A worldwide survey of genome sequence variation provides insight into the evolutionary history of the honeybee Apis mellifera

The honeybee Apis mellifera has major ecological and economic importance. We analyze patterns of genetic variation at 8.3 million SNPs, identified by sequencing 140 honeybee genomes from a worldwide sample of 14 populations at a combined total depth of 634×. These data provide insight into the evolutionary history and genetic basis of local adaptation in this species. We find evidence that population sizes have fluctuated greatly, mirroring historical fluctuations in climate, although contemporary populations have high genetic diversity, indicating the absence of domestication bottlenecks. Levels of genetic variation are strongly shaped by natural selection and are highly correlated with patterns of gene expression and DNA methylation. We identify genomic signatures of local adaptation, which are enriched in genes expressed in workers and in immune system– and sperm motility–related genes that might underlie geographic variation in reproduction, dispersal and disease resistance. This study provides a framework for future investigations into responses to pathogens and climate change in honeybees.Swedish Research Council Formas (grant 2010-1295).http://www.nature.comhb201

Predictability and performance of different non-linear mixed-effects models for HbA1c in patients with type 2 diabetes mellitus

To accurately predict the outcome of a late phase study, pharmacometric models can help in drug development. Making informed decision on which models to use will also facilitate drug development. This can depend on the mechanism of action for the drug as well as stability and runtime factors. This is an investigation of four published semi-mechanistic pharmacometric models to predict glycosylated red blood cells (HbA1c) in a late phase study of an anti-diabetic drug together with an assessment of their stability and power to detect drug effects. Mean plasma glucose (MPG), fasting plasma glucose (FPG) or FPG and fasting serum insulin (FSI) are used together with HbA1c as drivers for change in the models. We find that less complex models, with fewer differential equations, are quicker to run and more stable, and that MPG alone is superior to FPG or FPG and FSI to detect a drug effect. The findings are useful for drug development in the anti-diabetic area, and show that a less mechanistic model performs well under these conditions.

Pharmacometric Investigations of Prediction Precision and Advances of Models for Composite Scale Data

Clinical trials are needed to evaluate new treatments. In late-stage clinical trials, failures are mostly due to lack of efficacy. Fit-for-purpose analysis methods will likely increase the success rates and advance drug development by providing higher precision to support decisions such as go/no-go, dose selection, or sample size. This thesis presents new methods for analysis of composite scale data, and comparisons of prediction precision of new and standard analysis methods.  Composite scale data arise from questions/items rated with integers. A total score can be derived, which is discrete and bounded. Item response theory (IRT) models are the natural choice for such data, since they use the item-level information. However, when only the total score is available they cannot be used.  The bounded integer (BI) model is a new method for discrete, bounded outcomes. With composite scale total score data, it had superior fit compared to standard methods, because it respects the nature of the data. Further, a new method, formally linking IRT models to models for total score, was developed. The expected mean and variance, given an IRT model, was implemented in BI and continuous variable models. This improved fit, allowed estimation of IRT parameters, and allowed comparison of different model types. The prediction precision of both outcome and parameters were investigated with different methods, ranging from t-test to mechanistic pharmacometric models, for composite scale and continuous data. The most suitable method depended on the purpose, for example mechanistic models are superior at establishing a drug’s site of action. In conclusion, the choice of method should be based on the primary question, and also the data collected. The method should not be more complex than necessary, and the nature of the data respected. This thesis will help modellers select the most appropriate analysis method for a problem at hand.Zoom link: https://uu-se.zoom.us/j/63922730946 Passcode: 210115</p

Pharmacometric Investigations of Prediction Precision and Advances of Models for Composite Scale Data

Clinical trials are needed to evaluate new treatments. In late-stage clinical trials, failures are mostly due to lack of efficacy. Fit-for-purpose analysis methods will likely increase the success rates and advance drug development by providing higher precision to support decisions such as go/no-go, dose selection, or sample size. This thesis presents new methods for analysis of composite scale data, and comparisons of prediction precision of new and standard analysis methods.  Composite scale data arise from questions/items rated with integers. A total score can be derived, which is discrete and bounded. Item response theory (IRT) models are the natural choice for such data, since they use the item-level information. However, when only the total score is available they cannot be used.  The bounded integer (BI) model is a new method for discrete, bounded outcomes. With composite scale total score data, it had superior fit compared to standard methods, because it respects the nature of the data. Further, a new method, formally linking IRT models to models for total score, was developed. The expected mean and variance, given an IRT model, was implemented in BI and continuous variable models. This improved fit, allowed estimation of IRT parameters, and allowed comparison of different model types. The prediction precision of both outcome and parameters were investigated with different methods, ranging from t-test to mechanistic pharmacometric models, for composite scale and continuous data. The most suitable method depended on the purpose, for example mechanistic models are superior at establishing a drug’s site of action. In conclusion, the choice of method should be based on the primary question, and also the data collected. The method should not be more complex than necessary, and the nature of the data respected. This thesis will help modellers select the most appropriate analysis method for a problem at hand.Zoom link: https://uu-se.zoom.us/j/63922730946 Passcode: 210115</p

Predictability and performance of different non-linear mixed-effects models for HbA1c in patients with type 2 diabetes mellitus

To accurately predict the outcome of a late phase study, pharmacometric models can help in drug development. Making informed decision on which models to use will also facilitate drug development. This can depend on the mechanism of action for the drug as well as stability and runtime factors. This is an investigation of four published semi-mechanistic pharmacometric models to predict glycosylated red blood cells (HbA1c) in a late phase study of an anti-diabetic drug together with an assessment of their stability and power to detect drug effects. Mean plasma glucose (MPG), fasting plasma glucose (FPG) or FPG and fasting serum insulin (FSI) are used together with HbA1c as drivers for change in the models. We find that less complex models, with fewer differential equations, are quicker to run and more stable, and that MPG alone is superior to FPG or FPG and FSI to detect a drug effect. The findings are useful for drug development in the anti-diabetic area, and show that a less mechanistic model performs well under these conditions.

Predictability and performance of different non-linear mixed-effects models for HbA1c in patients with type 2 diabetes mellitus

To accurately predict the outcome of a late phase study, pharmacometric models can help in drug development. Making informed decision on which models to use will also facilitate drug development. This can depend on the mechanism of action for the drug as well as stability and runtime factors. This is an investigation of four published semi-mechanistic pharmacometric models to predict glycosylated red blood cells (HbA1c) in a late phase study of an anti-diabetic drug together with an assessment of their stability and power to detect drug effects. Mean plasma glucose (MPG), fasting plasma glucose (FPG) or FPG and fasting serum insulin (FSI) are used together with HbA1c as drivers for change in the models. We find that less complex models, with fewer differential equations, are quicker to run and more stable, and that MPG alone is superior to FPG or FPG and FSI to detect a drug effect. The findings are useful for drug development in the anti-diabetic area, and show that a less mechanistic model performs well under these conditions.

Comparison of Precision and Accuracy of Five Methods to Analyse Total Score Data

Total score (TS) data is generated from composite scales consisting of several questions/items, such as the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). The analysis method that most fully use the information gathered is item response theory (IRT) models, but these are complex and require item-level data which may not be available. Therefore, the TS is commonly analysed with standard continuous variable (CV) models, which do not respect the bounded nature of data. Bounded integer (BI) models do respect the data nature but are not as extensively researched. Mixed models for repeated measures (MMRM) are an alternative that requires few assumptions and handles dropout without bias. If an IRT model exists, the expected mean and standard deviation of TS can be computed through IRT-informed functions – which allows CV and BI models to estimate parameters on the IRT scale. The fit, performance on external data, and parameter precision (when applicable) of CV, BI and MMRM to analyse simulated TS data from the MDS-UPDRS motor subscale is investigated in this work. All models provided accurate predictions and residuals without trends, but the fit of CV and BI models was improved by IRT-informed functions. The IRT-informed BI model had more precise parameter estimates than the IRT-informed CV model. The IRT-informed models also had the best performance on external data, while the MMRM model was worst. In conclusion: 1) IRT-informed functions improve TS-analyses and 2) IRT-informed BI models had more precise IRT parameter estimates than IRT-informed CV models

An Item Response Theory-Informed Strategy to Model Total Score Data from Composite Scales

Composite scale data is widely used in many therapeutic areas and consists of several categorical questions/items that are usually summarized into a total score (TS). Such data is discrete and bounded by nature. The gold standard to analyse composite scale data is item response theory (IRT) models. However, IRT models require item-level data while sometimes only TS is available. This work investigates models for TS. When an IRT model exists, it can be used to derive the information as well as expected mean and variability of TS at any point, which can inform TS-analyses. We propose a new method: IRT-informed functions of expected values and standard deviation in TS-analyses. The most common models for TS-analyses are continuous variable (CV) models, while bounded integer (BI) models offer an alternative that respects scale boundaries and the nature of TS data. We investigate the method in CV and BI models on both simulated and real data. Both CV and BI models were improved in fit by IRT-informed disease progression, which allows modellers to precisely and accurately find the corresponding latent variable parameters, and IRT-informed SD, which allows deviations from homoscedasticity. The methodology provides a formal way to link IRT models and TS models, and to compare the relative information of different model types. Also, joint analyses of item-level data and TS data are made possible. Thus, IRT-informed functions can facilitate total score analysis and allow a quantitative analysis of relative merits of different analysis methods

A Bounded Integer Model for Rating and Composite Scale Data

Rating and composite scales are commonly used to assess treatment efficacy. The two main strategies for modelling such endpoints are to treat them as a continuous or an ordered categorical variable (CV or OC). Both strategies have disadvantages, including making assumptions that violate the integer nature of the data (CV) and requiring many parameters for scales with many response categories (OC). We present a method, called the bounded integer (BI) model, which utilises the probit function with fixed cut-offs to estimate the probability of a certain score through a latent variable. This method was successfully implemented to describe six data sets from four different therapeutic areas: Parkinson's disease, Alzheimer's disease, schizophrenia, and neuropathic pain. Five scales were investigated, ranging from 11 to 181 categories. The fit (likelihood) was better for the BI model than for corresponding OC or CV models (ΔAIC range 11-1555) in all cases but one (ΔAIC -63), while the number of parameters was the same or lower. Markovian elements were successfully implemented within the method. The performance in external validation, assessed through cross-validation, was also in favour of the new model (ΔOFV range 22-1694) except in one case (ΔOFV -70). A residual for diagnostic purposes is discussed. This study shows that the BI model respects the integer nature of data and is parsimonious in terms of number of estimated parameters

Dose-Response Mixed Models for Repeated Measures – a New Method for Assessment of Dose-Response

Purpose In this paper we investigated a new method for dose-response analysis of longitudinal data in terms of precision and accuracy using simulations. Methods The new method, called Dose-Response Mixed Models for Repeated Measures (DR-MMRM), combines conventional Mixed Models for Repeated Measures (MMRM) and dose-response modeling. Conventional MMRM can be applied for highly variable repeated measure data and is a way to estimate the drug effect at each visit and dose, however without any assumptions regarding the dose-response shape. Dose-response modeling, on the other hand, utilizes information across dose arms and describes the drug effect as a function of dose. Drug development in chronic kidney disease (CKD) is complicated by many factors, primarily by the slow progression of the disease and lack of predictive biomarkers. Recently, new approaches and biomarkers are being explored to improve efficiency in CKD drug development. Proteinuria, i.e. urinary albumin-to-creatinine ratio (UACR) is increasingly used in dose finding trials in patients with CKD. We use proteinuria to illustrate the benefits of DR-MMRM. Results The DR-MMRM had higher precision than conventional MMRM and less bias than a dose-response model on UACR change from baseline to end-of-study (DR-EOS). Conclusions DR-MMRM is a promising method for dose-response analysis