To shorten the time for drug development and regulatory approval, a growing number of clinical trials are being conducted in multiple regions simultaneously. One of the challenges to multi-regional clinical trials (MRCT) is how to utilize the data obtained from other regions within the entire trial to help make local approval decisions. In addition to the global efficacy, the evidence of consistency in treatment effects between the local region and the entire trial is usually required for regional approval. In recent years, a number of statistical models and consistency criteria have been proposed. The sample size requirement for the region of interest was also studied. However, there is no specific regional requirement being broadly accepted; sample size planning considering regional requirement of all regions of interest is not well developed; how to apply the adaptive design to MRCT has not been studied. In this dissertation, we have made a number of contributions. First, we propose a unified regional requirement for the consistency assessment of MRCT, which generalizes the requirements proposed by Ko et al. (2010), Chen et al. (2012) and Tsong et al. (2012), make recommendations for choosing the value of parameters defining the proposed requirement, and determine the sample size increase needed to preserve power. Second, we propose two optimal designs for MRCT: minimal total sample size design and maximal utility design, which will provide more effective sample size allocation to ensure certain overall power and assurance probabilities of all interested regions. We also introduce the factors which should be considered in designing MRCT and analyze how each factor affects sample size planning. Third, we propose an unblinded region-level adaptive design to perform sample size re-estimation and re-allocation at interim based on the observed values of each region. We can determine not only whether to stop the whole MRCT based on the conditional power, but also whether to stop any individual region based on the conditional success rate at interim. The simulation results support that the proposed adaptive design has better performance than the classical design in terms of overall power and success rate of each region.

This research aims to find architectural and urban design related solutions to enhance the living conditions of refugees who are trapped in protracted refugee situations. One key issue that could be considered the main problem-generating factor in protracted refugee situations, is the lack of a clear definition for the appropriate deign life-span of refugee camps in general. As an example of that refuge situation, focus within this research is upon the two refugee camp models found in Jordan; the Zaatari camp and the Azraq camp. Both were established in response of the recent civil war in Syria, started in 2011. A comparison between the two refugee camps resulted in identifying the strong points and the shortcomings of the current models. In addition, an assessment of the refugees’ needs is made based on the refugee camp’s design guidelines and the official reports published by the different humanitarian organizations. The above mentioned researched information is utilized to develop and apply an adaptable design solution that aims to overcome the unpredictable future of the refugee situation in Azraq refugee camp in Jordan. The proposal includes a long-term plan that may develop over time and which is divided to three stages depending on the longevity of the refugee camp. Each stage takes into consideration the changing requirements and needs that the refugees develop over time.

New possibilit ies exist for the development of novel hardware/software platforms havin g fast data acquisition capability with low power requirements. One application is a high speed Adaptive Design for Information (ADI) system that combines the advantages of feature-based data compression, low power nanometer CMOS technology, and stream computing [1]. We have developed a compressive sensing (CS) algorithm which linearly reduces the data at the analog front end, an approach which uses analog designs and computations instead of smaller feature size transistors for higher speed and lower power. A level-crossing sampling approach replaces Nyquist sampling. With an in-memory design, the new compressive sensing based instrumentation performs digitization only when there is enough variation in the input and when the random selection matrix chooses this input.

In adaptive design clinical trials, an endpoint at the final analysis that takes a long time to observe is not feasible to be used for making decisions at the interim analysis. For example, overall survival (OS) in oncology trials usually cannot be used to make interim decisions. However, biomarkers correlated to the final clinical endpoint can be used. Hence, considerable interest has been drawn towards the biomarker informed adaptive clinical trial designs. Shun et al. (2008) proposed a "biomarker informed two-stage winner design" with 2 active treatment arms and a control arm, and proposed a normal approximation method to preserve type I error. However, their method cannot be extended to designs with more than 2 active treatment arms. In this dissertation, we propose a novel statistical approach for biomarker informed two-stage winner design that can accommodate multiple active arms and control type I error. We further propose another biomarker informed adaptive design called "biomarker informed add-arm design for unimodal response". This design utilizes existing knowledge about the shape of dose-response relationship to optimize the procedure of selecting best candidate treatment for a larger trial. The key element of the proposed design is that, some inferior treatments do not need to be explored and the design is shown to be more efficient than biomarker informed two-stage winner design mathematically. Another important component in the study of biomarker informed adaptive designs is to model the relationship between the two endpoints. The conventional approach uses a one-level correlation model, which might be inappropriate if there is no solid historical knowledge of the two endpoints. A two-level correlation model is developed in this dissertation. In the new model a new variable that describes the mean level correlation is developed, so that the uncertainty of the historical knowledge could be more accurately reflected. We use this new model to study the "biomarker informed two-stage winner design" and the "biomarker informed add-arm design for unimodal response". We show the new proposed model performs better than conventional model via simulations. The concordance of inference based on biomarker and primary endpoint is further studied in a real case.

Superiority clinical trials are often designed with a planned interim analysis for the purpose of sample size re-estimation (SSR) when limited information is available at the start of the trial to estimate the required sample size. Typically these trials are designed with a two-arm internal pilot where subjects are enrolled to both treatment arms prior to the interim analysis. Circumstances may sometimes call for a trial with a single-arm internal pilot (enroll only in the control group). For a dichotomous outcome, Herson and Wittes proposed a SSR method (HW-SSR) that can be applied to single-arm internal pilot trials using an unblinded estimate of the control group outcome rate. Previous evaluations of the HW-SSR method reported conflicting results regarding the impact of the method on the two-sided Type I error rate and power of the final hypothesis test. In this research we evaluate the HW-SSR method under the null and alternative hypothesis in various scenarios to investigate the one-sided Type I error rate and power of trials with a two-arm internal pilot. We find that the one-sided Type I error rate is sometimes inflated and that the power is sometimes reduced. We propose a new method, the Critical Value and Power Adjusted Sample Size Re-estimation (CVPA-SSR) algorithm to adjust the critical value cutoff used in the final Z-test and the power critical value used in the interim SSR formula to preserve the nominal Type I error rate and the desired power. We conduct simulations for trials with single-arm and two-arm internal pilots to confirm that the CVPA-SSR algorithm does preserve the nominal Type I error rate and the desired power. We investigate the robustness of the CVPA-SSR algorithm for trials with single-arm and two-arm internal pilots when the assumptions used in designing the trial are incorrect. No Type I error inflation is observed but significant over- or under-powering of the trial occurs when the treatment effect used to design the trial is misspecified.

A common approach to adapt the design of a clinical trial based on interim results is sample size re-estimation (SSR). SSR allows an increase in the trial's sample size in order to maintain, at the desired nominal level, the desired power to reject the null hypothesis conditioned on the interim observed treatment effect and its variance (i.e., the conditional power). There are several established approaches to SSR for clinical studies with independent and identically distributed observations; however, no established methods have been developed for trials where there is more than one observation collected per subject where within-subject correlation exists. Without accurately accounting for the within-subject correlation in SSR, a sponsor may incorrectly estimate the trial's conditional power to obtain statistical significance at the final analysis and hence risk overestimating or underestimating the number of patients required to complete the trial as planned. In this dissertation, we propose an extension of Mehta and Pocock's promising zone approach to SSR that reconciles the within-subject correlation in the data for a variety of superiority clinical trials. We consider trials with continuous and binary primary endpoints, and further we explore cases where patients contribute both the same and varying numbers of observations to the analysis of the primary endpoint. Using a simulation study, we show that in each case, our proposed conditional power formula accurately calculates conditional power and our proposed SSR methodology preserves the nominal type I error rate under the null hypothesis and maintains adequate power under the alternative hypothesis. Additionally, we demonstrate the robustness of our methodology to the mis-specification of a variety of distributional assumptions regarding the underlying population from which the data arise. / 2024-06-21T00:00:00Z

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There are a number of available statistical methods for adaptive designs, among which the combination method of Bauer and Kohne's (1994) is well known and widely used. In this work, we revisit the the Bauer-Kohne method in three ways: overall FWER control for single-hypothesis in a two-stage adaptive design, overall FWER control for two-hypothesis in a two-stage adaptive design, and overall FDR control for multiple-hypothesis in a two-stage adaptive design. We first take the Bauer-Kohne method in a more direct manner to have more flexibility in the choice of the early rejection and acceptance boundaries as well as the second stage critical value based on the chosen combination function. Our goal is not to develop a new method, but focus primarily on developing a comprehensive understanding of two-stage designs. Rather than tying up the early rejection and acceptance boundaries by considering the second stage critical value to be the same as that of the level á combination test, as done in the original Bauer-Kohne method, we allow the second-stage critical value to be determined from prefixed early rejection and acceptance boundaries. An explicit formula is derived for the overall Type I error probability to determine the second stage critical value from these stopping boundaries not only for Fisher's combination function but also for other types of combination function. Tables of critical values corresponding to several different choices of early rejection and acceptance boundaries and these combination functions are presented. A dataset from a clinical study is used to apply the different methods based on directly computed second stage critical values from pre fixed stopping boundaries and discuss the outcomes in relation to those produced by the original Bauer-Kohne method. We then extend the Bauer-Kohne method to two-hypothesis setting and propose a stepwise-combination method for a two-stage adaptive design. In particular, we modify Holm's step-down procedure (1979) and suggest a step-down combination method to control the overall FWER at a desired level á. In many scientific studies requiring simultaneous testing of multiple null hypotheses, it is often necessary to carry out the multiple testing in two stages to decide which of the hypotheses can be rejected or accepted at the first stage and which should be followed up for further testing having combined their p-values from both stages. Unfortunately, no multiple testing procedure is available yet to perform this task meeting pre-specified boundaries on the first-stage p-values in terms of the false discovery rate (FDR) and maintaining a control over the overall FDR at a desired level. Our third goal in this work is to present two procedures, extending the classical Benjamini-Hochberg (BH) procedure and its adaptive version incorporating an estimate of the number of true null hypotheses from single-stage to a two-stage setting. These procedures are theoretically proved to control the overall FDR when the pairs of first- and second-stage p-values are independent and those corresponding to the null hypotheses are identically distributed as a pair (p1, p2) satisfying the p-clud property of Brannath, Posch and Bauer (2002, Journal of the American Statistical Association, 97, 236 -244). We consider two types of combination function, Fisher's and Simes', and present explicit formulas involving these functions towards carrying out the proposed procedures based on pre-determined critical values or through estimated FDR's. Simulations were carried to compare the proposed methods with class BH procedure using first stage data only and full data from both stages respectively. Our simulation studies indicate that the proposed procedures can have significant power improvement over the single-stage BH procedure based on the first stage data, at least under independence, and can continue to control the FDR under some dependence situations. Application of the proposed procedures to a real gene expression data set produces more discoveries compared to the single-stage BH procedure using the first stage data and full data as well. / Statistics

This thesis analyses long term and short term environmental effects on geothermal power plant performance and discusses adaptive ways to improve performance. Mokai 1 geothermal power plant has been used as a case study for this investigation. Mokai 1 is a combined cycle plant where the binary cycles are air-cooled. The plant performance of an air-cooled binary cycle geothermal power plant is dependent on the environment (resource characteristics as well as weather conditions). For modelling such a power plant, two time scales are of interest: the yearly basis for aggregate plant performance for design and operations; and the daily basis for hourly plant performances for an accurate dispatch prediction. Adaptive methodology for long term performance improvement has been introduced in this work which would save money and effort in the future by keeping the provisions to adapt to changes in resource characteristics based on geothermal reservoir modelling. The investigation was carried out using a steady state computer simulator of Mokai 1 geothermal power plant. The steady sate simulator was built specifically for this work. The deviation in performance of various components is less than 5% compared to the original plant design. The model is very generic and it can be used for other plants with simple adaptation or can be used for future plant design. One of the main contributions of this work is an iterative method for modelling the environmental effect on short term performance on the air-cooled organic Rankine cycle. The ambient temperature is identified as the most influencing parameter on short term performance which influences the performance of the whole cycle in two ways. Firstly, by changing the equilibrium pressure inside the condenser, the turbine outlet pressure changes and hence, the turbine pressure ratio also changes. The turbine pressure ratio is a major parameter determining power generated by a turbine; therefore, the plant output is affected. Secondly, by changing the condenser outlet temperature with the ambient temperature, the pump inlet and outlet condition and consequently vaporizer equilibrium temperature and pressure are influenced. The developed method sought the equilibrium conditions of both condenser and vaporizer iteratively. In short, ORC cycle shifts on the T-s plane depending on the ambient temperature. This method iteratively seeks the shifted ORC on the T,s plane. Two case studies have been carried out to demonstrate the method. The developed method shows robustness and converges exponentially. The model is effective for cycles that use saturated vapour as well as superheated vapour. The model essentially assumes steady state operation of the power cycle. The possible unit time where this model can be applied is bounded by the time required by a system to come into steady state. The saturated vapour cycle yielded average error 4.20% with maximum error 9.25% and the superheated vapour cycle yielded average error 2.12% with maximum error 5.60%. The main advantage of the developed method is that it requires a minimum number of inputs: condenser (p,T), vaporizer (p,T), condenser heat load, turbine efficiency (overall), pump work and the extremum conditions of all the components. These inputs should represent typical operating conditions of a plant. The model can predict the appropriate plant performance depending on the system heat input (geothermal fluid flow in this case) and the heat sink temperature. As the method is based on basic thermodynamics rather than empirical or semi-empirical approaches, this method is widely applicable. The main focus of this work is on the ORC but the developed method is applicable to any closed Rankine cycle. In addition, application of the developed iterative method to predict plant performance based on mean yearly weather data is also discussed in the thesis. Water-augmented cooling system and optimization of plant operating point parameters have been proposed as adaptive measures to improve short term performance. Developed iterative method has been used for the short term performance analysis. The water-augmented cooling system is specifically suitable to mitigate the reduced power output during the summer. The simulated average gain in power during the summer (Jan, Feb, Nov and Dec) of an ORC of Mokai 1 geothermal power plant by incorporating a water-augmented cooling system was 2.3% and the average gain for the whole year was 1.6% based on the weather data of Taupo for the year 2005. A cost benefit analysis showed that water-augmented cooling system is more economical compared to other alternative renewable energies considered to meet summer peak demand. From the green house gas emissions perspective, water-augmented cooling is a better option than the gas fired peaking plants. Adaptive approach for short term performance improvement by optimizing operating point parameters of an air-cooled binary cycle has huge potential with possible maximum improvement in power output by about 50%. The optimization takes in to account the effects of the geothermal resource characteristics and the weather conditions. The optimization is achieved by manipulating cycle mass flow rate and vaporizer equilibrium condition. Further study on the optimizing operating points to achieve improved short term performance has been recommended for future work.