A comparison of methods to estimate the survivor average causal effect in the presence of missing data: a simulation study

Unified semicompeting risks analysis of hepatitis natural history through mediation modeling

Natural history of hepatitis B or C is comprised of multiple milestones such as liver cirrhosis and liver cancer. To fully characterize its natural course, semicompeting risks represent a common problem where liver cirrhosis and liver cancer are both of interest, but only the former may be censored by the latter. Copula, frailty and multistate models serve as well-established analytics for semicompeting risks. Here, we cast the semicompeting risks in a mediation framework, with liver cirrhosis as a mediator and liver cancer as an outcome. We define the indirect and direct effects as the effects of an exposure on the liver cancer incidence mediated and not mediated through liver cirrhosis, respectively. With the estimands derived as conditional probabilities, we derive respective expressions under the copula, frailty, and multistate models. Next, we propose estimators based on nonparametric maximum likelihood or U-statistics and establish their asymptotic results. Numerical studies demonstrate that the efficiency of copula models leads to potential bias due to model misspecification. Moreover, the robustness of frailty models is accompanied by a loss in efficiency, and multistate models balance the efficiency and robustness. We demonstrate the utility of the proposed methods by a hepatitis study, showing that hepatitis B and C lead to a higher incidence of liver cancer by increasing liver cirrhosis incidence. Thus, mediation modeling provides a unified framework that accommodates various semicompeting risks models.

A Bayesian Approach for Estimating the Survivor Average Causal Effect When Outcomes Are Truncated by Death in Cluster-Randomized Trials

Many studies encounter clustering due to multicenter enrollment and nonmortality outcomes, such as quality of life, that are truncated due to death-that is, missing not at random and nonignorable. Traditional missing-data methods and target causal estimands are suboptimal for statistical inference in the presence of these combined issues, which are especially common in multicenter studies and cluster-randomized trials (CRTs) carried out among the elderly or seriously ill. Using principal stratification, we developed a Bayesian estimator that jointly identifies the always-survivor principal stratum in a clustered/hierarchical data setting and estimates the average treatment effect among them (i.e., the survivor average causal effect (SACE)). In simulations, we observed low bias and good coverage with our method. In a motivating CRT, the SACE and the estimate from complete-case analysis differed in magnitude, but both were small, and neither was incompatible with a null effect. However, the SACE estimate has a clear causal interpretation. The option to assess the rigorously defined SACE estimand in studies with informative truncation and clustering can provide additional insight into an important subset of study participants. Based on the simulation study and CRT reanalysis, we provide practical recommendations for using the SACE in CRTs and software code to support future research.

Racial Disparities in Length of Stay Among Severely Ill Patients Presenting With Sepsis and Acute Respiratory Failure

Importance

Although racial and ethnic minority patients with sepsis and acute respiratory failure (ARF) experience worse outcomes, how patient presentation characteristics, processes of care, and hospital resource delivery are associated with outcomes is not well understood.

Objective

To measure disparities in hospital length of stay (LOS) among patients at high risk of adverse outcomes who present with sepsis and/or ARF and do not immediately require life support and to quantify associations with patient- and hospital-level factors.

Design, Setting, and Participants

This matched retrospective cohort study used electronic health record data from 27 acute care teaching and community hospitals across the Philadelphia metropolitan and northern California areas between January 1, 2013, and December 31, 2018. Matching analyses were performed between June 1 and July 31, 2022. The study included 102 362 adult patients who met clinical criteria for sepsis (n = 84 685) or ARF (n = 42 008) with a high risk of death at the time of presentation to the emergency department but without an immediate requirement for invasive life support.

Exposures

Racial or ethnic minority self-identification.

Main Outcomes and Measures

Hospital LOS, defined as the time from hospital admission to the time of discharge or inpatient death. Matches were stratified by racial and ethnic minority patient identity, comparing Asian and Pacific Islander patients, Black patients, Hispanic patients, and multiracial patients with White patients in stratified analyses.

Results

Among 102 362 patients, the median (IQR) age was 76 (65-85) years; 51.5% were male. A total of 10.2% of patients self-identified as Asian American or Pacific Islander, 13.7% as Black, 9.7% as Hispanic, 60.7% as White, and 5.7% as multiracial. After matching racial and ethnic minority patients to White patients on clinical presentation characteristics, hospital capacity strain, initial intensive care unit admission, and the occurrence of inpatient death, Black patients experienced longer LOS relative to White patients in fully adjusted matches (sepsis: 1.26 [95% CI, 0.68-1.84] days; ARF: 0.97 [95% CI, 0.05-1.89] days). Length of stay was shorter among Asian American and Pacific Islander patients with ARF (-0.61 [95% CI, -0.88 to -0.34] days) and Hispanic patients with sepsis (-0.22 [95% CI, -0.39 to -0.05] days) or ARF (-0.47 [-0.73 to -0.20] days).

Conclusions and Relevance

In this cohort study, Black patients with severe illness who presented with sepsis and/or ARF experienced longer LOS than White patients. Hispanic patients with sepsis and Asian American and Pacific Islander and Hispanic patients with ARF both experienced shorter LOS. Because matched differences were independent of commonly implicated clinical presentation-related factors associated with disparities, identification of additional mechanisms that underlie these disparities is warranted.

A Bayesian semiparametric approach for inference on the population partly conditional mean from longitudinal data with dropout

Studies of memory trajectories using longitudinal data often result in highly nonrepresentative samples due to selective study enrollment and attrition. An additional bias comes from practice effects that result in improved or maintained performance due to familiarity with test content or context. These challenges may bias study findings and severely distort the ability to generalize to the target population. In this study, we propose an approach for estimating the finite population mean of a longitudinal outcome conditioning on being alive at a specific time point. We develop a flexible Bayesian semiparametric predictive estimator for population inference when longitudinal auxiliary information is known for the target population. We evaluate the sensitivity of the results to untestable assumptions and further compare our approach to other methods used for population inference in a simulation study. The proposed approach is motivated by 15-year longitudinal data from the Betula longitudinal cohort study. We apply our approach to estimate lifespan trajectories in episodic memory, with the aim to generalize findings to a target population.

Hypothesis test for causal mediation of time-to-event mediator and outcome

Hepatitis B has been a well-documented risk factor of liver cancer and mortality. To what extent hepatitis B affects mortality through increasing liver cancer incidence is of research interest and remains to be studied. We formulate the research question as a hypothesis testing problem of causal mediation where both the mediator and the outcome are time-to-event variables. The problem is closely related to semicompeting risks because time to the intermediate event may be censored by an occurrence of the outcome. We propose two hypothesis testing methods: a weighted log-rank test (WLR) and an intersection-union test (IUT). A test statistic of the WLR is constructed by adapting a nonparametric estimator of the mediation effect; however, the test may be conservative regarding its Type I Error rate. To address this, we further propose the IUT, the test statistic of which is constructed under the composite null hypothesis. Asymptotic properties of the two tests are studied, showing that the IUT is a size α test with better statistical power than the WLR. The theoretical properties are supported by extensive simulation studies under finite samples. Applying the proposed methods to the motivating hepatitis study, both WLR and IUT provided strong evidence that hepatitis B had a significant mediation effect on mortality via liver cancer incidence.

Correction to: A comparison of methods to estimate the survivor average causal effect in the presence of missing data: a simulation study

In the original publication of this article [1], the incorrect causal diagram was submitted as Fig. 1.