Comments on 'standard and reference-based conditional mean imputation': Regulators and trial statisticians be aware!

Accurate frequentist performance of a method is desirable in confirmatory clinical trials, but is not sufficient on its own to justify the use of a missing data method. Reference-based conditional mean imputation, with variance estimation justified solely by its frequentist performance, has the surprising and undesirable property that the estimated variance becomes smaller the greater the number of missing observations; as explained under jump-to-reference it effectively forces the true treatment effect to be exactly zero for patients with missing data.

BRL37344 stimulates GLUT4 translocation and glucose uptake in skeletal muscle via ß2-adrenoceptors without causing classical receptor desensitization.

The type 2 diabetes epidemic makes it important to find insulin-independent ways to improve glucose homeostasis. This study examines the mechanisms activated by a dual ß2-/ß3-adrenoceptor agonist, BRL37344, to increase glucose uptake in skeletal muscle and its effects on glucose homeostasis in vivo. We measured the effect of BRL37344 on glucose uptake, glucose transporter 4 (GLUT4) translocation, cAMP levels, ß2-adrenoceptor desensitization, ß-arrestin recruitment, Akt, AMPK, and mammalian target of rapamycin (mTOR) phosphorylation using L6 skeletal muscle cells as a model. We further tested the ability of BRL37344 to modulate skeletal muscle glucose metabolism in animal models (glucose tolerance tests and in vivo and ex vivo skeletal muscle glucose uptake). In L6 cells, BRL37344 increased GLUT4 translocation and glucose uptake only by activation of ß2-adrenoceptors, with a similar potency and efficacy to that of the nonselective ß-adrenoceptor agonist isoprenaline, despite being a partial agonist with respect to cAMP generation. GLUT4 translocation occurred independently of Akt and AMPK phosphorylation but was dependent on mTORC2. Furthermore, in contrast to isoprenaline, BRL37344 did not promote agonist-mediated desensitization and failed to recruit ß-arrestin1/2 to the ß2-adrenoceptor. In conclusion, BRL37344 improved glucose tolerance and increased glucose uptake into skeletal muscle in vivo and ex vivo through a ß2-adrenoceptor-mediated mechanism independently of Akt. BRL37344 was a partial agonist with respect to cAMP, but a full agonist for glucose uptake, and importantly did not cause classical receptor desensitization or internalization of the receptor.

Treatment policy estimands for recurrent event data using data collected after cessation of randomised treatment.

In the past, many clinical trials have withdrawn subjects from the study when they prematurely stopped their randomised treatment and have therefore only collected 'on-treatment' data. Thus, analyses addressing a treatment policy estimand have been restricted to imputing missing data under assumptions drawn from these data only. Many confirmatory trials are now continuing to collect data from subjects in a study even after they have prematurely discontinued study treatment as this event is irrelevant for the purposes of a treatment policy estimand. However, despite efforts to keep subjects in a trial, some will still choose to withdraw. Recent publications for sensitivity analyses of recurrent event data have focused on the reference-based imputation methods commonly applied to continuous outcomes, where imputation for the missing data for one treatment arm is based on the observed outcomes in another arm. However, the existence of data from subjects who have prematurely discontinued treatment but remained in the study has now raised the opportunity to use this 'off-treatment' data to impute the missing data for subjects who withdraw, potentially allowing more plausible assumptions for the missing post-study-withdrawal data than reference-based approaches. In this paper, we introduce a new imputation method for recurrent event data in which the missing post-study-withdrawal event rate for a particular subject is assumed to reflect that observed from subjects during the off-treatment period. The method is illustrated in a trial in chronic obstructive pulmonary disease (COPD) where the primary endpoint was the rate of exacerbations, analysed using a negative binomial model.

Estimating the variance for heterogeneity in arm-based network meta-analysis.

Network meta-analysis can be implemented by using arm-based or contrast-based models. Here we focus on arm-based models and fit them using generalized linear mixed model procedures. Full maximum likelihood (ML) estimation leads to biased trial-by-treatment interaction variance estimates for heterogeneity. Thus, our objective is to investigate alternative approaches to variance estimation that reduce bias compared with full ML. Specifically, we use penalized quasi-likelihood/pseudo-likelihood and hierarchical (h) likelihood approaches. In addition, we consider a novel model modification that yields estimators akin to the residual maximum likelihood estimator for linear mixed models. The proposed methods are compared by simulation, and 2 real datasets are used for illustration. Simulations show that penalized quasi-likelihood/pseudo-likelihood and h-likelihood reduce bias and yield satisfactory coverage rates. Sum-to-zero restriction and baseline contrasts for random trial-by-treatment interaction effects, as well as a residual ML-like adjustment, also reduce bias compared with an unconstrained model when ML is used, but coverage rates are not quite as good. Penalized quasi-likelihood/pseudo-likelihood and h-likelihood are therefore recommended.

Estimands: discussion points from the PSI estimands and sensitivity expert group.

ICH E9 Statistical Principles for Clinical Trials was issued in 1998. In October 2014, an addendum to ICH E9 was proposed relating to estimands and sensitivity analyses. In preparation for the release of the addendum, Statisticians in the Pharmaceutical Industry held a 1-day expert group meeting in February 2015. Topics debated included definition, development, implementation, education and communication challenges associated with estimands and sensitivity analyses. The topic of estimands is an important and relatively new one in clinical development. A clear message from the meeting was that estimands bridge the gap between study objectives and statistical methods. When defining estimands, an iterative process linking trial objectives, estimands, trial design, statistical and sensitivity analysis needs to be established. Each objective should have at least one distinct estimand, supported by sensitivity analyses. Because clinical trials are multi-faceted and expensive, it is unrealistic to restrict a study to a single objective and associated estimand. The actual set of estimands and sensitivity analyses for a study will depend on the study objectives, the disease setting and the needs of the various stakeholders. Copyright © 2016 John Wiley & Sons, Ltd.

Oral Trauma and Tooth Avulsion Following Explosion of E-Cigarette.

Electronic cigarettes (E-cigarettes), or personal vaporizers, were introduced in 2003 and have been available in the United States since 2007. In addition to the health and safety concerns of the aerosol delivery of nicotine through E-cigarettes, during the past 8 years, reports of explosions and fires caused by the E-cigarette devices have led the US Fire Administration to evaluate the safety of these devices. These explosions have been observed frequently enough that the US Department of Transportation has recently banned E-cigarette devices in checked baggage aboard airplanes. This report contributes to existing knowledge about the hazards related to E-cigarettes by describing oral hard and soft tissue injuries from an E-cigarette explosion.

Missing CD4+ cell response in randomized clinical trials of maraviroc and dolutegravir.

BACKGROUND: Missing data can compromise inferences from clinical trials, yet the topic has received little attention in the clinical trial community. Shortcomings in commonly used methods used to analyze studies with missing data (complete case, last- or baseline-observation carried forward) have been highlighted in a recent Food and Drug Administration-sponsored report. This report recommends how to mitigate the issues associated with missing data. We present an example of the proposed concepts using data from recent clinical trials. METHODS: CD4+ cell count data from the previously reported SINGLE and MOTIVATE studies of dolutegravir and maraviroc were analyzed using a variety of statistical methods to explore the impact of missing data. Four methodologies were used: complete case analysis, simple imputation, mixed models for repeated measures, and multiple imputation. We compared the sensitivity of conclusions to the volume of missing data and to the assumptions underpinning each method. RESULTS: Rates of missing data were greater in the MOTIVATE studies (35%-68% premature withdrawal) than in SINGLE (12%-20%). The sensitivity of results to assumptions about missing data was related to volume of missing data. Estimates of treatment differences by various analysis methods ranged across a 61 cells/mm3 window in MOTIVATE and a 22 cells/mm3 window in SINGLE. CONCLUSIONS: Where missing data are anticipated, analyses require robust statistical and clinical debate of the necessary but unverifiable underlying statistical assumptions. Multiple imputation makes these assumptions transparent, can accommodate a broad range of scenarios, and is a natural analysis for clinical trials in HIV with missing data.

Effects of simulated neural mobilization on fluid movement in cadaveric peripheral nerve sections: implications for the treatment of neuropathic pain and dysfunction.

BACKGROUND AND PURPOSE: Neural mobilization techniques are used clinically to treat neuropathic pain and dysfunction. While selected studies report efficacy of these techniques, the mechanisms of benefit are speculative. The purpose of this study was to evaluate the effects of in vitro simulated stretch/relax neural mobilization cycles on fluid dispersion within sections of unembalmed cadaveric peripheral nerve tissue. METHODS: Bilateral sciatic nerve sections were harvested from six cadavers. Matched pairs of nerve sections were secured in a tissue tester and injected with a plasma/Toluidine Blue dye solution. Once the initial dye spread stabilized, the experimental nerve sections underwent 25 stretch/relaxation cycles (e.g. simulated neural mobilization) produced by a mechanical tissue tester. Post-test dye spread measurements were compared to pre-test measurements as well as control findings (no simulated mobilization). Data were analyzed using paired t-tests. RESULTS: Individual dye spread measurements were reliable [ICC(3,1) = 0·99]. The post-test intraneural fluid movement (dye spread) in the experimental section increased significantly with simulated neural mobilization compared to pre-test measurements (3·2±2·1 mm; P = 0·015) and control measurements (3·3±2·7 mm; P = 0·013). CONCLUSION: Repetitive simulated neural mobilization, incorporating stretch/relax cycles, of excised cadaveric peripheral nerve tissue produced an increase in intraneural fluid dispersion. Neural mobilization may alter nerve tissue environment, promoting improved function and nerve health, by dispersing tissue fluid and diminishing intraneural swelling and/or pressure.

Self-mutilation of the lower lip in a child with dystonia secondary to megalencephalic leukoencephalopathy treated with botox injections: a case report.

PURPOSE: We report the case of a 10-year old boy who had been diagnosed with megalencephalic leukoencephalopathy several years earlier. Because of the patient's oral dystonic activity, a traumatic, nonhealing, chronic ulcer had developed on his lower lip. MATERIALS AND METHODS: Botox-A was injected into the mentalis, orbicularis oris, and bilateral masseter muscles. RESULTS: The patient showed decreased dystonia and gradual complete healing of the traumatic ulcer of the lower lip. CONCLUSIONS: The treatment of patients with self-mutilation to the lips will often be difficult. Traditionally, patients have been treated with various medications, oral appliances, and even tooth extraction. The results of the present case report suggest that Botox should be considered as a possible first-line strategy, along with oral appliances.

Missing data sensitivity analysis for recurrent event data using controlled imputation.

Statistical analyses of recurrent event data have typically been based on the missing at random assumption. One implication of this is that, if data are collected only when patients are on their randomized treatment, the resulting de jure estimator of treatment effect corresponds to the situation in which the patients adhere to this regime throughout the study. For confirmatory analysis of clinical trials, sensitivity analyses are required to investigate alternative de facto estimands that depart from this assumption. Recent publications have described the use of multiple imputation methods based on pattern mixture models for continuous outcomes, where imputation for the missing data for one treatment arm (e.g. the active arm) is based on the statistical behaviour of outcomes in another arm (e.g. the placebo arm). This has been referred to as controlled imputation or reference-based imputation. In this paper, we use the negative multinomial distribution to apply this approach to analyses of recurrent events and other similar outcomes. The methods are illustrated by a trial in severe asthma where the primary endpoint was rate of exacerbations and the primary analysis was based on the negative binomial model.

Analysis of longitudinal trials with protocol deviation: a framework for relevant, accessible assumptions, and inference via multiple imputation.

Protocol deviations, for example, due to early withdrawal and noncompliance, are unavoidable in clinical trials. Such deviations often result in missing data. Additional assumptions are then needed for the analysis, and these cannot be definitively verified from the data at hand. Thus, as recognized by recent regulatory guidelines and reports, clarity about these assumptions and their implications is vital for both the primary analysis and framing relevant sensitivity analysis. This article focuses on clinical trials with longitudinal quantitative outcome data. For the target population, we define two estimands, the de jure estimand, "does the treatment work under the best case scenario," and the de facto estimand, "what would be the effect seen in practice." We then carefully define the concept of a deviation from the protocol relevant to the estimand, or for short a deviation. Each patient's postrandomization data can then be divided into predeviation data and postdeviation data. We set out an accessible framework for contextually appropriate assumptions relevant to de facto and de jure estimands, that is, assumptions about the joint distribution of pre- and postdeviation data relevant to the clinical question at hand. We then show how, under these assumptions, multiple imputation provides a practical approach to estimation and inference. We illustrate with data from a longitudinal clinical trial in patients with chronic asthma.

Multiplicity: discussion points from the Statisticians in the Pharmaceutical Industry multiplicity expert group.

In May 2012, the Committee of Health and Medicinal Products issued a concept paper on the need to review the points to consider document on multiplicity issues in clinical trials. In preparation for the release of the updated guidance document, Statisticians in the Pharmaceutical Industry held a one-day expert group meeting in January 2013. Topics debated included multiplicity and the drug development process, the usefulness and limitations of newly developed strategies to deal with multiplicity, multiplicity issues arising from interim decisions and multiregional development, and the need for simultaneous confidence intervals (CIs) corresponding to multiple test procedures. A clear message from the meeting was that multiplicity adjustments need to be considered when the intention is to make a formal statement about efficacy or safety based on hypothesis tests. Statisticians have a key role when designing studies to assess what adjustment really means in the context of the research being conducted. More thought during the planning phase needs to be given to multiplicity adjustments for secondary endpoints given these are increasing in importance in differentiating products in the market place. No consensus was reached on the role of simultaneous CIs in the context of superiority trials. It was argued that unadjusted intervals should be employed as the primary purpose of the intervals is estimation, while the purpose of hypothesis testing is to formally establish an effect. The opposing view was that CIs should correspond to the test decision whenever possible.

Costas Sparse 2-D Arrays for High-Resolution Ultrasound Imaging.

Two-dimensional arrays enable volumetric ultrasound imaging but have been limited to small aperture size and hence low resolution due to the high cost and complexity of fabrication, addressing, and processing associated with large fully addressed arrays. Here, we propose Costas arrays as a gridded sparse 2-D array architecture for volumetric ultrasound imaging. Costas arrays have exactly one element for every row and column, such that the vector displacement between any pair of elements is unique. These properties ensure aperiodicity, which helps eliminate grating lobes. Compared with previously reported works, we studied the distribution of active elements based on an order-256 Costas layout on a wider aperture ( 96 λ×96 λ at 7.5 MHz center frequency) for high-resolution imaging. Our investigations with focused scanline imaging of point targets and cyst phantoms showed that Costas arrays exhibit lower peak sidelobe levels compared with random sparse arrays of the same size and offer comparable performance in terms of contrast compared with Fermat spiral arrays. In addition, Costas arrays are gridded, which could ease the manufacturing and has one element for each row/column, which enables simple interconnection strategies. Compared with state-of-the-art matrix probes, which are commonly 32×32 , the proposed sparse arrays achieve higher lateral resolution and a wider field of view.

Syk inhibition with fostamatinib leads to transitional B lymphocyte depletion.

Cell signaling initiated by the B cell receptor is critical to normal development of B lymphocytes, most notably at the transitional B cell stage. Inhibition of this signaling pathway with the syk inhibitor, fostamatinib, has produced significant efficacy in lymphoid malignancies and autoimmune conditions. Here, we demonstrate that short-term use of fostamatinib impairs B lymphocyte development at the transitional stage without affecting mature B cell populations. Additionally, IL-10 producing B cells remained relatively constant throughout the treatment period. These findings provide insight into the mechanism of action of B cell receptor inhibition in autoimmune disease. As the development of agents targeting B cell receptor signaling proceeds, monitoring for long-term consequences as well as functional evaluation of B cell subsets may further improve our understanding of this rapidly growing class of novel agents.

Evaluation of Criteria2Query: Towards Augmented Intelligence for Cohort Identification.

Electronic healthcare records data promises to improve the efficiency of patient eligibility screening, which is an important factor in the success of clinical trials and observational studies. To bridge the sociotechnical gap in cohort identification by end-users, who are clinicians or researchers unfamiliar with underlying EHR databases, we previously developed a natural language query interface named Criteria2Query (C2Q) that automatically transforms free-text eligibility criteria to executable database queries. In this study, we present a comprehensive evaluation of C2Q to generate more actionable insights to inform the design and evaluation of future natural language user interfaces for clinical databases, towards the realization of Augmented Intelligence (AI) for clinical cohort definition via e-screening.

The use of baseline covariates in crossover studies.

It is our experience that in many settings, crossover trials that have within-period baseline measurements are analyzed wrongly. A "conventional" analysis of covariance in this setting uses each baseline as a covariate for the following outcome variable in the same period but not for any other outcome. If used with random subject effects such an analysis leads to biased treatment comparisons; this is an example of cross-level bias. Using a postulated covariance structure that reflects the symmetry of the crossover setting, we quantify such bias and, at the same time, investigate potential gains and losses in efficiency through the use of the baselines. We then describe alternative methods of analysis that avoid the cross-level bias. The development is illustrated throughout with 2 example trials, one balanced and orthogonal and one highly unbalanced and nonorthogonal.

Statistical approaches for conducting network meta-analysis in drug development.

We introduce health technology assessment and evidence synthesis briefly, and then concentrate on the statistical approaches used for conducting network meta-analysis (NMA) in the development and approval of new health technologies. NMA is an extension of standard meta-analysis where indirect as well as direct information is combined and can be seen as similar to the analysis of incomplete-block designs. We illustrate it with an example involving three treatments, using fixed-effects and random-effects models, and using frequentist and Bayesian approaches. As most statisticians in the pharmaceutical industry are familiar with SAS® software for analyzing clinical trials, we provide example code for each of the methods we illustrate. One issue that has been overlooked in the literature is the choice of constraints applied to random effects, and we show how this affects the estimates and standard errors and propose a symmetric set of constraints that is equivalent to most current practice. Finally, we discuss the role of statisticians in planning and carrying out NMAs and the strategy for dealing with important issues such as heterogeneity.

Evaluations of Bayesian and maximum likelihood methods in PK models with below-quantification-limit data.

Pharmacokinetic (PK) data often contain concentration measurements below the quantification limit (BQL). While specific values cannot be assigned to these observations, nevertheless these observed BQL data are informative and generally known to be lower than the lower limit of quantification (LLQ). Setting BQLs as missing data violates the usual missing at random (MAR) assumption applied to the statistical methods, and therefore leads to biased or less precise parameter estimation. By definition, these data lie within the interval [0, LLQ], and can be considered as censored observations. Statistical methods that handle censored data, such as maximum likelihood and Bayesian methods, are thus useful in the modelling of such data sets. The main aim of this work was to investigate the impact of the amount of BQL observations on the bias and precision of parameter estimates in population PK models (non-linear mixed effects models in general) under maximum likelihood method as implemented in SAS and NONMEM, and a Bayesian approach using Markov chain Monte Carlo (MCMC) as applied in WinBUGS. A second aim was to compare these different methods in dealing with BQL or censored data in a practical situation. The evaluation was illustrated by simulation based on a simple PK model, where a number of data sets were simulated from a one-compartment first-order elimination PK model. Several quantification limits were applied to each of the simulated data to generate data sets with certain amounts of BQL data. The average percentage of BQL ranged from 25% to 75%. Their influence on the bias and precision of all population PK model parameters such as clearance and volume distribution under each estimation approach was explored and compared.

Missing data: discussion points from the PSI missing data expert group.

The Points to Consider Document on Missing Data was adopted by the Committee of Health and Medicinal Products (CHMP) in December 2001. In September 2007 the CHMP issued a recommendation to review the document, with particular emphasis on summarizing and critically appraising the pattern of drop-outs, explaining the role and limitations of the 'last observation carried forward' method and describing the CHMP's cautionary stance on the use of mixed models. In preparation for the release of the updated guidance document, statisticians in the Pharmaceutical Industry held a one-day expert group meeting in September 2008. Topics that were debated included minimizing the extent of missing data and understanding the missing data mechanism, defining the principles for handling missing data and understanding the assumptions underlying different analysis methods. A clear message from the meeting was that at present, biostatisticians tend only to react to missing data. Limited pro-active planning is undertaken when designing clinical trials. Missing data mechanisms for a trial need to be considered during the planning phase and the impact on the objectives assessed. Another area for improvement is in the understanding of the pattern of missing data observed during a trial and thus the missing data mechanism via the plotting of data; for example, use of Kaplan-Meier curves looking at time to withdrawal.

Kinematic Validation of Postural Sway Measured by Biodex Biosway (Force Plate) and SWAY Balance (Accelerometer) Technology.

BACKGROUND: The Biodex Biosway® Balance System and SWAY Balance® Mobile smartphone application (SBMA) are portable instruments that assess balance function with force plate and accelerometer technology, respectively. The validity of these indirect clinical measures of postural sway merits investigation. PURPOSE: The purpose of this study was to investigate the concurrent validity of standing postural sway measurements by using the portable Biosway and SBMA systems with kinematic measurements of the whole body Center of Mass (COM) derived from a gold-standard reference, a motion capture system. STUDY DESIGN: Cross-sectional; repeated measures. METHODS: Forty healthy young adults (21 female, 19 male) participated in this study. Participants performed 10 standing balance tasks that included combinations of standing on one or two legs, with eyes open or closed, on a firm surface or foam surface and voluntary rhythmic sway. Postural sway was measured simultaneously from SBMA, Biosway, and the motion capture system. The linear relationships between the measurements were analyzed. RESULTS: Significant correlations were found between Biosway and COM velocity for both progressively challenging single and double leg stances (τ b = 0.3 to 0.5, p < 0.01 to <0.0001). SBMA scores and COM velocity were significantly correlated only for single leg stances (τ b = -0.5 to -0.6, p < 0.0001). SBMA scores had near-maximal values with zero to near-zero variance in double leg stances, indicating a ceiling effect. CONCLUSION: The force plate-based Biodex Biosway is valid for assessing standing postural sway for a wide range of test conditions and challenges to standing balance, whereas an accelerometer-based SWAY Balance smartphone application is valid for assessing postural sway in progressively challenging single leg stance but is not sensitive enough to detect lower-magnitude postural sway changes in progressively challenging double leg stances.