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Study questionDo the rates at which women transition among different intensities of pregnancy planning vary with age, marital status and race/ethnicity?Summary answerRates of transition from low or moderate pregnancy probability groups (PPGs) to higher PPGs vary by age, marital status and race/ethnicity.What is known alreadyThe design of prospective studies of the effects of pre- and peri-conception exposures on fecundity, pregnancy and children's health is challenging because at any specific time only a small percentage of reproductive age women is attempting to conceive. To our knowledge, there has been no population-based, prospective study that repeatedly assessed pregnancy planning, which included women who were not already planning pregnancy at enrollment and whose ages spanned the female reproductive age range.Study design, size, durationA longitudinal study was carried out that repeatedly assessed pregnancy probability in 12 916 women for up to 21 months from January 2009 to September 2010.Participants/materials, setting, methodWe analyzed data from the National Children's Study Vanguard Study, a pilot study for a large-scale epidemiological birth cohort study of children and their parents. During the Vanguard Study, investigators followed population-based samples of reproductive age women in each of seven geographically dispersed and diverse study locations over time to identify when they sought to become pregnant, providing a unique opportunity to prospectively assess changes in pregnancy planning in a large sample of US women. At study entry and each follow-up contact, which occurred at 1, 3 or 6 month intervals depending on PPG, a questionnaire was used to assess behavior dimensions of pregnancy planning to assign women to low, moderate, high non-tryer and high tryer PPGs.Main results and the role of chanceCrude rates of pregnancy increased with higher assigned PPG, validating the utility of the instrument. The initial PPG and probabilities of transitioning from low or moderate PPG to higher PPG or pregnancy varied with age, marital status and race/ethnicity. Women aged 25 to

OBJECTIVE:This study aims to estimate the incidence of first hip or clinical vertebral fracture or major osteoporotic (hip, clinical vertebral, proximal humerus, or wrist) fracture in postmenopausal women undergoing their first bone mineral density (BMD) test before age 65 years.METHODS:We studied 4,068 postmenopausal women, aged 50 to 64 years without hip or clinical vertebral fracture or antifracture treatment at baseline, who were participating in the Women's Health Initiative BMD cohort study. BMD tests were performed between October 1993 and April 2005, with fracture follow-up through 2012. Outcomes were the time for 1% of women to sustain a hip or clinical vertebral fracture and the time for 3% of women to sustain a major osteoporotic fracture before initiating treatment, adjusting for clinical risk factors and accounting for competing risks. Women without osteoporosis and women with osteoporosis on their first BMD test were analyzed separately.RESULTS:During a maximum of 11.2 years of concurrent BMD and fracture follow-up, the adjusted estimated time for 1% of women to have a hip or clinical vertebral fracture was 12.8 years (95% CI, 8.0-20.4) for women aged 50 to 54 years without baseline osteoporosis, 7.6 years (95% CI, 4.8-12.1) for women aged 60 to 64 years without baseline osteoporosis, and 3.0 years (95% CI, 1.3-7.1) for all women aged 50 to 64 years with baseline osteoporosis. Results for major osteoporotic fracture were similar.CONCLUSIONS:Because of very low rates of major osteoporotic fracture, postmenopausal women aged 50 to 64 years without osteoporosis on their first BMD test are unlikely to benefit from frequent rescreening before age 65 years.

Background: The addition of oxaliplatin to adjuvant 5-fluorouracil (5-FU) improves survival of patients with stage III colon cancer in randomized clinical trials (RCTs). However, RCT participants are younger, healthier, and less racially diverse than the general cancer population. Thus, the benefit of oxaliplatin outside RCTs is uncertain.Subjects and Methods: Patients younger than 75 years with stage III colon cancer who received chemotherapy within 120 days of surgical resection were identified from five observational data sources-the Surveillance, Epidemiology, and End Results registry linked to Medicare claims (SEER-Medicare), the New York State Cancer Registry (NYSCR) linked to Medicaid and Medicare claims, the National Comprehensive Cancer Network (NCCN) Outcomes Database, and the Cancer Care Outcomes Research & Surveillance Consortium (CanCORS). Overall survival (OS) was compared among patients treated with oxaliplatin vs non-oxaliplatin-containing adjuvant chemotherapy. Overall survival for 4060 patients diagnosed during 2004-2009 was compared with pooled data from five RCTs (the Adjuvant Colon Cancer ENdpoinTs [ACCENT] group, n = 8292). Datasets were juxtaposed but not combined using Kaplan-Meier curves. Covariate and propensity score adjusted proportional hazards models were used to calculate adjusted survival hazard ratios (HR). Stratified analyses examined effect modifiers. All statistical tests were two-sided.Results: The survival advantage associated with the addition of oxaliplatin to adjuvant 5-FU was evident across diverse practice settings (3-year OS: RCTs, 86% [n = 1273]; SEER-Medicare, 80% [n = 1152]; CanCORS, 88% [n = 129]; NYSCR-Medicaid, 82% [n = 54]; NYSCR-Medicare, 79% [n = 180]; and NCCN, 86% [n = 438]). A statistically significant improvement in 3-year overall survival was seen in the largest cohort, SEER-Medicare, and in the NYSCR-Medicare cohort (non-oxaliplatin-containing vs oxaliplatin-containing adjuvant therapy, adjusted HR of death: pooled RCTs: HR = 0.80, 95% CI = 0.70 to 0.92, P = .002; SEER-Medicare: HR = 0.70, 95% CI = 0.60 to 0.82, P < .001; NYSCR-Medicare patients aged ≥65 years: HR = 0.58, 95% CI = 0.38 to 0.90, P = .02). The association between oxaliplatin treatment and better survival was maintained in older and minority group patients, as well as those with higher comorbidity.Conclusion: The addition of oxaliplatin to 5-FU appears to be associated with better survival among patients receiving adjuvant colon cancer treatment in the community. [ABSTRACT FROM AUTHOR]

We consider modeling the dependence of sensitivity and specificity on the disease prevalence in diagnostic accuracy studies. Many meta-analyses compare test accuracy across studies and fail to incorporate the possible connection between the accuracy measures and the prevalence. We propose a Pearson type correlation coefficient and an estimating equation-based regression framework to help understand such a practical dependence. The results we derive may then be used to better interpret the results from meta-analyses. In the biomedical examples analyzed in this paper, the diagnostic accuracy of biomarkers are shown to be associated with prevalence, providing insights into the utility of these biomarkers in low- and high-prevalence populations. [ABSTRACT FROM AUTHOR]

The accuracy of a single diagnostic test for binary outcome can be summarized by the area under the receiver operating characteristic (ROC) curve. Volume under the surface and hypervolume under the manifold have been proposed as extensions for multiple class diagnosis (Scurfield, 1996, 1998). However, the lack of simple inferential procedures for such measures has limited their practical utility. Part of the difficulty is that calculating such quantities may not be straightforward, even with a single test. The decision rule used to generate the ROC surface requires class probability assessments, which are not provided by the tests. We develop a method based on estimating the probabilities via some procedure, for example, multinomial logistic regression. Bootstrap inferences are proposed to account for variability in estimating the probabilities and perform well in simulations. The ROC measures are compared to the correct classification rate, which depends heavily on class prevalences. An example of tumor classification with microarray data demonstrates that this property may lead to substantially different analyses. The ROC-based analysis yields notable decreases in model complexity over previous analyses. [ABSTRACT FROM AUTHOR]

The standard approach to regression modeling for cause-specific hazards with prospective competing risks data specifies separate models for each failure type. An alternative proposed by Lunn and McNeil (1995) assumes the cause-specific hazards are proportional across causes. This may be more efficient than the standard approach, and allows the comparison of covariate effects across causes. In this paper, we extend Lunn and McNeil (1995) to nested case-control studies, accommodating scenarios with additional matching and non-proportionality. We also consider the case where data for different causes are obtained from different studies conducted in the same cohort. It is demonstrated that while only modest gains in efficiency are possible in full cohort analyses, substantial gains may be attained in nested case-control analyses for failure types that are relatively rare. Extensive simulation studies are conducted and real data analyses are provided using the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) study., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. [br]For permissions, please e-mail: journals.permissions@oup.com.)

We consider estimation of the semiparametric additive hazards model with an unspecified baseline hazard function where the effect of a continuous covariate has a specific shape but otherwise unspecified. Such estimation is particularly useful for a unimodal hazard function, where the hazard is monotone increasing and monotone decreasing with an unknown mode. A popular approach of the proportional hazards model is limited in such setting due to the complicated structure of the partial likelihood. Our model defines a quadratic loss function, and its simple structure allows a global Hessian matrix that does not involve parameters. Thus, once the global Hessian matrix is computed, a standard quadratic programming method can be applicable by profiling all possible locations of the mode. However, the quadratic programming method may be inefficient to handle a large global Hessian matrix in the profiling algorithm due to a large dimensionality, where the dimension of the global Hessian matrix and number of hypothetical modes are the same order as the sample size. We propose the quadratic pool adjacent violators algorithm to reduce computational costs. The proposed algorithm is extended to the model with a time-dependent covariate with monotone or U-shape hazard function. In simulation studies, our proposed method improves computational speed compared to the quadratic programming method, with bias and mean square error reductions. We analyze data from a recent cardiovascular study., (© 2024 John Wiley & Sons Ltd.)

In many phase 1 oncology trials of immunotherapies, no dose-limiting toxicities are observed and the maximum tolerated dose cannot be identified. In these settings, dose-finding can be guided by a biomarker of response rather than the occurrences of dose-limiting toxicity. The recommended phase 2 dose can be defined as the dose with mean response equal to a prespecified value of a continuous response biomarker. To target the mean of a continuous biomarker, we build on the idea of the continual reassessment method and the quasi-Bernoulli likelihood. We extend the design to a problem of finding the recommended phase 2 dose combination in a trial with multiple immunotherapies., (© 2023 John Wiley & Sons Ltd.)

Background: Cardiovascular disease (CVD) guidelines recommend using the Pooled Cohort Equation (PCE) to assess 10-year CVD risk based on traditional risk factors. Pregnancy-related factors have been associated with future CVD. We examined the contribution of two pregnancy-related factors, (1) history of a low birthweight (LBW) infant and (2) breastfeeding to CVD risk accounting for traditional risk factors as assessed by the PCE., Methods: A nationally representative sample of women, ages 40-79, with a history of pregnancy, but no prior CVD, was identified using NHANES 1999-2006. Outcomes included (1) CVD death and (2) CVD death plus CVD surrogates. We used Cox proportional hazards models to adjust for PCE risk score., Results: Among 3,758 women, 479 had a LBW infant and 1,926 reported breastfeeding. Mean follow-up time was 12.1 years. Survival models showed a consistent reduction in CVD outcomes among women with a history of breastfeeding. In cause-specific survival models, breastfeeding was associated with a 24% reduction in risk of CVD deaths (HR 0.76; 95% CI 0.45─1.27, p = 0.30) and a 33% reduction in risk of CVD deaths + surrogate CVD, though not statistically significant. (HR 0.77; 95% CI 0.52─1.14, p = 0.19). Survival models yielded inconclusive results for LBW with wide confidence intervals (CVD death: HR 0.98; 95% CI 0.47─2.05; p = 0.96 and CVD death + surrogate CVD: HR 1.29; 95% CI 0.74─2.25; p = 0.38)., Conclusion: Pregnancy-related factors may provide important, relevant information about CVD risk beyond traditional risk factors. While further research with more robust datasets is needed, it may be helpful for clinicians to counsel women about the potential impact of pregnancy-related factors, particularly the positive impact of breastfeeding, on cardiovascular health., (© 2022. The Author(s).)

Studies on the health effects of environmental mixtures face the challenge of limit of detection (LOD) in multiple correlated exposure measurements. Conventional approaches to deal with covariates subject to LOD, including complete-case analysis, substitution methods, and parametric modeling of covariate distribution, are feasible but may result in efficiency loss or bias. With a single covariate subject to LOD, a flexible semiparametric accelerated failure time (AFT) model to accommodate censored measurements has been proposed. We generalize this approach by considering a multivariate AFT model for the multiple correlated covariates subject to LOD and a generalized linear model for the outcome. A two-stage procedure based on semiparametric pseudo-likelihood is proposed for estimating the effects of these covariates on health outcome. Consistency and asymptotic normality of the estimators are derived for an arbitrary fixed dimension of covariates. Simulations studies demonstrate good large sample performance of the proposed methods vs conventional methods in realistic scenarios. We illustrate the practical utility of the proposed method with the LIFECODES birth cohort data, where we compare our approach to existing approaches in an analysis of multiple urinary trace metals in association with oxidative stress in pregnant women., (© 2022 John Wiley & Sons, Ltd.)

In the analysis for competing risks data, regression modeling of the cause-specific hazard functions has been usually conducted using the same time scale for all event types. However, when the true time scale is different for each event type, it would be appropriate to specify regression models for the cause-specific hazards on different time scales for different event types. Often, the proportional hazards model has been used for regression modeling of the cause-specific hazard functions. However, the proportionality assumption may not be appropriate in practice. In this article, we consider the additive risk model as an alternative to the proportional hazards model. We propose predictions of the cumulative incidence functions under the cause-specific additive risk models employing different time scales for different event types. We establish the consistency and asymptotic normality of the predicted cumulative incidence functions under the cause-specific additive risk models specified on different time scales using empirical processes and derive consistent variance estimators of the predicted cumulative incidence functions. Through simulation studies, we show that the proposed prediction methods perform well. We illustrate the methods using stage III breast cancer data obtained from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute., (© 2022 John Wiley & Sons Ltd.)

In prevalent cohort studies where subjects are recruited at a cross-section, the time to an event may be subject to length-biased sampling, with the observed data being either the forward recurrence time, or the backward recurrence time, or their sum. In the regression setting, assuming a semiparametric accelerated failure time model for the underlying event time, where the intercept parameter is absorbed into the nuisance parameter, it has been shown that the model remains invariant under these observed data set-ups and can be fitted using standard methodology for accelerated failure time model estimation, ignoring the length-bias. However, the efficiency of these estimators is unclear, owing to the fact that the observed covariate distribution, which is also length-biased, may contain information about the regression parameter in the accelerated life model. We demonstrate that if the true covariate distribution is completely unspecified, then the naive estimator based on the conditional likelihood given the covariates is fully efficient for the slope.

With known cause of death (CoD), competing risk survival methods are applicable in estimating disease-specific survival. Relative survival analysis may be used to estimate disease-specific survival when cause of death is either unknown or subject to misspecification and not reliable for practical usage. This method is popular for population-based cancer survival studies using registry data and does not require CoD information. The standard estimator is the ratio of all-cause survival in the cancer cohort group to the known expected survival from a general reference population. Disease-specific death competes with other causes of mortality, potentially creating dependence among the CoD. The standard ratio estimate is only valid when death from disease and death from other causes are independent. To relax the independence assumption, we formulate dependence using a copula-based model. Likelihood-based parametric method is used to fit the distribution of disease-specific death without CoD information, where the copula is assumed known and the distribution of other cause of mortality is derived from the reference population. We propose a sensitivity analysis, where the analysis is conducted across a range of assumed dependence structures. We demonstrate the utility of our method through simulation studies and an application to French breast cancer data., (© 2022 Walter de Gruyter GmbH, Berlin/Boston.)

Background: Preinvasive cancer conditions are often actively treated to minimize progression to life-threatening invasive cancers, but this creates challenges for analysis of invasive cancer risk. Conventional methods of treating preinvasive conditions as censoring events or targeting at the composite outcome could both lead to bias., Methods: We propose two solutions: one that provides exact estimates of risk based on distributional assumptions about progression, and one that provides risk bounds corresponding to extreme cases of no or complete progression. We compare these approaches through simulations and an analysis of the Sister Study data in the context of ductal carcinoma in situ (DCIS) and invasive breast cancer., Results: Simulations suggested important biases with conventional approaches, whereas the proposed estimate is consistent when progression parameters are correctly specified, and the risk bounds are robust in all scenarios. With Sister Study, the estimated lifetime risks for invasive breast cancer are 0.220 and 0.269 with DCIS censored or combined. Without detailed progression information, a sensitivity analysis suggested lifetime risk falls between the bounds of 0.214 and 0.269 across assumptions of 10%-95% of DCIS patients progressing to invasive cancer in an average of 1-10 years., Conclusions: When estimating invasive cancer risk while preinvasive conditions are actively treated, it is important to consider the implied assumptions and potential biases of conventional approaches. Although still not perfect, we proposed two practical solutions that provide improved understanding of the underlying mechanism of invasive cancer., Competing Interests: The authors report no conflicts of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

The causal effect of a treatment is of fundamental interest in the social, biological, and health sciences. Instrumental variable (IV) methods are commonly used to determine causal treatment effects in the presence of unmeasured confounding. In this work, we study a new binary IV framework with randomly censored outcomes where we propose to quantify the causal treatment effect by the concept of complier quantile causal effect (CQCE). The CQCE is identifiable under weaker conditions than the complier average causal effect when outcomes are subject to censoring, and it can provide useful insight into the dynamics of the causal treatment effect. Employing the special characteristic of the binary IV and adapting the principle of conditional score, we uncover a simple weighting scheme that can be incorporated into the standard censored quantile regression procedure to estimate CQCE. We develop robust nonparametric estimation of the derived weights in the first stage, which permits stable implementation of the second stage estimation based on existing software. We establish rigorous asymptotic properties for the proposed estimator, and confirm its validity and satisfactory finite-sample performance via extensive simulations. The proposed method is applied to a bone marrow transplant dataset to evaluate the causal effect of rituximab in diffuse large B-cell lymphoma patients.

Proportional rates models are frequently used for the analysis of recurrent event data with multiple event categories. When some of the event categories are missing, a conventional approach is to either exclude the missing data for a complete-case analysis or employ a parametric model for the missing event type. It is well known that the complete-case analysis is inconsistent when the missingness depends on covariates, and the parametric approach may incur bias when the model is misspecified. In this paper, we aim to provide a more robust approach using a rate proportion method for the imputation of missing event types. We show that the log-odds of the event type can be written as a semiparametric generalized linear model, facilitating a theoretically justified estimation framework. Comprehensive simulation studies were conducted demonstrating the improved performance of the semiparametric method over parametric procedures. Multiple types of Pseudomonas aeruginosa infections of young cystic fibrosis patients were analyzed to demonstrate the feasibility of our proposed approach.

Experimental manipulation of gut microbes in animal models alters fear behavior and relevant neurocircuitry. In humans, the first year of life is a key period for brain development, the emergence of fearfulness, and the establishment of the gut microbiome. Variation in the infant gut microbiome has previously been linked to cognitive development, but its relationship with fear behavior and neurocircuitry is unknown. In this pilot study of 34 infants, we find that 1-year gut microbiome composition (Weighted Unifrac; lower abundance of Bacteroides, increased abundance of Veillonella, Dialister, and Clostridiales) is significantly associated with increased fear behavior during a non-social fear paradigm. Infants with increased richness and reduced evenness of the 1-month microbiome also display increased non-social fear. This study indicates associations of the human infant gut microbiome with fear behavior and possible relationships with fear-related brain structures on the basis of a small cohort. As such, it represents an important step in understanding the role of the gut microbiome in the development of human fear behaviors, but requires further validation with a larger number of participants.

Canonical correlation analysis (CCA) is a common method used to estimate the associations between two different sets of variables by maximizing the Pearson correlation between linear combinations of the two sets of variables. We propose a version of CCA for transelliptical distributions with an elliptical copula using pairwise Kendall's tau to estimate a latent scatter matrix. Because Kendall's tau relies only on the ranks of the data this method does not make any assumptions about the marginal distributions of the variables, and is valid when moments do not exist. We establish consistency and asymptotic normality for canonical directions and correlations estimated using Kendall's tau. Simulations indicate that this estimator outperforms standard CCA for data generated from heavy tailed elliptical distributions. Our method also identifies more meaningful relationships when the marginal distributions are skewed. We also propose a method for testing for non-zero canonical correlations using bootstrap methods. This testing procedure does not require any assumptions on the joint distribution of the variables and works for all elliptical copulas. This is in contrast to permutation tests which are only valid when data are generated from a distribution with a Gaussian copula. This method's practical utility is shown in an analysis of the association between radial diffusivity in white matter tracts and cognitive tests scores for six-year-old children from the Early Brain Development Study at UNC-Chapel Hill. An R package implementing this method is available at github.com/blangworthy/transCCA.

The continual reassessment method (CRM) is a well-known design for dose-finding trials with the goal of estimating the maximum tolerated dose (MTD), the dose with a given probability of toxicity. The standard assumption is that the probability of toxicity monotonically increases with dose. We show that the CRM can still be consistent and correctly identify the MTD even when the dose-toxicity curve is not monotone as long as there is monotonicity of the true toxicity probabilities right below and right above the true MTD. In the case of multiple therapies, where it is unclear how to order combinations of dose levels of multiple therapies, our findings provide insight into the performance of the partial order CRM (POCRM). To select the correct dose combination at the end of a trial, the POCRM does not have to select a monotone ordering of drug combinations. We illustrate the connection between our results for the CRM with a nonmonotone dose-toxicity curve and the POCRM via simulations., (© 2021 John Wiley & Sons, Ltd.)

Instrumental variables (IV) are a useful tool for estimating causal effects in the presence of unmeasured confounding. IV methods are well developed for uncensored outcomes, particularly for structural linear equation models, where simple two-stage estimation schemes are available. The extension of these methods to survival settings is challenging, partly because of the nonlinearity of the popular survival regression models and partly because of the complications associated with right censoring or other survival features. Motivated by the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer screening trial, we develop a simple causal hazard ratio estimator in a proportional hazards model with right censored data. The method exploits a special characterization of IV which enables the use of an intuitive inverse weighting scheme that is generally applicable to more complex survival settings with left truncation, competing risks, or recurrent events. We rigorously establish the asymptotic properties of the estimators, and provide plug-in variance estimators. The proposed method can be implemented in standard software, and is evaluated through extensive simulation studies. We apply the proposed IV method to a data set from the Prostate, Lung, Colorectal and Ovarian cancer screening trial to delineate the causal effect of flexible sigmoidoscopy screening on colorectal cancer survival which may be confounded by informative noncompliance with the assigned screening regimen.

Instrumental variable (IV) analysis can be used to address bias due to unobserved confounding when estimating the causal effect of a treatment on an outcome of interest. However, if a proposed IV is correlated with unmeasured confounders and/or weakly correlated with the treatment, the standard IV estimator may be more biased than an ordinary least squares (OLS) estimator. Several methods have been proposed that compare the bias of the IV and OLS estimators relying on the belief that measured covariates can be used as proxies for the unmeasured confounder. Despite these developments, there is lack of discussion about approaches that can be used to formally test whether the IV estimator may be less biased than the OLS estimator. Thus, we have developed a testing framework to compare the bias and a criterion to select informative measured covariates for bias comparison and regression adjustment. We also have developed a bias-correction method, which allows one to use an invalid IV to correct the bias of the OLS or IV estimator. Numerical studies demonstrate that the proposed methods perform well with realistic sample sizes., (© 2020 John Wiley & Sons Ltd.)

Background: Thirty-day readmissions, emergency department visits, and observation stays are common after colorectal surgery (9%-25%, 8%-12%, and 3%-5%), yet it is unknown to what extent planned postdischarge care can decrease the frequency of emergency department visits., Objective: This study's aim was to determine whether early follow-up with the surgical team reduces 30-day emergency department visits., Design: This retrospective cohort study used a central data repository of clinical and administrative data for 2013 through 2018., Setting: This study was conducted in a large statewide health care system (10 affiliated hospitals, >300 practices)., Patients: All adult patients undergoing colorectal surgery were included unless they had a length of stay <1 day or died during the index hospitalization., Intervention: Early (<10 days after discharge) versus late (≥10 days) follow-up at the outpatient surgery clinic, or no outpatient surgery clinic follow-up, was assessed., Main Outcome Measures: The primary outcome measured was the time to 30-day postdischarge emergency department visit., Results: Our cohort included 3442 patients undergoing colorectal surgery; 38% of patients had an early clinic visit. Overall, 11% had an emergency department encounter between 11 and 30 days after discharge. Those with early follow-up had decreased emergency department encounters (adjusted HR 0.13; 95% CI, 0.08-0.22). An early clinic visit within 10 days, compared to 14 days, prevented an additional 142 emergency department encounters. Nationwide, this could potentially prevent 8433 unplanned visits each year with an estimated cost savings of $49 million annually., Limitations: We used retrospective data and were unable to assess for health care utilization outside our health system., Conclusions: Early follow-up within 10 days of adult colorectal surgery is associated with decreased subsequent emergency department encounters. See Video Abstract at http://links.lww.com/DCR/B330. EL SEGUIMIENTO TEMPRANO DESPUÉS DE LA CIRUGÍA COLORRECTAL REDUCE LAS VISITAS AL SERVICIO DE URGENCIAS POSTERIOR AL ALTA: Los readmisión a los treinta días, las visitas al servicio de urgencias y las estancias de observación son comunes después de la cirugía colorrectal, 9-25%, 8-12% y 3-5%, respectivamente. Sin embargo, se desconoce en qué medida la atención planificada posterior al alta puede disminuir la frecuencia de las visitas al servicio de urgencias.Determinar si el seguimiento temprano con el equipo quirúrgico reduce las visitas a 30 días al servicio de urgencias.Este estudio de cohorte retrospectivo utilizó un depósito central de datos clínicos y administrativos para 2013-2018.Gran sistema de salud estatal (10 hospitales afiliados,> 300 consultorios).Se incluyeron todos los pacientes adultos de cirugía colorrectal a menos que tuvieran una estadía <1 día o murieran durante el índice de hospitalización.Temprano (<10 días después del alta) versus tardío (≥10 días) o sin seguimiento en la clínica de cirugía ambulatoria.Tiempo para la visita al servicio de urgencias a 30 días después del alta.Nuestra cohorte incluyó 3.442 pacientes de cirugía colorrectal; El 38% de los pacientes tuvieron una visita temprana a clínica. En total, el 11% tuvo un encuentro con el servicio de urgencias entre 11 y 30 días después de ser dado de alta. Aquellos con seguimiento temprano disminuyeron las visitas al servicio de urgencias (HR 0,13; IC del 95%: 0,08 a 0,22). Además, una visita temprana a la clínica en un plazo de 10 días, en comparación con 14 días, evitó 142 encuentros adicionales en el servicio de urgencias. A nivel nacional, esto podría prevenir 8.433 visitas no planificadas cada año con un ahorro estimado de $ 49 millones anuales.Utilizamos datos retrospectivos y no pudimos evaluar la utilización de la atención médica fuera de nuestro sistema de salud.El seguimiento temprano dentro de los 10 días de la cirugía colorrectal en adultos se asocia con una disminución de los encuentros posteriores en el servicio de urgencias. Consulte Video Resumen en http://links.lww.com/DCR/B330. (Traducción-Dr. Gonzalo Hagerman).

This article provides methods of causal inference for competing risks data. The methods are formulated as structural nested mean models of causal effects directly related to the cumulative incidence function or subdistribution hazard, which reflect the survival experience of a subject in the presence of competing risks. The effect measures include causal risk differences, causal risk ratios, causal subdistribution hazard ratios, and causal effects of time-varying exposures. Inference is implemented by g-estimation using pseudo-observations, a technique to handle censoring. The finite-sample performance of the proposed estimators in simulated datasets and application to time-varying exposures in a cohort study of type 2 diabetes are also presented., (© The Author 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Preclinical evidence has demonstrated that common intratumor bacteria metabolize the chemotherapeutic drug gemcitabine. The significance of this bacterial metabolism pathway, relative to the known metabolic pathways by host enzymes, is not known. We hypothesized that bacterial metabolism is clinically significant and that "knockdown" by antibacterial therapy has the unintended effect of increasing the effective dose of gemcitabine, thereby increasing the risk for gemcitabine-associated toxicities., Materials and Methods: We reanalyzed the comparator arm of the MPACT trial (NCT01442974), made available through Project Data Sphere, LLC (CEO Roundtable on Cancer's Life Sciences Consortium, Cary, NC; www.projectdatasphere.org). In this arm, 430 patients with metastatic pancreatic adenocarcinoma were treated with gemcitabine. We used the Anderson-Gill survival model to compare the risk of developing an adverse event after antibacterial prescription with time unexposed to antibacterials. Adverse events of grade 3 and greater were considered at three levels of granularity: all aggregated into one endpoint, aggregated by class, and taken individually. Antibiotic exposures were analyzed in aggregate as well as by class., Results: Antibacterial exposure was associated with an increased risk of adverse events (hazard ratio [HR]: 1.77; confidence interval [CI]: 1.46-2.14), any hematologic adverse event (HR: 1.64; CI: 1.26-2.13), and any gastrointestinal adverse event (HR: 2.14; CI: 1.12-4.10) but not a constitutional (HR: 1.33; CI: 0.611-2.90) or hepatologic adverse event (HR: 0.99; CI: 0.363-2.71). Among specific adverse events, antibacterial exposure was associated with an increased risk of anemia (HR: 3.16; CI: 1.59-6.27), thrombocytopenia (HR: 2.52; CI: 1.31-4.85), leukopenia (HR: 3.91; CI: 1.46-10.5), and neutropenia (HR: 1.53; CI: 1.07-2.17) but not any other specific adverse events., Conclusion: Antibacterial exposure was associated with an increased risk of gemcitabine-associated, dose-limiting adverse events, including aggregate hematologic and gastrointestinal events, as well as four specific hematologic adverse events, suggesting that intratumor bacteria may be responsible for a clinically significant portion of gemcitabine metabolism. Alternative avenues of evidence will be necessary to confirm this preliminary finding and assess its generalizability. There is plentiful opportunity for similar analyses on other clinical trial data sets, where gemcitabine or other biomimetic small molecules were used., Implications for Practice: Patients treated with gemcitabine for metastatic pancreatic ductal adenocarcinoma have an increased rate of gemcitabine-associated toxicity during and after antibiotic therapy. This observation is consistent with preclinical evidence that intratumor bacteria metabolize gemcitabine to an inactive form. Further research is needed to determine whether this observation merits any changes in clinical practice., (© AlphaMed Press 2020.)

Objective: To determine the diagnostic value of acute infarcts in multiple cerebral circulations (AIMCC) on MRI diffusion-weighted imaging (DWI) for cardioembolism (CE) stroke subtype in adult patients hospitalized with acute ischemic stroke, we conducted a systematic literature review and meta-analysis., Methods: MEDLINE was searched via PubMed for articles reporting patients hospitalized with acute ischemic stroke with MRI DWI categorized as AIMCC vs other and use of Trial of Org 10172 in Acute Stroke Treatment (TOAST) Criteria for cardioembolism subtype. Measures of diagnostic accuracy were calculated from the retrieved studies., Results: Seven eligible articles comprised 5813 patients. Bivariate random effects models estimated sensitivity 0.19 (95% CI, 0.13 to 0.27), specificity 0.89 (0.86 to 0.91), positive predictive value 0.37 (0.30 to 0.45), negative predictive value 0.76 (0.7 to 0.82), positive likelihood ratio 1.70 (1.13 to 2.57) and negative likelihood ratio 0.91 (0.83 to 1)., Interpretation: The pattern of AIMCC on DWI is of limited diagnostic value. It is not sufficiently accurate to exclude cardiac pathology by a negative test nor does a positive test indicate a major increase in the probability of identifying a potential cardioembolic source., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Topical intra-nasal sprays are amongst the most commonly prescribed therapeutic options for sinonasal diseases in humans. However, inconsistency and ambiguity in instructions show a lack of definitive knowledge on best spray use techniques. In this study, we have identified a new usage strategy for nasal sprays available over-the-counter, that registers an average 8-fold improvement in topical delivery of drugs at diseased sites, when compared to prevalent spray techniques. The protocol involves re-orienting the spray axis to harness inertial motion of particulates and has been developed using computational fluid dynamics simulations of respiratory airflow and droplet transport in medical imaging-based digital models. Simulated dose in representative models is validated through in vitro spray measurements in 3D-printed anatomic replicas using the gamma scintigraphy technique. This work breaks new ground in proposing an alternative user-friendly strategy that can significantly enhance topical delivery inside human nose. While these findings can eventually translate into personalized spray usage instructions and hence merit a change in nasal standard-of-care, this study also demonstrates how relatively simple engineering analysis tools can revolutionize everyday healthcare. Finally, with respiratory mucosa as the initial coronavirus infection site, our findings are relevant to intra-nasal vaccines that are in-development, to mitigate the COVID-19 pandemic.

Left-truncation poses extra challenges for the analysis of complex time-to-event data. We propose a general semiparametric regression model for left-truncated and right-censored competing risks data that is based on a novel weighted conditional likelihood function. Targeting the subdistribution hazard, our parameter estimates are directly interpretable with regard to the cumulative incidence function. We compare different weights from recent literature and develop a heuristic interpretation from a cure model perspective that is based on pseudo risk sets. Our approach accommodates external time-dependent covariate effects on the subdistribution hazard. We establish consistency and asymptotic normality of the estimators and propose a sandwich estimator of the variance. In comprehensive simulation studies we demonstrate solid performance of the proposed method. Comparing the sandwich estimator with the inverse Fisher information matrix, we observe a bias for the inverse Fisher information matrix and diminished coverage probabilities in settings with a higher percentage of left-truncation. To illustrate the practical utility of the proposed method, we study its application to a large HIV vaccine efficacy trial dataset., (© 2020 Biometrika Trust.)

We consider scenarios in which the likelihood function for a semiparametric regression model factors into separate components, with an efficient estimator of the regression parameter available for each component. An optimal weighted combination of the component estimators, named an ensemble estimator, may be employed as an overall estimate of the regression parameter, and may be fully efficient under uncorrelatedness conditions. This approach is useful when the full likelihood function may be difficult to maximize, but the components are easy to maximize. It covers settings where the nuisance parameter may be estimated at different rates in the component likelihoods. As a motivating example we consider proportional hazards regression with prospective doubly censored data, in which the likelihood factors into a current status data likelihood and a left-truncated right-censored data likelihood. Variable selection is important in such regression modelling, but the applicability of existing techniques is unclear in the ensemble approach. We propose ensemble variable selection using the least squares approximation technique on the unpenalized ensemble estimator, followed by ensemble re-estimation under the selected model. The resulting estimator has the oracle property such that the set of nonzero parameters is successfully recovered and the semiparametric efficiency bound is achieved for this parameter set. Simulations show that the proposed method performs well relative to alternative approaches. Analysis of an AIDS cohort study illustrates the practical utility of the method., (© 2020 Biometrika Trust.)

Background: National chlamydia case rate trends are difficult to interpret because of biases from partial screening coverage, imperfect diagnostic tests, and underreporting. We examined the extent to which these time-varying biases could influence reported annual chlamydia case rates., Methods: Annual reported case rates among women aged 15 through 24 years from 2000 through 2017 were obtained from the Centers for Disease Control and Prevention's National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention AtlasPlus tool. Estimates of reporting completeness, diagnostic test sensitivity and specificity, and screening coverage were derived from literature review and expert opinion. We adjusted annual reported case rates for incomplete reporting, imperfect diagnostic tests, and partial screening coverage through a series of corrections, and calculated annual adjusted case rates of correctly diagnosed chlamydia., Results: Adjusted chlamydia case rates among young women were higher than reported case rates throughout the study period. Reported case rates increased over the study period, but adjusted rates declined from 12,900 to 7900 cases per 100,000 person-years between 2000 and 2007. After 2007, adjusted case rates declined to 7500 cases per 100,000 person-years in 2017. Bias from partial screening coverage had a larger impact on case rate magnitude and trend shape than bias from imperfect diagnostic tests or underreporting., Conclusions: Reported chlamydia case rates may be substantially lower than true chlamydia case rates because of incomplete reporting, imperfect diagnostic tests, and partial screening coverage. Because the magnitude of these biases has declined over time, the differences between reported and adjusted case rates have narrowed, revealing a sharp decline in adjusted case rates even as reported case rates have risen. The decline in adjusted case rates suggests that the rise in reported case rates should not be interpreted strictly as increasing chlamydia incidence, as the observed rise can be explained by improvements in screening coverage, diagnostic tests, and reporting.

In survival analysis, time-varying covariates are covariates whose value can change during follow-up. Outcomes in medical research are frequently subject to competing risks (events precluding the occurrence of the primary outcome). We review the types of time-varying covariates and highlight the effect of their inclusion in the subdistribution hazard model. External time-dependent covariates are external to the subject, can effect the failure process, but are not otherwise involved in the failure mechanism. Internal time-varying covariates are measured on the subject, can effect the failure process directly, and may also be impacted by the failure mechanism. In the absence of competing risks, a consequence of including internal time-dependent covariates in the Cox model is that one cannot estimate the survival function or the effect of covariates on the survival function. In the presence of competing risks, the inclusion of internal time-varying covariates in a subdistribution hazard model results in the loss of the ability to estimate the cumulative incidence function (CIF) or the effect of covariates on the CIF. Furthermore, the definition of the risk set for the subdistribution hazard function can make defining internal time-varying covariates difficult or impossible. We conducted a review of the use of time-varying covariates in subdistribution hazard models in articles published in the medical literature in 2015 and in the first 5 months of 2019. Seven percent of articles published included a time-varying covariate. Several inappropriately described a time-varying covariate as having an association with the risk of the outcome., (© 2019 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.)

Background: Sleep duration is associated with type 2 diabetes (T2D). However, few T2D risk scores include sleep duration. We aimed to develop T2D scores containing sleep duration and to estimate the additive value of sleep duration., Methods: We used data from 43,404 adults without T2D in the Beijing Health Management Cohort study. The participants were surveyed approximately every 2 years from 2007/2008 to 2014/2015. Sleep duration was calculated from the self-reported usual time of going to bed and waking up at baseline. Logistic regression was employed to construct the risk scores. Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to estimate the additional value of sleep duration., Results: After a median follow-up of 6.8 years, we recorded 2623 (6.04%) new cases of T2D. Shorter (both 6-8 h/night and <6 h/night) sleep durations were associated with an increased risk of T2D (odds ratio (OR) = 1.43, 95% confidence interval (CI) = 1.30-1.59; OR = 1.98, 95%CI = 1.63-2.41, respectively) compared with a sleep duration of >8 h/night in the adjusted model. Seven variables, including age, education, waist-hip ratio, body mass index, parental history of diabetes, fasting plasma glucose, and sleep duration, were selected to form the comprehensive score; the C -index was 0.74 (95% CI: 0.71-0.76) for the test set. The IDI and NRI values for sleep duration were 0.017 (95% CI: 0.012-0.022) and 0.619 (95% CI: 0.518-0.695), respectively, suggesting good improvement in the predictive ability of the comprehensive nomogram. The decision curves showed that women and individuals older than 50 had more net benefit., Conclusions: The performance of T2D risk scores developed in the study could be improved by containing the shorter estimated sleep duration, particularly in women and individuals older than 50., Competing Interests: The authors declare that they have no competing interests., (Copyright © 2020 Xiangtong Liu et al.)

This paper demonstrates the flexibility of a general approach for the analysis of discrete time competing risks data that can accommodate complex data structures, different time scales for different causes, and nonstandard sampling schemes. The data may involve a single data source where all individuals contribute to analyses of both cause-specific hazard functions, overlapping datasets where some individuals contribute to the analysis of the cause-specific hazard function of only one cause while other individuals contribute to analyses of both cause-specific hazard functions, or separate data sources where each individual contributes to the analysis of the cause-specific hazard function of only a single cause. The approach is modularized into estimation and prediction. For the estimation step, the parameters and the variance-covariance matrix can be estimated using widely available software. The prediction step utilizes a generic program with plug-in estimates from the estimation step. The approach is illustrated with three prognostic models for stage IV male oral cancer using different data structures. The first model uses only men with stage IV oral cancer from population-based registry data. The second model strategically extends the cohort to improve the efficiency of the estimates. The third model improves the accuracy for those with a lower risk of other causes of death, by bringing in an independent data source collected under a complex sampling design with additional other-cause covariates. These analyses represent novel extensions of existing methodology, broadly applicable for the development of prognostic models capturing both the cancer and noncancer aspects of a patient's health., (© 2019 John Wiley & Sons, Ltd.)

There is an unmet medical need in the area of Parkinson's disease (PD) to develop novel therapeutic approaches that can stop and reverse the underlying mechanisms responsible for the neuronal death. We previously demonstrated that systemically administered autologous macrophages transfected ex vivo to produce glial cell line-derived neurotrophic factor (GDNF) readily migrate to the mouse brain with acute toxin-induced neuroinflammation and ameliorate neurodegeneration in PD mouse models. We hypothesized that the high level of cytokines due to inflammatory process attracted GDNF-expressing macrophages and ensured targeted drug delivery to the PD brain. Herein, we validated a therapeutic potential of GDNF-transfected macrophages in a transgenic Parkin Q311X(A) mice with slow progression and mild brain inflammation. Systemic administration of GDNF-macrophages at a severe late stage of the disease leaded to a near complete restoration of motor functions in Parkin Q311X(A) mice and improved brain tissue integrity with healthy neuronal morphology. Furthermore, intravenous injections of GDNF-macrophages at an early stage of disease resulted in potent sustained therapeutic effects in PD mice for more than a year after the treatment. Importantly, multiple lines of evidence for therapeutic efficacy were observed including: diminished neuroinflammation and α-synuclein aggregation, increased survival of dopaminergic neurons, and improved locomotor functions. In summary, GDNF-transfected macrophages represent a promising therapeutic strategy for PD at both late- and early-stages of the disease., (Published by Elsevier B.V.)

Recurrent event data frequently occur in longitudinal studies when subjects experience more than one event during the observation period. Often, the occurrence of subsequent events is associated with the experience of previous events. Such dependence is commonly ignored in the application of standard recurrent event methodology. In this paper, we utilize a Cox-type regression model with time-varying triggering effect depending on the number and timing of previous events to enhance both model fit and prediction. Parameter estimation and statistical inference is achieved via the partial likelihood. A statistical test procedure is provided to assess the existence of the triggering effects. We demonstrate our approach via comprehensive simulation studies and a real data analysis on chronic pseudomonas infections in young cystic fibrosis patients. Our model provides significantly better predictions than standard recurrent event models., (© 2019 John Wiley & Sons, Ltd.)

Background and Objectives: Intravenous iron therapy for chronic anemia management is largely driven by dosing protocols that differ in intensity with respect to dosing approach ( i.e. , dose, frequency, and duration). Little is known about the safety of these protocols., Design, Setting, Participants, & Measurements: Using clinical data from a large United States dialysis provider linked to health care utilization data from Medicare, we constructed a cohort of patients with ESKD aged ≥65 years who initiated and continued center-based hemodialysis for ≥90 days between 2009 and 2012, and initiated at least one of the five common intravenous iron administration strategies; ranked by intensity (the amount of iron given at moderate-to-high iron indices), the order of strategies was 3 (least intensive), 2 (less intensive), 1 (reference), 4 (more intensive), and 5 (most intensive). We estimated the effect of continuous exposure to these strategies on cumulative risks of mortality and infection-related events with dynamic Cox marginal structural models., Results: Of 13,249 eligible patients, 1320 (10%) died and 1627 (12%) had one or more infection-related events during the 4-month follow-up. The most and least commonly initiated strategy was strategy 2 and 5, respectively. Compared with the reference strategy 1, more intensive strategies (4 and 5) demonstrated a higher risk of all-cause mortality ( e.g. , most intensive strategy 5: 60-day risk difference: 1.3%; 95% confidence interval [95% CI], 0.8% to 2.1%; 120-day risk difference: 3.1%; 95% CI, 1.0% to 5.6%). Similarly, higher risks were observed for infection-related morbidity and mortality among more intensive strategies ( e.g. , strategy 5: 60-day risk difference: 1.8%; 95% CI, 1.2% to 2.6%; 120-day risk difference: 4.3%; 95% CI, 2.2% to 6.8%). Less intensive strategies (2 and 3) demonstrated lower risks of all-cause mortality and infection-related events., Conclusions: Among dialysis patients surviving 90 days, subsequent intravenous iron administration strategies promoting more intensive iron treatment at moderate-to-high iron indices levels are associated with higher risks of mortality and infection-related events., (Copyright © 2019 by the American Society of Nephrology.)

Propensity-score matching is a popular analytic method to remove the effects of confounding due to measured baseline covariates when using observational data to estimate the effects of treatment. Time-to-event outcomes are common in medical research. Competing risks are outcomes whose occurrence precludes the occurrence of the primary time-to-event outcome of interest. All non-fatal outcomes and all cause-specific mortality outcomes are potentially subject to competing risks. There is a paucity of guidance on the conduct of propensity-score matching in the presence of competing risks. We describe how both relative and absolute measures of treatment effect can be obtained when using propensity-score matching with competing risks data. Estimates of the relative effect of treatment can be obtained by using cause-specific hazard models in the matched sample. Estimates of absolute treatment effects can be obtained by comparing cumulative incidence functions (CIFs) between matched treated and matched control subjects. We conducted a series of Monte Carlo simulations to compare the empirical type I error rate of different statistical methods for testing the equality of CIFs estimated in the matched sample. We also examined the performance of different methods to estimate the marginal subdistribution hazard ratio. We recommend that a marginal subdistribution hazard model that accounts for the within-pair clustering of outcomes be used to test the equality of CIFs and to estimate subdistribution hazard ratios. We illustrate the described methods by using data on patients discharged from hospital with acute myocardial infarction to estimate the effect of discharge prescribing of statins on cardiovascular death., (© 2018 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.)

Direct regression modeling of the subdistribution has become popular for analyzing data with multiple, competing event types. All general approaches so far are based on non-likelihood based procedures and target covariate effects on the subdistribution. We introduce a novel weighted likelihood function that allows for a direct extension of the Fine-Gray model to a broad class of semiparametric regression models. The model accommodates time-dependent covariate effects on the subdistribution hazard. To motivate the proposed likelihood method, we derive standard nonparametric estimators and discuss a new interpretation based on pseudo risk sets. We establish consistency and asymptotic normality of the estimators and propose a sandwich estimator of the variance. In comprehensive simulation studies we demonstrate the solid performance of the weighted NPMLE in the presence of independent right censoring. We provide an application to a very large bone marrow transplant dataset, thereby illustrating its practical utility.

Context.—: Measurement of interpathologist diagnostic agreement (IPDA) should allow pathologists to improve current diagnostic criteria and disease classifications., Objectives.—: To determine how IPDA for pathologists' diagnoses of non-small cell lung carcinoma (NSCLC) is affected by the addition of a set of mucin and immunohistochemical (IHC) stains to hematoxylin-eosin (H&E) alone, by recent NSCLC reclassifications, by simplification of these classifications, and by pathologists' practice location, pulmonary pathology expertise, practice duration, and lung carcinoma case exposure., Design.—: We used a Web-based survey to present core images of 54 NSCLC cases to 22 practicing pathologists for diagnosis, initially as H&E only, then as H&E plus mucin and 4 IHC stains. Each case was diagnosed according to published 2004, 2011, and 2015 NSCLC classifications. Cohen's kappa was calculated for the 231 pathologist pairs as a measure of IPDA., Results.—: Twenty-two pathologists diagnosed 54 NSCLC cases by using 4 published classifications. IPDA is significantly higher for H&E/mucin/IHC diagnoses than for H&E-only diagnoses. IPDA for H&E/mucin/IHC diagnoses is highest with the 2015 classification. IPDA is estimated higher after collapse of stated diagnoses into subhead or dichotomized classes. IPDA for H&E/mucin/IHC diagnoses with the 2015 World Health Organization classification is similar for community and academic pathologists, and is higher when pathologists have pulmonary pathology expertise, have more than 6 years of practice experience, or diagnose more than 100 new lung carcinoma cases per year., Conclusions.—: Higher IPDA is associated with use of mucin and IHC stains, with the 2015 NSCLC classification, and with pathologists' pulmonary pathology expertise, practice duration, and frequency of lung carcinoma cases.

Regression methodology has been well developed for competing risks data with continuous event times, both for the cause-specific hazard and cumulative incidence functions. However, in many applications, including those from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute, the event times may be observed discretely. Naive application of continuous time regression methods to such data is not appropriate. We propose maximum likelihood inferences for estimation of model parameters for the discrete time cause-specific hazard functions, develop predictions for the associated cumulative incidence functions, and derive consistent variance estimators for the predicted cumulative incidence functions. The methods are readily implemented using standard software for generalized estimating equations, where models for different causes may be fitted separately. For the SEER data, it may be desirable to model different event types on different time scales and the methods are generalized to accommodate such scenarios, extending earlier work on continuous time data. Simulation studies demonstrate that the methods perform well in realistic set-ups. The methodology is illustrated with stage III colon cancer data from SEER., (© 2018, The International Biometric Society.)

Polytomous discrimination index is a novel and important diagnostic accuracy measure for multi-category classification. After reconstructing its probabilistic definition, we propose a nonparametric approach to the estimation of polytomous discrimination index based on an empirical sample of biomarker values. In this paper, we provide the finite-sample and asymptotic properties of the proposed estimators and such analytic results may facilitate the statistical inference. Simulation studies are performed to examine the performance of the nonparametric estimators. Two real data examples are analysed to illustrate our methodology.

In statistical modeling, it is often of interest to evaluate non-negative quantities that capture heterogeneity in the population such as variances, mixing proportions and dispersion parameters. In instances of covariate-dependent heterogeneity, the implied homogeneity hypotheses are nonstandard and existing inferential techniques are not applicable. In this paper, we develop a quasi-score test statistic to evaluate homogeneity against heterogeneity that varies with a covariate profile through a regression model. We establish the limiting null distribution of the proposed test as a functional of mixtures of chi-square processes. The methodology does not require the full distribution of the data to be entirely specified. Instead, a general estimating function for a finite dimensional component of the model that is of interest is assumed but other characteristics of the population are left completely unspecified. We apply the methodology to evaluate the excess zero proportion in zero-inflated models for count data. Our numerical simulations show that the proposed test can greatly improve efficiency over tests of homogeneity that neglect covariate information under the alternative hypothesis. An empirical application to dental caries indices demonstrates the importance and practical utility of the methodology in detecting excess zeros in the data.