobrez) and a linear transformation method were applied to these data. The agreement between measures was assessed by the concordance correlation coefficient (r(CCC)), and paired t-tests were used to compare means. RESULTS: While agreement between the estimation algorithms was good (ranged from 0.72 to 0.81), there were statistically significant (p<0.001) and clinically meaningful differences between the scores: mean scores were higher with Dobrez than with Cheung or the linear transformation method. Scores were also statistically significantly different (p<0.001) between studies. CONCLUSIONS: In the absence of prospectively collected utility data, the use of mapping algorithms is feasible, however, the optimal algorithm is not clear. There were significant differences between studies, which highlight the need for validation of these algorithms in specific settings. If cost analyses incorporate mapping algorithms to obtain utility estimates, investigators should take the variability into account.

A mathematical model for maximizing the value of phase 3 drug development portfolios incorporating budget constraints and risk.

We describe a value-driven approach to optimizing pharmaceutical portfolios. Our approach incorporates inputs from research and development and commercial functions by simultaneously addressing internal and external factors. This approach differentiates itself from current practices in that it recognizes the impact of study design parameters, sample size in particular, on the portfolio value. We develop an integer programming (IP) model as the basis for Bayesian decision analysis to optimize phase 3 development portfolios using expected net present value as the criterion. We show how this framework can be used to determine optimal sample sizes and trial schedules to maximize the value of a portfolio under budget constraints. We then illustrate the remarkable flexibility of the IP model to answer a variety of 'what-if' questions that reflect situations that arise in practice. We extend the IP model to a stochastic IP model to incorporate uncertainty in the availability of drugs from earlier development phases for phase 3 development in the future. We show how to use stochastic IP to re-optimize the portfolio development strategy over time as new information accumulates and budget changes occur.

Shortcomings in the clinical evaluation of new drugs: acute myeloid leukemia as paradigm.

Drugs introduced over the past 25 years have benefitted many patients with acute myeloid leukemia (AML) and provided cure for some. Still, AML remains difficult to treat, and most patients will eventually die from their disease. Therefore, novel drugs and drug combinations are under intense investigation, and promising results eagerly awaited and embraced. However, drug development is lengthy and costs are staggering. While the phase 1-phase 2-phase 3 sequence of clinical drug testing has remained inviolate for decades, it appears intrinsically inefficient, and scientific flaws have been noted by many authors. Of major concern is the high frequency of false-positive results obtained in phase 2 studies. Here, we review features of phase 2 trials in AML that may contribute to this problem, particularly lack of control groups, patient heterogeneity, selection bias, and choice of end points. Recognizing these problems and challenges should provide us with opportunities to make drug development more efficient and less costly. We also suggest strategies for trial design improvement. Although our focus is on the treatment of AML, the principles that we highlight should be broadly applicable to the evaluation of new treatments for a variety of diseases.