Understanding the Impact of Critical Illness on Families: A Call for Standardization of Outcomes and Longitudinal Research.

No abstract available

A simple principal stratum estimator for failure to initiate treatment

A common intercurrent event affecting many trials is when some participants do not begin their assigned treatment. For example, in a trial comparing two different methods for fluid delivery during surgery, some participants may have their surgery cancelled. Similarly, in a double-blind drug trial, some participants may not receive any dose of study medication. The commonly used intention-to-treat analysis preserves the randomisation structure, thus protecting against biases from post-randomisation exclusions. However, it estimates a treatment policy effect (i.e. addresses the question "what is the effect of the intervention, regardless of whether the participant actually begins treatment?"), which may not be the most clinically relevant estimand. A principal stratum approach, estimating the treatment effect in the subpopulation of participants who would initiate treatment (regardless of treatment arm), may be a more clinically relevant estimand for many trials. We show that a simple principal stratum estimator based on a "modified intention-to-treat" population, where participants who experience the intercurrent event are excluded, is unbiased for the principal stratum estimand under certain assumptions that are likely to be plausible in many trials, namely that participants who initiate the intervention under one treatment condition would also do so under the other treatment condition. We provide several examples of trials where this assumption is plausible, and several instances where it is not. We conclude that this simple principal stratum estimator can be a useful strategy for handling failure to initiate treatment

Using modified intention-to-treat as a principal stratum estimator for failure to initiate treatment

BACKGROUND: A common intercurrent event affecting many trials is when some participants do not begin their assigned treatment. For example, in a double-blind drug trial, some participants may not receive any dose of study medication. Many trials use a 'modified intention-to-treat' approach, whereby participants who do not initiate treatment are excluded from the analysis. However, it is not clear (a) the estimand being targeted by such an approach and (b) the assumptions necessary for such an approach to be unbiased. METHODS: Using potential outcome notation, we demonstrate that a modified intention-to-treat analysis which excludes participants who do not begin treatment is estimating a principal stratum estimand (i.e. the treatment effect in the subpopulation of participants who would begin treatment, regardless of which arm they were assigned to). The modified intention-to-treat estimator is unbiased for the principal stratum estimand under the assumption that the intercurrent event is not affected by the assigned treatment arm, that is, participants who initiate treatment in one arm would also do so in the other arm (i.e. if someone began the intervention, they would also have begun the control, and vice versa). RESULTS: We identify two key criteria in determining whether the modified intention-to-treat estimator is likely to be unbiased: first, we must be able to measure the participants in each treatment arm who experience the intercurrent event, and second, the assumption that treatment allocation will not affect whether the participant begins treatment must be reasonable. Most double-blind trials will satisfy these criteria, as the decision to start treatment cannot be influenced by the allocation, and we provide an example of an open-label trial where these criteria are likely to be satisfied as well, implying that a modified intention-to-treat analysis which excludes participants who do not begin treatment is an unbiased estimator for the principal stratum effect in these settings. We also give two examples where these criteria will not be satisfied (one comparing an active intervention vs usual care, where we cannot identify which usual care participants would have initiated the active intervention, and another comparing two active interventions in an unblinded manner, where knowledge of the assigned treatment arm may affect the participant's choice to begin or not), implying that a modified intention-to-treat estimator will be biased in these settings. CONCLUSION: A modified intention-to-treat analysis which excludes participants who do not begin treatment can be an unbiased estimator for the principal stratum estimand. Our framework can help identify when the assumptions for unbiasedness are likely to hold, and thus whether modified intention-to-treat is appropriate or not

Adipokines and the Right Ventricle: The MESA-RV Study.

ObjectiveObesity is associated with changes in both right (RV) and left (LV) ventricular morphology, but the biological basis of this finding is not well established. We examined whether adipokine levels were associated with RV morphology and function in a population-based multiethnic sample free of clinical cardiovascular disease.MethodsWe examined relationships of leptin, resistin, TNF-α, and adiponectin with RV morphology and function (from cardiac MRI) in participants (n = 1,267) free of clinical cardiovascular disease from the Multi-Ethnic Study of Atherosclerosis (MESA)-RV study. Multivariable regressions (linear, quantile [25th and 75th] and generalized additive models [GAM]) were used to examine the independent association of each adipokine with RV mass, RV end-diastolic volume (RVEDV), RV end-systolic volume (RVESV), RV stroke volume (RVSV) and RV ejection fraction (RVEF).ResultsHigher leptin levels were associated with significantly lower levels of RV mass, RVEDV, RVESV and stroke volume, but not RVEF, after adjustment for age, gender, race, height and weight. These associations were somewhat attenuated but still significant after adjustment for traditional risk factors and covariates, and were completely attenuated when correcting for the respective LV measures. There were no significant interactions of age, gender, or race/ethnicity on the relationship between the four adipokines and RV structure or function.ConclusionsLeptin levels are associated with favorable RV morphology in a multi-ethnic population free of cardiovascular disease, however these associations may be explained by a yet to be understood bi-ventricular process as this association was no longer present after adjustment for LV values. These findings complement the associations previously shown between adipokines and LV structure and function in both healthy and diseased patients. The mechanisms linking adipokines to healthy cardiovascular function require further investigation

Eliminating ambiguous treatment effects using estimands

Most reported treatment effects in medical research studies are ambiguously defined, which can lead to misinterpretation of study results. This is because most studies do not attempt to describe what the treatment effect represents, and instead require readers to deduce this based on the reported statistical methods. However, this approach is fraught, as many methods provide counterintuitive results. For example, some methods include data from all patients, yet the resulting treatment effect applies only to a subset of patients, whereas other methods will exclude certain patients while results will apply to everyone. Additionally, some analyses provide estimates pertaining to hypothetical settings where patients never die or discontinue treatment. Herein we introduce estimands as a solution to the aforementioned problem. An estimand is a clear description of what the treatment effect represents, thus saving readers the necessity of trying to infer this from study methods and potentially getting it wrong. We provide examples of how estimands can remove ambiguity from reported treatment effects and describe their current use in practice. The crux of our argument is that readers should not have to infer what investigators are estimating; they should be told explicitly

Estimands in cluster-randomized trials: choosing analyses that answer the right question

Background Cluster-randomized trials (CRTs) involve randomizing groups of individuals (e.g. hospitals, schools or villages) to different interventions. Various approaches exist for analysing CRTs but there has been little discussion around the treatment effects (estimands) targeted by each. Methods We describe the different estimands that can be addressed through CRTs and demonstrate how choices between different analytic approaches can impact the interpretation of results by fundamentally changing the question being asked, or, equivalently, the target estimand. Results CRTs can address either the participant-average treatment effect (the average treatment effect across participants) or the cluster-average treatment effect (the average treatment effect across clusters). These two estimands can differ when participant outcomes or the treatment effect depends on the cluster size (referred to as ‘informative cluster size’), which can occur for reasons such as differences in staffing levels or types of participants between small and large clusters. Furthermore, common estimators, such as mixed-effects models or generalized estimating equations with an exchangeable working correlation structure, can produce biased estimates for both the participant-average and cluster-average treatment effects when cluster size is informative. We describe alternative estimators (independence estimating equations and cluster-level analyses) that are unbiased for CRTs even when informative cluster size is present. Conclusion We conclude that careful specification of the estimand at the outset can ensure that the study question being addressed is clear and relevant, and, in turn, that the selected estimator provides an unbiased estimate of the desired quantity

Temporal Trends in Incidence, Sepsis-Related Mortality, and Hospital-Based Acute Care After Sepsis.

OBJECTIVES: A growing number of patients survive sepsis hospitalizations each year and are at high risk for readmission. However, little is known about temporal trends in hospital-based acute care (emergency department treat-and-release visits and hospital readmission) after sepsis. Our primary objective was to measure temporal trends in sepsis survivorship and hospital-based acute care use in sepsis survivors. In addition, because readmissions after pneumonia are subject to penalty under the national readmission reduction program, we examined whether readmission rates declined after sepsis hospitalizations related to pneumonia. DESIGN AND SETTING: Retrospective, observational cohort study conducted within an academic healthcare system from 2010 to 2015. PATIENTS: We used three validated, claims-based approaches to identify 17,256 sepsis or severe sepsis hospitalizations to examine trends in hospital-based acute care after sepsis. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: From 2010 to 2015, sepsis as a proportion of medical and surgical admissions increased from 3.9% to 9.4%, whereas in-hospital mortality rate for sepsis hospitalizations declined from 24.1% to 14.8%. As a result, the proportion of medical and surgical discharges at-risk for hospital readmission after sepsis increased from 2.7% to 7.8%. Over 6 years, 30-day hospital readmission rates declined modestly, from 26.4% in 2010 to 23.1% in 2015, driven largely by a decline in readmission rates among survivors of nonsevere sepsis, and nonpneumonia sepsis specifically, as the readmission rate of severe sepsis survivors was stable. The modest decline in 30-day readmission rates was offset by an increase in emergency department treat-and-release visits, from 2.8% in 2010 to a peak of 5.4% in 2014. CONCLUSIONS: Owing to increasing incidence and declining mortality, the number of sepsis survivors at risk for hospital readmission rose significantly between 2010 and 2015. The 30-day hospital readmission rates for sepsis declined modestly but were offset by a rise in emergency department treat-and-release visits

Altitude and regional gradients in chronic kidney disease prevalence in Costa Rica: Data from the Costa Rican Longevity and Healthy Aging Study

Objectives Recent studies in Central America indicate that mortality attributable to chronic kidney disease (CKD) is rising rapidly. We sought to determine the prevalence and regional variation of CKD and the relationship of biologic and socio-economic factors to CKD risk in the older-adult population of Costa Rica. Methods We used data from the Costa Rican Longevity and Health Aging Study (CRELES). The cohort was comprised of 2657 adults born before 1946 in Costa Rica, chosen through a sampling algorithm to represent the national population of Costa Ricans >60 years of age. Participants answered questionnaire data and completed laboratory testing. The primary outcome of this study was CKD, defined as an estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m2. Results The estimated prevalence of CKD for older Costa Ricans was 20% (95% CI 18.5–21.9%). In multivariable logistic regression, older age (adjusted odds ratio [aOR] 1.08 per year, 95% CI 1.07–1.10, P < 0.001) was independently associated with CKD. For every 200 m above sea level of residence, subjects' odds of CKD increased 26% (aOR 1.26 95% CI 1.15–1.38, P < 0.001). There was large regional variation in adjusted CKD prevalence, highest in Limon (40%, 95% CI 30–50%) and Guanacaste (36%, 95% CI 26–46%) provinces. Regional and altitude effects remained robust after adjustment for socio-economic status. Conclusions We observed large regional and altitude-related variations in CKD prevalence in Costa Rica, not explained by the distribution of traditional CKD risk factors. More studies are needed to explore the potential association of geographic and environmental exposures with the risk of CKD.National Institute of Diabetes and Digestive and Kidney Diseases of the United States National Institutes of Health/[K23-DK105207-01]//Estados UnidosWellcome Trust///Estados UnidosNational Heart, Lung And Blood Institute///Estados UnidosUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Sociales::Centro Centroamericano de Población (CCP

On the mixed-model analysis of covariance in cluster-randomized trials

In the analyses of cluster-randomized trials, a standard approach for covariate adjustment and handling within-cluster correlations is the mixed-model analysis of covariance (ANCOVA). The mixed-model ANCOVA makes stringent assumptions, including normality, linearity, and a compound symmetric correlation structure, which may be challenging to verify and may not hold in practice. When mixed-model ANCOVA assumptions are violated, the validity and efficiency of the model-based inference for the average treatment effect are currently unclear. In this article, we prove that the mixed-model ANCOVA estimator for the average treatment effect is consistent and asymptotically normal under arbitrary misspecification of its working model. Under equal randomization, we further show that the model-based variance estimator for the mixed-model ANCOVA estimator remains consistent, clarifying that the confidence interval given by standard software is asymptotically valid even under model misspecification. Beyond robustness, we also provide a caveat that covariate adjustment via mixed-model ANCOVA may lead to precision loss compared to no adjustment when the covariance structure is misspecified, and describe when a cluster-level ANCOVA becomes more efficient. These results hold under both simple and stratified randomization, and are further illustrated via simulations as well as analyses of three cluster-randomized trials

The implications of outcome truncation in reproductive medicine RCTs: a simulation platform for trialists and simulation study.

From Europe PMC via Jisc Publications RouterHistory: ppub 2021-08-01, epub 2021-08-06Publication status: PublishedFunder: Wellcome Trust; Grant(s): 204796/Z/16/ZBackgroundRandomised controlled trials in reproductive medicine are often subject to outcome truncation, where the study outcomes are only defined in a subset of the randomised cohort. Examples include birthweight (measurable only in the subgroup of participants who give birth) and miscarriage (which can only occur in participants who become pregnant). These outcomes are typically analysed by making a comparison between treatment arms within the subgroup (for example, comparing birthweights in the subgroup who gave birth or miscarriages in the subgroup who became pregnant). However, this approach does not represent a randomised comparison when treatment influences the probability of being observed (i.e. survival). The practical implications of this for the design and interpretation of reproductive trials are unclear however.MethodsWe developed a simulation platform to investigate the implications of outcome truncation for reproductive medicine trials. We used this to perform a simulation study, in which we considered the bias, type 1 error, coverage, and precision of standard statistical analyses for truncated continuous and binary outcomes. Simulation settings were informed by published assisted reproduction trials.ResultsIncreasing treatment effect on the intermediate variable, strength of confounding between the intermediate and outcome variables, and the presence of an interaction between treatment and confounder were found to adversely affect performance. However, within parameter ranges we would consider to be more realistic, the adverse effects were generally not drastic. For binary outcomes, the study highlighted that outcome truncation could cause separation in smaller studies, where none or all of the participants in a study arm experience the outcome event. This was found to have severe consequences for inferences.ConclusionWe have provided a simulation platform that can be used by researchers in the design and interpretation of reproductive medicine trials subject to outcome truncation and have used this to conduct a simulation study. The study highlights several key factors which trialists in the field should consider carefully to protect against erroneous inferences. Standard analyses of truncated binary outcomes in small studies may be highly biassed, and it remains to identify suitable approaches for analysing data in this context