Constructing time-invariant dynamic surveillance rules for optimal monitoring schedules.

Dynamic surveillance rules (DSRs) are sequential surveillance decision rules informing monitoring schedules in clinical practice, which can adapt over time according to a patient's evolving characteristics. In many clinical applications, it is desirable to identify and implement optimal time-invariant DSRs, where the parameters indexing the decision rules are shared across different decision points. We propose a new criterion for DSRs that accounts for benefit-cost tradeoff during the course of disease surveillance. We develop two methods to estimate the time-invariant DSRs optimizing the proposed criterion, and establish asymptotic properties for the estimated parameters of biomarkers indexing the DSRs. The first approach estimates the optimal decision rules for each individual at every stage via regression modeling, and then estimates the time-invariant DSRs via a classification procedure with the estimated time-varying decision rules as the response. The second approach proceeds by optimizing a relaxation of the empirical objective, where a surrogate function is utilized to facilitate computation. Extensive simulation studies are conducted to demonstrate the superior performances of the proposed methods. The methods are further applied to the Canary Prostate Active Surveillance Study (PASS).

Simultaneous spatial smoothing and outlier detection using penalized regression, with application to childhood obesity surveillance from electronic health records.

Electronic health records (EHRs) have become a platform for data-driven granular-level surveillance in recent years. In this paper, we make use of EHRs for early prevention of childhood obesity. The proposed method simultaneously provides smooth disease mapping and outlier information for obesity prevalence that are useful for raising public awareness and facilitating targeted intervention. More precisely, we consider a penalized multilevel generalized linear model. We decompose regional contribution into smooth and sparse signals, which are automatically identified by a combination of fusion and sparse penalties imposed on the likelihood function. In addition, we weigh the proposed likelihood to account for the missingness and potential nonrepresentativeness arising from the EHR data. We develop a novel alternating minimization algorithm, which is computationally efficient, easy to implement, and guarantees convergence. Simulation studies demonstrate superior performance of the proposed method. Finally, we apply our method to the University of Wisconsin Population Health Information Exchange database.

Restricted survival benefit with right-censored data.

The hazard ratio is widely used to quantify treatment effects. However, it may be difficult to interpret for patients and practitioners, especially when the hazard ratio is not constant over time. Alternative measures of the treatment effects have been proposed such as the difference of the restricted mean survival times, the difference in survival proportions at some fixed follow-up time, or the net chance of a longer survival. In this paper, we propose the restricted survival benefit (RSB), a quantity that can incorporate multiple useful measurements of treatment effects. Hence, it provides a framework for a comprehensive assessment of the treatment effects. We provide estimation and inference procedures for the RSB that accommodate censored survival outcomes, using methods of the inverse-probability-censoring-weighted U$U$ -statistic and the jackknife empirical likelihood. We conduct extensive simulation studies to examine the numerical performance of the proposed method, and we analyze data from a randomized Phase III clinical trial (SWOG S0777) using the proposed method.

Corticosterone induced apoptosis of mouse oviduct epithelial cells independent of the TNF-α system.

It has been reported in recent studies that restraint stress on pregnant mice during the preimplantation stage elevated corticotrophin-releasing hormone (CRH) and glucocorticoid levels in the serum and oviducts; furthermore, CRH and corticosterone (CORT) impacted preimplantation embryos indirectly by triggering the apoptosis of oviductal epithelial cells (OECs) through activation of the Fas system. However, it remains unclear whether TNF-α signaling is involved in CRH- and/or glucocorticoid-induced apoptosis of OECs. In the present study, it was shown that culture with either CRH or CORT induced significant apoptosis of OECs. The culture of OECs with CRH augmented both FasL expression and TNF-α expression. However, culture with CORT increased FasL, but decreased TNF-α, expression significantly. Although knocking down/knocking out FasL expression in OECs significantly ameliorated the proapoptotic effects of both CRH and CORT, knocking down/knocking out TNF-α expression relieved only the proapoptotic effect of CRH but not that of CORT. Taken together, our results demonstrated that CRH-induced OEC apoptosis involved both Fas signaling and TNF-α signaling. Conversely, CORT-induced OEC apoptosis involved only the Fas, but not the TNF-α, signaling pathway. The data obtained are crucial for our understanding of the mechanisms by which various categories of stress imposed on pregnant females impair embryo development, as well as for the development of measures to protect the embryo from the adverse effects of stress.

Glucocorticoids impair oocyte competence and trigger apoptosis of ovarian cells via activating the TNF-α system.

Mechanisms by which female stress and particularly glucocorticoids impair oocyte competence are largely unclear. Although one study demonstrated that glucocorticoids triggered apoptosis in ovarian cells and oocytes by activating the FasL/Fas system, other studies suggested that they might induce apoptosis through activating other signaling pathways as well. In this study, both in vivo and in vitro experiments were conducted to test the hypothesis that glucocorticoids might trigger apoptosis in oocytes and ovarian cells through activating the TNF-α system. The results showed that cortisol injection of female mice (1.) impaired oocyte developmental potential and mitochondrial membrane potential with increased oxidative stress; (2.) induced apoptosis in mural granulosa cells (MGCs) with increased oxidative stress in the ovary; and (3.) activated the TNF-α system in both ovaries and oocytes. Culture with corticosterone induced apoptosis and activated the TNF-α system in MGCs. Knockdown or knockout of TNF-α significantly ameliorated the pro-apoptotic effects of glucocorticoids on oocytes and MGCs. However, culture with corticosterone downregulated TNF-α expression significantly in oviductal epithelial cells. Together, the results demonstrated that glucocorticoids impaired oocyte competence and triggered apoptosis in ovarian cells through activating the TNF-α system and that the effect of glucocorticoids on TNF-α expression might vary between cell types.

Designing precision medicine trials to yield a greater population impact.

Traditionally, a clinical trial is conducted comparing treatment to standard care for all patients. However, it could be inefficient given patients' heterogeneous responses to treatments, and rapid advances in the molecular understanding of diseases have made biomarker-based clinical trials increasingly popular. We propose a new targeted clinical trial design, termed as Max-Impact design, which selects the appropriate subpopulation for a clinical trial and aims to optimize population impact once the trial is completed. The proposed design not only gains insights on the patients who would be included in the trial but also considers the benefit to the excluded patients. We develop novel algorithms to construct enrollment rules for optimizing population impact, which are fairly general and can be applied to various types of outcomes. Simulation studies and a data example from the SWOG Cancer Research Network demonstrate the competitive performance of our proposed method compared to traditional untargeted and targeted designs.

Learning-based biomarker-assisted rules for optimized clinical benefit under a risk constraint.

Novel biomarkers, in combination with currently available clinical information, have been sought to improve clinical decision making in many branches of medicine, including screening, surveillance, and prognosis. Statistical methods are needed to integrate such diverse information to develop targeted interventions that balance benefit and harm. In the specific setting of disease detection, we propose novel approaches to construct a multiple-marker-based decision rule by directly optimizing a benefit function, while controlling harm at a maximally tolerable level. These new approaches include plug-in and direct-optimization-based algorithms, and they allow for the construction of both nonparametric and parametric rules. A study of asymptotic properties of the proposed estimators is provided. Simulation results demonstrate good clinical utilities for the resulting decision rules under various scenarios. The methods are applied to a biomarker study in prostate cancer surveillance.

Constructing dynamic treatment regimes with shared parameters for censored data.

Dynamic treatment regimes are sequential decision rules that adapt throughout disease progression according to a patient's evolving characteristics. In many clinical applications, it is desirable that the format of the decision rules remains consistent over time. Unlike the estimation of dynamic treatment regimes in regular settings, where decision rules are formed without shared parameters, the derivation of the shared decision rules requires estimating shared parameters indexing the decision rules across different decision points. Estimation of such rules becomes more complicated when the clinical outcome of interest is a survival time subject to censoring. To address these challenges, we propose two novel methods: censored shared-Q-learning and censored shared-O-learning. Both methods incorporate clinical preferences into a qualitative rule, where the parameters indexing the decision rules are shared across different decision points and estimated simultaneously. We use simulation studies to demonstrate the superior performance of the proposed methods. The methods are further applied to the Framingham Heart Study to derive treatment rules for cardiovascular disease.

Multicategory Outcome Weighted Margin-based Learning for Estimating Individualized Treatment Rules.

Due to heterogeneity for many chronic diseases, precise personalized medicine, also known as precision medicine, has drawn increasing attentions in the scientific community. One main goal of precision medicine is to develop the most effective tailored therapy for each individual patient. To that end, one needs to incorporate individual characteristics to detect a proper individual treatment rule (ITR), by which suitable decisions on treatment assignments can be made to optimize patients' clinical outcome. For binary treatment settings, outcome weighted learning (OWL) and several of its variations have been proposed recently to estimate the ITR by optimizing the conditional expected outcome given patients' information. However, for multiple treatment scenarios, it remains unclear how to use OWL effectively. It can be shown that some direct extensions of OWL for multiple treatments, such as one-versus-one and one-versus-rest methods, can yield suboptimal performance. In this paper, we propose a new learning method, named Multicategory Outcome weighted Margin-based Learning (MOML), for estimating ITR with multiple treatments. Our proposed method is very general and covers OWL as a special case. We show Fisher consistency for the estimated ITR, and establish convergence rate properties. Variable selection using the sparse l 1 penalty is also considered. Analysis of simulated examples and a type 2 diabetes mellitus observational study are used to demonstrate competitive performance of the proposed method.

Quantifying treatment effects using the personalized chance of longer survival.

The hazard ratio is widely used to measure or to summarize the magnitude of treatment effects, but it is justifiably difficult to interpret in a meaningful way to patients and perhaps for clinicians as well. In addition, it is most meaningful when the hazard functions are approximately proportional over time. We propose a new measure, termed personalized chance of longer survival. The measure, which quantifies the probability of living longer with one treatment over the another, accounts for individualized characteristics to directly address personalized treatment effects. Hence, the measure is patient focused, which can be used to evaluate subgroups easily. We believe it is intuitive to understand and clinically interpretable in the presence of nonproportionality. Furthermore, because it estimates the probability of living longer by some fixed amount of time, it encodes the probabilistic part of treatment effect estimation. We provide nonparametric estimation and inference procedures that can accommodate censored survival outcomes. We conduct extensive simulation studies, which characterize performance of the proposed method, and data from a large randomized Phase III clinical trial (SWOG S0819) are analyzed using the proposed method.

Frequency and Predictors of Communication About High Blood Pressure in Rheumatoid Arthritis Visits.

BACKGROUND/OBJECTIVE: Given heightened cardiovascular disease (CVD) risk in rheumatoid arthritis (RA) and that higher blood pressure (BP) represents greater CVD risk, we hypothesized that higher BP would predict more BP-related communication in rheumatology visits. We examined predictors of documented BP communication during RA clinic visits. METHODS: This was a retrospective cohort study of RA patients identified in electronic health record records with uncontrolled hypertension (HTN) receiving both primary and rheumatology care. Trained abstractors reviewed RA visit notes for "BP communication" using a standardized tool to elicit documentation about BP or HTN beyond recording vital signs. We used multivariate logistic regression to examine the impact of BP category (American Heart Association: ideal normotension, pre-HTN, and stages I and II HTN) on odds ratios (95% confidence intervals) of BP communication. RESULTS: Among 1267 RA patients, 40% experienced BP elevations meeting the definition of uncontrolled HTN. Of 2677 eligible RA visits, 22% contained any documented BP communication. After adjustment, models predicted only 31% of visits with markedly high BPs 160/100 mm Hg or greater would contain BP communication. Compared with stage I, stage II elevation did not significantly increase communication (odds ratio, 2.0 [95% confidence interval, 1.4-2.8] vs. 1.5 [1.2-2.2]), although both groups' odds exceeded pre-HTN and normotension. Less than 10% of eligible visits resulted in documented action steps recommending follow-up of high BP. CONCLUSIONS: Regardless of BP magnitude, most RA clinic visits lacked documented communication about BP despite compounded CVD risk. Future work should study how rheumatology clinics can facilitate follow-up of high BPs to address HTN as the most common and reversible CVD risk factor.

Greedy outcome weighted tree learning of optimal personalized treatment rules.

We propose a subgroup identification approach for inferring optimal and interpretable personalized treatment rules with high-dimensional covariates. Our approach is based on a two-step greedy tree algorithm to pursue signals in a high-dimensional space. In the first step, we transform the treatment selection problem into a weighted classification problem that can utilize tree-based methods. In the second step, we adopt a newly proposed tree-based method, known as reinforcement learning trees, to detect features involved in the optimal treatment rules and to construct binary splitting rules. The method is further extended to right censored survival data by using the accelerated failure time model and introducing double weighting to the classification trees. The performance of the proposed method is demonstrated via simulation studies, as well as analyses of the Cancer Cell Line Encyclopedia (CCLE) data and the Tamoxifen breast cancer data.

Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy.

A personalized treatment strategy formalizes evidence-based treatment selection by mapping patient information to a recommended treatment. Personalized treatment strategies can produce better patient outcomes while reducing cost and treatment burden. Thus, among clinical and intervention scientists, there is a growing interest in conducting randomized clinical trials when one of the primary aims is estimation of a personalized treatment strategy. However, at present, there are no appropriate sample size formulae to assist in the design of such a trial. Furthermore, because the sampling distribution of the estimated outcome under an estimated optimal treatment strategy can be highly sensitive to small perturbations in the underlying generative model, sample size calculations based on standard (uncorrected) asymptotic approximations or computer simulations may not be reliable. We offer a simple and robust method for powering a single stage, two-armed randomized clinical trial when the primary aim is estimating the optimal single stage personalized treatment strategy. The proposed method is based on inverting a plugin projection confidence interval and is thereby regular and robust to small perturbations of the underlying generative model. The proposed method requires elicitation of two clinically meaningful parameters from clinical scientists and uses data from a small pilot study to estimate nuisance parameters, which are not easily elicited. The method performs well in simulated experiments and is illustrated using data from a pilot study of time to conception and fertility awareness.

The Association Between Use of Brain CT for Atraumatic Headache and 30-Day Emergency Department Revisitation.

OBJECTIVE: The purpose of this article is to describe the association between initial CT for atraumatic headache and repeat emergency department (ED) visitation within 30 days of ED discharge. MATERIALS AND METHODS: A retrospective observational study was performed at an academic urban ED with more than 85,000 annual visits. All adult patients with a chief complaint of headache from January through December 2010 who were discharged after ED evaluation were included in the analysis. Patients were excluded if they were transferred, died in the ED, or had a diagnosis indicating a traumatic mechanism. A propensity score-matched logistic regression model was used to determine whether the use of brain CT was associated with the primary outcome of ED revisitation within 30 days, controlling for potential confounding variables. RESULTS: Of 80,619 total patient visits to the ED during the study period, 922 ED discharges with a chief complaint of headache were included. A total of 139 (15.1%) patients revisited within 30 days. The return rate was 11.2% among patients who underwent CT at their initial visit and 21.1% among those who did not. In the adjusted analysis, controlling for age, race, sex, insurance status, triage vital signs, laboratory values, and triage pain level, the odds ratio for revisitation given CT performance was 0.49 (95% CI, 0.27-0.86). CONCLUSION: After adjustment for clinical factors, we found that patients who underwent a brain CT examination for atraumatic headache at an initial ED visit were less likely to return to the ED within 30 days. Future appropriate use quality metrics regarding ED imaging use may need to incorporate downstream health care use.

Prospective evaluation of the ability of clinical scoring systems and physician-determined likelihood of appendicitis to obviate the need for CT.

OBJECTIVE: To determine whether clinical scoring systems or physician gestalt can obviate the need for computed tomography (CT) in patients with possible appendicitis. METHODS: Prospective, observational study of patients with abdominal pain at an academic emergency department (ED) from February 2012 to February 2014. Patients over 11 years old who had a CT ordered for possible appendicitis were eligible. All parameters needed to calculate the scores were recorded on standardised forms prior to CT. Physicians also estimated the likelihood of appendicitis. Test characteristics were calculated using clinical follow-up as the reference standard. Receiver operating characteristic curves were drawn. RESULTS: Of the 287 patients (mean age (range), 31 (12-88) years; 60% women), the prevalence of appendicitis was 33%. The Alvarado score had a positive likelihood ratio (LR(+)) (95% CI) of 2.2 (1.7 to 3) and a negative likelihood ratio (LR(-)) of 0.6 (0.4 to 0.7). The modified Alvarado score (MAS) had LR(+) 2.4 (1.6 to 3.4) and LR(-) 0.7 (0.6 to 0.8). The Raja Isteri Pengiran Anak Saleha Appendicitis (RIPASA) score had LR(+) 1.3 (1.1 to 1.5) and LR(-) 0.5 (0.4 to 0.8). Physician-determined likelihood of appendicitis had LR(+) 1.3 (1.2 to 1.5) and LR(-) 0.3 (0.2 to 0.6). When combined with physician likelihoods, LR(+) and LR(-) was 3.67 and 0.48 (Alvarado), 2.33 and 0.45 (RIPASA), and 3.87 and 0.47 (MAS). The area under the curve was highest for physician-determined likelihood (0.72), but was not statistically significantly different from the clinical scores (RIPASA 0.67, Alvarado 0.72, MAS 0.7). CONCLUSIONS: Clinical scoring systems performed equally well as physician gestalt in predicting appendicitis. These scores do not obviate the need for imaging for possible appendicitis when a physician deems it necessary.

Regularized outcome weighted subgroup identification for differential treatment effects.

To facilitate comparative treatment selection when there is substantial heterogeneity of treatment effectiveness, it is important to identify subgroups that exhibit differential treatment effects. Existing approaches model outcomes directly and then define subgroups according to interactions between treatment and covariates. Because outcomes are affected by both the covariate-treatment interactions and covariate main effects, direct modeling outcomes can be hard due to model misspecification, especially in presence of many covariates. Alternatively one can directly work with differential treatment effect estimation. We propose such a method that approximates a target function whose value directly reflects correct treatment assignment for patients. The function uses patient outcomes as weights rather than modeling targets. Consequently, our method can deal with binary, continuous, time-to-event, and possibly contaminated outcomes in the same fashion. We first focus on identifying only directional estimates from linear rules that characterize important subgroups. We further consider estimation of comparative treatment effects for identified subgroups. We demonstrate the advantages of our method in simulation studies and in analyses of two real data sets.

Discussion of combining biomarkers to optimize patient treatment recommendations.

Kang, Janes and Huang propose an interesting boosting method to combine biomarkers for treatment selection. The method requires modeling the treatment effects using markers. We discuss an alternative method, outcome weighted learning. This method sidesteps the need for modeling the outcomes, and thus can be more robust to model misspecification.

Estimation of optimal dynamic treatment regimes.

BACKGROUND: Recent advances in medical research suggest that the optimal treatment rules should be adaptive to patients over time. This has led to an increasing interest in studying dynamic treatment regime, a sequence of individualized treatment rules, one per stage of clinical intervention, which maps present patient information to a recommended treatment. There has been a recent surge of statistical work for estimating optimal dynamic treatment regimes from randomized and observational studies. The purpose of this article is to review recent methodological progress and applied issues associated with estimating optimal dynamic treatment regimes. METHODS: We discuss sequential multiple assignment randomized trials, a clinical trial design used to study treatment sequences. We use a common estimator of an optimal dynamic treatment regime that applies to sequential multiple assignment randomized trials data as a platform to discuss several practical and methodological issues. RESULTS: We provide a limited survey of practical issues associated with modeling sequential multiple assignment randomized trials data. We review some existing estimators of optimal dynamic treatment regimes and discuss practical issues associated with these methods including model building, missing data, statistical inference, and choosing an outcome when only non-responders are re-randomized. We mainly focus on the estimation and inference of dynamic treatment regimes using sequential multiple assignment randomized trials data. Dynamic treatment regimes can also be constructed from observational data, which may be easier to obtain in practice; however, care must be taken to account for potential confounding.

Inference about the expected performance of a data-driven dynamic treatment regime.

BACKGROUND: A dynamic treatment regime (DTR) comprises a sequence of decision rules, one per stage of intervention, that recommends how to individualize treatment to patients based on evolving treatment and covariate history. These regimes are useful for managing chronic disorders, and fit into the larger paradigm of personalized medicine. The Value of a DTR is the expected outcome when the DTR is used to assign treatments to a population of interest. PURPOSE: The Value of a data-driven DTR, estimated using data from a Sequential Multiple Assignment Randomized Trial, is both a data-dependent parameter and a non-smooth function of the underlying generative distribution. These features introduce additional variability that is not accounted for by standard methods for conducting statistical inference, for example, the bootstrap or normal approximations, if applied without adjustment. Our purpose is to provide a feasible method for constructing valid confidence intervals (CIs) for this quantity of practical interest. METHODS: We propose a conceptually simple and computationally feasible method for constructing valid CIs for the Value of an estimated DTR based on subsampling. The method is self-tuning by virtue of an approach called the double bootstrap. We demonstrate the proposed method using a series of simulated experiments. RESULTS: The proposed method offers considerable improvement in terms of coverage rates of the CIs over the standard bootstrap approach. LIMITATIONS: In this article, we have restricted our attention to Q-learning for estimating the optimal DTR. However, other methods can be employed for this purpose; to keep the discussion focused, we have not explored these alternatives. CONCLUSION: Subsampling-based CIs provide much better performance compared to standard bootstrap for the Value of an estimated DTR.

Ordered Ag/Si nanowires array: wide-range surface-enhanced Raman spectroscopy for reproducible biomolecule detection.

Surface-enhanced Raman scattering (SERS) systems utilizing the interparticle nanogaps as hot spots have demonstrated ultrasensitive single-molecule detection with excellent selectivity yet the electric fields are too confined in the small nanogaps to enable reproducible biomolecule detections. Here, guided by finite-difference-time-domain simulation, we report hexagonal-packed silver-coated silicon nanowire (Ag/SiNW) arrays as a nanogap-free SERS system with wide-range electric fields and controlled interwire separation. Significantly, the system achieves a SERS detection of long double-strand DNA of 25-50 nm in length with a relative standard deviation (RSD) of 14% for measurements of above 4000 spots over an area of 200 × 200 µm(2). The high reproducibility in the SERS detection is attributed to (1) the large interwire spacing of 150 nm that allows access and excitation of large biomolecules; and (2) 600 nm wide-range electric field generated by propagating surface plasmons along the surface of continuous Ag coating on a SiNW. Moreover, a reproducible multiplex SERS measurement is also demonstrated with RSDs of 7-16% with an enhancement factor of ~10(6). The above results show that the ordered Ag/SiNW array system may serve as an excellent SERS platform for practical chemical and biological detection.