Direct and indirect treatment effects in the presence of semicompeting risks.

Semicompeting risks refer to the phenomenon that the terminal event (such as death) can censor the nonterminal event (such as disease progression) but not vice versa. The treatment effect on the terminal event can be delivered either directly following the treatment or indirectly through the nonterminal event. We consider 2 strategies to decompose the total effect into a direct effect and an indirect effect under the framework of mediation analysis in completely randomized experiments by adjusting the prevalence and hazard of nonterminal events, respectively. They require slightly different assumptions on cross-world quantities to achieve identifiability. We establish asymptotic properties for the estimated counterfactual cumulative incidences and decomposed treatment effects. We illustrate the subtle difference between these 2 decompositions through simulation studies and two real-data applications in the Supplementary Materials.

Causal inference for time-to-event data with a cured subpopulation.

When studying the treatment effect on time-to-event outcomes, it is common that some individuals never experience failure events, which suggests that they have been cured. However, the cure status may not be observed due to censoring which makes it challenging to define treatment effects. Current methods mainly focus on estimating model parameters in various cure models, ultimately leading to a lack of causal interpretations. To address this issue, we propose 2 causal estimands, the timewise risk difference and mean survival time difference, in the always-uncured based on principal stratification as a complement to the treatment effect on cure rates. These estimands allow us to study the treatment effects on failure times in the always-uncured subpopulation. We show the identifiability using a substitutional variable for the potential cure status under ignorable treatment assignment mechanism, these 2 estimands are identifiable. We also provide estimation methods using mixture cure models. We applied our approach to an observational study that compared the leukemia-free survival rates of different transplantation types to cure acute lymphoblastic leukemia. Our proposed approach yielded insightful results that can be used to inform future treatment decisions.

Improving efficiency of inference in clinical trials with external control data.

Suppose we are interested in the effect of a treatment in a clinical trial. The efficiency of inference may be limited due to small sample size. However, external control data are often available from historical studies. Motivated by an application to Helicobacter pylori infection, we show how to borrow strength from such data to improve efficiency of inference in the clinical trial. Under an exchangeability assumption about the potential outcome mean, we show that the semiparametric efficiency bound for estimating the average treatment effect can be reduced by incorporating both the clinical trial data and external controls. We then derive a doubly robust and locally efficient estimator. The improvement in efficiency is prominent especially when the external control data set has a large sample size and small variability. Our method allows for a relaxed overlap assumption, and we illustrate with the case where the clinical trial only contains a treated group. We also develop doubly robust and locally efficient approaches that extrapolate the causal effect in the clinical trial to the external population and the overall population. Our results also offer a meaningful implication for trial design and data collection. We evaluate the finite-sample performance of the proposed estimators via simulation. In the Helicobacter pylori infection application, our approach shows that the combination treatment has potential efficacy advantages over the triple therapy.

Defining estimands in clinical trials: A unified procedure.

The ICH E9 (R1) addendum proposes five strategies to define estimands by addressing intercurrent events. However, mathematical forms of these targeted quantities are lacking, which might lead to discordance between statisticians who estimate these quantities and clinicians, drug sponsors, and regulators who interpret them. To improve the concordance, we provide a unified four-step procedure for constructing the mathematical estimands. We apply the procedure for each strategy to derive the mathematical estimands and compare the five strategies in practical interpretations, data collection, and analytical methods. Finally, we show that the procedure can help ease tasks of defining estimands in settings with multiple types of intercurrent events using two real clinical trials.

Quantile partially linear additive model for data with dropouts and an application to modeling cognitive decline.

The National Alzheimer's Coordinating Center Uniform Data Set includes test results from a battery of cognitive exams. Motivated by the need to model the cognitive ability of low-performing patients we create a composite score from ten tests and propose to model this score using a partially linear quantile regression model for longitudinal studies with non-ignorable dropouts. Quantile regression allows for modeling non-central tendencies. The partially linear model accommodates nonlinear relationships between some of the covariates and cognitive ability. The data set includes patients that leave the study prior to the conclusion. Ignoring such dropouts will result in biased estimates if the probability of dropout depends on the response. To handle this challenge, we propose a weighted quantile regression estimator where the weights are inversely proportional to the estimated probability a subject remains in the study. We prove that this weighted estimator is a consistent and efficient estimator of both linear and nonlinear effects.

Electron correlation and relativistic effects on the electronic properties of a plutonium and americium mixed oxide (PuAmO4): from single-particle approximation to dynamical mean-field theory.

First-principles calculations were performed on a plutonium and americium mixed oxide (PuAmO4), aiming at revealing the effects of electron correlation, Pu/Am 5f-conduction electrons' hybridization, and relativity on its electronic properties. The many-body calculation suggests that the spin-orbit-coupling (SOC)-splitting of j = 5/2 and j = 7/2 manifolds are both in the weakly and moderately correlated states, respectively, implying that the jj coupling scheme is more appropriate for Pu/Am 5f electrons. The density of states, 5f occupation numbers, and Green's functions all suggest that both Pu and Am 5f electrons exhibit the coexistence of the localized and delocalized states. The admixture of 5fn atomic configurations, Pu/Am 5f-conduction electrons' hybridization, and dual characteristics of 5f electrons yield average occupation numbers of 5f electrons n5f = 4.78 and 5.86 for Pu and Am ions, respectively. Within the DFT+DMFT calculation, the weighted-summation-derived occupation numbers in terms of 5f4/5f5/5f6 and 5f5/5f6 configurations for Pu and Am 5f electrons, respectively, are in reasonable agreement with those of other DFT-based calculations.

Comparing the accuracy of screening tests with verification of disease status restricted to test positives.

As a fundamental component of health care, disease screening is of highly importance. Oftentimes, two screening tests for a specific disease are compared in order to determine an optimal screening policy, for example, the digital rectal examination (DRE) and serum prostate specific antigen (PSA) level for screening prostate cancer. Ideally, if a gold standard test is given to each individual being screened to establish their true disease status, the difference in accuracy measures between two tests can be evaluated. In practice, however, it is common that only individuals who test positive on at least one screening test are to receive gold standard tests, which are often invasive and cannot be applied to those with negative results on both tests due to ethical reasons. Under such circumstances, estimates of the differences in accuracy measures between two tests cannot be determined, thus the inference problem within this framework is challenging. In this article, using sensitivity and specificity as measures of test accuracy, we show that their difference between two tests is interval-identified, as bounded by estimable sharp bounds. Here, we develop the asymptotic normality for the estimators of the bounds and construct confidence intervals for the difference by utilizing the method for solving inference problem for partially identified parameters. The performance of constructed confidence intervals for the difference and their sharp bounds are evaluated via simulation studies. We also apply the proposed method to the prostate cancer example to compare the accuracy of DRE and PSA.

A novel regression method for the analysis of multireader multicase-free-response receiver operating characteristics studies.

In diagnostic radiology, the multireader multicase (MRMC) design and the free-response receiver operating characteristics (FROC) method are often used in combination. The cross-correlated data generated by the MRMC-FROC study leads to difficulties in the corresponding analysis, and the need to include covariates in the model further complicates the subsequent analysis. In this paper, we propose a regression approach based on three new measures with good interpretability. The correlation structure of the original test results is taken directly into account in the estimation procedure. The proposed method also allows the inclusion of continuous or discrete covariates. Consistent and asymptotically normal estimators are derived for the new measures. Simulation studies are conducted to evaluate the performance of the proposed approach. The simulation results show that the regression approach performs well under a wide range of scenarios. We also apply the proposed regression approach to a diagnostic study of computer-aided diagnosis in lung cancer.

Extending Hui-Walter framework to correlated outcomes with application to diagnosis tests of an eye disease among premature infants.

Diagnostic accuracy, a measure of diagnostic tests for correctly identifying patients with or without a target disease, plays an important role in evidence-based medicine. Diagnostic accuracy of a new test ideally should be evaluated by comparing to a gold standard; however, in many medical applications it may be invasive, costly, or even unethical to obtain a gold standard for particular diseases. When the accuracy of a new candidate test under evaluation is assessed by comparison to an imperfect reference test, bias is expected to occur and result in either overestimates or underestimates of its true accuracy. In addition, diagnostic test studies often involve repeated measurements of the same patient, such as the paired eyes or multiple teeth, and generally lead to correlated and clustered data. Using the conventional statistical methods to estimate diagnostic accuracy can be biased by ignoring the within-cluster correlations. Despite numerous statistical approaches have been proposed to tackle this problem, the methodology to deal with correlated and clustered data in the absence of a gold standard is limited. In this article, we propose a method based on the composite likelihood function to derive simple and intuitive closed-form solutions for estimates of diagnostic accuracy, in terms of sensitivity and specificity. Through simulation studies, we illustrate the relative advantages of the proposed method over the existing methods that simply treat an imperfect reference test as a gold standard in correlated and clustered data. Compared with the existing methods, the proposed method can reduce not only substantial bias, but also the computational burden. Moreover, to demonstrate the utility of this approach, we apply the proposed method to the study of National-Eye-Institute-funded Telemedicine Approaches to Evaluating of Acute-Phase Retinopathy of Prematurity (e-ROP), for estimating accuracies of both the ophthalmologist examination and the image evaluation.

Estimating c-level partial correlation graphs with application to brain imaging.

Alzheimer's disease (AD) is a chronic neurodegenerative disease that changes the functional connectivity of the brain. The alteration of the strong connections between different brain regions is of particular interest to researchers. In this article, we use partial correlations to model the brain connectivity network and propose a data-driven procedure to recover a $c$-level partial correlation graph based on PET data, which is the graph of the absolute partial correlations larger than a pre-specified constant $c$. The proposed procedure is adaptive to the "large p, small n" scenario commonly seen in whole brain studies, and it incorporates the variation of the estimated partial correlations, which results in higher power compared to the existing methods. A case study on the FDG-PET images from AD and normal control (NC) subjects discovers new brain regions, Sup Frontal and Mid Frontal in the frontal lobe, which have different brain functional connectivity between AD and NC.

Estimation of incubation period and generation time based on observed length-biased epidemic cohort with censoring for COVID-19 outbreak in China.

The incubation period and generation time are key characteristics in the analysis of infectious diseases. The commonly used contact-tracing-based estimation of incubation distribution is highly influenced by the individuals' judgment on the possible date of exposure, and might lead to significant errors. On the other hand, interval censoring-based methods are able to utilize a much larger set of traveling data but may encounter biased sampling problems. The distribution of generation time is usually approximated by observed serial intervals. However, it may result in a biased estimation of generation time, especially when the disease is infectious during incubation. In this paper, the theory from renewal process is partially adopted by considering the incubation period as the interarrival time, and the duration between departure from Wuhan and onset of symptoms as the mixture of forward time and interarrival time with censored intervals. In addition, a consistent estimator for the distribution of generation time based on incubation period and serial interval is proposed for incubation-infectious diseases. A real case application to the current outbreak of COVID-19 is implemented. We find that the incubation period has a median of 8.50 days (95% confidence interval [CI] [7.22; 9.15]). The basic reproduction number in the early phase of COVID-19 outbreak based on the proposed generation time estimation is estimated to be 2.96 (95% CI [2.15; 3.86]).

Summary concordance index for meta-analysis of prognosis studies with a survival outcome.

In prognosis studies to evaluate association between a continuous biomarker and a survival outcome, investigators often classify subjects into two subclasses of the high- and low-expression groups and apply simple survival analysis techniques of the Kaplan-Meier method and the logrank test. The high- and low-expressions are defined according to whether or not the observation of the biomarker is higher than the cut-off value, which is heterogeneous across studies. The heterogeneous definitions of the cut-off value make it difficult to apply the standard meta-analysis techniques. We propose a method to estimate the concordance index for a survival outcome synthesizing published prognosis studies, in which the Kaplan-Meier estimates for the high- and low-expression groups are reported. We illustrate our proposed method with a real dataset for meta-analysis of prognosis studies evaluating Ki-67 in early breast cancer and evaluate its performance with a simulation study.

Causal mediation analysis with sure outcomes of random events model.

Mediation analysis is a useful tool in randomized trials for understanding how a treatment works, in particular how much of the treatment's effect on an outcome is explained by a mediator variable. The traditional approach to mediation analysis makes sequential ignorability assumption which precludes the existence of unobserved confounders between the mediator and outcome variables. Since the randomized experiment does not randomize the mediator, sequential ignorability may not be plausible. In this article, based on a statistical model termed sure outcomes of random events model, we propose an alternative approach to causal mediation analysis without relying on the sequential ignorability assumption for the case of binary treatment and mediator variables. When the outcome is also binary, we establish the identifiability of the average natural direct and indirect effects in the presence of an unobserved confounder between mediator and outcome variables. More importantly, if the identifiability conditions are violated, we provide new bounds that are narrower than those in the previous studies, and these bound results are extended to the case of an arbitrary bounded outcome. Simulation studies show good performance for the proposed estimators in finite samples. Finally, we use a job training intervention on the mental health study to illustrate our approach.

A powerful test for the maximum treatment effect in thorough QT/QTc studies.

Parallel-group thorough QT/QTc studies focus on the change of QT/QTc values at several time-matched points from a pretreatment day (baseline) to a posttreatment day for different groups of treatment. The International Council for Harmonisation E14 stresses that QTc prolongation beyond a threshold represents high cardiac risk and calls for a test on the largest time-matched treatment effect (QTc prolongation). QT/QTc analysis usually assumes a jointly multivariate normal (MVN) distribution of pretreatment and posttreatment QT/QTc values, with a blocked compound symmetry covariance matrix. Existing methods use an analysis of covariance (ANCOVA) model including day-averaged baseline as a covariate to deal with the MVN model. However, the ANCOVA model tends to underestimate the variation of the estimator for treatment effects, resulting in the inflation of empirical type I error rate when testing whether the largest QTc prolongation is beyond a threshold. In this article, we propose two new methods to estimate the time-matched treatment effects under the MVN model, including maximum likelihood estimation and ordinary-least-square-based two-stage estimation. These two methods take advantage of the covariance structure and are asymptotically efficient. Based on these estimators, powerful tests for QT/QTc prolongation are constructed. Simulation shows that the proposed estimators have smaller mean square error, and the tests can control the type I error rate with high power. The proposed methods are applied on testing the carryover effect of diltiazem to inhibit dofetilide in a randomized phase 1 trial.

Changes in depressive-like behaviors induced by spinal cord injury based on the hypothalamic-pituitary-adrenal axis and hippocampal neurogenesis.

A reduction in sucrose preference is a key characteristic of depressive-like behaviors after spinal cord injury as judged by the sucrose preference test, the hypothalamic-pituitary-adrenal axis and adult hippocampal neurogenesis. Male rats were divided into three groups: control, sham and spinal cord injury groups. The spinal cord injury rats received a severe mid-thoracic contusion. The Basso, Beattie and Bresnahan score was used to assess motor function. The sucrose preference test and forced swim test were used to evaluate depressive-like behaviors. Serum corticosterone levels were examined by enzyme-linked immunosorbent assay and hippocampal glucocorticoid receptor levels were examined by Western blot to evaluate the function of the hypothalamic-pituitary-adrenal axis. Adult hippocampal neurogenesis was assessed by testing hippocampal brain-derived neurotrophic factor and tropomyosin receptor kinase B levels by Western blot and doublecortin levels by immunohistochemistry. Data showed that spinal cord injury impaired motor function. The spinal cord injury rats exhibited decreased sucrose preference on day six, which continued to decrease until day twelve, followed by a plateau phase. Additionally, the immobility time of the spinal cord injury rats was increased on day thirty-four. Moreover, serum corticosterone levels in the spinal cord injury group peaked on day seven, was decreased by day twenty-one and was increased again on day thirty-five. Serum corticosterone levels were significantly negatively correlated with sucrose preference and positively correlated with immobility time. Finally, hippocampal doublecortin levels on days twenty-one and thirty-five were lower in the spinal cord injury group than in the other groups. These results suggest that hyperactivation of the hypothalamic-pituitary-adrenal axis and the inhibition of adult hippocampal neurogenesis may be part of the underlying mechanism responsible for depressive-like behaviors after spinal cord injury.

Identification of the optimal treatment regimen in the presence of missing covariates.

Covariates associated with treatment-effect heterogeneity can potentially be used to make personalized treatment recommendations towards best clinical outcomes. Methods for treatment-selection rule development that directly maximize treatment-selection benefits have attracted much interest in recent years, due to the robustness of these methods to outcome modeling. In practice, the task of treatment-selection rule development can be further complicated by missingness in data. Here, we consider the identification of optimal treatment-selection rules for a binary disease outcome when measurements of an important covariate from study participants are partly missing. Under the missing at random assumption, we develop a robust estimator of treatment-selection rules under the direct-optimization paradigm. This estimator targets the maximum selection benefits to the population under correct specification of at least one mechanism from each of the two sets-missing data or conditional covariate distribution, and treatment assignment or disease outcome model. We evaluate and compare performance of the proposed estimator with alternative direct-optimization estimators through extensive simulation studies. We demonstrate the application of the proposed method through a real data example from an Alzheimer's disease study for developing covariate combinations to guide the treatment of Alzheimer's disease.

Chinese clinical named entity recognition with variant neural structures based on BERT methods.

Clinical Named Entity Recognition (CNER) is a critical task which aims to identify and classify clinical terms in electronic medical records. In recent years, deep neural networks have achieved significant success in CNER. However, these methods require high-quality and large-scale labeled clinical data, which is challenging and expensive to obtain, especially data on Chinese clinical records. To tackle the Chinese CNER task, we pre-train BERT model on the unlabeled Chinese clinical records, which can leverage the unlabeled domain-specific knowledge. Different layers such as Long Short-Term Memory (LSTM) and Conditional Random Field (CRF) are used to extract the text features and decode the predicted tags respectively. In addition, we propose a new strategy to incorporate dictionary features into the model. Radical features of Chinese characters are used to improve the model performance as well. To the best of our knowledge, our ensemble model outperforms the state of the art models which achieves 89.56% strict F1 score on the CCKS-2018 dataset and 91.60% F1 score on CCKS-2017 dataset.

[Clinical Characteristics of Aldosterone Producing Adenoma and Idiopathic Hyperaldosteronism with Obstructive Sleep Apnea Hypopnea Syndrome].

OBJECTIVE: To investigate the clinical characteristics of aldosterone producing adenoma (APA) and idiopathic hyperaldosteronism (IHA) complicated with obstructive sleep apnea hypopnea syndrome (OSAHS) and the effect of OSAHS on renin-angiotensin-aldosterone system (RAAS) in APA and IHA patients. METHODS: The clinical data of 127 patients with primary aldosteronism (PA) diagnosed from May 2010 to Aug. 2019 were retrospectively analyzed. There were 70 cases of APA, 53 cases of IHA. Another 4 cases were primary adrenal hyperplasia (PAH), so not included into further analysis. According to the results of polysomnography, the 123 patients of APA or IHA were divided into OSAHS group (96 cases) and non-OSAHS group (27 cases ). The patients with OSAHS were divided into mild, moderate and severe subgroups based on apnea hypopnea index (AHI).The clinical characteristics, biochemical parameters, plasma renin activity, aldosterone levels, and the ratio of aldosterone to renin activity (ARR) in the patients of APA and IHA complicated with OSAHS were compared with those of the patients without OSAHS. RESULTS: There were 49 OSAHS cases (49/70, 70.0%) in APA patients. and 47 OSAHS cases (47/53, 88.7%) in IHA patients. The age, male ratio, body mass index (BMI), waist circumference, triglyceride, blood uric acid, and blood creatinine in APA patients with OSAHS were higher than those in APA patients without OSAHS ( P<0.05), while high-density lipoprotein and estimated glomerular filtration rate (eGFR) were lower ( P<0.05). Compared to the patients without OSAHS, IHA-OSAHS patients had higher BMI and waist circumference ( P<0.05). Moderate/severe OSAHS-APA patients exhibited higher plasma renin activity levels and lower ARR values than the APA patients with no/mild OSAHS ( P<0.05). There were no significant differences in plasma renin activity, aldosterone levels, and ARR values between moderate/severe OSAHS-IHA group and no/mild OSAHS-IHA group. CONCLUSION: The prevalence of OSAHS is significantly higher in the patients with PA than normal population, and OSAHS may aggravate glycose, lipid and uric acid metabolism in PA patients. Moderate/severe OSAHS can increase renin levels and decrease ARR values in APA patients, but has no significant effect on RAAS in IHA patients.

Estimating causal effects of treatment in RCTs with provider and subject noncompliance.

Subject noncompliance is a common problem in the analysis of randomized clinical trials (RCTs). With cognitive behavioral interventions, the addition of provider noncompliance further complicates making causal inference. As a motivating example, we consider an RCT of a motivational interviewing (MI)-based behavioral intervention for treating problem drug use. Treatment receipt depends on compliance of both a therapist (provider) and a patient (subject), where MI is received when the therapist adheres to the MI protocol and the patient actively participates in the intervention. However, therapists cannot be forced to follow protocol and patients cannot be forced to cooperate in an intervention. In this article, we (1) define a causal estimand of interest based on a principal stratification framework, the average causal effect of treatment among provider-subject pairs that comply with assignment or ACE(cc); (2) explore possible assumptions that identify ACE(cc); (3) develop novel estimators of ACE(cc); (4) evaluate estimators' statistical properties via simulation; and (5) apply our proposed methods for estimating ACE(cc) to data from our motivating example.

Sensitivity analysis for publication bias in meta-analysis of diagnostic studies for a continuous biomarker.

Publication bias is one of the most important issues in meta-analysis. For standard meta-analyses to examine intervention effects, the funnel plot and the trim-and-fill method are simple and widely used techniques for assessing and adjusting for the influence of publication bias, respectively. However, their use may be subjective and can then produce misleading insights. To make a more objective inference for publication bias, various sensitivity analysis methods have been proposed, including the Copas selection model. For meta-analysis of diagnostic studies evaluating a continuous biomarker, the summary receiver operating characteristic (sROC) curve is a very useful method in the presence of heterogeneous cutoff values. To our best knowledge, no methods are available for evaluation of influence of publication bias on estimation of the sROC curve. In this paper, we introduce a Copas-type selection model for meta-analysis of diagnostic studies and propose a sensitivity analysis method for publication bias. Our method enables us to assess the influence of publication bias on the estimation of the sROC curve and then judge whether the result of the meta-analysis is sufficiently confident or should be interpreted with much caution. We illustrate our proposed method with real data.