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Uncontrolled glycated hemoglobin (HbA1c) levels are associated with adverse events among complex diabetic patients. These adverse events present serious health risks to affected patients and are as...

We discuss the results on improving the generalizability of individualized treatment rule following the work in Kallus [1] and Mo et al. [5]. We note that the advocated weights in Kallus [1] are connected to the efficient score of the contrast function. We further propose a likelihood-ratio-based method (LR-ITR) to accommodate covariate shifts, and compare it to the CTE-DR-ITR method proposed in Mo et al. [5]. We provide the upper-bound on the risk function of the target population when both the covariate shift and the contrast function shift are present. Numerical studies show that LR-ITR can outperform CTE-DR-ITR when there is only covariate shift.

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, which 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 non-representativeness 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. This article is protected by copyright. All rights reserved.

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.

Purpose: Interpersonal trust is linked to therapeutic factors of patient care, including adherence to treatment, continuity with a provider, perceived effectiveness of care, and clinical outcomes. Differences in interpersonal trust across groups may contribute to health disparities. We explored whether differences in interpersonal trust varied across three racial/ethnic groups. Additionally, we explored how different health care factors were associated with differences in trust. Methods: We conducted a cross-sectional, computer-administered survey with 600 racially and ethnically diverse adults in Chicago, IL, from a wide variety of neighborhoods. We used staged ordinal logistic regression models to analyze the association between interpersonal trust and variables of interest. Results: Interpersonal trust did not differ by racial or ethnic group. However, individuals with 0–2 annual doctor visits, those reporting having a “hard time” getting health care services, those answering “yes” to “Did you not follow advice or treatment plan because it cost too much?,” and those reporting waiting more than 6 days/never getting an appointment had significantly increased odds of low trust. We did not find differences across racial/ethnic groups. Conclusion: Our study suggests that access to health care and interactions within the health care setting negatively impact individual's trust in their physician.

The hazard ratio (HR) 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 (RMST), 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 (IPCW) 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.

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.

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.

The Early Detection Initiative (EDI) is an innovative study designed to build a platform that will evaluate novel approaches to early detection of resectable pancreatic ductal adenocarcinoma (PDAC) in patients over age 50 with new onset hyperglycemia and diabetes (NOD). This randomized controlled trial (RCT) of algorithm-based screening for PDAC uses a modified Zelen’s design with post-randomization consent. Eligible subjects are identified through electronic medical record (EMR) surveillance and randomized 1:1 to the Observation or Intervention Arm. The Enriching New-onset Diabetes for Pancreatic Cancer (ENDPAC) score, an algorithm that further risk stratifies NOD based on age and changes in weight and glycemic parameters, is calculated in the Intervention Arm. Consenting subjects with ENDPAC >0 have Computerized Tomography (CT) scan imaging for PDAC detection; potential harm, including over-diagnosis, is also estimated. At the time of abstract submission >2000 individuals have been randomized and >50 subjects consented for CT imaging. EDI version 2 adds several innovations in response to challenges encountered and experience gained so far. Most significantly, EDI v2 has a platform design with a common control arm and allows for multiple intervention arms. This opens the door for evaluating future new biomarkers for PDAC early detection in a high–risk NOD population, including new blood-based biomarkers for the early detection of cancer that are now commercially available. EDI v2 focuses primarily on co-efficacy endpoints (PDAC stage shift in ENDPAC >0 group, and in all patients, excluding non-consented patients); effectiveness evaluation becomes a secondary objective. This addresses the challenges of low consent rate (~25%) due to lack of awareness of connection between NOD and PDAC and low acceptance for a novel research screening modality. The analyses of primary endpoints were adjusted to accommodate potential over-diagnosis due to screening and imbalance between ENDPAC >0 and ENDPAC ≤0 groups due to low consent rate and likely imperfect enrichment by ENDPAC, when evaluating all patients. Electronic eligibility checks and consent shorten the recruitment time, a critical factor for stage shift due to rapid progression of PDAC. A pre-randomization screening pilot study is planned to enhance the post-randomization consent rate. Intervention to Control randomization ratio is changed to 2:1, allowing more rapid recruitment and, when factoring the consent rate, doubling the sample size of the observation arm. Through these innovations, EDI v2 maintains the full advantage of Zelen’s design that made the trial feasible in sample size and cost. Simulation studies demonstrate that our approach correctly controls the type-1 error and the trial has adequate statistical power. The EDI trial represents an innovative and contemporary approach to developing and refining an early detection strategy for pancreatic cancer. Citation Format: Ying-Qi Zhao, Suresh T. Chari, Anirban Maitra, Lynn M. Matrisian, Eva E. Shrader, Bechien U. Wu, Avinash Kambadakone, Barbara Kenner, Jo Ann S. Rinaudo, Sudhir Srivastava, Ying Huang, Ziding Feng. The Early Detection Initiative trial version 2: A platform trial to test novel approaches to pancreatic cancer screening in patients with new onset hyperglycemia and diabetes [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A028.

The individualized treatment recommendation (ITR) is an important analytic framework for precision medicine. The goal of ITR is to assign the best treatments to patients based on their individual characteristics. From the machine learning perspective, the solution to the ITR problem can be formulated as a weighted classification problem to maximize the mean benefit from the recommended treatments given patients’ characteristics. Several ITR methods have been proposed in both the binary setting and the multicategory setting. In practice, one may prefer a more flexible recommendation that includes multiple treatment options. This motivates us to develop methods to obtain a set of near-optimal individualized treatment recommendations alternative to each other, called alternative individualized treatment recommendations (A-ITR). We propose two methods to estimate the optimal A-ITR within the outcome weighted learning (OWL) framework. Simulation studies and a real data analysis for Type 2 diabetic patients with injectable antidiabetic treatments are conducted to show the usefulness of the proposed A-ITR framework. We also show the consistency of these methods and obtain an upper bound for the risk between the theoretically optimal recommendation and the estimated one. An R package aitr has been developed, found at https://github.com/menghaomiao/aitr.

Pancreatic ductal adenocarcinoma (PDAC) is the only leading cause of cancer death without an early detection strategy. In retrospective studies, 0.5–1% of subjects >50 years of age who newly develop biochemically-defined diabetes have been diagnosed with PDAC within 3 years of meeting new onset hyperglycemia and diabetes (NOD) criteria. The Enriching New-onset Diabetes for Pancreatic Cancer (ENDPAC) algorithm further risk stratifies NOD subjects based on age and changes in weight and diabetes parameters. We present the methodology for the Early Detection Initiative (EDI), a randomized controlled trial of algorithm-based screening in patients with NOD for early detection of PDAC. We hypothesize that study interventions (risk stratification with ENDPAC and imaging with Computerized Tomography (CT) scan) in NOD will identify earlier stage PDAC. EDI uses a modified Zelen’s design with post-randomization consent. Eligible subjects will be identified through passive surveillance of electronic medical records and eligible study participants randomized 1:1 to the Intervention or Observation arm. The sample size is 12,500 subjects. The ENDPAC score will be calculated only in those randomized to the Intervention arm, with 50% (n=3125) expected to have a high ENDPAC score. Consenting subjects in the high ENDPAC group will undergo CT imaging for PDAC detection and an estimate of potential harm. The effectiveness and efficacy evaluation will compare proportions of late stage PDAC between Intervention and Observation arm per randomization assignment or per protocol, respectively, with a planned interim analysis. The study is designed to improve the detection of sporadic PDAC when surgical intervention is possible.

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.

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.

Dynamic treatment regimens (DTRs) are sequential treatment decisions tailored by patient’s evolving features and intermediate outcomes at each treatment stage. Patient heterogeneity and the complexity and chronicity of many diseases call for learning optimal DTRs that can best tailor treatment according to each individual’s time-varying characteristics (eg, intermediate response over time). In this paper, we propose a robust and efficient approach referred to as Augmented Outcome-weighted Learning (AOL) to identify optimal DTRs from sequential multiple assignment randomized trials. We improve previously proposed outcome-weighted learning to allow for negative weights. Furthermore, to reduce the variability of weights for numeric stability and improve estimation accuracy, in AOL, we propose a robust augmentation to the weights by making use of predicted pseudooutcomes from regression models for Q-functions. We show that AOL still yields Fisher-consistent DTRs even if the regression models are misspecified and that an appropriate choice of the augmentation guarantees smaller stochastic errors in value function estimation for AOL than the previous outcome-weighted learning. Finally, we establish the convergence rates for AOL. The comparative advantage of AOL over existing methods is demonstrated through extensive simulation studies and an application to a sequential multiple assignment randomized trial for major depressive disorder.

BACKGROUND/OBJECTIVE: Given heightened cardiovascular (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: Retrospective cohort study of RA patients identified in EHR records with uncontrolled hypertension 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 hypertension beyond recording vital signs. We used multivariate logistic regression to examine the impact of BP category (American Heart Association: ideal normotension, prehypertension, stage I and stage II hypertension) on odds ratios (OR, 95% confidence intervals) of BP-communication. RESULTS: Among 1,267 RA patients, 40% experienced BP elevations meeting the definition of uncontrolled hypertension. Of 2,677 eligible RA visits, 22% contained any documented BP communication. After adjustment, models predicted only 31% of visits with markedly high BPs ≥160/100 would contain BP-communication. Compared to stage I, stage II elevation did not significantly increase communication (OR 2.0, 1.4–2.8 vs. 1.5, 1.2–2.2), although both groups’ odds exceeded prehypertension 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 blood pressure despite compounded CVD risk. Future work should study how rheumatology clinics can facilitate follow-up of high BPs to address hypertension as the most common and reversible CVD risk factor.

Individualized treatment rules (ITRs) recommend treatment according to patient characteristics. There is a growing interest in developing novel and efficient statistical methods in constructing ITRs. We propose an improved doubly robust estimator of the optimal ITRs. The proposed estimator is based on a direct optimization of an augmented inverse-probability weighted estimator of the expected clinical outcome over a class of ITRs. The method enjoys two key properties. First, it is doubly robust, meaning that the proposed estimator is consistent when either the propensity score or the outcome model is correct. Second, it achieves the smallest variance among the class of doubly robust estimators when the propensity score model is correctly specified, regardless of the specification of the outcome model. Simulation studies show that the estimated ITRs obtained from our method yield better results than those obtained from current popular methods. Data from the Sequenced Treatment Alternatives to Relieve Depression study is analyzed as an illustrative example. Supplementary materials for this article are available online.

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.

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.

Background Direct observation of clinical skills is a cornerstone of competency-based education and training. Ensuring direct observation in a consistent fashion has been a significant challenge for residency programs. Objective The purpose of this study was to evaluate the effects of a novel evaluation system, designed to achieve ongoing direct observation of residents, examine changes in resident observation practices, and understand faculty attitudes toward direct observation and the evaluation system. Methods Internal medicine residents on an ambulatory block rotation participated in a new evaluation system, which replaced a single end-of-rotation summative evaluation with 9 formative evaluations based on direct observation. Faculty received training in direct observation and use of the forms, and residents were given responsibility to collect 9 observations per rotation. Faculty members contacted residents at the beginning and middle of the rotation to ensure completion of the observations. Residents and faculty also completed postrotation surveys to gauge the impact of the new system. Results A total of 507 patient encounters were directly observed, and 52 of 57 (91%) residents completed all 9 observations. Residents reported considerably more direct observation than prior to the intervention, and most reported changes to their clinical skills based on faculty feedback. Faculty reported improvements in their attitudes, increased their use of direct observation, and preferred the new system to the old one. Conclusions A novel evaluation system replacing summative evaluations with multiple formative evaluations based on direct observation was successful in achieving high rates of observations, and improving faculty attitudes toward direct observation.

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.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.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).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.

A review is given of different non-destructive techniques for fruit quality classification. As a hot research topic in the field of International Agricultural and Food Engineering, fruit quality classification has great influence on meeting consumers’ requirements for food quality and safety. Recently, a range of non-destructive techniques has been used for evaluating fruit quality. The main non-destructive techniques for fruit classification include using electrical properties, acoustic properties, optical properties, sonic vibration properties of fruits or THz, NMR, x-rays, electronic nose, machine vision technology, near-infrared spectroscopy and hyperspectral imaging to evaluate fruits without destruction. This review focus on summing up different types of non-destructive fruit classification techniques, including their pros and cons. Hyperspectral imaging techniques which were developed in recent years has been involved.

Summary 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.

Rheumatoid arthritis (RA) and inflammatory bowel disease (IBD) patient populations face similar risks of chronic immunosuppression including corticosteroid use. We compared the receipt of preventive services between IBD and RA populations according to published quality metrics.We defined a single-center cohort of patients with IBD or RA receiving specialty and primary care. Electronic health record abstraction assessed quality metrics, sociodemographics, comorbidity, and utilization. Comparisons used multivariate odds ratios and Student's t-tests.218 RA and 190 IBD patients were included. In multivariate analysis, IBD patients were less likely to receive pneumococcal vaccination (OR=0.29, 95% CI: 0.11-0.85), while RA patients underwent glucocorticoid-induced osteoporosis screening more often (100% vs. 82.5%, p = 0.023).Gastroenterologists can improve care quality for IBD patients by assuming greater responsibility for preventive care in IBD patients and/or collaborating with primary care and health systems to improve preventive care delivery.

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.

Treatment rules based on individual patient characteristics that are easy to interpret and disseminate are important in clinical practice. Properly planned and conducted randomized clinical trials are used to construct individualized treatment rules. However, it is often a concern that trial participants lack representativeness, so it limits the applicability of the derived rules to a target population. In this work, we use data from a single trial study to propose a two-stage procedure to derive a robust and parsimonious rule to maximize the benefit in the target population. The procedure allows a wide range of possible covariate distributions in the target population, with minimal assumptions on the first two moments of the covariate distribution. The practical utility and favorable performance of the methodology are demonstrated using extensive simulations and a real data application.

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 aim of this study is to determine the effects of early intravenous (IV) infusion later followed by transendocardial (TE) injection of allogeneic mesenchymal stem cells (MSCs) following myocardial infarction (MI). Twenty-four swine underwent balloon occlusion reperfusion MI and were randomized into 4 groups: IV MSC (or placebo) infusion (post-MI day 2) and TE MSC (or placebo) injection targeting the infarct border with 2D X-ray fluoroscopy fused to 3D magnetic resonance (XFM) co-registration (post-MI day 14). Continuous ECG recording, MRI, and invasive pressure-volume analyses were performed. IV MSC plus TE MSC treated group was superior to other groups for contractility reserve (p = 0.02) and freedom from VT (p = 0.03) but had more lymphocytic foci localized to the peri-infarct region (p = 0.002). No differences were observed in post-MI remodeling parameters. IV followed by XFM targeted TE MSC therapy improves contractility reserve and suppresses VT but does not affect post-MI remodeling and may cause an immune response.

Individualized treatment rules recommend treatments on the basis of individual patient characteristics. A high-quality treatment rule can produce better patient outcomes, lower costs and less treatment burden. If a treatment rule learned from data is to be used to inform clinical practice or provide scientific insight, it is crucial that it be interpretable; clinicians may be unwilling to implement models they do not understand, and black-box models may not be useful for guiding future research. The canonical example of an interpretable prediction model is a decision tree. We propose a method for estimating an optimal individualized treatment rule within the class of rules that are representable as decision trees. The class of rules we consider is interpretable but expressive. A novel feature of this problem is that the learning task is unsupervised, as the optimal treatment for each patient is unknown and must be estimated. The proposed method applies to both categorical and continuous treatments and produces favourable marginal mean outcomes in simulation experiments. We illustrate it using data from a study of major depressive disorder.

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.

As a new strategy for treatment which takes individual heterogeneity into consideration, personalized medicine is of growing interest. Discovering individualized treatment rules (ITRs) for patients who have heterogeneous responses to treatment is one of the important areas in developing personalized medicine. As more and more information per individual is being collected in clinical studies and not all of the information is relevant for treatment discovery, variable selection becomes increasingly important in discovering individualized treatment rules. In this article, we develop a variable selection method based on penalized outcome weighted learning through which an optimal treatment rule is considered as a classification problem where each subject is weighted proportional to his or her clinical outcome. We show that the resulting estimator of the treatment rule is consistent and establish variable selection consistency and the asymptotic distribution of the estimators. The performance of the proposed approach is demonstrated via simulation studies and an analysis of chronic depression data.

Background Childhood obesity remains a public health concern, and tracking local progress may require local surveillance systems. Electronic health record data may provide a cost-effective solution. Purpose To demonstrate the feasibility of estimating childhood obesity rates using de-identified electronic health records for the purpose of public health surveillance and health promotion. Methods Data were extracted from the Public Health Information Exchange (PHINEX) database. PHINEX contains de-identified electronic health records from patients primarily in south central Wisconsin. Data on children and adolescents (aged 2–19 years, 2011–2012, n =93,130) were transformed in a two-step procedure that adjusted for missing data and weighted for a national population distribution. Weighted and adjusted obesity rates were compared to the 2011–2012 National Health and Nutrition Examination Survey (NHANES). Data were analyzed in 2014. Results The weighted and adjusted obesity rate was 16.1% (95% CI=15.8, 16.4). Non-Hispanic white children and adolescents (11.8%, 95% CI=11.5, 12.1) had lower obesity rates compared to non-Hispanic black (22.0%, 95% CI=20.7, 23.2) and Hispanic (23.8%, 95% CI=22.4, 25.1) patients. Overall, electronic health record–derived point estimates were comparable to NHANES, revealing disparities from preschool onward. Conclusions Electronic health records that are weighted and adjusted to account for intrinsic bias may create an opportunity for comparing regional disparities with precision. In PHINEX patients, childhood obesity disparities were measurable from a young age, highlighting the need for early intervention for at-risk children. The electronic health record is a cost-effective, promising tool for local obesity prevention efforts.

Individualized treatment rules recommend treatments based on individual patient characteristics in order to maximize clinical benefit. When the clinical outcome of interest is survival time, estimation is often complicated by censoring. We develop nonparametric methods for estimating an optimal individualized treatment rule in the presence of censored data. To adjust for censoring, we propose a doubly robust estimator which requires correct specification of either the censoring model or survival model, but not both; the method is shown to be Fisher consistent when either model is correct. Furthermore, we establish the convergence rate of the expected survival under the estimated optimal individualized treatment rule to the expected survival under the optimal individualized treatment rule. We illustrate the proposed methods using simulation study and data from a Phase III clinical trial on non-small cell lung cancer.

The problem of combining markers to optimize treatment selection has recently received significant attention among statistical researchers. We congratulate Kang, Jane, and Huang (2014; hereafter KJH) on an elegant contribution to this important area. KJH provide a novel application of boosting to find the best marker combination. In particular, with a binary outcome, the optimal treatment rule is determined by the conditional probability of having disease, i.e., the risk, given the covariates and the treatment. If the risk models are correctly specified, the optimal rule can be deduced accordingly. While a generalized linear model is a simple and popular option, it may suffer from model misspecification. The proposed method in KJH achieves a measure of robustness to such model misspecification through the use of boosting, combined with iteratively reweighting each subject’s potential misclassification based on treatment benefit in the previous iteration. Although KJH indicate that the purpose of the proposed method is to classify subjects according to the unobserved optimal treatment decision rule, the approach does not utilize a clear objective function for optimization. Risk modeling is required to estimate the optimal rules and to further update the weights at each step. As shown in the simulation results, the performances vary with different working models. An alternative approach is given in Zhao et al. (2012) who propose outcome weighted learning (OWL) which estimates the optimal treatment decision rule through a weighted classification procedure that incorporates outcome information. To be more specific, the optimization target, which directly leads to the optimal treatment decision rule, can be viewed as a weighted classification error where each subject is weighted proportional to his or her clinical outcome. In the next section, we will briefly introduce the idea of OWL and modify it to the binary outcome setup. In Section 3, we present simulation studies comparing OWL with the boosting method proposed by KJH. We conclude with a brief discussion in Section 4.

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.

ummary A dynamic treatment regime consists of a set of decision rules that dictate how to individualize treatment to patients based on available treatment and covariate history. A common method for estimating an optimal dynamic treatment regime from data is Q-learning which involves nonsmooth operations of the data. This nonsmoothness causes standard asymptotic approaches for inference like the bootstrap or Taylor series arguments to breakdown if applied without correction. Here, we consider the m-out-of-n bootstrap for constructing confidence intervals for the parameters indexing the optimal dynamic regime. We propose an adaptive choice of m and show that it produces asymptotically correct confidence sets under fixed alternatives. Furthermore, the proposed method has the advantage of being conceptually and computationally much simple than competing methods possessing this same theoretical property. We provide an extensive simulation study to compare the proposed method with currently available inference procedures. The results suggest that the proposed method delivers nominal coverage while being less conservative than alternatives. The proposed methods are implemented in the qLearn R-package and have been made available on the Comprehensive R-Archive Network (http://cran.r-project.org/). Analysis of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study is used as an illustrative example.

We present closed form sample size and power formulas motivated by the study of a psycho-social intervention in which the experimental group has the intervention delivered in teaching subgroups whereas the control group receives usual care. This situation is different from the usual clustered randomized trial because subgroup heterogeneity only exists in one arm. We take this modification into consideration and present formulas for the situation in which we compare a continuous outcome at both a single point in time and longitudinally over time. In addition, we present the optimal combination of parameters such as the number of subgroups and number of time points for minimizing sample size and maximizing power subject to constraints such as the maximum number of measurements that can be taken (i.e., a proxy for cost).