2,085 research outputs found

    Analysis of Adverse Events in Drug Safety: A Multivariate Approach Using Stratified Quasi-least Squares

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    Safety assessment in drug development involves numerous statistical challenges, and yet statistical methodologies and their applications to safety data have not been fully developed, despite a recent increase of interest in this area. In practice, a conventional univariate approach for analysis of safety data involves application of the Fisher\u27s exact test to compare the proportion of subjects who experience adverse events (AEs) between treatment groups; This approach ignores several common features of safety data, including the presence of multiple endpoints, longitudinal follow-up, and a possible relationship between the AEs within body systems. In this article, we propose various regression modeling strategies to model multiple longitudinal AEs that are biologically classified into different body systems via the stratified quasi-least squares (SQLS) method. We then analyze safety data from a clinical drug development program at Wyeth Research that compared an experimental drug with a standard treatment using SQLS, which could be a superior alternative to application of the Fisher\u27s exact test

    Evaluation and Improvement of Machine Learning Algorithms in Drug Discovery

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    Drug discovery plays a critical role in today’s society for treating and preventing sickness and possibly deadly viruses. In early drug discovery development, the main challenge is to find candidate molecules to be used as drugs to treat a disease. This also means assessing key properties that are wanted in the inter- action between molecules and proteins. It is a very difficult problem because the molecular space is so big and complex. Drug discovery development is es- timated to take around 12–15 years on average, and the costs of developing a single drug amount to $2.8 billion dollars in the US. Modern drug discovery and drug development often start with finding candi- date drug molecules (‘compounds’) that can bind to a target, usually a protein in our body. Since there are billions of possible molecules to test, this becomes an endless search for compounds that show promising bioactivity. The search method is called high-throughput screening (HTS), or virtual HTS (VHTS) in a virtual environment. The traditional approach to HTS has been to test every compound one by one. More recent approaches have seen the use of robotics and of features extracted from the molecule, combining them with machine learning algorithms, in an effort to make the process more automated. Research has shown that this will still lead to human errors and bias. So, how can we use machine learning algorithms to make this approach more cost-efficient and more robust to human errors? This project tried to address these issues and led to two scientific papers as a result. The first paper explores how common evaluation metrics used for classification can actually be unsuited to the task, leading to severe consequences when put into a real application. The argument is based on basic principles of Decision Theory, which is recognized in the field of machine learning but has not been put into much use. It makes a distinction between predicting the most probable class and predicting the most valuable class in terms of the “cost” or “gains” for the classes. In an algorithm for classifying a particular disease in a patient, the wrong classification could lead to a life or death situation. The principles also apply to drug discovery, where the cost of further developing and optimizing a "useless" drug could be huge. The goal of the classifier should therefore not be to guess the correct class but to choose the optimal class, and the metric must depend on the type of classification problem. Thus, we show that common metrics such as precision, balanced accuracy, F1-score, Area Under The Curve, Matthews Correlation Coefficient, and Fowlkes-Mallows index are affected by this problem, and propose an evaluation method grounded on the foundations of Decision Theory to provide a solution to this problem. The metric presented, called utility, takes into account gains and losses for each correct or incorrect classification of the confusion matrix. For this to work effectively, the output of the machine learning algorithm needs to be a set of sensible probabilities for each class. This brings us to the second paper. Machine learning algorithms usually output a set of real numbers for the classes they try to predict, which, possibly after some transformation (for exam- ple the ‘softmax’ function), are meant to represent probabilities for the classes. However, the problem is that these numbers cannot be reliably interpreted as actual probabilities, in the sense of degrees of belief. In the paper, we propose the implementation of a probability transducer to transform the output of the algorithm into sensible probabilities. These are then used in conjunction with the utilities to choose the class with the maximal expected utility. The results show that the transducer gives better scores, in terms of the utilities, for all cases compared to the standard method used in machine learning.Masteroppgave i Programutvikling samarbeid med HVLPROG399MAMN-PRO

    Effectiveness of a peer-delivered, psychosocial intervention on maternal depression and child development at 3 years postnatal: a cluster randomised trial in Pakistan

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    Maternal depression has a recurring course that can influence offspring outcomes. Evidence on how to treat maternal depression to improve longer-term maternal outcomes and reduce intergenerational transmission of psychopathology is scarce, particularly for task-shifted, low-intensity, and scalable psychosocial interventions. We evaluated the effects of a peer-delivered, psychosocial intervention on maternal depression and child development at 3 years postnatal

    Adherence to and effectiveness of Highly Active Antiretroviral Treatment for HIV infection: assessing the bidirectional relationship

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    It is well-established that high adherence to HAART is a major determinant of virological and immunological success. Furthermore, psycho-social research has identified a wide range of adherence factors. Our objective was to assess the bi-directional relationship between adherence and response to treatment among patients enrolled in the ANRS CO8 APROCOCOPILOTE study. An econometric approach was implemented through a bivariate twoequation simultaneous system, studying the factors associated with both adherence and undetectability of HIV plasma viral load. Our results highlight that good biological results induced by adherence reinforce continued adherence. This strengthens the argument that patients who do not experience rapid improvements in their immunological and clinical statuses after HAART initiation should be prioritized when developing adherence support interventions. Furthermore, it rules out the hypothesis that HAART leads to "false reassurance" among HIV infected patients.Adherence ; HIV ; relationship between adherence and effectiveness ; simultaneous equations ; GEE

    Improved Performance and Stability of the Knockoff Filter and an Approach to Mixed Effects Modeling of Sequentially Randomized Trials

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    The knockoff filter is a variable selection technique for linear regression with finite-sample control of the regression false discovery rate (FDR). The regression FDR is the expected proportion of selected variables which, in fact, have no effect in the regression model. The knockoff filter constructs a set of synthetic variables which are known to be irrelevant to the regression and, by serving as negative controls, help identify relevant variables. The first two thirds of this thesis describe tradeoffs between power and collinearity due to tuning choices in the knockoff filter and provide a stabilization method to reduce variance and improve replicability of the selected variable set using the knockoff filter. The final third of this thesis develops an approach for mixed modeling and estimation for sequential multiple assignment randomized trials (SMARTs). SMARTs are an important data collection tool for informing the construction of dynamic treatment regimens (DTRs), which use cumulative patient information to recommend specific treatments during the course of an intervention. A common primary aim in a SMART is the marginal mean comparison between two or more of the DTRs embedded in the trial, and the mixed modeling approach is developed for these primary aim comparisons based on a continuous, longitudinal outcome. The method is illustrated using data from a SMART in autism research.PHDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163099/1/luers_1.pd

    Driver helper dispatching problems: Three essays

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    The driver helper dispatching problems (DHDPs) have received scant research attention in past literature. In this three essay format dissertation, we proposed two ideas: 1) minimizing of the total cost as the new objective function to replace minimizing the total distance cost that is mostly used in past traveling salesman problem (TSP) and vehicle routing problem (VRP) algorithms and 2) dispatching vehicle either with a helper or not as part of the routing decision. The first study shows that simply separating a single with-helper route into two different types of sub-routes can significantly reduce total costs. It also proposes a new dependent driver helper (DDH) model to boost the utilization rate of the helpers to higher levels. In the second study, a new hybrid driver helper (HDH) model is proposed to solve DHDPs. The proposed HDH model provides the flexibility to relax the constraints that a helper can only work at one predetermined location in current-practice independent driver helper (IDH) model and that a helper always travels with the vehicle in the current-practice DDH model. We conducted a series of full-factorial experiments to prove that the proposed HDH model performs better than both two current solutions in terms of savings in both cost and time. The last study proposes a mathematical model to solve the VRPTW version of DHDPs and conducts a series of full factorial computational experiments. The results show that the proposed model can achieve more cost savings while reducing a similar level of dispatched vehicles as the current-practice DDH solution. All these three studies also investigate the conditions under which the proposed models would work most, or least, effectively
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