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.

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.

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).

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.

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.

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.

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.

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.

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.

Dropouts in the AB/BA crossover design.

Missing data arise in crossover trials, as they do in any form of clinical trial. Several papers have addressed the problems that missing data create, although almost all of these assume that the probability that a planned observation is missing does not depend on the value that would have been observed; that is, the data are missing at random (MAR). In many applications, this assumption is likely to be untenable; in which case, the data are missing not at random (MNAR). We investigate the effect on estimates of the treatment effect that assume data are MAR when data are actually MNAR. We also propose using the assumption of no carryover treatment effect, which is usually required for this design, to permit the estimation of a treatment effect when data are MNAR. The results are applied to a trial comparing two treatments for neuropathic pain and show that the estimate of treatment effect is sensitive to the assumption of MAR.

A measure of explained variation for event history data.

There is no shortage of proposed measures of prognostic value of survival models in the statistical literature. They come under different names, including explained variation, correlation, explained randomness, and information gain, but their goal is common: to define something analogous to the coefficient of determination R(2) in linear regression. None however have been uniformly accepted, none have been extended to general event history data, including recurrent events, and many cannot incorporate time-varying effects or covariates. We present here a measure specifically tailored for use with general dynamic event history regression models. The measure is applicable and interpretable in discrete or continuous time; with tied data or otherwise; with time-varying, time-fixed, or dynamic covariates; with time-varying or time-constant effects; with single or multiple event times; with parametric or semiparametric models; and under general independent censoring/observation. For single-event survival data with neither censoring nor time dependency it reduces to the concordance index. We give expressions for its population value and the variance of the estimator and explore its use in simulations and applications. A web link to R software is provided.

An approach to estimation in relative survival regression.

The goal of relative survival methodology is to compare the survival experience of a cohort with that of the background population. Most often an additive excess hazard model is employed, which assumes that each person's hazard is a sum of 2 components--the population hazard obtained from life tables and an excess hazard attributable to the specific condition. Usually covariate effects on the excess hazard are assumed to have a proportional hazards structure with parametrically modelled baseline. In this paper, we introduce a new fitting procedure using the expectation-maximization algorithm, treating the cause of death as missing data. The method requires no assumptions about the baseline excess hazard thus reducing the risk of bias through misspecification. It accommodates the possibility of knowledge of cause of death for some patients, and as a side effect, the method yields an estimate of the ratio between the excess and the population hazard for each subject. More importantly, it estimates the baseline excess hazard flexibly with no additional degrees of freedom spent. Finally, it is a generalization of the Cox model, meaning that all the wealth of options in existing software for the Cox model can be used in relative survival. The method is applied to a data set on survival after myocardial infarction, where it shows how a particular form of the hazard function could be missed using the existing methods.

Regret-regression for optimal dynamic treatment regimes.

We consider optimal dynamic treatment regime determination in practice. Model building, checking, and comparison have had little or no attention so far in this literature. Motivated by an application on optimal dosage of anticoagulants, we propose a modeling and estimation strategy that incorporates the regret functions of Murphy (2003, Journal of the Royal Statistical Society, Series B 65, 331-366) into a regression model for observed responses. Estimation is quick and diagnostics are available, meaning a variety of candidate models can be compared. The method is illustrated using simulation and the anticoagulation application.

Longitudinal perspectives on event history analysis.

We discuss event histories from the point of view of longitudinal data analysis, comparing several possible inferential objectives. We show that the Nelson-Aalen estimate of a cumulative intensity may be derived as a limiting solution to a sequence of generalized estimating equations for intermittently observed longitudinal count data. We outline a potential use for the theory in interval-censored recurrent-event models, and demonstrate its applicability using data from a Toronto arthritis clinic. We also discuss connections with rate models, along with some implications for the longitudinal analyst.

A martingale residual diagnostic for longitudinal and recurrent event data.

One method of assessing the fit of an event history model is to plot the empirical standard deviation of standardised martingale residuals. We develop an alternative procedure which is valid also in the presence of measurement error and applicable to both longitudinal and recurrent event data. Since the covariance between martingale residuals at times t0 and t > t0 is independent of t, a plot of these covariances should, for fixed t (0), have no time trend. A test statistic is developed from the increments in the estimated covariances, and we investigate its properties under various types of model misspecification. Applications of the approach are presented using two Brazilian studies measuring daily prevalence and incidence of infant diarrhoea and a longitudinal study into treatment of schizophrenia.

Exploring changes in treatment effects across design stages in adaptive trials.

The recently published Committee for Medicinal Products for Human Use reflection paper on flexible designs highlights a controversial issue regarding the interpretation of adaptive trials. The guideline suggests that a test for heterogeneity should be preplanned and if treatment effect estimates differ significantly between design stages then data collected before and after the interim analysis might not be combined in a formal analysis. In this paper we investigate error rates for such a procedure in the presence of calendar-time effects. Furthermore, we present an alternative testing strategy based on change point methods. In a simulation study we demonstrate that our procedure performs well in comparison to that suggested by the guideline.

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.

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.