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An adaptive trial design to optimize dose-schedule regimes with delayed outcomes
This paper proposes a two-stage phase I-II clinical trial design to optimize doseschedule regimes of an experimental agent within ordered disease subgroups in terms of the toxicity-efficacy trade-off. The design is motivated by settings where prior biological information indicates it is certain that efficacy will improve with ordinal subgroup level. We formulate a flexible Bayesian hierarchical model to account for associations among subgroups and regimes, and to characterize ordered subgroup effects. Sequentially adaptive decisionmaking is complicated by the problem, arising from the motivating application, that efficacy is scored on day 90 and toxicity is evaluated within 30 days from the start of therapy, while the patient accrual rate is fast relative to these outcome evaluation intervals. To deal with this in a practical manner, we take a likelihood-based approach that treats unobserved toxicity and efficacy outcomes as missing values, and use elicited utilities that quantify the efficacy-toxicity trade-off as a decision criterion. Adaptive randomization is used to assign patients to regimes while accounting for subgroups, with randomization probabilities depending on the posterior predictive distributions of utilities. A simulation study is presented to evaluate the design’s performance under a variety of scenarios, and to assess its sensitivity to the amount of missing data, the prior, and model misspecification.
Journal Article
Three randomized controlled trials evaluating the impact of “spin” in health news stories reporting studies of pharmacologic treatments on patients’/caregivers’ interpretation of treatment benefit
Background
News stories represent an important source of information. We aimed to evaluate the impact of “spin” (i.e., misrepresentation of study results) in health news stories reporting studies of pharmacologic treatments on patients’/caregivers’ interpretation of treatment benefit.
Methods
We conducted three two-arm, parallel-group, Internet-based randomized trials (RCTs) comparing the interpretation of news stories reported with or without spin. Each RCT considered news stories reporting a different type of study: (1) pre-clinical study, (2) phase I/II non-RCT, and (3) phase III/IV RCT. For each type of study, we identified news stories reported with spin that had earned mention in the press. Two versions of the news stories were used: the version with spin and a version rewritten without spin. Participants were patients/caregivers involved in Inspire, a large online community of more than one million patients/caregivers. The primary outcome was participants’ interpretation assessed by one specific question “What do you think is the probability that ‘treatment X’ would be beneficial to patients?” (scale, 0 [very unlikely] to 10 [very likely]).
Results
For each RCT, 300 participants were randomly assigned to assess a news story with spin (
n
= 150) or without spin (
n
= 150), and 900 participants assessed a news story. Participants were more likely to consider that the treatment would be beneficial to patients when the news story was reported with spin. The mean (SD) score for the primary outcome for abstracts reported with and without spin for pre-clinical studies was 7.5 (2.2) versus 5.8 (2.8) (mean difference [95% CI] 1.7 [1.0–2.3],
p
< 0.001); for phase I/II non-randomized trials, 7.6 (2.2) versus 5.8 (2.7) (mean difference 1.8 [1.0–2.5],
p
< 0.001); and for phase III/IV RCTs, 7.2 (2.3) versus 4.9 (2.8) (mean difference 2.3 [1.4–3.2],
p
< 0.001).
Conclusions
Spin in health news stories reporting studies of pharmacologic treatments affects patients’/caregivers’ interpretation.
Trial registration
ClinicalTrials.gov,
NCT03094078
,
NCT03094104
,
NCT03095586
Journal Article
Blockchain and clinical trial : securing patient data
\"This book aims to highlight the gaps and the transparency issues in the clinical research and trials processes and how there is a lack of information flowing back to researchers and patients involved in those trials. Lack of data transparency is an underlying theme within the clinical research world and causes issues of corruption, fraud, errors and a problem of reproducibility. Blockchain can prove to be a method to ensure a much more joined up and integrated approach to data sharing and improving patient outcomes. Surveys undertaken by creditable organisations in the healthcare industry are analysed in this book that show strong support for using blockchain technology regarding strengthening data security, interoperability and a range of beneficial use cases where mostly all respondents of the surveys believe blockchain will be important for the future of the healthcare industry. Another aspect considered in the book is the coming surge of healthcare wearables using Internet of Things (IoT) and the prediction that the current capacity of centralised networks will not cope with the demands of data storage. The benefits are great for clinical research, but will add more pressure to the transparency of clinical trials and how this is managed unless a secure mechanism like, blockchain is used\"--Publisher's description.
Book
Long-term safety of secukinumab in patients with moderate-to-severe plaque psoriasis, psoriatic arthritis, and ankylosing spondylitis: integrated pooled clinical trial and post-marketing surveillance data
Background
Secukinumab, a fully human immunoglobulin G1-kappa monoclonal antibody that directly inhibits interleukin (IL)-17A, has been shown to have robust efficacy in the treatment of moderate-to-severe psoriasis (PsO), psoriatic arthritis (PsA), and ankylosing spondylitis (AS) demonstrating a rapid onset of action and sustained long-term clinical responses with a consistently favorable safety profile in multiple Phase 2 and 3 trials. Here, we report longer-term pooled safety and tolerability data for secukinumab across three indications (up to 5 years of treatment in PsO and PsA; up to 4 years in AS).
Methods
The integrated clinical trial safety dataset included data pooled from 21 randomized controlled clinical trials of secukinumab 300 or 150 or 75 mg in PsO (14 Phase 3 trials and 1 Phase 4 trial), PsA (3 Phase 3 trials), and AS (3 Phase 3 trials), along with post-marketing safety surveillance data with a cut-off date of June 25, 2017. Adverse events (AEs) were reported as exposure-adjusted incident rates (EAIRs) per 100 patient-years. Analyses included all patients who received ≥ 1 dose of secukinumab.
Results
A total of 5181, 1380, and 794 patients from PsO, PsA, and AS clinical trials representing secukinumab exposures of 10,416.9, 3866.9, and 1943.1 patient-years, respectively, and post-marketing data from patients with a cumulative exposure to secukinumab of ~ 96,054 patient-years were included in the analysis. The most frequent AE was upper respiratory tract infection. EAIRs across PsO, PsA, and AS indications were generally low for serious infections (1.4, 1.9, and 1.2, respectively),
Candida
infections (2.2, 1.5, and 0.7, respectively), inflammatory bowel disease (0.01, 0.05, and 0.1, respectively), and major adverse cardiac events (0.3, 0.4, and 0.6, respectively). No cases of tuberculosis reactivation were reported. The incidence of treatment-emergent anti-drug antibodies was low with secukinumab across all studies, with no discernible loss of efficacy, unexpected alterations in pharmacokinetics, or association with immunogenicity-related AEs.
Conclusions
Secukinumab demonstrated a favorable safety profile over long-term treatment in patients with PsO, PsA, and AS. This comprehensive assessment demonstrated that the safety profile of secukinumab was consistent with previous reports in patients with PsO, PsA, and AS, supporting its long-term use in these chronic conditions.
Journal Article
Model‐Based Population Pharmacokinetic Analysis of Nivolumab in Patients With Solid Tumors
Nivolumab is a fully human monoclonal antibody that inhibits programmed death‐1 activation. The clinical pharmacology profile of nivolumab was analyzed by a population pharmacokinetics model that assessed covariate effects on nivolumab concentrations in 1,895 patients who received 0.3–10.0 mg/kg nivolumab in 11 clinical trials. Nivolumab pharmacokinetics is linear with a time‐varying clearance. A full covariate model was developed to assess covariate effects on pharmacokinetic parameters. Nivolumab clearance and volume of distribution increase with body weight. The final model included the effects of baseline performance status (PS), baseline body weight, and baseline estimated glomerular filtration rate (eGFR), sex, and race on clearance, and effects of baseline body weight and sex on volume of distribution in the central compartment. Sex, PS, baseline eGFR, age, race, baseline lactate dehydrogenase, mild hepatic impairment, tumor type, tumor burden, and programmed death ligand‐1 expression had a significant but not clinically relevant (<20%) effect on nivolumab clearance.
Journal Article
