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Dynamic treatment regimes (DTRs) are sequential decision rules for individual patients that can adapt over time to an evolving illness. The goal is to accommodate heterogeneity among patients and find the DTR which will produce the best long-term outcome if implemented. We introduce two new statistical learning methods for estimating the optimal DTR, termed backward outcome weighted learning (BOWL), and simultaneous outcome weighted learning (SOWL). These approaches convert individualized treatment selection into an either sequential or simultaneous classification problem, and can thus be applied by modifying existing machine learning techniques. The proposed methods are based on directly maximizing over all DTRs a nonparametric estimator of the expected long-term outcome; this is fundamentally different than regression-based methods, for example, Q -learning, which indirectly attempt such maximization and rely heavily on the correctness of postulated regression models. We prove that the resulting rules are consistent, and provide finite sample bounds for the errors using the estimated rules. Simulation results suggest the proposed methods produce superior DTRs compared with Q -learning especially in small samples. We illustrate the methods using data from a clinical trial for smoking cessation. Supplementary materials for this article are available online.

There is an urgent need for childhood surveillance systems to design, implement, and evaluate interventions at the local level. We estimated obesity prevalence for individuals aged 5–17 years using a southcentral Wisconsin EHR data repository, Public Health Information Exchange (PHINEX, 2007–2012). The prevalence estimates were calculated by aggregating the estimated probability of each individual being obese, which was obtained via a generalized linear mixed model. We incorporated the random effects at the area level into our model. A weighted procedure was employed to account for missingness in EHR data. A non-parametric kernel smoothing method was used to obtain the prevalence estimates for locations with no or little data (<20 individuals) from the EHR. These estimates were compared to results from newly available obesity atlas (2015–2016) developed from various EHRs with greater statewide representation. The mean of the zip code level obesity prevalence estimates for males and females aged 5–17 years is 16.2% (SD 2.72%); 17.9% (SD 2.14%) for males and 14.4% (SD 2.00%) for females. The results were comparable to the Wisconsin Health Atlas (WHA) estimates, a much larger dataset of local community EHRs in Wisconsin. On average, prevalence estimates were 2.12% lower in this process than the WHA estimates, with lower estimation occurring more frequently for zip codes without data in PHINEX. Using this approach, we can obtain estimates for local areas that lack EHRs data. Generally, lower prevalence estimates were produced for those locations not represented in the PHINEX database when compared to WHA estimates. This underscores the need to ensure that the reference EHRs database can be made sufficiently similar to the geographic areas where synthetic estimates are being created.

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.

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 highdimensional 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.

Objective To investigate the role of COLEC12 in osteosarcoma and observe the relationship between COLEC12 knockdown and the inflammation of osteosarcoma. Then, further explore whether the process is regulated by TLR4. Method GEPIA and TCGA systems were used to predict the potential function of COLEC12. Western blot and RT‐PCR were used to analyze the protein expression, or mRNA level, of COLEC12 in different tissue or cell lines. The occurrence and development of osteosarcoma were observed by using COLEC12 knockdown lentivirus. The inflammation indexes of osteosarcoma, in vitro and in vivo, were explored. TLR4 knockdown lentivirus was applied to the relationship between COLEC12 and TLR4. Results COLEC12 expression in SARC tumor tissue was higher than in normal, and a high expression of COLEC12 in SARC patients had a worse prognostic outcome. Pairwise gene correlation analysis revealed a potential relationship between COLEC12 and TLR4. The COLEC12 expression and mRNA level in the tumor or Saos‐2 cells were increased. COLEC12 knockdown lentivirus could inhibit osteosarcoma development, in vivo and vitro, through reducing tumor volume and weight, weakening tumor proliferation, migration, and invasion, and enhancing apoptosis. Furthermore, COLEC12 knockdown could increase inflammation of osteosarcoma, in vivo and in vitro, through inducing myeloperoxidase (MPO), TLR4, NF‐κB, and C3, and expression of related inflammatory factors. Finally, TLR4 knockdown lentivirus inhibits the progress of inflammation after COLEC12 regulation, in vivo and vitro. Conclusion COLEC12 may be able to regulate apoptosis and inflammation of osteosarcoma, and TLR4 may be the downstream target factor of COLEC12 in inflammation. The potential mechanisms of inflammation by COLEC12 mediated through TLR4 in osteosarcoma. COLEC12 could reduce TLR4 expression, weaken NF‐κB and C3 signaling pathway to release inflammatory factors downstream, restrain immune defense to tumor cells, inhibit apoptosis of tumor cells, and cut down lives of mouses.

Despite the fact that the inflammatory bowel diseases (IBD) and their treatments may affect physical appearance, the effect of IBD on body image is poorly understood. The aims of this study were to determine whether body image dissatisfaction (BID) changes over time in patients with IBD and to examine the demographic and disease-related variables associated with decreased body image.MethodsAdults aged 18 and above in the Ocean State Crohn's and Colitis Area Registry with at least 2 years of follow-up were eligible for this study. All patients were enrolled within 6 months of IBD diagnosis and followed prospectively. BID was assessed using a modified version of the Adapted Satisfaction With Appearance questionnaire. Total Adapted Satisfaction With Appearance scores and 2 subscores were calculated. To assess for changes over time, general linear models for correlated data were used for continuous outcomes, and generalized estimating equations were used for discrete outcomes.ResultsTwo hundred seventy-four patients were studied. BID was found to be stable over time among men and women with IBD despite overall improvements in disease activity. No differences were found in BID according to IBD subtype. Female gender, greater disease activity, higher symptom burden, longer duration of steroid use, dermatologic and musculoskeletal manifestations of IBD, and ileocolonic disease location among patients with Crohn's disease were associated with greater BID. Greater BID was associated with lower health-related quality of life.ConclusionsBID remains stable in an incident cohort of IBD despite improved disease activity and is associated with lower health-related quality of life.

The effect of the inflammatory bowel diseases (IBD) on menstrual function is largely unknown. The aims of this study were to determine whether changes in menstrual function occur in the year before IBD diagnosis or in the initial years after diagnosis.MethodsWomen aged 18 years and older in the Ocean State Crohn's and Colitis Area Registry with at least 2 years of follow-up were eligible for this study. All patients were enrolled within 6 months of IBD diagnosis and followed prospectively. Menstrual cycle characteristics were retrospectively assessed. To assess for changes over time, general linear models for correlated data were used for continuous outcomes, and generalized estimating equations were used for discrete outcomes.ResultsOne hundred twenty-one patients were studied. Twenty-five percent of patients experienced a change in cycle interval in the year before IBD diagnosis and 21% experienced a change in the duration of flow. Among women with dysmenorrhea, 40% experienced a change in the intensity of their menstrual pain and 31% experienced a change in its duration. Overall cycle regularity increased over time. Quality of life was significantly lower in women without regular cycles across all time points.ConclusionsChanges in menstrual function occur frequently in the year before IBD diagnosis; therefore, screening for menstrual irregularities should be considered in women with newly diagnosed IBD. Patients can be reassured that cycles typically become more regular over time.

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.

Previous studies show that stressful events after ovulation in sows significantly impaired the embryo cleavage with a significant elevation of blood cortisol. However, the effects of corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH) and cortisol on fertilization and embryo development remain to be specified, and whether they damage pig embryos directly or indirectly is unclear. This study demonstrated that embryo development was unaffected when pig parthenotes were cultured with different concentrations of CRH/ACTH/cortisol. However, embryo development was significantly impaired when the embryos were cocultured with pig oviductal epithelial cells (OECs) in the presence of CRH/cortisol or cultured in medium that was conditioned with CRH/cortisol-pretreated OECs (CRH/cortisol-CM). Fertilization in CRH/cortisol-CM significantly increased the rates of polyspermy. CRH and cortisol induced apoptosis of OECs through FAS and TNFα signaling. The apoptotic OECs produced less growth factors but more FASL and TNFα, which induced apoptosis in embryos. Pig embryos were not sensitive to CRH because they expressed no CRH receptor but the CRH-binding protein, and they were tolerant to cortisol because they expressed more 11-beta hydroxysteroid dehydrogenase 2 (HSD11B2) than HSD11B1. When used at a stress-induced physiological concentration, while culture with either CRH or cortisol alone showed no effect, culture with both significantly increased apoptosis in OECs. In conclusion, CRH and cortisol impair pig fertilization and preimplantation embryo development indirectly by inducing OEC apoptosis via the activation of the FAS and TNFα systems. ACTH did not show any detrimental effect on pig embryos, nor OECs.

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.