Modeling multilevel sleep transitional data via Poisson log-linear multilevel models

This paper proposes Poisson log-linear multilevel models to investigate population variability in sleep state transition rates. We specifically propose a Bayesian Poisson regression model that is more flexible, scalable to larger studies, and easily fit than other attempts in the literature. We further use hierarchical random effects to account for pairings of individuals and repeated measures within those individuals, as comparing diseased to non-diseased subjects while minimizing bias is of epidemiologic importance. We estimate essentially non-parametric piecewise constant hazards and smooth them, and allow for time varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming piecewise constant hazards. This relationship allows us to synthesize two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed.

MODIFIED TEST STATISTICS BY INTER-VOXEL VARIANCE SHRINKAGE WITH AN APPLICATION TO fMRI

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique which is commonly used to quantify changes in blood oxygenation and flow coupled to neuronal activation. One of the primary goals of fMRI studies is to identify localized brain regions where neuronal activation levels vary between groups. Single voxel t-tests have been commonly used to determine whether activation related to the protocol differs across groups. Due to the generally limited number of subjects within each study, accurate estimation of variance at each voxel is difficult. Thus, combining information across voxels in the statistical analysis of fMRI data is desirable in order to improve efficiency. Here we construct a hierarchical model and apply an Empirical Bayes framework on the analysis of group fMRI data, employing techniques used in high throughput genomic studies. The key idea is to shrink residual variances by combining information across voxels, and subsequently to construct an improved test statistic in lieu of the classical t-statistic. This hierarchical model results in a shrinkage of voxel-wise residual sample variances towards a common value. The shrunken estimator for voxelspecific variance components on the group analyses outperforms the classical residual error estimator in terms of mean squared error. Moreover, the shrunken test-statistic decreases false positive rate when testing differences in brain contrast maps across a wide range of simulation studies. This methodology was also applied to experimental data regarding a cognitive activation task.

COVARIATE-ADJUSTED NONPARAMETRIC ANALYSIS OF MAGNETIC RESONANCE IMAGES USING MARKOV CHAIN MONTE CARLO

Permutation tests are useful for drawing inferences from imaging data because of their flexibility and ability to capture features of the brain that are difficult to capture parametrically. However, most implementations of permutation tests ignore important confounding covariates. To employ covariate control in a nonparametric setting we have developed a Markov chain Monte Carlo (MCMC) algorithm for conditional permutation testing using propensity scores. We present the first use of this methodology for imaging data. Our MCMC algorithm is an extension of algorithms developed to approximate exact conditional probabilities in contingency tables, logit, and log-linear models. An application of our non-parametric method to remove potential bias due to the observed covariates is presented.

Mapping quantitative trait loci for osteochondrosis in fetlock and hock joints and palmar/plantar osseus fragments in fetlock joints of South German Coldblood horses

The aim of this study was to identify quantitative trait loci (QTL) for osteochondrosis (OC) and palmar/plantar osseous fragments (POF) in fetlock joints in a whole-genome scan of 219 South German Coldblood horses. Symptoms of OC and POF were checked by radiography in 117 South German Coldblood horses at a mean age of 17 months. The radiographic examination comprised the fetlock and hock joints of all limbs. The genome scan included 157 polymorphic microsatellite markers. All microsatellite markers were equally spaced over the 31 autosomes and the X chromosome, with an average distance of 17.7 cM and a mean polymorphism information content (PIC) of 63%. Sixteen chromosomes harbouring putative QTL regions were further investigated by genotyping the animals with 93 additional markers. QTL that had chromosome-wide significance by non-parametric Z-means and LOD scores were found on 10 chromosomes. This included seven QTL for fetlock OC and one QTL on ECA18 associated with hock OC and fetlock OC. Significant QTL for POF in fetlock joints were located on equine chromosomes 1, 4, 8, 12 and 18. This genome scan is an important step towards the identification of genes responsible for OC in horses.

ECG-triggered muscular counterpulsation for treatment of low cardiac output

BACKGROUND: Skeletal muscular counterpulsation (MCP) has been used as a new noninvasive technique for treatment of low cardiac output. The MCP method is based on ECG-triggered skeletal muscle stimulation. The purpose of the present study was to evaluate acute hemodynamic changes induced by MCP in the experimental animal. METHODS: Eight anaesthetized pigs (43+/-4 kg) were studied at rest and after IV â-blockade (10 mg propranolol) before and after MCP. Muscular counterpulsation was performed on both thighs using trains (75 ms duration) of multiple biphasic electrical impulses with a width of 1 ms and a frequency of 200 Hz at low (10 V) and high (30 V) amplitude. ECG-triggering was used to synchronize stimulation to a given time point. LV pressure-volume relations were determined using the conductance catheter. After baseline measurements, MCP was carried out for 10 minutes at low and high stimulation amplitude. The optimal time point for MCP was determined from LV pressure-volume loops using different stimulation time points during systole and diastole. Best results were observed during end-systole and, therefore, this time point was used for stimulation. RESULTS: Under control conditions, MCP was associated with a significant decrease in pulmonary vascular resistance (-18%), a decrease in systemic vascular resistance (-11%) and stroke work index (-4%), whereas cardiac index (+2%) and ejection fraction (+6%) increased slightly. Pressure-volume loops showed a leftward shift with a decrease in end-systolic volume. After â-blockade, cardiac function decreased (HR, MAP, EF, dP/dt max), but it improved with skeletal muscle stimulation (HR +10% and CI +17%, EF +5%). There was a significant decrease in pulmonary (-19%) and systemic vascular resistance (-29%). CONCLUSIONS: In the animal model, ECG-triggered skeletal muscular counterpulsation is associated with a significant improvement in cardiac function at baseline and after IV â-blockade. Thus, MCP represents a new, non-invasive technique which improves cardiac function by diastolic compression of the peripheral arteries and veins, with a decrease in systemic vascular resistance and increase in cardiac output.

Relation of white matter anisotropy to visual memory in 17 healthy subjects

We performed a Rey visual design learning test (RVDLT) in 17 subjects and measured intervoxel coherence (IC) by DTI as an indication of connectivity to investigate if visual memory performance would depend on white matter structure in healthy persons. IC considers the orientation of the adjacent voxels and has a better signal-to-noise ratio than the commonly used fractional anisotropy index. Voxel-based t-test analysis of the IC values was used to identify neighboring voxel clusters with significant differences between 7 low and 10 high test performers. We detected 9 circumscribed significant clusters (p< .01) with lower IC values in low performers than in high performers, with centers of gravity located in left and right superior temporal region, corpus callosum, left superior longitudinal fascicle, and left optic radiation. Using non-parametric correlation analysis, IC and memory performance were significantly correlated in each of the 9 clusters (r< .61 to r< .81; df=15, p< .01 to p< .0001). The findings provide in vivo evidence for the contribution of white matter structure to visual memory in healthy people.

Truncated recombinant human SP-D attenuates emphysema and type II cell changes in SP-D deficient mice

BACKGROUND: Surfactant protein D (SP-D) deficient mice develop emphysema-like pathology associated with focal accumulations of foamy alveolar macrophages, an excess of surfactant phospholipids in the alveolar space and both hypertrophy and hyperplasia of alveolar type II cells. These findings are associated with a chronic inflammatory state. Treatment of SP-D deficient mice with a truncated recombinant fragment of human SP-D (rfhSP-D) has been shown to decrease the lipidosis and alveolar macrophage accumulation as well as production of proinflammatory chemokines. The aim of this study was to investigate if rfhSP-D treatment reduces the structural abnormalities in parenchymal architecture and type II cells characteristic of SP-D deficiency. METHODS: SP-D knock-out mice, aged 3 weeks, 6 weeks and 9 weeks were treated with rfhSP-D for 9, 6 and 3 weeks, respectively. All mice were sacrificed at age 12 weeks and compared to both PBS treated SP-D deficient and wild-type groups. Lung structure was quantified by design-based stereology at the light and electron microscopic level. Emphasis was put on quantification of emphysema, type II cell changes and intracellular surfactant. Data were analysed with two sided non-parametric Mann-Whitney U-test. MAIN RESULTS: After 3 weeks of treatment, alveolar number was higher and mean alveolar size was smaller compared to saline-treated SP-D knock-out controls. There was no significant difference concerning these indices of pulmonary emphysema within rfhSP-D treated groups. Type II cell number and size were smaller as a consequence of treatment. The total volume of lamellar bodies per type II cell and per lung was smaller after 6 weeks of treatment. CONCLUSION: Treatment of SP-D deficient mice with rfhSP-D leads to a reduction in the degree of emphysema and a correction of type II cell hyperplasia and hypertrophy. This supports the concept that rfhSP-D might become a therapeutic option in diseases that are characterized by decreased SP-D levels in the lung.

Spatial and temporal analysis of land cover and landscape structure change in Zagros Forests

The Zagros oak forests in Western Iran are critically important to the sustainability of the region. These forests have undergone dramatic declines in recent decades. We evaluated the utility of the non-parametric Random Forest classification algorithm for land cover classification of Zagros landscapes, and selected the best spatial and spectral predictive variables. The algorithm resulted in high overall classification accuracies (>85%) and also equivalent classification accuracies for the datasets from the three different sensors. We evaluated the associations between trends in forest area and structure with trends in socioeconomic and climatic conditions, to identify the most likely driving forces creating deforestation and landscape structure change. We used available socioeconomic (urban and rural population, and rural income), and climatic (mean annual rainfall and mean annual temperature) data for two provinces in northern Zagros. The most correlated driving force of forest area loss was urban population, and climatic variables to a lesser extent. Landscape structure changes were more closely associated with rural population. We examined the effects of scale changes on the results from spatial pattern analysis. We assessed the impacts of eight years of protection in a protected area in northern Zagros at two different scales (both grain and extent). The effects of protection on the amount and structure of forests was scale dependent. We evaluated the nature and magnitude of changes in forest area and structure over the entire Zagros region from 1972 to 2009. We divided the Zagros region in 167 Landscape Units and developed two measures— Deforestation Sensitivity (DS) and Connectivity Sensitivity (CS) — for each landscape unit as the percent of the time steps that forest area and ECA experienced a decrease of greater than 10% in either measure. A considerable loss in forest area and connectivity was detected, but no sudden (nonlinear) changes were detected at the spatial and temporal scale of the study. Connectivity loss occurred more rapidly than forest loss due to the loss of connecting patches. More connectivity was lost in southern Zagros due to climatic differences and different forms of traditional land use.

EVALUATING INFRASTRUCTURE PERFORMANCE TO ASSIST FACILITY MANAGEMENT TOWARD SUSTAINABLE SYSTEMS

Civil infrastructure provides essential services for the development of both society and economy. It is very important to manage systems efficiently to ensure sound performance. However, there are challenges in information extraction from available data, which also necessitates the establishment of methodologies and frameworks to assist stakeholders in the decision making process. This research proposes methodologies to evaluate systems performance by maximizing the use of available information, in an effort to build and maintain sustainable systems. Under the guidance of problem formulation from a holistic view proposed by Mukherjee and Muga, this research specifically investigates problem solving methods that measure and analyze metrics to support decision making. Failures are inevitable in system management. A methodology is developed to describe arrival pattern of failures in order to assist engineers in failure rescues and budget prioritization especially when funding is limited. It reveals that blockage arrivals are not totally random. Smaller meaningful subsets show good random behavior. Additional overtime failure rate is analyzed by applying existing reliability models and non-parametric approaches. A scheme is further proposed to depict rates over the lifetime of a given facility system. Further analysis of sub-data sets is also performed with the discussion of context reduction. Infrastructure condition is another important indicator of systems performance. The challenges in predicting facility condition are the transition probability estimates and model sensitivity analysis. Methods are proposed to estimate transition probabilities by investigating long term behavior of the model and the relationship between transition rates and probabilities. To integrate heterogeneities, model sensitivity is performed for the application of non-homogeneous Markov chains model. Scenarios are investigated by assuming transition probabilities follow a Weibull regressed function and fall within an interval estimate. For each scenario, multiple cases are simulated using a Monte Carlo simulation. Results show that variations on the outputs are sensitive to the probability regression. While for the interval estimate, outputs have similar variations to the inputs. Life cycle cost analysis and life cycle assessment of a sewer system are performed comparing three different pipe types, which are reinforced concrete pipe (RCP) and non-reinforced concrete pipe (NRCP), and vitrified clay pipe (VCP). Life cycle cost analysis is performed for material extraction, construction and rehabilitation phases. In the rehabilitation phase, Markov chains model is applied in the support of rehabilitation strategy. In the life cycle assessment, the Economic Input-Output Life Cycle Assessment (EIO-LCA) tools are used in estimating environmental emissions for all three phases. Emissions are then compared quantitatively among alternatives to support decision making.

Geospatial analysis of western juniper (Juniperus occidentalis) encroachment utilizing remotely sensed data

Utilizing remote sensing methods to assess landscape-scale ecological change are rapidly becoming a dominant force in the natural sciences. Powerful and robust non-parametric statistical methods are also actively being developed to compliment the unique characteristics of remotely sensed data. The focus of this research is to utilize these powerful, robust remote sensing and statistical approaches to shed light on woody plant encroachment into native grasslands--a troubling ecological phenomenon occurring throughout the world. Specifically, this research investigates western juniper encroachment within the sage-steppe ecosystem of the western USA. Western juniper trees are native to the intermountain west and are ecologically important by means of providing structural diversity and habitat for many species. However, after nearly 150 years of post-European settlement changes to this threatened ecosystem, natural ecological processes such as fire regimes no longer limit the range of western juniper to rocky refugia and other areas protected from short fire return intervals that are historically common to the region. Consequently, sage-steppe communities with high juniper densities exhibit negative impacts, such as reduced structural diversity, degraded wildlife habitat and ultimately the loss of biodiversity. Much of today's sage-steppe ecosystem is transitioning to juniper woodlands. Additionally, the majority of western juniper woodlands have not reached their full potential in both range and density. The first section of this research investigates the biophysical drivers responsible for juniper expansion patterns observed in the sage-steppe ecosystem. The second section is a comprehensive accuracy assessment of classification methods used to identify juniper tree cover from multispectral 1 m spatial resolution aerial imagery.

Reflectance Transfer for Material Editing and Relighting

We present a new approach to diffuse reflectance estimation for dynamic scenes. Non-parametric image statistics are used to transfer reflectance properties from a static example set to a dynamic image sequence. The approach allows diffuse reflectance estimation for surface materials with inhomogeneous appearance, such as those which commonly occur with patterned or textured clothing. Material editing is also possible by transferring edited reflectance properties. Material reflectance properties are initially estimated from static images of the subject under multiple directional illuminations using photometric stereo. The estimated reflectance together with the corresponding image under uniform ambient illumination form a prior set of reference material observations. Material reflectance properties are then estimated for video sequences of a moving person captured under uniform ambient illumination by matching the observed local image statistics to the reference observations. Results demonstrate that the transfer of reflectance properties enables estimation of the dynamic surface normals and subsequent relighting combined with material editing. This approach overcomes limitations of previous work on material transfer and relighting of dynamic scenes which was limited to surfaces with regions of homogeneous reflectance. We evaluate our approach for relighting 3D model sequences reconstructed from multiple view video. Comparison to previous model relighting demonstrates improved reproduction of detailed texture and shape dynamics.

Virtual implant planning in the edentulous maxilla: criteria for decision making of prosthesis design

OBJECTIVES To evaluate prosthetic parameters in the edentulous anterior maxilla for decision making between fixed and removable implant prosthesis using virtual planning software. MATERIAL AND METHODS CT- or DVT-scans of 43 patients (mean age 62 ± 8 years) with an edentulous maxilla were analyzed with the NobelGuide software. Implants (≥3.5 mm diameter, ≥10 mm length) were virtually placed in the optimal three-dimensional prosthetic position of all maxillary front teeth. Anatomical and prosthetic landmarks, including the cervical crown point (C-Point), the acrylic flange border (F-Point), and the implant-platform buccal-end (I-Point) were defined in each middle section to determine four measuring parameters: (1) acrylic flange height (FLHeight), (2) mucosal coverage (MucCov), (3) crown-Implant distance (CID) and (4) buccal prosthesis profile (ProsthProfile). Based on these parameters, all patients were assigned to one of three classes: (A) MucCov ≤ 0 mm and ProsthProfile≥45(0) allowing for fixed prosthesis, (B) MucCov = 0-5 mm and/or ProsthProfile = 30(0) -45(0) probably allowing for fixed prosthesis, and (C) MucCov ≥ 5 mm and/or ProsthProfile ≤ 30(0) where removable prosthesis is favorable. Statistical analyses included descriptive methods and non-parametric tests. RESULTS Mean values were for FLHeight 10.0 mm, MucCov 5.6 mm, CID 7.4 mm, and ProsthProfile 39.1(0) . Seventy percent of patients fulfilled class C criteria (removable), 21% class B (probably fixed), and 2% class A (fixed), while in 7% (three patients) bone volume was insufficient for implant planning. CONCLUSIONS The proposed classification and virtual planning procedure simplify the decision-making process regarding type of prosthesis and increase predictability of esthetic treatment outcomes. It was demonstrated that in the majority of cases, the space between the prosthetic crown and implant platform had to be filled with prosthetic materials.

In vitro precision of fit of computer-aided designed and computer-aided manufactured titanium screw-retained fixed dental prostheses before and after ceramic veneering

OBJECTIVE To compare the precision of fit of full-arch implant-supported screw-retained computer-aided designed and computer-aided manufactured (CAD/CAM) titanium-fixed dental prostheses (FDP) before and after veneering. The null-hypothesis was that there is no difference in vertical microgap values between pure titanium frameworks and FDPs after porcelain firing. MATERIALS AND METHODS Five CAD/CAM titanium grade IV frameworks for a screw-retained 10-unit implant-supported reconstruction on six implants (FDI tooth positions 15, 13, 11, 21, 23, 25) were fabricated after digitizing the implant platforms and the cuspid-supporting framework resin pattern with a laser scanner (CARES(®) Scan CS2; Institut Straumann AG, Basel, Switzerland). A bonder, an opaquer, three layers of porcelain, and one layer of glaze were applied (Vita Titankeramik) and fired according to the manufacturer's preheating and fire cycle instructions at 400-800°C. The one-screw test (implant 25 screw-retained) was applied before and after veneering of the FDPs to assess the vertical microgap between implant and framework platform with a scanning electron microscope. The mean microgap was calculated from interproximal and buccal values. Statistical comparison was performed with non-parametric tests. RESULTS All vertical microgaps were clinically acceptable with values <90 μm. No statistically significant pairwise difference (P = 0.98) was observed between the relative effects of vertical microgap of unveneered (median 19 μm; 95% CI 13-35 μm) and veneered FDPs (20 μm; 13-31 μm), providing support for the null-hypothesis. Analysis within the groups showed significantly different values between the five implants of the FDPs before (P = 0.044) and after veneering (P = 0.020), while a monotonous trend of increasing values from implant 23 (closest position to screw-retained implant 25) to 15 (most distant implant) could not be observed (P = 0.169, P = 0.270). CONCLUSIONS Full-arch CAD/CAM titanium screw-retained frameworks have a high accuracy. Porcelain firing procedure had no impact on the precision of fit of the final FDPs. All implant microgap measurements of each FDP showed clinically acceptable vertical misfit values before and after veneering. Thus, the results do not only show accurate performance of the milling and firing but show also a reproducible scanning and designing process.

Gonadotrophin stimulation for in vitro fertilization significantly alters the hormone milieu in follicular fluid: a comparative study between natural cycle IVF and conventional IVF.

STUDY QUESTION Is the steroid hormone profile in follicular fluid (FF) at the time of oocyte retrieval different in naturally matured follicles, as in natural cycle IVF (NC-IVF), compared with follicles stimulated with conventional gonadotrophin stimulated IVF (cIVF)? SUMMARY ANSWER Anti-Mullerian hormone (AMH), testosterone (T) and estradiol (E2) concentrations are ∼3-fold higher, androstenedione (A2) is ∼1.5-fold higher and luteinizing hormone (LH) is ∼14-fold higher in NC-IVF than in cIVF follicles, suggesting an alteration of the follicular metabolism in conventional gonadotrophin stimulated IVF. WHAT IS KNOWN ALREADY In conventional IVF, the implantation rate of unselected embryos appears to be lower than in NC-IVF, which is possibly due to negative effects of the stimulation regimen on follicular metabolism. In NC-IVF, the intrafollicular concentration of AMH has been shown to be positively correlated with the oocyte fertilization and implantation rates. Furthermore, androgen treatment seems to improve the ovarian response in low responders. STUDY DESIGN, SIZE, DURATION This cross-sectional study involving 36 NC-IVF and 40 cIVF cycles was performed from 2011 to 2013. Within this population, 13 women each underwent 1 NC-IVF and 1 cIVF cycle. cIVF was performed by controlled ovarian stimulation with HMG and GnRH antagonists. PARTICIPANTS/MATERIALS, SETTING, METHODS Follicular fluid was collected from the leading follicles. AMH, T, A2, dehydroepiandrosterone (DHEA), E2, FSH, LH and progesterone (P) were determined by immunoassays in 76 women. Aromatase activity in follicular fluid cells was analysed by a tritiated water release assay in 33 different women. For statistical analysis, the non-parametric Mann-Whitney U or Wilcoxon tests were used. MAIN RESULTS AND ROLE OF CHANCE In follicular fluid from NC-IVF and from cIVF, median levels were 32.8 and 10.7 pmol/l for AMH (P < 0.0001), 47.2 and 18.8 µmol/l for T (P < 0.0001), 290 and 206 nmol/l for A2 (P = 0.0035), 6.7 and 5.6 pg/ml for DHEA (n.s.), 3292 and 1225 nmol/l for E2 (P < 0.0001), 4.9 and 7.2 mU/ml for FSH (P < 0.05), 14.4 and 0.9 mU/ml for LH (P < 0.0001) and 62 940 and 54 710 nmol/l for P (n.s.), respectively. Significant differences in follicular fluid concentrations for AMH, E2 and LH were also found in the 13 patients who underwent both NC-IVF and cIVF when they were analysed separately in pairs. Hormone analysis in serum excluded any relevant impact of AMH, T, A2, and E2 serum concentration on the follicular fluid hormone concentrations. Median serum concentrations were 29.4 and 0.9 mU/ml for LH (P < 0.0001) and 2.7 and 23.5 nmol/l for P (P < 0.0001) after NC-IVF and c-IVF, respectively. Positive correlations were seen for FF-AMH with FF-T (r = 0.35, P = 0.0002), FF-T with FF-LH (r = 0.48, P < 0.0001) and FF-E2 with FF-T (r = 0.75, P < 0.0001). The analysis of aromatase activity was not different in NC-IVF and cIVF follicular cells. LIMITATION, REASONS FOR CAUTION Any association between the hormone concentrations and the implantation potential of the oocytes could not be investigated as the oocytes in cIVF were not treated individually in the IVF laboratory. Since both c-IVF and NC-IVF follicles were stimulated by hCG before retrieval, the endocrine milieu in the natural cycle does not represent the pure physiological situation. WIDER IMPLICATIONS OF THE FINDINGS The endocrine follicular milieu and the concentration of putative markers of oocyte quality, such as AMH, are significantly different in gonadotrophin-stimulated conventional IVF compared with natural cycle IVF. This could be a cause for the suggested lower oocyte quality in cIVF compared with naturally matured oocytes. The reasons for the reduced AMH concentration might be low serum and follicular fluid LH concentrations due to LH suppression, leading initially to low follicular androgen concentrations and then to low follicular AMH production. STUDY FUNDING/COMPETING INTERESTS Funding for this study was obtained from public universities (for salaries) and private industry (for consumables). Additionally, the study was supported by an unrestricted grant from MSD Merck Sharp & Dohme GmbH and IBSA Institut Biochimique SA. The authors are clinically involved in low-dose monofollicular stimulation and IVF therapies, using gonadotrophins from all gonadotrophin distributors on the Swiss market, including Institut Biochimique SA and MSD Merck Sharp & Dohme GmbH. Otherwise, the authors have no competing interests. TRIAL REGISTRATION NUMBER Not applicable.

A BAYESIAN APPROACH TO DOSE-RESPONSE ASSESSMENT AND DRUG-DRUG INTERACTION ANALYSIS: APPLICATION TO IN VITRO STUDIES

The considerable search for synergistic agents in cancer research is motivated by the therapeutic benefits achieved by combining anti-cancer agents. Synergistic agents make it possible to reduce dosage while maintaining or enhancing a desired effect. Other favorable outcomes of synergistic agents include reduction in toxicity and minimizing or delaying drug resistance. Dose-response assessment and drug-drug interaction analysis play an important part in the drug discovery process, however analysis are often poorly done. This dissertation is an effort to notably improve dose-response assessment and drug-drug interaction analysis. The most commonly used method in published analysis is the Median-Effect Principle/Combination Index method (Chou and Talalay, 1984). The Median-Effect Principle/Combination Index method leads to inefficiency by ignoring important sources of variation inherent in dose-response data and discarding data points that do not fit the Median-Effect Principle. Previous work has shown that the conventional method yields a high rate of false positives (Boik, Boik, Newman, 2008; Hennessey, Rosner, Bast, Chen, 2010) and, in some cases, low power to detect synergy. There is a great need for improving the current methodology. We developed a Bayesian framework for dose-response modeling and drug-drug interaction analysis. First, we developed a hierarchical meta-regression dose-response model that accounts for various sources of variation and uncertainty and allows one to incorporate knowledge from prior studies into the current analysis, thus offering a more efficient and reliable inference. Second, in the case that parametric dose-response models do not fit the data, we developed a practical and flexible nonparametric regression method for meta-analysis of independently repeated dose-response experiments. Third, and lastly, we developed a method, based on Loewe additivity that allows one to quantitatively assess interaction between two agents combined at a fixed dose ratio. The proposed method makes a comprehensive and honest account of uncertainty within drug interaction assessment. Extensive simulation studies show that the novel methodology improves the screening process of effective/synergistic agents and reduces the incidence of type I error. We consider an ovarian cancer cell line study that investigates the combined effect of DNA methylation inhibitors and histone deacetylation inhibitors in human ovarian cancer cell lines. The hypothesis is that the combination of DNA methylation inhibitors and histone deacetylation inhibitors will enhance antiproliferative activity in human ovarian cancer cell lines compared to treatment with each inhibitor alone. By applying the proposed Bayesian methodology, in vitro synergy was declared for DNA methylation inhibitor, 5-AZA-2'-deoxycytidine combined with one histone deacetylation inhibitor, suberoylanilide hydroxamic acid or trichostatin A in the cell lines HEY and SKOV3. This suggests potential new epigenetic therapies in cell growth inhibition of ovarian cancer cells.