Asymptotics for a Class of Dynamic Recurrent Event Models

Asymptotic properties, both consistency and weak convergence, of estimators arising in a general class of dynamic recurrent event models are presented. The class of models take into account the impact of interventions after each event occurrence, the impact of accumulating event occurrences, the induced informative and dependent right-censoring mechanism due to the data-accrual scheme, and the effect of covariate processes on the recurrent event occurrences. The class of models subsumes as special cases many of the recurrent event models that have been considered in biostatistics, reliability, and in the social sciences. The asymptotic properties presented have the potential of being useful in developing goodness-of-fit and model validation procedures, confidence intervals and confidence bands constructions, and hypothesis testing procedures for the finite- and infinite-dimensional parameters of a general class of dynamic recurrent event models, albeit the models without frailties.Comment: 26 page

Bayes multiple decision functions

This paper deals with the problem of simultaneously making many M binary decisions based on one realization of a random data matrix X. M is typically large and X will usually have M rows associated with each of the M decisions to make, but for each row the data may be low dimensional. Such problems arise in many practical areas such as the biological and medical sciences, where the available dataset is from microarrays or other high-throughput technology and with the goal being to decide which among of many genes are relevant with respect to some phenotype of interest; in the engineering and reliability sciences; in astronomy; in education; and in business. A Bayesian decision-theoretic approach to this problem is implemented with the overall loss function being a cost-weighted linear combination of Type I and Type II loss functions. The class of loss functions considered allows for use of the false discovery rate (FDR), false nondiscovery rate (FNR), and missed discovery rate (MDR) in assessing the quality of decision. Through this Bayesian paradigm, the Bayes multiple decision function (BMDF) is derived and an efficient algorithm to obtain the optimal Bayes action is described. In contrast to many works in the literature where the rows of the matrix X are assumed to be stochastically independent, we allow a dependent data structure with the associations obtained through a class of frailty-induced Archimedean copulas. In particular, non-Gaussian dependent data structure, which is typical with failure-time data, can be entertained. The numerical implementation of the determination of the Bayes optimal action is facilitated through sequential Monte Carlo techniques. The theory developed could also be extended to the problem of multiple hypotheses testing, multiple classification and prediction, and high-dimensional variable selection. The proposed procedure is illustrated for the simple versus simple hypotheses setting and for the composite hypotheses setting through simulation studies. The procedure is also applied to a subset of a microarray data set from a colon cancer study

Median Confidence Regions in a Nonparametric Model

The nonparametric measurement error model (NMEM) postulates that Xi=Δ+ϵi,i=1,2,…,n;Δ∈R with ϵi,i=1,2,…,n, IID from F(⋅)∈Fc,0, where Fc,0 is the class of all continuous distributions with median 0, so Δ is the median parameter of X. This paper deals with the problem of constructing a confidence region (CR) for Δ under the NMEM. Aside from the NMEM, the problem setting also arises in a variety of situations, including inference about the median lifetime of a complex system arising in engineering, reliability, biomedical, and public health settings, as well as in the economic arena such as when dealing with household income. Current methods of constructing CRs for Δ are discussed, including the T-statistic based CR and the Wilcoxon signed-rank statistic based CR, arguably the two default methods in applied work when a confidence interval about the center of a distribution is desired. A ‘bottom-to-top’ approach for constructing CRs is implemented, which starts by imposing reasonable invariance or equivariance conditions on the desired CRs, and then optimizing with respect to their mean contents on subclasses of Fc,0. This contrasts with the usual approach of using a pivotal quantity constructed from test statistics and/or estimators and then ‘pivoting’ to obtain the CR. Applications to a real car mileage data set and to Proschan’s famous air-conditioning data set are illustrated. Simulation studies to compare performances of the different CR methods were performed. Results of these studies indicate that the sign-statistic based CR and the optimal CR focused on symmetric distributions satisfy the confidence level requirement, though they tended to have higher contents; while three of the bootstrap-based CR procedures and one of the newly-developed adaptive CR tended to be a tad more liberal, but with smaller contents. A critical recommendation for practitioners is that, under the NMEM, the T-statistic based and Wilcoxon signed-rank statistic based CRs should not be used since they either have very degraded coverage probabilities or inflated contents under some of the allowable error distributions under the NMEM

Maximum Agreement Linear Prediction via the Concordance Correlation Coefficient

This paper examines distributional properties and predictive performance of the estimated maximum agreement linear predictor (MALP) introduced in Bottai, Kim, Lieberman, Luta, and Pena (2022) paper in The American Statistician, which is the linear predictor maximizing Lin's concordance correlation coefficient (CCC) between the predictor and the predictand. It is compared and contrasted, theoretically and through computer experiments, with the estimated least-squares linear predictor (LSLP). Finite-sample and asymptotic properties are obtained, and confidence intervals are also presented. The predictors are illustrated using two real data sets: an eye data set and a bodyfat data set. The results indicate that the estimated MALP is a viable alternative to the estimated LSLP if one desires a predictor whose predicted values possess higher agreement with the predictand values, as measured by the CCC

Reviving the Philippine Economy under a Responsible New Normal

After the reclassification of areas under enhanced community quarantine (ECQ) to general community quarantine (GCQ), the urgent task for the Philippine government is to provide an exit plan to revive the Philippine economy. Given the significant economic damage resulting from the shutdown of roughly 75 percent of the country’s total production in the National Capital Region (NCR) and in the CALABARZON and Central Luzon areas, a gradual reopening of the economy will be necessary to prevent further economic damage that could not only be difficult to repair, but also long to overcome. Indeed, based on recent directives from the government, a substantial number of industries and services have thus been allowed to operate in both the ECQ and GCQ areas. However, as the Philippine government begins to calibrate the opening of sectors, there remain concerns as to how this process will affect jobs and livelihoods now and beyond. In this context, an economic recovery plan that talks about short-term, a transition, and full recovery phases— encompassing a revision of the current Philippine Development Plan without losing sight of the long-term goals envisioned in Ambisyon Natin 2040— is still needed. Indeed, a key component of AmBisyon 2040 has been of building resiliency over the long-term, which includes resiliency in health and economic shocks apart from natural disasters. At the same time, this recovery plan should also be accompanied by structural reforms to enhance its implementation. The Department of Finance has crafted a four-pillar socio-economic strategy aimed at: (a) supporting the more vulnerable sectors of society; (b) increasing medical resources to contain the virus and offer safety to front-liners; (c) keeping the economy afloat through financial emergency initiatives; and (d) creating jobs and sustaining the economy. Yet while enumerating the costs of these plans, the said strategy lacked details on how the country could achieve some of the goals without the availability of widespread testing and adequate health facilities. Loan guarantees, cash transfers, and other forms of subsidies can revive disrupted supply chains but cannot restore productivity in the middle of a persisting health crisis, while the uncertainty of a possible outbreak can keep workers from supplying goods and services. It is crucial to have these programs and institutions in place since a number of cities, regions and provinces have started to reopen. A modified community quarantine without the necessary health system investments, protection measures, and economic recovery plan risks amounting to an unregulated herd immunity strategy. Opting for herd immunity allows governments to blame the failure of the health and economic system on the virus, rather than on bad governance. Under current GCQ protocols, the burden on containing the virus is mostly transferred to the public. Unless the government provides mass testing, the problem of information is aggravated, probably raising the transmission risks. Moreover, unregulated herd immunity will be differentially felt by the poor. As healthy workers may recover their earnings from the modified quarantine, the poor, who have limited access to the health services and are thus more susceptible to the virus, are unlikely to benefit from this system. In effect, this will only exacerbate the inequality that prevails in the country. Moving towards a responsible new normal requires a strategy that addresses both people’s wellbeing and the socio-economic weaknesses exposed by COVID-19. Thus, the strategy should have the following elements

American ginseng suppresses inflammation and DNA damage associated with mouse colitis

Ulcerative colitis (UC) is a dynamic, idiopathic, chronic inflammatory condition associated with a high colon cancer risk. American ginseng has antioxidant properties and targets many of the players in inflammation. The aim of this study was to test whether American ginseng extract prevents and treats colitis. Colitis in mice was induced by the presence of 1% dextran sulfate sodium (DSS) in the drinking water or by 1% oxazolone rectally. American ginseng extract was mixed in the chow at levels consistent with that currently consumed by humans as a supplement (75 p.p.m., equivalent to 58 mg daily). To test prevention of colitis, American ginseng extract was given prior to colitis induction. To test treatment of colitis, American ginseng extract was given after the onset of colitis. In vitro studies were performed to examine mechanisms. Results indicate that American ginseng extract not only prevents but it also treats colitis. Inducible nitric oxide synthase and cyclooxygenase-2 (markers of inflammation) and p53 (induced by inflammatory stress) are also downregulated by American ginseng. Mucosal and DNA damage associated with colitis is at least in part a result of an oxidative burst from overactive leukocytes. We therefore tested the hypothesis that American ginseng extract can inhibit leukocyte activation and subsequent epithelial cell DNA damage in vitro and in vivo. Results are consistent with this hypothesis. The use of American ginseng extract represents a novel therapeutic approach for the prevention and treatment of UC