Explore the vast range of titles available.

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

Recruitment for clinical trials continues to be a challenge, as patient recruitment is the single biggest cause of trial delays. Around 80% of trials fail to meet the initial enrollment target and timeline, and these delays can result in lost revenue of as much as US $8 million per day for drug developing companies. This study aimed to conduct a systematic review and meta-analysis examining the effectiveness of online recruitment of participants for clinical trials compared with traditional in-clinic/offline recruitment methods. Data on recruitment rates (the average number of patients enrolled in the study per month and per day of active recruitment) and conversion rates (the percentage of participants screened who proceed to enroll into the clinical trial), as well as study characteristics and patient demographics were collected from the included studies. Differences in online and offline recruitment rates and conversion rates were examined using random effects models. Further, a nonparametric paired Wilcoxon test was used for additional analysis on the cost-effectiveness of online patient recruitment. All data analyses were conducted in R language, and P<.05 was considered significant. In total, 3861 articles were screened for inclusion. Of these, 61 studies were included in the review, and 23 of these were further included in the meta-analysis. We found online recruitment to be significantly more effective with respect to the recruitment rate for active days of recruitment, where 100% (7/7) of the studies included had a better online recruitment rate compared with offline recruitment (incidence rate ratio [IRR] 4.17, P=.04). When examining the entire recruitment period in months we found that 52% (12/23) of the studies had a better online recruitment rate compared with the offline recruitment rate (IRR 1.11, P=.71). For cost-effectiveness, we found that online recruitment had a significantly lower cost per enrollee compared with offline recruitment (US $72 vs US $199, P=.04). Finally, we found that 69% (9/13) of studies had significantly better offline conversion rates compared with online conversion rates (risk ratio 0.8, P=.02). Targeting potential participants using online remedies is an effective approach for patient recruitment for clinical research. Online recruitment was both superior in regard to time efficiency and cost-effectiveness compared with offline recruitment. In contrast, offline recruitment outperformed online recruitment with respect to conversion rate.

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.

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.

Despite efforts to improve research equity in clinical trials, a lack of representativeness continues to threaten the generalizability of clinical trial evidence and leads to several ethical and economic consequences. Decentralized clinical trials (DCTs) and pragmatic clinical trials (PCTs), novel clinical trial models that use technology to enable alternative data collection methods and integrate studies into clinical care, hold great promise for addressing representativeness challenges but also face several limitations. Leveraging technology and clinical care settings to conduct trial visits and collect trial data inherently limits participation from people without reliable access to technology and consistent medical care. Further, representativeness needs to be improved across the full spectrum of clinical trials, from trials conducted in early product development phases to trials conducted after a product is approved, but the DCT and PCT elements that can improve representativeness are more likely to be deployed after seminal evidence for a new medical product’s regulatory approval has already been generated. We propose three solutions to help ensure the defining aspects of DCTs and PCTs increase representativeness and the generalizability of evidence generated in clinical trials conducted at all phases in the product lifecycle. We suggest that efforts should be centered around collaborative work with regulators to define data standards and validate outcomes when alternative data sources and collection methods are used, the creation of technical support resources and transparent processes for the conduct of trials facilitated by technology, and the incorporation of health equity principles into the design and deployment of technology-based tools used to facilitate DCTs and PCTs.

Common Sense Oncology (CSO) prioritises treatments providing meaningful benefits for people with cancer. Here, we describe CSO principles aimed at improving the design, analysis, and reporting of randomised, controlled, phase 3 clinical trials evaluating cancer treatments. These principles include: (1) control treatment should be the best current standard of care; (2) the preferred primary endpoint is overall survival or a validated surrogate; (3) an absolute measure of benefit should be provided, such as the difference between groups in median overall survival times or the proportion of surviving patients at a prespecified time; (4) health-related quality of life should be at least a secondary endpoint; (5) toxicity should be described objectively without subjective language diminishing its importance; (6) trials should be designed to show or rule out clinically meaningful differences in outcomes, rather than a statistically significant difference alone; (7) censoring should be detailed, and sensitivity analyses done to determine its possible effects; (8) experimental treatments known to improve overall survival at later disease stages should be offered and funded for patients progressing in the control group; and (9) reports of trials should include a lay-language summary. We include checklists to guide compliance with these principles. By encouraging adherence, CSO aims to ensure that clinical trials yield results that are scientifically robust and meaningful to patients.

Today, more than 75 percent of pharmaceutical drug trials in the United States are being conducted in the private sector. Once the sole province of academic researchers, these important studies are now being outsourced to non-academic physicians. According to Jill A. Fisher, this major change in the way medical research is performed is the outcome of two problems in U.S. health care: decreasing revenue for physicians and decreasing access to treatment for patients. As physicians report diminishing income due to restrictive relationships with insurers, increasing malpractice insurance premiums, and inflated overhead costs to operate private practices, they are attracted to pharmaceutical contract research for its lucrative return. Clinical trials also provide limited medical access to individuals who have no or inadequate health insurance because they offer \"free\" doctors' visits, diagnostic tests, and medications to participants. Focusing on the professional roles of those involved, as well as key research practices, Fisher assesses the risks and advantages for physicians and patients alike when pharmaceutical drug studies are used as an alternative to standard medical care. A volume in the Critical Issues in Health and Medicine series, edited by Rima D. Apple and Janet Golden

Factorial designs can allow efficient evaluation of multiple treatments within a single trial. We evaluated the design, analysis, and reporting in a sample of factorial trials. Review of 2 × 2 factorial trials evaluating health-related interventions and outcomes in humans. Using Medline, we identified articles published between January 2015 and March 2018. We randomly selected 100 articles for inclusion. Most trials (78%) did not provide a rationale for using a factorial design. Only 63 trials (63%) assessed the interaction for the primary outcome, and 39/63 (62%) made a further assessment for at least one secondary outcome. 12/63 trials (19%) identified a significant interaction for the primary outcome and 16/39 trials (41%) for at least one secondary outcome. Inappropriate methods of analysis to protect against potential negative effects from interactions were common, with 18 trials (18%) choosing the analysis method based on a preliminary test for interaction, and 13% (n = 10/75) of those conducting a factorial analysis including an interaction term in the model. Reporting of factorial trials was often suboptimal, and assessment of interactions was poor. Investigators often used inappropriate methods of analysis to try to protect against adverse effects of interactions.

Conventionally, evaluation of a new drug, A, is done in three phases. Phase I is based on toxicity to determine a \"maximum tolerable dose\" (MTD) of A, phase II is conducted to decide whether A at the MTD is promising in terms of response probability, and if so a large randomized phase III trial is conducted to compare A to a control treatment, C, usually based on survival time or progression free survival time. It is widely recognized that this paradigm has many flaws. A recent approach combines the first two phases by conducting a phase I-II trial, which chooses an optimal dose based on both efficacy and toxicity, and evaluation of A at the selected optimal phase I-II dose then is done in a phase III trial. This paper proposes a new design paradigm, motivated by the possibility that the optimal phase I-II dose may not maximize mean survival time with A. We propose a hybridized design, which we call phase I-II/III, that combines phase I-II and phase III by allowing the chosen optimal phase I-II dose of A to be re-optimized based on survival time data from phase I-II patients and the first portion of phase III. The phase I-II/III design uses adaptive randomization in phase I-II, and relies on a mixture model for the survival time distribution as a function of efficacy, toxicity, and dose. A simulation study is presented to evaluate the phase I-II/III design and compare it to the usual approach that does not re-optimize the dose of A in phase III.