A Framework for Design of Two-Stage Adaptive Procedures

The main objective of this dissertation is to introduce a framework for two-stage adaptive procedures in which the blind is broken at the end of Stage I. Using our framework, it is possible to control many aspects of an experiment including the Type I error rate, power and maximum total sample size. Our framework also enables us to compare different procedures under the same formulation. We conduct an ANOVA type study to learn the effects of different components of the design specification on the performance characteristics of the resulting design. In addition, we consider conditions for the monotonicity of the power function of a two-stage adaptive procedure.To foster the practicality of our framework, two extensions are considered. The first one is an application of our framework to the settings with unequal sample sizes. We show how to design a two-stage adaptive procedure having unequal sample sizes for the treatment and control groups. Also we illustrate how to modify an ongoing two-stage adaptive trial when some observations are missing in Stage I and/or in Stage II. Second, we extend the framework to unknown population variance. Our framework can construct a design that incorporates updating the variance estimate at the end of Stage I and modifies the design of Stage II accordingly. All the procedures we present protect the Type I error rate and allow specification of the power and the maximum sample size.We also consider the problem of switching design objectives between testing noninferiority and testing superiority. Our framework can be used to design a two-stage adaptive procedure that simultaneously tests both noninferiority and superiority hypotheses with controlled error probabilities. The sample size for Stage I is chosen for the main study objective, but that objective may be switched for Stage II based on the unblinded observations from Stage I. Our framework offers a technique to specify certain design criteria such as the various Type I error rates, power and maximum sample sizes

A Bayesian Approach for the Cox Proportional Hazards Model with Covariates Subject to Detection Limit

The research on biomarkers has been limited in its effectiveness because biomarker levels can only be measured within the thresholds of assays and laboratory instruments, a challenge referred to as a detection limit (DL) problem. In this paper, we propose a Bayesian approach to the Cox proportional hazards model with explanatory variables subject to lower, upper, or interval DLs. We demonstrate that by formulating the time-to-event outcome using the Poisson density with counting process notation, implementing the proposed approach in the OpenBUGS and JAGS is straightforward. We have conducted extensive simulations to compare the proposed Bayesian approach to the other four commonly used methods and to evaluate its robustness with respect to the distribution assumption of the biomarkers. The proposed Bayesian approach and other methods were applied to an acute lung injury study, in which a panel of cytokine biomarkers was studied for the biomarkers' association with ventilation-free survival

Enhancing Cancer Care of Rural Dwellers through Telehealth and Engagement (ENCORE): Protocol to Evaluate Effectiveness of a Multi-Level Telehealth-Based Intervention to Improve Rural Cancer Care Delivery

BACKGROUND: Despite lower cancer incidence rates, cancer mortality is higher among rural compared to urban dwellers. Patient, provider, and institutional level factors contribute to these disparities. The overarching objective of this study is to leverage the multidisciplinary, multispecialty oncology team from an academic cancer center in order to provide comprehensive cancer care at both the patient and provider levels in rural healthcare centers. Our specific aims are to: 1) evaluate the clinical effectiveness of a multi-level telehealth-based intervention consisting of provider access to molecular tumor board expertise along with patient access to a supportive care intervention to improve cancer care delivery; and 2) identify the facilitators and barriers to future larger scale dissemination and implementation of the multi-level intervention. METHODS: Coordinated by a National Cancer Institute-designated comprehensive cancer center, this study will include providers and patients across several clinics in two large healthcare systems serving rural communities. Using a telehealth-based molecular tumor board, sequencing results are reviewed, predictive and prognostic markers are discussed, and treatment plans are formulated between expert oncologists and rural providers. Simultaneously, the rural patients will be randomized to receive an evidence-based 6-week self-management supportive care program, Cancer Thriving and Surviving, versus an education attention control. Primary outcomes will be provider uptake of the molecular tumor board recommendation and patient treatment adherence. A mixed methods approach guided by the Consolidated Framework for Implementation Research that combines qualitative key informant interviews and quantitative surveys will be collected from both the patient and provider in order to identify facilitators and barriers to implementing the multi-level intervention. DISCUSSION: The proposed study will leverage information technology-enabled, team-based care delivery models in order to deliver comprehensive, coordinated, and high-quality cancer care to rural and/or underserved populations. Simultaneous attention to institutional, provider, and patient level barriers to quality care will afford the opportunity for us to broadly share oncology expertise and develop dissemination and implementation strategies that will enhance the cancer care delivered to patients residing within underserved rural communities. TRIAL REGISTRATION: Clinicaltrials.gov , NCT04758338 . Registered 17 February 2021 - Retrospectively registered, http://www.clinicaltrials.gov/

Androgen Regulated Genes in Human Prostate Xenografts in Mice: Relation to BPH and Prostate Cancer

Benign prostatic hyperplasia (BPH) and prostate carcinoma (CaP) are linked to aging and the presence of androgens, suggesting that androgen regulated genes play a major role in these common diseases. Androgen regulation of prostate growth and development depends on the presence of intact epithelial-stromal interactions. Further, the prostatic stroma is implicated in BPH. This suggests that epithelial cell lines are inadequate to identify androgen regulated genes that could contribute to BPH and CaP and which could serve as potential clinical biomarkers. In this study, we used a human prostate xenograft model to define a profile of genes regulated in vivo by androgens, with an emphasis on identifying candidate biomarkers. Benign transition zone (TZ) human prostate tissue from radical prostatectomies was grafted to the sub-renal capsule site of intact or castrated male immunodeficient mice, followed by the removal or addition of androgens, respectively. Microarray analysis of RNA from these tissues was used to identify genes that were; 1) highly expressed in prostate, 2) had significant expression changes in response to androgens, and, 3) encode extracellular proteins. A total of 95 genes meeting these criteria were selected for analysis and validation of expression in patient prostate tissues using quantitative real-time PCR. Expression levels of these genes were measured in pooled RNAs from human prostate tissues with varying severity of BPH pathologic changes and CaP of varying Gleason score. A number of androgen regulated genes were identified. Additionally, a subset of these genes were over-expressed in RNA from clinical BPH tissues, and the levels of many were found to correlate with disease status. Our results demonstrate the feasibility, and some of the problems, of using a mouse xenograft model to characterize the androgen regulated expression profiles of intact human prostate tissues

Peer Review Model Kepemimpinan Modern di Program Pascasarjana Universitas Ahmad Dahlan

Logistic regression model for in-hospital mortality in patients without head injury. (DOCX 14 kb

A Bayesian Approach for the Cox Proportional Hazards Model with Covariates Subject to Detection Limit

The research on biomarkers has been limited in its effectiveness because biomarker levels can only be measured within the thresholds of assays and laboratory instruments, a challenge referred to as a detection limit (DL) problem. In this paper, we propose a Bayesian approach to the Cox proportional hazards model with explanatory variables subject to lower, upper, or interval DLs. We demonstrate that by formulating the time-to-event outcome using the Poisson density with counting process notation, implementing the proposed approach in the OpenBUGS and JAGS is straightforward. We have conducted extensive simulations to compare the proposed Bayesian approach to the other four commonly used methods and to evaluate its robustness with respect to the distribution assumption of the biomarkers. The proposed Bayesian approach and other methods were applied to an acute lung injury study, in which a panel of cytokine biomarkers was studied for the biomarkers’ association with ventilation-free survival