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An important strategy for identifying principal causal effects (popular estimands in settings with noncompliance) is to invoke the principal ignorability (PI) assumption. As PI is untestable, it is important to gauge how sensitive effect estimates are to its violation. We focus on this task for the common one‐sided noncompliance setting where there are two principal strata, compliers and noncompliers. Under PI, compliers and noncompliers share the same outcome‐mean‐given‐covariates function under the control condition. For sensitivity analysis, we allow this function to differ between compliers and noncompliers in several ways, indexed by an odds ratio, a generalized odds ratio, a mean ratio, or a standardized mean difference sensitivity parameter. We tailor sensitivity analysis techniques (with any sensitivity parameter choice) to several types of PI‐based main analysis methods, including outcome regression, influence function (IF) based and weighting methods. We discuss range selection for the sensitivity parameter. We illustrate the sensitivity analyses with several outcome types from the JOBS II study. This application estimates nuisance functions parametrically – for simplicity and accessibility. In addition, we establish rate conditions on nonparametric nuisance estimation for IF‐based estimators to be asymptotically normal – with a view to inform nonparametric inference. [ABSTRACT FROM AUTHOR]

A Real-World Evidence (RWE) Scientific Working Group (SWG) of the American Statistical Association Biopharmaceutical Section (ASA BIOP) has been reviewing statistical considerations for the generation of RWE to support regulatory decision-making. As part of the effort, the working group is addressing estimands in RWE studies. Constructing the right estimand--the target of estimation--which reflects the research question and the study objective, is one of the key components in formulating a clinical study. ICH E9(R1) describes statistical principles for constructing estimands in clinical trials with a focus on five attributes--population, treatment, endpoints, intercurrent events, and population-level summary. However, defining estimands for clinical studies using real-world data (RWD), that is, RWE studies, requires additional considerations due to, for example, heterogeneity of study population, complexity of treatment regimes, different types and patterns of intercurrent events, and complexities in choosing study endpoints. This article reviews the essential components of estimands and causal inference framework, discusses considerations in constructing estimands for RWE studies, highlights similarities and differences in traditional clinical trial and RWE study estimands, and provides a roadmap for choosing appropriate estimands for RWE studies. [ABSTRACT FROM AUTHOR]

Purpose: Guided Care (GC) is a model of health care for multimorbid older adults that is provided by a registered nurse who works with the patients' primary care physician (PCP). The purpose of this study was to determine whether GC improves patients' primary caregivers' depressive symptoms, strain, productivity, and perceptions of the quality of care recipients' chronic illness care. Design and Methods: A cluster-randomized controlled trial of GC was conducted within 14 PCP teams. The study sample included 196 primary caregivers who completed baseline and 18-month surveys and whose care recipients remained alive and enrolled in the GC study for 18 months. Caregiver outcomes included the following: depressive symptoms (Center for Epidemiological Studies-Depression scale), strain (Modified Caregiver Strain Index), the quality of care recipients' chronic illness care [Patient Assessment of Chronic Illness Care (PACIC)], and personal productivity (Work Productivity and Activity Impairment questionnaire, adapted for caregiving). Results: In multivariate regression models, between-group differences in depression, strain, work productivity, and regular activity productivity were not statistically significant after 18 months, but GC caregivers reported the overall quality of their recipients' chronic illness care to be significantly higher (adjusted beta = 0.40, 95% confidence interval: 0.14-0.67). Quality was significantly higher in 4 of 5 PACIC subscales, reflecting the dimensions of goal setting, coordination of care, decision support, and patient activation. Implications: GC improved the quality of chronic illness care received by multimorbid care recipients but did not improve caregivers' depressive symptoms, affect, or productivity.

Background: Results from observational studies and randomized clinical trials (RCTs) have led to the consensus that hydroxychloroquine (HCQ) and chloroquine (CQ) are not effective for COVID-19 prevention or treatment. Pooling individual participant data, including unanalyzed data from trials terminated early, enables more detailed investigation of the efficacy and safety of HCQ/CQ among subgroups of hospitalized patients. Methods: We searched ClinicalTrials.gov in May and June 2020 for US-based RCTs evaluating HCQ/CQ in hospitalized COVID-19 patients in which the outcomes defined in this study were recorded or could be extrapolated. The primary outcome was a 7-point ordinal scale measured between day 28 and 35 post enrollment; comparisons used proportional odds ratios. Harmonized de-identified data were collected via a common template spreadsheet sent to each principal investigator. The data were analyzed by fitting a prespecified Bayesian ordinal regression model and standardizing the resulting predictions. Results: Eight of 19 trials met eligibility criteria and agreed to participate. Patient-level data were available from 770 participants (412 HCQ/CQ vs 358 control). Baseline characteristics were similar between groups. We did not find evidence of a difference in COVID-19 ordinal scores between days 28 and 35 post-enrollment in the pooled patient population (odds ratio, 0.97; 95% credible interval, 0.76–1.24; higher favors HCQ/CQ), and found no convincing evidence of meaningful treatment effect heterogeneity among prespecified subgroups. Adverse event and serious adverse event rates were numerically higher with HCQ/CQ vs control (0.39 vs 0.29 and 0.13 vs 0.09 per patient, respectively). Conclusions: The findings of this individual participant data meta-analysis reinforce those of individual RCTs that HCQ/CQ is not efficacious for treatment of COVID-19 in hospitalized patients. [ABSTRACT FROM AUTHOR]

Purpose: Guided Care (GC) is a model of health care for multimorbid older adults that is provided by a registered nurse who works with the patients' primary care physician (PCP). The purpose of this study was to determine whether GC improves patients' primary caregivers' depressive symptoms, strain, productivity, and perceptions of the quality of care recipients' chronic illness care. Design and Methods: A cluster-randomized controlled trial of GC was conducted within 14 PCP teams. The study sample included 196 primary caregivers who completed baseline and 18-month surveys and whose care recipients remained alive and enrolled in the GC study for 18 months. Caregiver outcomes included the following: depressive symptoms (Center for Epidemiological Studies-Depression scale), strain (Modified Caregiver Strain Index), the quality of care recipients' chronic illness care [Patient Assessment of Chronic Illness Care (PACIC)], and personal productivity (Work Productivity and Activity Impairment questionnaire, adapted for caregiving). Results: In multivariate regression models, between-group differences in depression, strain, work productivity, and regular activity productivity were not statistically significant after 18 months, but GC caregivers reported the overall quality of their recipients' chronic illness care to be significantly higher (adjusted beta = 0.40, 95% confidence interval : 0.14-0.67). Quality was significantly higher in 4 of 5 PACIC subscales, reflecting the dimensions of goal setting, coordination of care, decision support, and patient activation. Implications: GC improved the quality of chronic illness care received by multimorbid care recipients but did not improve caregivers' depressive symptoms, affect, or productivity. Key Words: Caregiving, Chronic disease, Nursing, Primary care, Guided Care doi: 10.1093/geront/gnp124

Background. The Guided Care Program t-or Families and Friends (GCPFF) is one component of 'Guided Care' (GC), a model of primary care for chronically ill older adults that is facilitated by a registered nurse who has completed a supplemental educational curriculum. Methods. The GCPFF melds support for family caregivers with the delivery of coordinated and comprehensive chronic care and seeks to improve the health and well-being of both patients and their family caregivers. The GCPFF encompasses (a) an initial meeting between the nurse and the patient's primary caregiver, (b) education and referral to community resources, (c) ongoing 'coaching.' (d) a six-session group Caregiver Workshop, and (e) monthly Support Group meetings, all facilitated by the patient's GC nurse. Results. A cluster-randomized controlled trial of GC is underway in 14 primary care physician teams. Of 904 consented patients, 450 (49.8%) identified a primary caregiver: 308 caregivers met eligibility criteria, consented to participate, and completed a baseline interview. At 6-month follow-up, intervention group caregivers' mean Center for Epidemiological Studies Depression (CESD) and Caregiver Strain Index (CSI) scores were respectively 0.97 points (p =. 14) and 1.14 points (p = .06) lower than control group caregivers'. Among caregivers who provided more than 14 hours of weekly assistance at baseline, intervention group caregivers' mean CESD and CSI scores were respectively 1.23 points (p = .20) and 1.83 points (p = .04) lower than control group caregivers'. Conclusions. The GCPFF may benefit family caregivers of chronically ill older adults. Outcomes will continue to be monitored at 18-months follow-up. Key Words: Caregiving--Chronic disease--Nursing Primary care.

We hypothesized that the influence of acute kidney injury (AKI) on the sensitivity of the lung to an injurious process varies with the severity of the injurious process. Thus, we thought that AKI would exacerbate lung injury from low degrees of lung trauma but attenuate lung injury from higher degrees of lung trauma. C57BL/6 mice underwent AKI (30-min kidney ischemia) or sham surgery, followed at 24 h by 4 h of spontaneous breathing (SB), mechanical ventilation with low tidal volume (7 ml/kg, LTV), or mechanical ventilation with high tidal volume (30 ml/kg, HTV). Compared with LTV, median bronchoalveolar lavage (BAL) protein leak was significantly lower with SB and greater with HTV in both sham and AKI mice. Compared with LTV, median Evans blue dye-labeled albumin extravasation in lungs (L-EBD) was also significantly lower with SB and greater with HTV. L-EBD showed a significant interaction between ventilatory mode and kidney health, such that AKI attenuated the L-EBD rise seen in HTV vs. LTV sham mice. An interaction between ventilatory mode and kidney health could also be seen in BAL neutrophil number (PMN). Thus, AKI attenuated the BAL PMN rise seen in HTV vs. LTV sham mice. These data support the presence of a complex interaction between mechanical ventilation and AKI in which the sensitivity of the lung to trauma varies with the magnitude of the trauma and may involve a modification of pulmonary neutrophil activity by AKI. acute kidney injury; Evans blue dye

In this paper, we present a method for conducting global sensitivity analysis of randomized trials in which binary outcomes are scheduled to be collected on participants at prespecified points in time after randomization and these outcomes may be missing in a nonmonotone fashion. We introduce a class of missing data assumptions, indexed by sensitivity parameters, which are anchored around the missing not at random assumption introduced by Robins (Statistics in Medicine, 1997). For each assumption in the class, we establish that the joint distribution of the outcomes is identifiable from the distribution of the observed data. Our estimation procedure uses the plug‐in principle, where the distribution of the observed data is estimated using random forests. We establish n$\sqrt {n}$ asymptotic properties for our estimation procedure. We illustrate our methodology in the context of a randomized trial designed to evaluate a new approach to reducing substance use, assessed by testing urine samples twice weekly, among patients entering outpatient addiction treatment. We evaluate the finite sample properties of our method in a realistic simulation study. Our methods have been implemented in an R package entitled slabm. [ABSTRACT FROM AUTHOR]

Background. The quality of health care for older Americans with multiple chronic conditions is suboptimal. We designed 'Guided Care' (GC) to enhance quality of care by integrating a registered nurse, intensively trained in chronic care, into primary care practices to work with physicians in providing comprehensive chronic care to 50-60 multimorbid older patients. Methods. We hypothesized that GC would improve the quality of health care for this population. In 2006, we began a cluster-randomized controlled trial of GC at eight practices (n = 49 physicians). Older patients of these practices were eligible to participate if they were at risk for using health services heavily during the coming year. Teams of two to five physicians and their at-risk older patients were randomized to either GC or usual care (UC). Six months after baseline, participants rated the quality of their health care by answering validated closed-ended questions from telephone interviewers who were masked to group assignment. Results. Of the 13,534 older patients screened, 2391 (17.7%) were eligible to participate in the study, of which 904 (37.8%) gave informed consent and were cluster-randomized. After 6 months, 93.8% and 93.2% of the GC and UC participants who remained alive and eligible completed telephone interviews. GC participants were more likely than UC participants to rate their care highly (adjusted odds ratio = 2.0, 95% confidence interval, 1.2-3.4, p = .006), and primary care physicians were more likely to be satisfied with their interactions with chronically ill older patients and their families (p < .05). Conclusions. GC improves important aspects of the quality of health care for multimorbid older persons. Additional data will become available as this trial continues. Key Words: Multimorbid--Care management--Randomized trial--Self-management--Quality of care.

The difference in mortality between level 1 trauma centers and hospitals without a trauma center in justification of the expense of maintaining in trauma center is examined. The findings show that the risk of death is significantly lower when care is provided in the trauma center than in a non-trauma center and argue for continued efforts at regionalization.

We develop a Bayesian nonparametric (BNP) approach to evaluate the causal effect of treatment in a randomized trial where a nonterminal event may be censored by a terminal event, but not vice versa (i.e., semi-competing risks). Based on the idea of principal stratification, we define a novel estimand for the causal effect of treatment on the nonterminal event. We introduce identification assumptions, indexed by a sensitivity parameter, and show how to draw inference using our BNP approach. We conduct simulation studies and illustrate our methodology using data from a brain cancer trial. The R code implementing our model and algorithm is available for download at https://github.com/YanxunXu/BaySemiCompeting. [ABSTRACT FROM AUTHOR]

Randomized trials are often designed to collect outcomes at fixed points in time after randomization. In practice, the number and timing of outcome assessments can vary among participants (i.e. irregular assessment). In fact, the timing of assessments may be associated with the outcome of interest (i.e. informative assessment). For example, in a trial evaluating the effectiveness of treatments for major depressive disorder, not only did the timings of outcome assessments vary among participants but symptom scores were associated with assessment frequency. This type of informative observation requires appropriate statistical analysis. Although analytic methods have been developed, they are rarely used. In this article, we review the literature on irregular assessments with a view toward developing recommendations for analyzing trials with irregular and potentially informative assessment times. We show how the choice of analytic approach hinges on assumptions about the relationship between the assessment and outcome processes. We argue that irregular assessment should be treated with the same care as missing data, and we propose that trialists adopt strategies to minimize the extent of irregularity; describe the extent of irregularity in assessment times; make their assumptions about the relationships between assessment times and outcomes explicit; adopt analytic techniques that are appropriate to their assumptions; and assess the sensitivity of trial results to their assumptions. [ABSTRACT FROM AUTHOR]