The objectives of this dissertation were to evaluate genetic and environmental relationships between lamb and ewe traits including body weight, fleece weight and quality, prolificacy, body condition, ewe stayability and lamb survival. Average heritability estimates for lamb birth weight (BWT), weaning weight (WW), maternal weaning weight, yearling body weight, fleece weight, spinning count and staple length were 0.19, 0.09, 0.08, 0.35, 0.38, 0.25, and 0.31 respectively. Heritability estimates for adult traits averaged 0.43 for body weight (AW), 0.13 for body condition (AC), and 0.12 for number of lambs born per ewe lambing (NLB). Correlations between direct additive AW and direct additive and maternal lamb weights ranged from 0.21 to 0.96 (P < 0.05) and 0.29 to 0.53 (P < 0.05), respectively, with residual correlations ranging from 0.05 to 0.95. Correlations of lamb traits with adult body condition and NLB were generally not different from zero; genetic and residual correlations ranged from -0.52 to 0.69 and -.39 to 0.31, respectively. Ewe stayability was analyzed as overall stayability (STAYn|2) which indicated the presence or absence of a ewe at n yrs of age, given that she was present at 2 yrs of age, and marginal stayability (STAYn|1-n) recording the presences of a ewe at n yrs of age, given that she was in the flock the previous year. Additive variance in ewe stayability was only found in stayability at 5 and 6 yr of age (P < 0.05). Heritability estimates for STAY5|4 and STAY6|2 from multiple trait analyses with other traits averaged 0.08 and 0.10, respectively. Phenotypic correlations between STAY and all other traits were near zero, ranging from -0.04 to 0.03. The estimated correlations between additive effects on STAY5|4 and STAY6|2 and additive maternal effects on WW were positive (both 0.46; P < 0.05). Genetic correlations between STAY5|4 and WW, adult weight, and NLB were 0.06, 0.13 and -0.06 (P > 0.10), respectively. However, genetic correlations between STAY6|2 and WW, adult weight, and NLB were negative (-0.17, -0.32 (P < 0.05) and -0.03, respectively). Significant genetic variation was thus present in stayability, with nonzero genetic correlations present between STAY, maternal milk, WW, and adult weight. Survival analysis was performed using a proportional hazards model to measure the probability of lamb death before weaning. Lamb survival was recorded as the day of age at death. Records were censored if a live lamb was artificially removed from their litter before death. Fixed effects on survival included ewe age, litter size, sex, and linear and quadratic BWT. Average age of death was 13.7 d. Censoring of records before weaning occurred in 12.9% of the total lambs born. Risk ratios indicated lambs from yearlings and ewes older than 5 yr had the greater risk of death, as did triplet and quadruplet lambs. Linear and quadratic BWT effects on lamb survival were found (P < 0.05) and accounted for most of the litter size effects in large litters. The influence of informative censoring was considered by assuming that lambs censored by 3 d of age had died at the time of censoring. Heritability of lamb survival at 3 d of age (estimated using an animal model in MTDFREML) was near zero, ranging from 0.00 to 0.01. The lack of additive variance suggests that improvement in lamb survival should be made through changes in management practices. / Ph. D.

There has been confusion in choosing a proper survival model between two popular survival models of discrete and continuous survival analysis. This study aimed to provide empirical outcomes of two survival models in educational contexts and suggest a guideline for researchers who should adopt a suitable survival model. For the model specification, the study paid attention to three factors of time metrics, censoring proportions, and sample sizes. To arrive at comprehensive understanding of the three factors, the study investigated the separate and combined effect of these factors. Furthermore, to understand the interaction mechanism of those factors, this study examined the role of the factors to determine hazard rates which have been known to cause the discrepancies between discrete and continuous survival models. To provide empirical evidence from different combinations of the factors in the use of survival analysis, this study built a series of discrete and continuous survival models using secondary data and simulated data. In the first study, using empirical data from the National Longitudinal Survey of Youth 1997 (NLSY97), this study compared analyses results from the two models having different sizes of time metrics. In the second study, by having various specifications with combination of two other factors of censoring proportions and sample sizes, this study simulated datasets to build two models and compared the analysis results. The major finding of the study is that discrete models are recommended in the conditions of large units of time metrics, low censoring proportion, or small sample sizes. Particularly, discrete model produced better outcomes for conditions with low censoring proportion (20%) and small number (i.e., four) of large time metrics (i.e., year) regardless of sample sizes. Close examination of those conditions of time metrics, censoring proportion, and sample sizes showed that the conditions resulted into high hazards (i.e., 0.20). In conclusion, to determine a proper model, it is recommended to examine hazards of each of the time units with the specific factors of time metrics, censoring proportion and sample sizes. / Ph. D.

New agricultural technologies can improve productivity to meet the increased demand for food that places pressure on agricultural production systems in developing countries. Because technological innovation is one of major factors shaping agriculture in both developing and developed countries, it is important to identify factors that help or that hinder the adoption process. Adoption analysis can assist policy makers in making informed decisions about dissemination of technologies that are under consideration. It is also important to estimate the impact of a technology. This dissertation contains three essays that estimate factors affecting integrated pest management (IPM) adoption and the impact of IPM on sweet gourd farming in Bangladesh. The first essay estimates factors that affect the timing of IPM adoption in Bangladesh. It employs duration models, fully parametric and semiparametric, and (i) compares results from different estimation methods to provide the best model for the data, and (ii) identifies factors that affect the length of time before Bangladeshi farmers adopt an agricultural technology. The paper provides two conclusions: 1) even though the non-parametric estimate of the hazard function indicated a non-monotone model such as log-normal or log-logistic, no differences are found in the sign and significance of the estimated coefficients between the non-monotone and monotone models. 2) economic factors do not directly influence the adoption decision but rather factors related to information diffusion and farmer's non-economic characteristics such as age and education. Particularly, farmer's age and education, membership in an association, training, distance of the farmer's house from local and town markets, and farmer's perception about the use of IPM affect the length of time to adoption. Farm size is the only variable closely related to economic factors that is found to be significant and it decreases the length of time to adoption. The second paper measures Bangladeshi farmers' attitudes toward risk and ambiguity using experimental data. In different sessions, the experiment allows farmers to make decisions alone and communicate with peers in groups of 3 and 6 to see how social exchanges among peers affect attitudes toward uncertainty. Combining the measured attributes to household survey data, the paper investigates the factors affecting those attributes as well as the role of risk aversion and ambiguity aversion in technology choice by farmers who: face uncertainty alone, in a group of 3, or in a group of 6. It finds that Bangladeshi farmers in the sample are mostly risk and ambiguity averse. Their risk and ambiguity aversion, moreover, differ when they face the uncertain prospects alone from when they can communicate with other peer farmers before making decisions. In addition, farmer's demographic characteristics affect both risk and ambiguity aversion. Finally, findings suggest that the roles of risk and ambiguity aversion in technology adoption depend on which measure of uncertainty behavior is incorporated in the adoption model. While risk aversion increases the likelihood of technology adoption when farmers face uncertainty alone, only ambiguity aversion matters and it reduces the likelihood of technology adoption when farmers face uncertainty in groups of three. Neither risk aversion nor ambiguity aversion matter when farmers face uncertainty in groups of six. The third paper presents an impact assessment of integrated pest management on sweet gourd in Bangladesh. It employs an instrumental variable and marginal treatment effects approach to estimate the impact of IPM on yield and cost of sweet gourd in Bangladesh. The estimation methods consider both homogeneous and heterogeneous treatment effects. The paper finds that IPM adoption has a 7% - 34% yield advantage over traditional pest management practices. Results regarding the effect of IPM adoption on cost are mixed. IPM adoption alters production costs from -1.2% cost to +42%, depending on the estimation method employed. However, most of the cost changes are not statistically significant. Therefore, while we confidently argue that the IPM adoption provides a yield advantage over non-adoption, we do not find a robust effect regarding a cost advantage of adoption. / Ph. D.

No / SMEs create employment, wealth and a potential for future growth. In Palestine they can also mean survival and freedom. In Palestine they are not a choice but a necessity for sustainable development. But by their nature SMEs are vulnerable in a business environment characterized by uncertainty. To give the managers of SMEs in Palestine a realistic chance of success they need training to enable them to meet the challenge of running their enterprises effectively. Drawing on original research undertaken within Palestine this book explores how the challenge is being met (and considers how it might be even more successfully met) by enabling and empowering the owners and managers of these pioneering businesses.

Yes / Background The HOX genes are a family of homeodomain-containing transcription factors that determine cellular identity during development and which are dys-regulated in some cancers. In this study we examined the expression and oncogenic function of HOX genes in mesothelioma, a cancer arising from the pleura or peritoneum which is associated with exposure to asbestos. Methods We tested the sensitivity of the mesothelioma-derived lines MSTO-211H, NCI-H28, NCI-H2052, and NCI-H226 to HXR9, a peptide antagonist of HOX protein binding to its PBX co-factor. Apoptosis was measured using a FACS-based assay with Annexin, and HOX gene expression profiles were established using RT-QPCR on RNA extracted from cell lines and primary mesotheliomas. The in vivo efficacy of HXR9 was tested in a mouse MSTO-211H flank tumor xenograft model. Results We show that HOX genes are significantly dysregulated in malignant mesothelioma. Targeting HOX genes with HXR9 caused apoptotic cell death in all of the mesothelioma-derived cell lines, and prevented the growth of mesothelioma tumors in a mouse xenograft model. Furthermore, the sensitivity of these lines to HXR9 correlated with the relative expression of HOX genes that have either an oncogenic or tumor suppressive function in cancer. The analysis of HOX expression in primary mesothelioma tumors indicated that these cells could also be sensitive to the disruption of HOX activity by HXR9, and that the expression of HOXB4 is strongly associated with overall survival. Conclusion HOX genes are a potential therapeutic target in mesothelioma, and HOXB4 expression correlates with overall survival. / The authors gratefully acknowledge the support of the British Lung Foundation, grant number ICAPPG10-1. KJH acknowledges support from the ICR/RM NIHR Biomedical Research Centre.

Grass carp Ctenopharyngodon idella have been stocked throughout the world due to their utility as a biological control. In the United States, the species has been used to successfully control invasive, aquatic weeds such as hydrilla Hydrilla verticillata. Despite the large body of research surrounding the use of grass carp, few studies have demonstrated widely applicable methods for evaluating the success of weed control based on grass carp behavior and population dynamics. Classic methods of biological control using grass carp often rely on a single, large stocking of fish. Few of these studies have demonstrated success in achieving intermediate levels of weed control. Managers would be better equipped to make decisions regarding stocking and maintenance grass carp populations with better information about behavior, survival, and population structure. Improved decision making could result in reduced cost and increased effectiveness of stocking. In order to examine current knowledge gaps for management, I investigated the movements and habitat use of grass carp, post-stocking survival, age-specific survival rates, and population dynamics of grass carp in Lake Gaston, North Carolina and Virginia. I characterized relationships between grass carp behavior and environmental factors using radio-telemetry. The average rate of movement for grass carp in Lake Gaston was about 137 m/d. Rapid dispersal after stocking was followed by long periods of no movement. However, when time after stocking was held constant in models of behavior, fish moved about 200 m/d more in the second year after stocking than in the first year, and were found closer to shore. On average, grass carp were found about 40 m from shore in about 2.5-3.5 m of water, although mean depth of water at grass carp locations varied seasonally, being shallowest in summer and deepest in winter. Although depth of water at grass carp locations did not vary by stocking location, Grass carp were found closer to shorelines in the upper reservoir than in the lower reservoir. I found significant relationships between grass carp behavior and hydrological processes such as lake elevation and dam releases in the reservoir, as well as with other environmental factors such as water temperature, photoperiod, and weather conditions. The results of this study should be useful in better understanding how behavior can affect management decisions. Specifically, grass carp behavior appears to change with age and environmental conditions within large reservoir systems. Future research should focus more closely on the effects of large-scale flow dynamics on grass carp behavior. I estimated age-1 survival of grass carp from mark-recapture models designed for radio-tagged animals, and characterized relationships between age-1 survival and factors under the control of management, such as stocking locations and size at stocking. . According to the most-plausible model developed in this study, survival of age-1 grass carp in Lake Gaston varied throughout the year, and the probability of an individual grass carp surviving to the end of its first year (±SE) was 0.57(±0.10). According to the second-most-plausible model developed in this study, grass carp survival varied between stocking locations, and was twice as high in the upper reservoir (0.87±0.09) than in the lower reservoir (0.43±0.11). The differences in survival between stocking locations suggest that the cost-effectiveness of grass carp stocking could be improved by focusing stocking efforts in specific regions of Lake Gaston. Furthermore, none of the models developed in this study that incorporated the effects of size (length and weight) or condition factor accounted for a meaningful amount of the total model weights. These results suggest that costs of grass carp stocking could be reduced in Lake Gaston by using a smaller minimum size (352 mm, TL) than is commonly referred to in the literature (450 mm, TL). I used grass carp collected by bowfishers in Lake Gaston to characterize the age, growth, and survival of grass carp in the system. From these data, I characterized relationships between fish population dynamics and annual hydrilla coverage. Grass carp collected from Lake Gaston ranged in age 1-16 years. Growth of grass carp in Gaston was described by the von Bertalanffy growth function as Lt = 1297(1-e -0.1352 (t+1.52)). I estimated mortality from the von Bertalanffy growth parameters using methods based on growth, temperature, and age; and with each mortality estimate I estimated population size and standing biomass of grass carp. Use of age-specific mortality rates produced lower estimates of grass carp numbers and standing biomass in Lake Gaston than did the use of a single, instantaneous mortality rate for all ages. I determined that growth of grass carp slowed considerably after the fourth year and that slowed growth, in combination with changes in mortality, resulted in a decrease in the amount of hydrilla controlled by a given cohort after four years in Lake Gaston. This phenomenon resulted in an approximately linear relationship between the biomass of grass carp at year i and hectares of hydrilla at year i+3. Based on this relationship, I predicted that the biomass of grass carp necessary to reduce hydrilla coverage to the target level of 120 ha in Lake Gaston is about 91,184 kg (±38,146 kg) and that the current biomass of grass carp in Lake Gaston is about 108,073 kg (±3,609 kg). I conclude that grass carp biomass is at or near levels that should reduce hydrilla coverage to 120 ha between 2013 and 2018. This research provides an effective means for synthesis of information that is critical to understanding sterile, triploid grass carp populations when assumptions of other methods cannot be met. The results of this study should be of immediate utility to hydrilla management efforts in Lake Gaston and other systems. Furthermore, the age-specific mortality rates developed in this study should be useful as starting values for grass carp management in similar systems. / Master of Science

In clinical trials with failure-time primary outcomes, also known as "event-driven" designs, the statistical information is determined by total observed events. Examples of failure-time clinical trial endpoints include: time to death and time to disease progression. In trials with event-driven designs, the interim and final analyses are performed after a pre-specified number of events have been observed, based on a priori design considerations, rather than after observing patients for a pre-specified period of time. The timing of these analyses represent important milestones in the conduct of the study. In particular, if a trial requires review of interim analyses by a Data Monitoring Committee (DMC), convening the DMC members requires much advance planning and effort. In addition, advanced knowledge of when these milestones will occur can allow trial sponsors to make informed decisions regarding resources and financial planning. It is therefore of interest to predict when a pre-specified number of events will be observed based on accumulating data. Parametric and semi-parametric methods have been proposed for event prediction when data are right censored. In cases when the underlying failure time distribution is unknown or accumulated events are relatively sparse, these methods may not provide accurate or efficient prediction. We propose a method to predict the number of events that is a fully Bayesian non-parametric approach in modeling the survival probabilities that is more flexible and generalizes to interval censored data. We use a Gibbs sampler to sample from the posterior of the survival distribution to obtain point and interval estimates for the specified number of events. We compare the accuracy and precision of this approach to proposed parametric and semi-parametric methods under a variety of data generating mechanisms, beginning with right-censored data. We then extend the study to interval-censored data, comparing the methods under data generated from varying assessment intervals. Finally we consider the scenario in which we are blinded to treatment assignment, incorporating a Bayesian approach to determine the probability of membership to a particular treatment group. We demonstrate the proposed method offers greater flexibility and has the ability to match or outperform existing methods under multiple clinical trial scenarios.

No / Improved survival in patients with hematological malignancy (HM) admitted to the intensive care unit (ICU) has largely been reported in uncontrolled cohorts from single academic institutions. We compared hospital mortality between 147 patients with HM and 147 general medical admissions to five non-specialist ICUs. The proportion of patients surviving to hospital discharge was significantly worse in patients with HM (27% vs. 56%; p < 0.001). Six-month and 1-year survival in patients with HM was 21% and 18%, respectively. HM, greater age, mechanical ventilation (MV) and acute physiology and chronic health evaluation (APACHE) II score were independent predictors of poor outcome. For patients with HM, culture proven infection, age, MV and inotropes were negative predictors. Disease-specific factors including hematological diagnosis, neutropenia, remission status, prior stem cell transplant, time from diagnosis to admission and degree of prior treatment were not predictive. Overall survival of patients with HM was worse than that recently reported from specialist units.

This master thesis is aimed towards answering the question What is the contribution from a company’s sector with regards to its survival of a financial crisis? with the sub question Can we use survival analysis on financial data to answer this?. Thus survival analysis is used to answer our main question which is seldom used on financial data. This is interesting since it will study how well survival analysis can be used on financial data at the same time as it will evaluate if all companies experiences a financial crisis in the same way. The dataset consists of all companies traded on the Swedish stock market during 2008. The results show that the survival method is very suitable the data that is used. The sector a company operated in has a significant effect. However the power is to low too give any indication of specific differences between the different sectors. Further on it is found that the group of smallest companies had much better survival than larger companies.

Survival Analysis generally uses the median survival time as a common summary statistic. While the median possesses the desirable characteristic of being unbiased, there are times when it is not the best statistic to describe the data at hand. Royston and Parmar (2011) provide an argument that the restricted mean survival time should be the summary statistic used when the proportional hazards assumption is in doubt. Work in Restricted Means dates back to 1949 when J.O. Irwin developed a calculation for the standard error of the restricted mean using Greenwood’s formula. Since then the development of the restricted mean has been thorough in the literature, but its use in practical analyses is still limited. One area that is not well developed in the literature is the choice of the time point to which the mean is restricted. The aim of this dissertation is to develop a data driven method that allows the user to find a cut-point to use to restrict the mean. Three methods are developed. The first is a simple method that locates the time at which the maximum distance between two curves exists. The second is a method adapted from a Renyi-type test, typically used when proportional hazards assumptions are not met, where the Renyi statistics are plotted and piecewise regression model is fit. The join point of the two pieces is where the meant will be restricted. Third is a method that applies a nonlinear model fit to the hazard estimates at each event time, the model allows for the hazards between the two groups to be different up until a certain time, after which the groups hazards are the same. The time point where the two groups’ hazards become the same is the time to which the mean is restricted. The methods are evaluated using MSE and bias calculations, and bootstrap techniques to estimate the variance.