Contrasting principal stratum and hypothetical strategy estimands in multi-period crossover trials with incomplete data.

Complete case analyses of complete crossover designs provide an opportunity to make comparisons based on patients who can tolerate all treatments. It is argued that this provides a means of estimating a principal stratum strategy estimand, something which is difficult to do in parallel group trials. While some trial users will consider this a relevant aim, others may be interested in hypothetical strategy estimands, that is, the effect that would be found if all patients completed the trial. Whether these estimands differ importantly is a question of interest to the different users of the trial results. This paper derives the difference between principal stratum strategy and hypothetical strategy estimands, where the former is estimated by a complete-case analysis of the crossover design, and a model for the dropout process is assumed. Complete crossover designs, that is, those where all treatments appear in all sequences, and which compare t treatments over p periods with respect to a continuous outcome are considered. Numerical results are presented for Williams designs with four and six periods. Results from a trial of obstructive sleep apnoea-hypopnoea (TOMADO) are also used for illustration. The results demonstrate that the percentage difference between the estimands is modest, exceeding 5% only when the trial has been severely affected by dropouts or if the within-subject correlation is low.

A Health Care System's Approach to Support Nursing Leaders in Mitigating Burnout Amid a COVID-19 World Pandemic.

Burnout has been demonstrating its presence in the nursing profession for decades. The advent of the world pandemic exacerbated the impact of burnout, and health care workers are suffering. In this article, the authors offer a review of burnout and its effect on the nursing profession. The authors describe a health care system's response to support its 48000 nurses. On the basis of critical drivers that influence the state of engagement of any nurses, we implemented a program allowing us to proactively partner with core leaders to support the emotional well-being of their caregivers. We provide focused coaching and support to leaders and their teams experiencing the highest stress levels. Finally, this article offers concrete interventions that nurse leaders should consider to support their respective nurses.

Degrees of necessity and of sufficiency: Further results and extensions, with an application to covid-19 mortality in Austria.

The purpose of this paper is to extend to ordinal and nominal outcomes the measures of degree of necessity and of sufficiency defined by the authors for dichotomous and survival outcomes in a previous paper. A cause, represented by certain values of prognostic factors, is considered necessary for an event if, without the cause, the event cannot develop. It is considered sufficient for an event if the event is unavoidable in the presence of the cause. The degrees of necessity and sufficiency, ranging from zero to one, are simple, intuitive functions of unconditional and conditional probabilities of an event such as disease or death. These probabilities often will be derived from logistic regression models; the measures, however, do not require any particular model. In addition, we study in detail the relationship between the proposed measures and the related explained variation summary for dichotomous outcomes, which are the common root for the developments for ordinal, nominal, and survival outcomes. We introduce and analyze the Austrian covid-19 data, with the aim of quantifying effects of age and other potentially prognostic factors on covid-19 mortality. This is achieved by standard regression methods but also in terms of the newly proposed measures. It is shown how they complement the toolbox of prognostic factor studies, in particular when comparing the importance of prognostic factors of different types. While the full model's degree of necessity is extremely high (0.933), its low degree of sufficiency (0.179) is responsible for the low proportion of explained variation (0.193).

Mitigating potential public health problems associated with edible cannabis products through adequate regulation: A landscape analysis.

An edible cannabis product (ECP) manufactured with food ingredients is subject to the same types of contamination as any conventional food product. Physical, microbial, and chemical hazards are a potential threat to anyone consuming cannabinoid-containing products by mouth. Preventing the unintentional ingestion of ECPs is also a concern for public health professionals. An analysis of the regulatory landscape in the United States (US) was conducted to identify best practices specific to ECPs and to pinpoint preventative safety measures that had not been extensively implemented. Widespread adoption of some of the more useful precedents set by US jurisdictions, as examined in this work, could be of great value in protecting public health.

Random cancers as supported by registry data.

There has been considerable interest in recent years in quantifying the rate of unavoidable or so-called random cancers, as opposed to cancers linked to environmental, genetic or other factors. We propose a data-based approach to estimate an upper limit to this probability, based on an analysis of multiple registry data. The argument is that the cumulative hazards for random cancers cannot exceed the minimum reliable cumulative hazard observed across the registries. We propose a Monte Carlo method to identify this upper limit and apply the method to data on nine different cancers recorded by 423 registries. We compare our values with estimates obtained from a random mutations argument.

Topological data analysis of high resolution diabetic retinopathy images.

Diabetic retinopathy is a complication of diabetes that produces changes in the blood vessel structure in the retina, which can cause severe vision problems and even blindness. In this paper, we demonstrate that by identifying topological features in very high resolution retinal images, we can construct a classifier that discriminates between healthy patients and those with diabetic retinopathy using summary statistics of these features. Topological data analysis identifies the features as connected components and holes in the images and describes the extent to which they persist across the image. These features are encoded in persistence diagrams, summaries of which can be used to discrimate between diabetic and healthy patients. The method has the potential to be an effective automated screening tool, with high sensitivity and specificity.

A dual frailty model for lifetime analysis in maritime transportation.

We consider changes in ownership of commercial shipping vessels from an event history perspective. Each change in ownership can be influenced by the properties of the vessel itself, its age and history to date, the characteristics of both the seller and the buyer, and time-varying market conditions. Similar factors can affect the process of deciding when to scrap the vessel as no longer being economically viable. We consider a multi-state approach in which states are defined by the owning companies, a sale marks a transition, and scrapping of the vessel corresponds to moving to an absorbing state. We propose a dual frailty model that attempts to capture unexplained heterogeneity in the data, with one frailty term for the seller and one for the buyer. We describe a Monte Carlo Markov chain estimation procedure and verify its accuracy through simulations. We investigate the consequences of mistakenly ignoring frailty in these circumstances. We compare results with and without the inclusion of frailty.

Modelling and sample size reestimation for longitudinal count data with incomplete follow up.

We consider modelling and inference as well as sample size estimation and reestimation for clinical trials with longitudinal count data as outcomes. Our approach is general but is rooted in design and analysis of multiple sclerosis trials where lesion counts obtained by magnetic resonance imaging are important endpoints. We adopt a binomial thinning model that allows for correlated counts with marginal Poisson or negative binomial distributions. Methods for sample size planning and blinded sample size reestimation for randomised controlled clinical trials with such outcomes are developed. The models and approaches are applicable to data with incomplete observations. A simulation study is conducted to assess the effectiveness of sample size estimation and blinded sample size reestimation methods. Sample sizes attained through these procedures are shown to maintain the desired study power without inflating the type I error. Data from a recent trial in patients with secondary progressive multiple sclerosis illustrate the modelling approach.

Sample size re-estimation for clinical trials with longitudinal negative binomial counts including time trends.

In some diseases, such as multiple sclerosis, lesion counts obtained from magnetic resonance imaging (MRI) are used as markers of disease progression. This leads to longitudinal, and typically overdispersed, count data outcomes in clinical trials. Models for such data invariably include a number of nuisance parameters, which can be difficult to specify at the planning stage, leading to considerable uncertainty in sample size specification. Consequently, blinded sample size re-estimation procedures are used, allowing for an adjustment of the sample size within an ongoing trial by estimating relevant nuisance parameters at an interim point, without compromising trial integrity. To date, the methods available for re-estimation have required an assumption that the mean count is time-constant within patients. We propose a new modeling approach that maintains the advantages of established procedures but allows for general underlying and treatment-specific time trends in the mean response. A simulation study is conducted to assess the effectiveness of blinded sample size re-estimation methods over fixed designs. Sample sizes attained through blinded sample size re-estimation procedures are shown to maintain the desired study power without inflating the Type I error rate and the procedure is demonstrated on MRI data from a recent study in multiple sclerosis.

Dynamic modelling of n-of-1 data: powerful and flexible data analytics applied to individualised studies.

N-of-1 studies are based on repeated observations within an individual or unit over time and are acknowledged as an important research method for generating scientific evidence about the health or behaviour of an individual. Statistical analyses of n-of-1 data require accurate modelling of the outcome while accounting for its distribution, time-related trend and error structures (e.g., autocorrelation) as well as reporting readily usable contextualised effect sizes for decision-making. A number of statistical approaches have been documented but no consensus exists on which method is most appropriate for which type of n-of-1 design. We discuss the statistical considerations for analysing n-of-1 studies and briefly review some currently used methodologies. We describe dynamic regression modelling as a flexible and powerful approach, adaptable to different types of outcomes and capable of dealing with the different challenges inherent to n-of-1 statistical modelling. Dynamic modelling borrows ideas from longitudinal and event history methodologies which explicitly incorporate the role of time and the influence of past on future. We also present an illustrative example of the use of dynamic regression on monitoring physical activity during the retirement transition. Dynamic modelling has the potential to expand researchers' access to robust and user-friendly statistical methods for individualised studies.

Explained variation for recurrent event data.

Although there are many suggested measures of explained variation for single-event survival data, there has been little attention to explained variation for recurrent event data. We describe an existing rank-based measure and we investigate a new statistic based on observed and expected event count processes. Both methods can be used for all models. Adjustments for missing data are proposed and demonstrated through simulation to be effective. We compare the population values of the two statistics and illustrate their use in comparing an array of non-nested models for data on recurrent episodes of infant diarrhoea.

Joint modelling of repeated measurements and time-to-event outcomes: flexible model specification and exact likelihood inference.

Random effects or shared parameter models are commonly advocated for the analysis of combined repeated measurement and event history data, including dropout from longitudinal trials. Their use in practical applications has generally been limited by computational cost and complexity, meaning that only simple special cases can be fitted by using readily available software. We propose a new approach that exploits recent distributional results for the extended skew normal family to allow exact likelihood inference for a flexible class of random-effects models. The method uses a discretization of the timescale for the time-to-event outcome, which is often unavoidable in any case when events correspond to dropout. We place no restriction on the times at which repeated measurements are made. An analysis of repeated lung function measurements in a cystic fibrosis cohort is used to illustrate the method.

Gross Motor Function Measure-66 trajectories in children recovering after severe acquired brain injury.

AIM: To explore the appropriateness of using the interval-scale version of the Gross Motor Function Measure (GMFM-66) in paediatric acquired brain injury (ABI), and to characterize GMFM-66 recovery trajectories and factors that affect them. METHOD: An observational study of gross motor recovery trajectories during rehabilitation at a single specialist paediatric in-patient rehabilitation centre using repeated GMFM-66 observations. The cohort comprised children rehabilitating after severe ABI of various causes. RESULTS: A total of 287 GMFM observations were made on 74 children (45 males, 29 females; age-at-injury range 0.3-17.3y, median age 11.3y, interquartile range 6.6-15.0y). Differences in item-difficulty estimates between this sample and the cerebral palsy population in which the GMFM-66 was initially developed are not detectable at this sample size. Changes in GMFM over time show lag-exponential forms. Children sustaining hypoxic-ischaemic injuries made the slowest and least complete recoveries. Older children made faster gross motor recoveries after controlling for aetiology. The time at which gross motor ability began to rise coincided approximately with admission to the rehabilitation facility. INTERPRETATION: Aetiology is strongly associated with gross motor recovery after ABI. Younger age at injury was associated with slower recovery. Comparable item-difficulty scores in this sample and in the cerebral palsy population suggest comparable sequences of gross motor ability reacquisition.

Doubly Robust Estimation of Optimal Dynamic Treatment Regimes.

We compare methods for estimating optimal dynamic decision rules from observational data, with particular focus on estimating the regret functions defined by Murphy (in J. R. Stat. Soc., Ser. B, Stat. Methodol. 65:331-355, 2003). We formulate a doubly robust version of the regret-regression approach of Almirall et al. (in Biometrics 66:131-139, 2010) and Henderson et al. (in Biometrics 66:1192-1201, 2010) and demonstrate that it is equivalent to a reduced form of Robins' efficient g-estimation procedure (Robins, in Proceedings of the Second Symposium on Biostatistics. Springer, New York, pp. 189-326, 2004). Simulation studies suggest that while the regret-regression approach is most efficient when there is no model misspecification, in the presence of misspecification the efficient g-estimation procedure is more robust. The g-estimation method can be difficult to apply in complex circumstances, however. We illustrate the ideas and methods through an application on control of blood clotting time for patients on long term anticoagulation.

Synergistic wetland treatment of sewage and mine water: pollutant removal performance of the first full-scale system.

Wetland systems are now well-established unit processes in the treatment of diverse wastewater streams. However, the development of wetland technology for sewage treatment followed an entirely separate trajectory from that for polluted mine waters. In recent years, increased networking has led to recognition of possible synergies which might be obtained by hybridising approaches to achieve co-treatment of otherwise distinct sewage and mine-derived wastewaters. As polluted discharges from abandoned mines often occur in or near the large conurbations to which the former mining activities gave rise, there is ample scope for such co-treatment in many places worldwide. The first full-scale co-treatment wetland anywhere in the world receiving large inflows of both partially-treated sewage (∼100 L s(-)(1)) and mine water (∼300 L s(-1)) was commissioned in Gateshead, England in 2005, and a performance evaluation has now been made. The evaluation is based entirely on routinely-collected water quality data, which the operators gather in fulfillment of their regulatory obligations. The principal parameters of concern in the sewage effluent are suspended solids, BOD5, ammoniacal nitrogen (NH4-N) and phosphate (P); in the mine water the only parameter of particular concern is total iron (Fe). Aerobic treatment processes are appropriate for removal of BOD5, NH4-N and Fe; for the removal of P, reaction with iron to form ferric phosphate solids is a likely pathway. With these considerations in mind, the treatment wetland was designed as a surface-flow aerobic system. Sample concentration level and daily flow rate date from April 2007 until March 2011 have been analyzed using nonparametric statistical methods. This has revealed sustained, high rates of absolute removal of all pollutants from the combined wastewater flow, quantified in terms of differences between influent and effluent loadings (i.e. mass per unit time). In terms of annual mass retention rates, for instance, the wetland system sequesters the following percentages of the key pollutants: BOD5: 41%; Fe 89%; NH4-N: 66%; dissolved P: 59%; total P: 46%; suspended solids: 66%. For similar wastewater chemistries, application of this type of co-treatment elsewhere could reasonably be based on the observed areally-normalized mass removal rates for the various pollutants found in this investigation.

Dropout in crossover and longitudinal studies: is complete case so bad?

We discuss inference for longitudinal clinical trials subject to possibly informative dropout. A selection of available methods is reviewed for the simple case of trials with two timepoints. Using data from two such clinical trials, each with two treatments, we demonstrate that different analysis methods can at times lead to quite different conclusions from the same data. We investigate properties of complete-case estimators for the type of trials considered, with emphasis on interpretation and meaning of parameters. We contrast longitudinal and crossover designs and argue that for crossover studies there are often good reasons to prefer a complete case analysis. More generally, we suggest that there is merit in an approach in which no untestable assumptions are made. Such an approach would combine a dropout analysis, an analysis of complete-case data only, and a careful statement of justified conclusions.

Two-period, two-treatment crossover designs subject to non-ignorable missing data.

In common with most forms of designed experiment, crossover trials can be affected by missing data. Attempts to devise designs that can mitigate the possible effects of missing data, such as loss of efficiency, or even inestimability of certain contrasts, have been proposed. However, a potentially serious effect of missing data that has not been addressed in designs hitherto is that the treatment effects may be biassed because of the nature of the missingness process. We investigate this problem in two-treatment, two-period crossover designs. In particular, we consider the robustness of the analysis under a missing at random assumption when, in fact, the data are non-ignorably missing. We show that the conventional AB/BA design still has good properties, although the design with sequences AB, BA, AA, and BB may be preferred if the chance of dropout depends primarily on the difference between the responses in the two periods.

A novel isolator-based system promotes viability of human embryos during laboratory processing.

In vitro fertilisation (IVF) and related technologies are arguably the most challenging of all cell culture applications. The starting material is a single cell from which one aims to produce an embryo capable of establishing a pregnancy eventually leading to a live birth. Laboratory processing during IVF treatment requires open manipulations of gametes and embryos, which typically involves exposure to ambient conditions. To reduce the risk of cellular stress, we have developed a totally enclosed system of interlinked isolator-based workstations designed to maintain oocytes and embryos in a physiological environment throughout the IVF process. Comparison of clinical and laboratory data before and after the introduction of the new system revealed that significantly more embryos developed to the blastocyst stage in the enclosed isolator-based system compared with conventional open-fronted laminar flow hoods. Moreover, blastocysts produced in the isolator-based system contained significantly more cells and their development was accelerated. Consistent with this, the introduction of the enclosed system was accompanied by a significant increase in the clinical pregnancy rate and in the proportion of embryos implanting following transfer to the uterus. The data indicate that protection from ambient conditions promotes improved development of human embryos. Importantly, we found that it was entirely feasible to conduct all IVF-related procedures in the isolator-based workstations.