Data for the period 2015-04 to 2015-09 was analyzed in order to examine the possible relationship between teat wash failure and the result on milk yield for dairy cows. Data provided by Sveriges Lantrbruksuniversitet over 49 093 specific milking events were used. Two linear mixed-effects models and one basic OLS-model were estimated. In order to perform the analysis a lot of data manipulation also had to be performed. The data analysis was divided into to two parts. First the variable of interest (teatwash) was examined by constructing two versions of the different models; an unrestricted- and a restricted version were teatwash had been excluded. Because of the large sample and linear mixed-effect models an out-of-sample forecasting method was used as the primary evaluation criteria. The prediction errors were evaluated on the basis of root mean squared error (RMSE) and mean squared error (MSE). The difference between the unrestricted- and restricted models was very small and no indication of a relationship between teat wash failure and milk yield was found. The second part involved the comparison of prediction errors between the two mixed-effect models and the OLS-model. Surprisingly, the basic OLS-model resulted in the lowest prediction error although obvious breach of assumptions.

Genome structure can be classified into three categories: primary structure, secondary structure and tertiary structure, and they are all important for gene transcription regulation. In this research, we utilize the structural information to characterize the correlations and interactions among genes, and involve such information into the Linear Mixed-Effects (LME) model to improve the accuracy of gene expression prediction. In particular, we use chromatin features as predictors and each gene is an observation. Before model training and testing, genes are grouped according to the genome structural information. We use four gene grouping methods: 1) grouping genes according to sliding windows on primary structure; 2) grouping anchor genes in chromatin loop structure; 3) grouping genes in the CTCF-anchored domain; and 4) grouping genes in the chromatin domains obtained from Hi-C experiments. We compare the prediction accuracy between LME model and linear regression model. If all chromatin feature predictors are included into the models, based on the primary structure only (Method 1), the LME models improve prediction accuracy by up to 1%. Based on the tertiary structure only (Methods 2-4), for the genes that can be grouped according the tertiary interaction data, LME models improve prediction accuracy by up to 2.1%. For individual chromatin feature predictors, the LME models improve from 2% to 26 %, in which improvement is more significant for chromatin features that have lower original predictive ability. For future research we propose a model that combines the primary and tertiary structure to infer the correlations among genes to further improve the prediction.

Det förefaller tillfredsställande att minska sin osäkerhet. Med tanke på hur generell osäkerhet är som psykologiskt fenomen och hur viktig känslan av tillfredsställelse är som grund för beteende förtjänar sambandet att utredas närmare. Det finns idag ingen tydlig kvantifiering av psykologisk osäkerhet och huruvida grad av minskad osäkerhet predicerar tillfredsställelse är oklart. I denna studie undersöktes sambandet genom ett datoriserat experiment, där deltagarna skattade sin osäkerhet på olika ords betydelser och sedan skattade sin överraskning och tillfredsställelse när de fått veta rätt svar. Experimentet genomfördes på 18 deltagare rekryterade via annonser på universitetet och relaterade hemsidor. I direkt motsats till hypotesen visade resultaten att ju högre den initiala osäkerheten var, desto lägre blev tillfredsställelsen av att eliminera den. Sambandet förklaras av att prestation hade stor betydelse för tillfredsställelse där rätta svar ledde till högre tillfredsställelse och felaktiga svar ledde till lägre tillfredsställelse. Osäkerhet hade inte någon effekt på tillfredsställelse när effekten av prestation kontrollerades för.  Deltagarna besvarade även ett personlighetstest som visade att grad av Neuroticism var relaterat till ett starkare negativt samband mellan tillfredsställelse och lösande av osäkerhet, kontrollerat för prestation. Våra resultat tyder på att upplevda krav på prestation kan överskugga tillfredsställelsen vid lösande av osäkerhet. Effekten av prestation på tillfredsställelse i relation till osäkerhet är inte tidigare utförligt undersökt och mer forskning kan ge ny information om inställningen till inlärning. / It appears satisfying to decrease ones uncertainty. Considering how general uncertainty is as a psychological phenomenon, and how important the sense of satisfaction is as a basis for behavior, this connection deserves to be further examined. As of today, there is no clear quantification of psychological uncertainty, and whether degree of decreased uncertainty predicts satisfaction is unclear. In this study, this connection was examined through a computerized experiment where participants estimated their uncertainty on the meaning of different words and then estimated their surprise and satisfaction when receiving the correct answer. The experiment was performed on 18 participants recruited with posters on campus and related internet sites. Contrary to the hypothesis, the results showed that the higher the initial uncertainty, the lower the satisfaction was when eliminating it. The connection is explained by the impact of performance on satisfaction, where correct answers lead to higher satisfaction and incorrect answers lead to lower satisfaction. Uncertainty had no effect on satisfaction when the effect of performance was accounted for. The participants also answered a personality questionnaire which showed that higher degrees of Neuroticism was related to a stronger negative connection between satisfaction and the resolution of uncertainty, when performance was accounted for. Our results suggest that perceived performance demands may overshadow the satisfaction received when resolving uncertainty. The effect of performance on satisfaction in relation to uncertainty has not been extensively examined and further studies may provide new information about the attitude towards learning.

With societies aging all around the world, the global burden of degenerative diseases is expected to increase exponentially. From the perspective drug development, degenerative diseases represent an especially challenging class. Clinical trials, in this context often termed disease progression studies, are long, costly, require many individuals, and have low success rates. Therefore, it is crucial to use informative study designs and to analyze efficiently the obtained trial data. The development of novel approaches intended towards facilitating both the design and the analysis of disease progression studies was the aim of this thesis. This aim was pursued in three stages (i) the characterization and extension of pharmacometric software, (ii) the development of new methodology around statistical power, and (iii) the demonstration of application benefits. The optimal design software PopED was extended to simplify the application of optimal design methodology when planning a disease progression study. The performance of non-linear mixed effect estimation algorithms for trial data analysis was evaluated in terms of bias, precision, robustness with respect to initial estimates, and runtime. A novel statistic allowing for explicit optimization of study design for statistical power was derived and found to perform superior to existing methods. Monte-Carlo power studies were accelerated through application of parametric power estimation, delivering full power versus sample size curves from a few hundred Monte-Carlo samples. Optimal design and an explicit optimization for statistical power were applied to the planning of a study in Alzheimer's disease, resulting in a 30% smaller study size when targeting 80% power. The analysis of ADAS-cog score data was improved through application of item response theory, yielding a more exact description of the assessment score, an increased statistical power and an enhanced insight in the assessment properties. In conclusion, this thesis presents novel pharmacometric methods that can help addressing the challenges of designing and planning disease progression studies.

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The present study provides an example of implementing the difference in differences (DD) estimator for a two-group, pretest-posttest design with K-12 educational intervention data. The goal is to explore the basis for causal inference via Rubin's potential outcomes framework. The DD method is introduced to educational researchers, as it is seldom implemented in educational research. DD analytic methods' mathematical formulae and assumptions are explored to understand the opportunity and the challenges of using the DD estimator for causal inference in educational research. For this example, the teacher intervention effect is estimated with multi-cohort student outcome data. First, the DD method is used to detect the average treatment effect (ATE) with linear regression as a baseline model. Second, the analysis is repeated using linear regression with cluster robust standard errors. Finally, a linear mixed effects analysis is provided with a random intercept model. Resulting standard errors, parameter estimates, and inferential statistics are compared among these three analyses to explore the best holistic analytic method for this context.

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The ongoing opioid crisis presents a significant public health challenge particularly for people who use opioids (PWUO). Naloxone is an opioid antagonist crucial to reducing opioid overdose mortality. Inconsistencies exist among PWUO in obtaining, carrying, discussing, and administering naloxone. Using sequential mixed methods, this study was aimed at investigating the use of implementation intentions on naloxone use among PWUO. Semi-structured interviews were conducted with 83 PWUO to gather individual experiences with using naloxone and contextual details regarding its use. An essentialist thematic analysis with inductive coding revealed valuable insights into where, for whom, and when naloxone is implemented. The analysis identified major themes such as caring for others' needs, knowledge gaps, reinforcement through overdose experiences, duality of overdose and compassion, and stigma. Minor themes related to syringe services program implementation and drug use were identified. Building on these qualitative findings a quantitative analysis determined the impact of implementation intentions on naloxone implementation. Participants were randomly assigned to develop implementation intentions or goal intentions for the use of naloxone. Follow-up surveys assessed changes in participants' intentions to obtain, carry, discuss, and administer naloxone and their actual implementation over a 6-month period. At the 3-month follow-up the experimental condition exhibited statistically significant positive intentions to obtain naloxone and engage in discussions about naloxone in social contexts of drug use. Changes in the magnitude of naloxone implementation were observed at the 3- and 6-month timepoints. Specifically, the self-reported discussion of naloxone showed noticeable changes in implementation frequency over time. This suggests that while implementation intentions may not have statistically significant effects on the use of naloxone it had some influence on the frequency of discussing naloxone prior to drug use. This work makes a valuable contribution to the existing literature because of its attempt to apply the Theory of Planned Behavior and implementation intentions in a novel way. Though the experimental hypothesis was not supported statistically significant observations were made for some behaviors at the 3-month follow-up. The pragmatic nature of the setting enhances the relevance of the findings and provides valuable insights for future interventions supporting PWUO. / Doctor of Philosophy / The ongoing crisis of opioid addiction poses a significant public health challenge particularly for individuals who use opioids. Naloxone is a medication that can reverse opioid overdoses and it plays a crucial role in saving lives. People who use opioids often face difficulties in accessing, carrying, discussing, and using naloxone consistently. This study was aimed at investigating the use of naloxone by employing qualitative and quantitative methods. We conducted interviews with 83 individuals who use opioids to explore their experiences and gather insights into naloxone use. These interviews provided valuable information about when, where, and for whom naloxone is used. Several important themes emerged including the significance of helping others, knowledge gaps, the influence of personal experiences, the conflict between the fear of overdose and caring for others, and the stigma associated with drug use. We investigated the impact of a specific approach called "implementation intentions" in improving naloxone use. Participants were randomly assigned to create specific plans or general goals for naloxone use. Through surveys conducted over a 6-month period we examined changes in participants' intentions and actions related to naloxone use. Although the specific approach did not yield significant improvements, we observed changes in how people discussed naloxone over time. This study contributes to the existing research by introducing innovative ideas to support positive behavioral changes among individuals who use opioids. The real-world setting in which the study took place enhances the applicability of the findings and offers valuable insights for future programs supporting individuals who use opioids.

My thesis examines nonlinearity between catch and effort. I use a meta-analysis of published literature and generalized linear mixed-effects models (GLMM) on both fixed and mobile gear fisheries of Atlantic Canada. The meta-analysis examines the proportionality of catch to effort using the slope of the reduced major axis (RMA) log-log regression, which accounts for “errors-in-variables”. The GLMMs explored proportionality while accounting for variation among fishing vessels. Both analyses found evidence for disproportionality between catch and effort. Catch that increases disproportionally to effort could result from either facilitation or recruitment of effort into the fishery. Catch increases that are less than proportional are expected from competitive interactions among fishers or gear saturation. The GLMM also revealed that the level of aggregation (by set, trip, monthly, or annually) can affect the apparent proportionality between catch and effort. In general, catch and effort should not be considered to be proportional. / May 2016