Chen and Heitjan (Sensitivity of estimands in clinical trials with imperfect compliance. Int J Biostat. 2023) used linear extrapolation to estimate the population average causal effect (PACE) from the complier average causal effect (CACE) in multiple randomized trials with all-or-none compliance. For extrapolating from CACE to PACE in this setting and in the paired availability design involving different availabilities of treatment among before-and-after studies, we recommend the sensitivity analysis in Baker and Lindeman (J Causal Inference, 2013) because it is not restricted to a linear model, as it involves various random effect and trend models.

UNASSIGNED: Several governments from African countries, including the Democratic Republic of the Congo (DRC), implemented stringent public health measures to curb COVID-19 transmission in the early phases of the pandemic. While these restrictive measures are believed to have contributed to lowering case incidence and related mortality in DRC, data on the population\'s knowledge and adherence are limited. This study aimed to assess the knowledge, perception, attitudes, and practices of COVID-19 preventive measures and associated factors among adult residents of Matadi, thereby generating evidence for a strategy adjustment as the COVID-19 response is transitioning from emergency to control status.
UNASSIGNED: We used data from a population-based cross-sectional study conducted in October 2021. Consenting participants were enrolled through a multi-stage cluster sampling approach and administered a pre-tested structured questionnaire using a mobile application (Epicollect 5). We analyzed adult participants\' data using STATA 15.1. Univariable and multivariable analyses were applied to identify factors associated with good knowledge, good perception, positive attitude and good practice.
UNASSIGNED: We included 1,269 adult respondents for the secondary analysis. One respondent in six was female. The median age was 36 years (IQR 24-50). Most respondents (76.5%) had good knowledge. Respondents aged 40-49 years and those with vocational education level were 1.7 time (AOR 1.75, 95% CI 1.07-2.87) and twice as likely (AOR 2.06, 95% CI 1.01-4.21) to have good knowledge. Preventive measures were perceived as efficient by 45% of respondents. Good perception was associated with education level, profession, average household monthly income and good knowledge. Only 40% of respondents had a positive attitude. A positive attitude was associated with age, education level, and good knowledge. Respondents having good practice represented 5.8%. Good practice was associated with good knowledge, attitude and perception.
UNASSIGNED: Most respondents were knowledgeable, had a good perception of government-related COVID-19 preventive measures, a moderately positive attitude and an extremely low level of good practice. Current COVID-19 preventive strategies, including vaccination rollout, need adjustment into high-efficiency, context-based and risk group-specific interventions. Evidence generated by this study will improve preparedness and response to future outbreaks.

UNASSIGNED: Our study explores how New York City (NYC) communities of various socioeconomic strata were uniquely impacted by the COVID-19 pandemic.
UNASSIGNED: New York City ZIP codes were stratified into three bins by median income: high-income, middle-income, and low-income. Case, hospitalization, and death rates obtained from NYCHealth were compared for the period between March 2020 and April 2022.
UNASSIGNED: COVID-19 transmission rates among high-income populations during off-peak waves were higher than transmission rates among low-income populations. Hospitalization rates among low-income populations were higher during off-peak waves despite a lower transmission rate. Death rates during both off-peak and peak waves were higher for low-income ZIP codes.
UNASSIGNED: This study presents evidence that while high-income areas had higher transmission rates during off-peak periods, low-income areas suffered greater adverse outcomes in terms of hospitalization and death rates. The importance of this study is that it focuses on the social inequalities that were amplified by the pandemic.

UNASSIGNED: Evaluate the classification accuracy of biomarkers of nicotine exposure for identifying noncompliance in randomized controlled trials (RCTs) of very low nicotine content (VLNC) cigarettes.
UNASSIGNED: We combined data from 2 studies to evaluate the classification accuracy of biomarkers of nicotine exposure for identifying noncompliance in RCTs of VLNC cigarettes. Using a novel approach that did not require knowledge of each participant\'s compliance status, we modeled the distributions of total nicotine equivalents (TNE), total cotinine, and anatabine in compliant and noncompliant participants using a mixture model. Estimates of the classification accuracy were derived from the estimated densities.
UNASSIGNED: TNE and total cotinine had near-perfect classification accuracy, but TNE had better classification accuracy than total cotinine (p = 0.03) and anatabine (p = 0.014). The classification accuracy of TNE and total cotinine decreased as self-reported study cigarettes increased; anatabine was similar, but the results were more statistically variable.
UNASSIGNED: TNE and total cotinine are better classifiers of compliance than anatabine. These results will be useful in determining biomarker thresholds for identifying noncompliance and will aid in the interpretation of future RCTs of VLNC cigarettes.

Significant progress has been achieved in enhancing early outcomes for individuals with maple syrup urine disease (MSUD), a rare metabolic disorder that leads to the accumulation of branched-chain amino acids leucine, isoleucine, and valine, where leucine is known as the primary neurotoxic metabolite. Newborn screening is helpful in early diagnosis and implementation of dietary treatment, thus reducing neurological deterioration and complications in young children. However, patients face the life-long challenge of maintaining metabolic control through adherence to a strict low-leucine diet to avoid long-term consequences of chronic hyperleucinemia, which include cognitive deficits, mood disorders, and movement disorders. This case report exemplifies the complex involvement of MSUD in adult survivors. Despite presenting early in life, the patient thrived until the onset of psychiatric symptoms. The subject of this case is a 25-year-old woman with MSUD, who remained in her usual state of health until presentation to the emergency department (ED) with psychosis and altered mental status. However, due to a lack of medical records and poor communication, there was a delay in considering MSUD as a primary cause of her psychiatric symptoms. Although a genetics consultation was later arranged and efforts were made to decrease plasma leucine to the therapeutic range, these interventions proved inadequate in halting her deterioration in health. Her condition worsened within 72 h, culminating in her untimely death. This case emphasizes the comorbidity of psychiatric involvement in MSUD, which contributes to metabolic decompensation that can lead to cerebral edema and death. This case also highlights the pressing need for enhanced strategies for the acute management and long-term care of MSUD patients with psychiatric involvement, particularly in scenarios where mental disturbance could lead to noncompliance.

Post-randomization events, also known as intercurrent events, such as treatment noncompliance and censoring due to a terminal event, are common in clinical trials. Principal stratification is a framework for causal inference in the presence of intercurrent events. The existing literature on principal stratification lacks generally applicable and accessible methods for time-to-event outcomes. In this paper, we focus on the noncompliance setting. We specify 2 causal estimands for time-to-event outcomes in principal stratification and provide a nonparametric identification formula. For estimation, we adopt the latent mixture modeling approach and illustrate the general strategy with a mixture of Bayesian parametric Weibull-Cox proportional hazards model for the outcome. We utilize the Stan programming language to obtain automatic posterior sampling of the model parameters. We provide analytical forms of the causal estimands as functions of the model parameters and an alternative numerical method when analytical forms are not available. We apply the proposed method to the ADAPTABLE (Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-Term Effectiveness) trial to evaluate the causal effect of taking 81 versus 325 mg aspirin on the risk of major adverse cardiovascular events. We develop the corresponding R package PStrata.

UNASSIGNED: To demonstrate that using an instrumental variable (IV) with monotonicity reduces the accuracy of propensity score (PS) weighted estimators for the average treatment effect (ATE).
UNASSIGNED: Monotonicity in the relationship between a binary IV and a binary treatment variable is an important assumption to identify the ATE for compliers who would only take treatment when encouraged by the IV. We perform theoretical and numerical investigations to study the impact of using the IV that satisfies monotonicity on the PS of treatment in terms of the positivity assumption, which requires that the PS be strictly between 0 and 1, and the accuracy of PS weighted estimators. Two versions of monotonicity that result in one-sided or two-sided noncompliance are considered.
UNASSIGNED: The PS adjusting for the IV always violates the positivity assumption when noncompliance occurs in one direction (one-sided noncompliance) and is more extreme than without the IV under two-sided noncompliance. These results are valid if the probability of being encouraged to get treatment and the compliance score, the probability of being a complier, are strictly between 0 and 1.
UNASSIGNED: Using a binary IV with monotonicity as a covariate for the PS model makes the estimated PSs unnecessarily extreme, reducing the accuracy of the PS weighted estimators.

We pay tribute to Marshall Joffe, PhD, and his substantial contributions to the field of causal inference with focus in biostatistics and epidemiology. By compiling narratives written by us, his colleagues, we not only present highlights of Marshall\'s research and their significance for causal inference but also offer a portrayal of Marshall\'s personal accomplishments and character. Our discussion of Marshall\'s research notably includes (but is not limited to) handling of posttreatment variables such as noncompliance, employing G-estimation for treatment effects on failure-time outcomes, estimating effects of time-varying exposures subject to time-dependent confounding, and developing a causal framework for case-control studies. We also provide a description of some of Marshall\'s unpublished work, which is accompanied by a bonus anecdote. We discuss future research directions related to Marshall\'s research. While Marshall\'s impact in causal inference and the world outside of it cannot be wholly captured by our words, we hope nonetheless to present some of what he has done for our field and what he has meant to us and to his loved ones.

Medical cost data often consist of zero values as well as extremely right-skewed positive values. A two-part model is a popular choice for analyzing medical cost data, where the first part models the probability of a positive cost using logistic regression and the second part models the positive cost using a lognormal or Gamma distribution. To address the unmeasured confounding in studies on cost outcome under two-part models, two instrumental variable (IV) methods, two-stage residual inclusion (2SRI) and two-stage prediction substitution (2SPS) are widely applied. However, previous literature demonstrated that both the 2SRI and the 2SPS could fail to consistently estimate the causal effect among compliers under standard IV assumptions for binary and survival outcomes. Our simulation studies confirmed that it continued to be the case for a two-part model, which is another nonlinear model. In this article, we develop a model-based IV approach, Instrumental Variable with Two-Part model (IV2P), to obtain a consistent estimate of the causal effect among compliers for cost outcome under standard IV assumptions. In addition, we develop sensitivity analysis approaches to allow the evaluation of the sensitivity of the causal conclusions to potential quantified violations of the exclusion restriction assumption and the randomization of IV assumption. We apply our method to a randomized cash incentive study to evaluate the effect of a primary care visit on medical cost among low-income adults newly covered by a primary care program.

UNASSIGNED: To assess the understanding of the patients with common mental disorders, towards issues related to their mental health.
UNASSIGNED: This qualitative study was conducted from December 2018 to April 2020. Thirty-four patients, suffering from common mental disorders, were interviewed in public and private sector hospitals of Peshawar. The interviews were recorded, transcribed, translated into English, and themes were generated from their responses. Content analysis was carried out on the data obtained. The themes resulting from each interview were then further comparatively analyzed.
UNASSIGNED: The mean age of the sample was 31.9±10.61 years. Most of the patients (n=24, 70.6%) were aware that the nature of their illness was a psychological one with a majority (n=17, 50%) describing it with the symptoms of headache or burden on the head. Most of the patients (n=14, 41.1%) were unaware of the general public opinion towards mental disorders but those who were aware described these with stigmatizing descriptions e.g., \"people call them crazy\" etc. Most of the patients (n=20, 58.8%) were unaware about their own opinion regarding their illness and some said that they tried to conceal their illness from others. Unfortunately, most of the patients (n=19, 55.8%) were not aware of mental healthcare professionals or the existence of psychiatry as a profession.
UNASSIGNED: Stigma, both public and personal, was quite high, which caused patients to feel compelled to conceal their illness. There was also a general lack of knowledge with regard to mental disorders in our society. The general public opinion about mental health professionals was not favorable.