Selective Inhibition of NaV1.8 with VX-548 for Acute Pain.

BACKGROUND: The NaV1.8 voltage-gated sodium channel, expressed in peripheral nociceptive neurons, plays a role in transmitting nociceptive signals. The effect of VX-548, an oral, highly selective inhibitor of NaV1.8, on control of acute pain is being studied. METHODS: After establishing the selectivity of VX-548 for NaV1.8 inhibition in vitro, we conducted two phase 2 trials involving participants with acute pain after abdominoplasty or bunionectomy. In the abdominoplasty trial, participants were randomly assigned in a 1:1:1:1 ratio to receive one of the following over a 48-hour period: a 100-mg oral loading dose of VX-548, followed by a 50-mg maintenance dose every 12 hours (the high-dose group); a 60-mg loading dose of VX-548, followed by a 30-mg maintenance dose every 12 hours (the middle-dose group); hydrocodone bitartrate-acetaminophen (5 mg of hydrocodone bitartrate and 325 mg of acetaminophen every 6 hours); or oral placebo every 6 hours. In the bunionectomy trial, participants were randomly assigned in a 2:2:1:2:2 ratio to receive one of the following over a 48-hour treatment period: oral high-dose VX-548; middle-dose VX-548; low-dose VX-548 (a 20-mg loading dose, followed by a 10-mg maintenance dose every 12 hours); oral hydrocodone bitartrate-acetaminophen (5 mg of hydrocodone bitartrate and 325 mg of acetaminophen every 6 hours); or oral placebo every 6 hours. The primary end point was the time-weighted sum of the pain-intensity difference (SPID) over the 48-hour period (SPID48), a measure derived from the score on the Numeric Pain Rating Scale (range, 0 to 10; higher scores indicate greater pain) at 19 time points after the first dose of VX-548 or placebo. The main analysis compared each dose of VX-548 with placebo. RESULTS: A total of 303 participants were enrolled in the abdominoplasty trial and 274 in the bunionectomy trial. The least-squares mean difference between the high-dose VX-548 and placebo groups in the time-weighted SPID48 was 37.8 (95% confidence interval [CI], 9.2 to 66.4) after abdominoplasty and 36.8 (95% CI, 4.6 to 69.0) after bunionectomy. In both trials, participants who received lower doses of VX-548 had results similar to those with placebo. Headache and constipation were common adverse events with VX-548. CONCLUSIONS: As compared with placebo, VX-548 at the highest dose, but not at lower doses, reduced acute pain over a period of 48 hours after abdominoplasty or bunionectomy. VX-548 was associated with adverse events that were mild to moderate in severity. (Funded by Vertex Pharmaceuticals; VX21-548-101 and VX21-548-102 ClinicalTrials.gov numbers, NCT04977336 and NCT05034952.).

Design and analysis of drop-the-losers studies using binary endpoints in the rare disease setting.

The drop-the-losers design combines a phase 2 trial of k treatments and a confirmatory phase 3 trial under a single adaptive protocol, thereby gaining efficiency over a traditional clinical development approach. Such designs may be particularly useful in the rare disease setting, where conserving sample size is paramount, and control arms may not be feasible. We propose an unconditional exact likelihood (UEL) testing and inference procedure for these designs for a binary endpoint using small sample sizes, comparing its operating characteristics to existing methods. Additional practical considerations are evaluated, including the choice of stagewise sample sizes and effect of ties.

Developing and evaluating risk prediction models with panel current status data.

Panel current status data arise frequently in biomedical studies when the occurrence of a particular clinical condition is only examined at several prescheduled visit times. Existing methods for analyzing current status data have largely focused on regression modeling based on commonly used survival models such as the proportional hazards model and the accelerated failure time model. However, these procedures have the limitations of being difficult to implement and performing sub-optimally in relatively small sample sizes. The performance of these procedures is also unclear under model misspecification. In addition, no methods currently exist to evaluate the prediction performance of estimated risk models with panel current status data. In this paper, we propose a simple estimator under a general class of nonparametric transformation (NPT) models by fitting a logistic regression working model and demonstrate that our proposed estimator is consistent for the NPT model parameter up to a scale multiplier. Furthermore, we propose nonparametric estimators for evaluating the prediction performance of the risk score derived from model fitting, which is valid regardless of the adequacy of the fitted model. Extensive simulation results suggest that our proposed estimators perform well in finite samples and the regression parameter estimators outperform existing estimators under various scenarios. We illustrate the proposed procedures using data from the Framingham Offspring Study.

Estimation and inference for semi-competing risks based on data from a nested case-control study.

In semi-competing risks, the occurrence of some non-terminal event is subject to a terminal event, usually death. While existing methods for semi-competing risks data analysis assume complete information on all relevant covariates, data on at least one covariate are often not readily available in practice. In this setting, for standard univariate time-to-event analyses, researchers may choose from several strategies for sub-sampling patients on whom to collect complete data, including the nested case-control study design. Here, we consider a semi-competing risks analysis through the reuse of data from an existing nested case-control study for which risk sets were formed based on either the non-terminal or the terminal event. Additionally, we introduce the supplemented nested case-control design in which detailed data are collected on additional events of the other type. We propose estimation with respect to a frailty illness-death model through maximum weighted likelihood, specifying the baseline hazard functions either parametrically or semi-parametrically via B-splines. Two standard error estimators are proposed: (i) a computationally simple sandwich estimator and (ii) an estimator based on a perturbation resampling procedure. We derive the asymptotic properties of the proposed methods and evaluate their small-sample properties via simulation. The designs/methods are illustrated with an investigation of risk factors for acute graft-versus-host disease among N = 8838 patients undergoing hematopoietic stem cell transplantation, for which death is a significant competing risk.

Design and analysis of nested case-control studies for recurrent events subject to a terminal event.

The process by which patients experience a series of recurrent events, such as hospitalizations, may be subject to death. In cohort studies, one strategy for analyzing such data is to fit a joint frailty model for the intensities of the recurrent event and death, which estimates covariate effects on the two event types while accounting for their dependence. When certain covariates are difficult to obtain, however, researchers may only have the resources to subsample patients on whom to collect complete data: one way is using the nested case-control (NCC) design, in which risk set sampling is performed based on a single outcome. We develop a general framework for the design of NCC studies in the presence of recurrent and terminal events and propose estimation and inference for a joint frailty model for recurrence and death using data arising from such studies. We propose a maximum weighted penalized likelihood approach using flexible spline models for the baseline intensity functions. Two standard error estimators are proposed: a sandwich estimator and a perturbation resampling procedure. We investigate operating characteristics of our estimators as well as design considerations via a simulation study and illustrate our methods using two studies: one on recurrent cardiac hospitalizations in patients with heart failure and the other on local recurrence and metastasis in patients with breast cancer.

Clinical trial transparency: a reassessment of industry compliance with clinical trial registration and reporting requirements in the United States.

OBJECTIVE: To evaluate the accuracy of a 2015 cross-sectional analysis published in the BMJ Open which reported that pharmaceutical industry compliance with clinical trial registration and results reporting requirements under US law was suboptimal and varied widely among companies. DESIGN: We performed a reassessment of the data reported in Miller et al to evaluate whether statutory compliance analyses and conclusions were valid. DATA SOURCES: Information from the Dryad Digital Repository, ClinicalTrials.gov, Drugs@FDA and direct communications with sponsors. MAIN OUTCOME MEASURES: Compliance with the clinical trial registration and results reporting requirements under the Food and Drug Administration Amendments Act (FDAAA). RESULTS: Industry compliance with FDAAA disclosure requirements was notably higher than reported by Miller et al. Among trials subject to FDAAA, Miller et al reported that, per drug, a median of 67% (middle 50% range: 0%-100%) of trials fully complied with registration and results reporting requirements. On reanalysis of the data, we found that a median of 100% (middle 50% range: 93%-100%) of clinical trials for a particular drug fully complied with the law. When looking at overall compliance at the trial level, our reassessment yields 94% timely registration and 90% timely results reporting among the 49 eligible trials, and an overall FDAAA compliance rate of 86%. CONCLUSIONS: The claim by Miller et al that industry compliance is below legal standards is based on an analysis that relies on an incomplete dataset and an interpretation of FDAAA that requires disclosure of study results for drugs that have not yet been approved for any indication. On reanalysis using a different interpretation of FDAAA that focuses on whether results were disclosed within 30 days of drug approval, we found that industry compliance with US statutory disclosure requirements for the 15 reviewed drugs was consistently high.

Beyond Composite Endpoints Analysis: Semicompeting Risks as an Underutilized Framework for Cancer Research.

BACKGROUND: Composite endpoints (CEP), such as progression-free survival, are commonly used in cancer research. Notwithstanding their popularity, however, CEP analyses suffer from a number of drawbacks, especially when death is combined with a nonterminal event (ie, progression or recurrence), exemplifying the semicompeting risks setting. We investigated the semicompeting risks framework as a complementary analysis strategy that avoids certain drawbacks of CEPs. METHODS: The illness-death model under the semicompeting risks framework was compared with standard analysis approaches: CEP analyses and (separate) univariate analyses for each component endpoint. Data from a previously published phase III randomized clinical trial in metastatic colon cancer including 1419 participants in the N9741 trial (conducted between 1997 and 2003) were used to determine the impact of the loss of information associated with combining multiple endpoints, as well as of ignoring the potentially informative role of death. A simulation study was conducted to further explore these issues. RESULTS: Failure to account for critical features of semicompeting risks data can lead to potentially severely misleading conclusions. Advantages of semicompeting risks analyses include a clear delineation of treatment effects on both events, the ability to draw conclusions about a patient's joint risk of the two events, and an assessment of the dependence between the two event types. CONCLUSIONS: Embedding and analyzing component outcomes in the semicompeting risks framework, either as a supplement or alternative to CEP analyses, represents an important, underutilized, and feasible opportunity for cancer research.

Learning About Missing Data Mechanisms in Electronic Health Records-based Research: A Survey-based Approach.

BACKGROUND: Bias due to missing data is a major concern in electronic health record (EHR)-based research. As part of an ongoing EHR-based study of weight change among patients treated for depression, we conducted a survey to investigate determinants of missingness in the available weight information and to evaluate the missing-at-random assumption. METHODS: We identified 8,345 individuals enrolled in a large EHR-based health care system who had monotherapy treatment for depression from April 2008 to March 2010. A stratified sample of 1,153 individuals completed a detailed survey. Logistic regression was used to investigate determinants of whether a patient (1) had an opportunity to be weighed at treatment initiation (baseline), and (2) had a weight measurement recorded. Parallel analyses were conducted to investigate missingness during follow-up. Throughout, inverse-probability weighting was used to adjust for the design and survey nonresponse. Analyses were also conducted to investigate potential recall bias. RESULTS: Missingness at baseline and during follow-up was associated with numerous factors not routinely collected in the EHR including whether or not the patient had ever chosen not to be weighed, external weight control activities, and self-reported baseline weight. Patient attitudes about their weight and perceptions regarding the potential impact of their depression treatment on weight were not related to missingness. CONCLUSION: Adopting a comprehensive strategy to investigate missingness early in the research process gives researchers information necessary to evaluate key assumptions. While the survey presented focuses on outcome data, the overarching strategy can be applied to any and all data elements subject to missingness.

curatedOvarianData: clinically annotated data for the ovarian cancer transcriptome.

This article introduces a manually curated data collection for gene expression meta-analysis of patients with ovarian cancer and software for reproducible preparation of similar databases. This resource provides uniformly prepared microarray data for 2970 patients from 23 studies with curated and documented clinical metadata. It allows users to efficiently identify studies and patient subgroups of interest for analysis and to perform meta-analysis immediately without the challenges posed by harmonizing heterogeneous microarray technologies, study designs, expression data processing methods and clinical data formats. We confirm that the recently proposed biomarker CXCL12 is associated with patient survival, independently of stage and optimal surgical debulking, which was possible only through meta-analysis owing to insufficient sample sizes of the individual studies. The database is implemented as the curatedOvarianData Bioconductor package for the R statistical computing language, providing a comprehensive and flexible resource for clinically oriented investigation of the ovarian cancer transcriptome. The package and pipeline for producing it are available from http://bcb.dfci.harvard.edu/ovariancancer.