cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5039626
title: Dataset for: The Mathematics of Random Mutation and Natural Selection for Multiple Simultaneous Selection Pressures and the Evolution of Antimicrobial Drug Resistance

description: The random mutation and natural selection phenomenon act in a mathematically predictable behavior which when understood leads to approaches to reduce and prevent the failure of the use of these selection pressures when treating infections and cancers. The underlying principle to impair the random mutation and natural selection phenomenon is to use combination therapy which forces the population to evolve to multiple selection pressures simultaneously which invoke the multiplication rule of probabilities simultaneously as well. Recently it has been seen that combination therapy for the treatment of malaria has failed to prevent the emergence of drug-resistant variants. Using this empirical example and the principles of probability theory, the derivation of the equations describing this treatment failure is done. These equations give guidance as to how to use combination therapy for the treatment of cancers and infectious diseases and prevent the emergence of drug resistance.
DOI: 10.6084/m9.figshare.5039650
title: Dataset for: Objective Bayesian model selection for Cox regression

description: There is now a large literature on objective Bayesian model selection in the linear model based on the $g$-prior. The methodology has been recently extended to generalized linear models using test-based Bayes factors (TBFs). In this paper we show that TBFs can also be applied to the Cox proportional hazards model. If the goal is to select a single model, then both the maximum {\em a posteriori} and the median probability model can be calculated. For clinical prediction of survival, we shrink the model-specific log hazard ratio estimates with subsequent calculation of the Breslow estimate of the cumulative baseline hazard function. A Bayesian model average can also be employed. We illustrate the proposed methodology with the analysis of survival data on primary biliary cirrhosis (PBC) patients and the development of a clinical prediction model for future cardiovascular events based on data from the SMART cohort study. Cross-validation is applied to compare the predictive performance with alternative model selection approaches based on Harrell's c-Index, the calibration slope and the integrated Brier score. Finally a novel application of Bayesian variable selection to optimal conditional prediction via landmarking is described.
DOI: 10.6084/m9.figshare.5039671.v1
title: Dataset for: Phosphorus accumulates faster than nitrogen globally in freshwater ecosystems under anthropogenic impacts

description: Combined effects of cumulative nutrient inputs and biogeochemical processes that occur in freshwater under anthropogenic eutrophication could lead to myriad shifts in nitrogen (N):phosphorus (P) stoichiometry in global freshwater ecosystems, but this is not yet well-assessed. Here we evaluated the characteristics of N and P stoichiometries in bodies of freshwater and their herbaceous macrophytes across human-impact levels, regions and periods. Freshwater and its macrophytes had higher N and P concentrations and lower N:P ratios in heavily than lightly human-impacted environments, further evidenced by spatiotemporal comparisons across eutrophication gradients. N and P concentrations in freshwater ecosystems were positively correlated and N:P was negatively correlated with population density in China. These results indicate a faster accumulation of P than N in human-impacted freshwater ecosystems, which could have large effects on the trophic webs and biogeochemical cycles of estuaries and coastal areas by freshwater loadings, and reinforce the importance of rehabilitating these ecosystems.
DOI: 10.6084/m9.figshare.5039650.v1
title: Dataset for: Objective Bayesian model selection for Cox regression

description: There is now a large literature on objective Bayesian model selection in the linear model based on the $g$-prior. The methodology has been recently extended to generalized linear models using test-based Bayes factors (TBFs). In this paper we show that TBFs can also be applied to the Cox proportional hazards model. If the goal is to select a single model, then both the maximum {\em a posteriori} and the median probability model can be calculated. For clinical prediction of survival, we shrink the model-specific log hazard ratio estimates with subsequent calculation of the Breslow estimate of the cumulative baseline hazard function. A Bayesian model average can also be employed. We illustrate the proposed methodology with the analysis of survival data on primary biliary cirrhosis (PBC) patients and the development of a clinical prediction model for future cardiovascular events based on data from the SMART cohort study. Cross-validation is applied to compare the predictive performance with alternative model selection approaches based on Harrell's c-Index, the calibration slope and the integrated Brier score. Finally a novel application of Bayesian variable selection to optimal conditional prediction via landmarking is described.
DOI: 10.6084/m9.figshare.5039593
title: ISSR markers for analysis of molecular diversity and genetic structure of Indian teak (Tectona grandis L.f.) populations

description: Twenty nine populations of teak (Tectona grandis L.f.) were collected from central and peninsular India for analysis of genetic diversity and structure. Genomic DNA from ten randomly selected individuals of each population was extracted and amplified using five ISSR primers (UBC-801, 834, 880, 899 and 900). The genetic data profile of the same is being uploaded with information of climatic data of the sampled location.<br>
DOI: 10.6084/m9.figshare.5039626.v1
title: Dataset for: The Mathematics of Random Mutation and Natural Selection for Multiple Simultaneous Selection Pressures and the Evolution of Antimicrobial Drug Resistance

description: The random mutation and natural selection phenomenon act in a mathematically predictable behavior which when understood leads to approaches to reduce and prevent the failure of the use of these selection pressures when treating infections and cancers. The underlying principle to impair the random mutation and natural selection phenomenon is to use combination therapy which forces the population to evolve to multiple selection pressures simultaneously which invoke the multiplication rule of probabilities simultaneously as well. Recently it has been seen that combination therapy for the treatment of malaria has failed to prevent the emergence of drug-resistant variants. Using this empirical example and the principles of probability theory, the derivation of the equations describing this treatment failure is done. These equations give guidance as to how to use combination therapy for the treatment of cancers and infectious diseases and prevent the emergence of drug resistance.
DOI: 10.17028/rd.lboro.5039605.v1
title: Individual, employment and psychosocial factors influencing walking to work: Implications for intervention design - dataset

description: This dataset contains baseline data for 1,544 participants in the Walking Works study and was used for analysis in the paper: Individual, employment and psychosocial factors influencing walking to work: implications for intervention design (Adams et al., 2017) doi:10.1371/journal.pone.0171374. <br>The data was collected as part of the Walking Works project which aimed to increase walking on the journey to and from work and during the working day. The project engaged with five employers in England based in the North East, Yorkshire, East Midlands, West Midlands and London. Walking initiatives were developed through recruiting employees as volunteer ‘walking champions’ and providing resources (such as materials to promote a national walk to work campaign and pedometer challenges) to encourage people to walk for all or some of their journey to work, as well as to walk more during the working day. <br>Consent was given to take part in the study on the basis that only researchers at Loughborough University would see the data provided, therefore we are not able to make the files openly available.<br>
DOI: 10.6084/m9.figshare.5039539
title: Dataset for: A Bayesian approach to sequential meta-analysis

description: As evidence accumulates within a meta-analysis, it is desirable to determine when the results could be considered conclusive to guide systematic review updates and future trial designs. Adapting sequential testing methodology from clinical trials for application to pooled meta-analytic effect size estimates appears well suited for this objective. In this paper we describe a Bayesian sequential meta-analysis method, in which an informative heterogeneity prior is employed and stopping rule criteria are applied directly to the posterior distribution for the treatment effect parameter. Using simulation studies, we examine how well this approach performs under different parameter combinations by monitoring the proportion of sequential meta-analyses that reach incorrect conclusions (to yield error rates), the number of studies required to reach conclusion, and the resulting parameter estimates. By adjusting the stopping rule thresholds, the overall error rates can be controlled within the target levels and are no higher than those of alternative frequentist and semi-Bayes methods for the majority of the simulation scenarios. To illustrate the potential application of this method, we consider two contrasting meta-analyses using data from the Cochrane Library and compare the results of employing different sequential methods, while examining the effect of the heterogeneity prior in the proposed Bayesian approach.
DOI: 10.6084/m9.figshare.5039527
title: Dataset for: A graphical model approach to systematically missing data in meta-analysis of observational studies

description: When studies in meta-analysis include different sets of confounders, simple analyses can cause a bias (omitting confounders that are missing in certain studies) or precision loss (omitting studies with incomplete confounders, i.e., a complete-case meta-analysis). To overcome these types of issues, a previous study proposed modelling the high correlation between partially and fully adjusted regression coefficient estimates in a bivariate meta-analysis. When multiple differently adjusted regression coefficient estimates are available, we propose exploiting such correlations in a graphical model. Compared to a previously suggested bivariate meta-analysis method, such a graphical model approach is likely to reduce the number of parameters in complex missing data settings by omitting the direct relationships between some of the estimates. We propose a structure-learning rule whose justification relies on the missingness pattern being monotone. This rule was tested using epidemiological data from a multi-centre survey. In the analysis of risk factors for early retirement, the method showed a smaller difference from a complete data odds ratio, and greater precision than a commonly used complete-case meta-analysis. Three real-world applications with monotone missing patterns are provided, namely, the association between 1) the fibrinogen level and coronary heart disease, 2) the intima media thickness and vascular risk, and 3) allergic asthma and depressive episodes. The proposed method allows for the inclusion of published summary data, which makes it particularly suitable for applications involving both micro- and summary data.
DOI: 10.6084/m9.figshare.5039539.v1
title: Dataset for: A Bayesian approach to sequential meta-analysis

description: As evidence accumulates within a meta-analysis, it is desirable to determine when the results could be considered conclusive to guide systematic review updates and future trial designs. Adapting sequential testing methodology from clinical trials for application to pooled meta-analytic effect size estimates appears well suited for this objective. In this paper we describe a Bayesian sequential meta-analysis method, in which an informative heterogeneity prior is employed and stopping rule criteria are applied directly to the posterior distribution for the treatment effect parameter. Using simulation studies, we examine how well this approach performs under different parameter combinations by monitoring the proportion of sequential meta-analyses that reach incorrect conclusions (to yield error rates), the number of studies required to reach conclusion, and the resulting parameter estimates. By adjusting the stopping rule thresholds, the overall error rates can be controlled within the target levels and are no higher than those of alternative frequentist and semi-Bayes methods for the majority of the simulation scenarios. To illustrate the potential application of this method, we consider two contrasting meta-analyses using data from the Cochrane Library and compare the results of employing different sequential methods, while examining the effect of the heterogeneity prior in the proposed Bayesian approach.
DOI: 10.6084/m9.figshare.5039593.v1
title: ISSR markers for analysis of molecular diversity and genetic structure of Indian teak (Tectona grandis L.f.) populations

description: Twenty nine populations of teak (Tectona grandis L.f.) were collected from central and peninsular India for analysis of genetic diversity and structure. Genomic DNA from ten randomly selected individuals of each population was extracted and amplified using five ISSR primers (UBC-801, 834, 880, 899 and 900). The genetic data profile of the same is being uploaded with information of climatic data of the sampled location.<br>
DOI: 10.6084/m9.figshare.5039485
title: Dataset for: Modeling Zero-Modified Count and Semicontinuous Data in Health Services Research, Part 2: Case Studies

description: This article is the second installment of a two-part tutorial on the analysis of zero-modified count and semicontinuous data. Part 1, which appears as a companion piece in this issue of Statistics in Medicine, provides a general background and overview of the topic, with particular emphasis on applications to health services research. Here, we present three case studies highlighting various approaches for the analysis of zero-modified data. The first case study describes methods for analyzing zero-inflated longitudinal count data. Case Study 2 considers the use of hurdle models for the analysis of spatiotemporal count data. The third case study discusses an application of marginalized two-part models to the analysis of semicontinuous health expenditure data.
DOI: 10.6084/m9.figshare.5039527.v1
title: Dataset for: A graphical model approach to systematically missing data in meta-analysis of observational studies

description: When studies in meta-analysis include different sets of confounders, simple analyses can cause a bias (omitting confounders that are missing in certain studies) or precision loss (omitting studies with incomplete confounders, i.e., a complete-case meta-analysis). To overcome these types of issues, a previous study proposed modelling the high correlation between partially and fully adjusted regression coefficient estimates in a bivariate meta-analysis. When multiple differently adjusted regression coefficient estimates are available, we propose exploiting such correlations in a graphical model. Compared to a previously suggested bivariate meta-analysis method, such a graphical model approach is likely to reduce the number of parameters in complex missing data settings by omitting the direct relationships between some of the estimates. We propose a structure-learning rule whose justification relies on the missingness pattern being monotone. This rule was tested using epidemiological data from a multi-centre survey. In the analysis of risk factors for early retirement, the method showed a smaller difference from a complete data odds ratio, and greater precision than a commonly used complete-case meta-analysis. Three real-world applications with monotone missing patterns are provided, namely, the association between 1) the fibrinogen level and coronary heart disease, 2) the intima media thickness and vascular risk, and 3) allergic asthma and depressive episodes. The proposed method allows for the inclusion of published summary data, which makes it particularly suitable for applications involving both micro- and summary data.
DOI: 10.6084/m9.figshare.5039485.v1
title: Dataset for: Modeling Zero-Modified Count and Semicontinuous Data in Health Services Research, Part 2: Case Studies

description: This article is the second installment of a two-part tutorial on the analysis of zero-modified count and semicontinuous data. Part 1, which appears as a companion piece in this issue of Statistics in Medicine, provides a general background and overview of the topic, with particular emphasis on applications to health services research. Here, we present three case studies highlighting various approaches for the analysis of zero-modified data. The first case study describes methods for analyzing zero-inflated longitudinal count data. Case Study 2 considers the use of hurdle models for the analysis of spatiotemporal count data. The third case study discusses an application of marginalized two-part models to the analysis of semicontinuous health expenditure data.
DOI: 10.6084/m9.figshare.5039386
title: Dataset for: A marginal structural model for recurrent events in the presence of time-dependent confounding: non-specific effects of vaccines on child hospitalisations

description: Using a Danish register-based study on childhood vaccination and hospitalisation as motivation, a marginal structural model for recurrent events is studied. The model addresses a number of challenges which may be seen more generally in large register-based cohort studies. One is to adjust for a time-dependent confounder when studying the effect of a time-varying exposure on a recurrent event based on an analysis in continuous time. Another is to report results via a measure which is easy to interpret and communicate even though quite elaborate treatment regimes are considered. Lastly, the implementation of continuously updated weights implies a substantial computationally demanding workload.
DOI: 10.6084/m9.figshare.5039338
title: Dataset for: A rank test for bivariate event time outcomes when one event is a surrogate

description: In many clinical settings, improving patient survival is of interest but a practical surrogate, such as time to disease progression, is instead used as a clinical trial's primary endpoint. A time-to-first endpoint (e.g. death or disease progression) is commonly analyzed but may not be adequate to summarize patient outcomes if a subsequent event contains important additional information. We consider a surrogate outcome very generally, as one correlated with the true endpoint of interest. Settings of interest include those where the surrogate indicates a beneficial outcome so that the usual time-to-first endpoint of death or surrogate event is nonsensical. We present a new two-sample test for bivariate, interval-censored time-to-event data, where one endpoint is a surrogate for the second, less frequently observed endpoint of true interest. This test examines whether patient groups have equal clinical severity. If the true endpoint rarely occurs, the proposed test acts like a weighted logrank test on the surrogate; if it occurs for most individuals, then our test acts like a weighted logrank test on the true endpoint. If the surrogate is a useful statistical surrogate, our test can have better power than tests based on the surrogate that naively handle the true endpoint. In settings where the surrogate is not valid (treatment affects the surrogate but not the true endpoint), our test incorporates the information regarding the lack of treatment effect from the observed true endpoints and hence is expected to have a dampened treatment effect compared to tests based on the surrogate alone.
DOI: 10.6084/m9.figshare.5039353
title: Dataset for: Testing of Evaluation Bias for Progression Free Survival Endpoint in Oncology Clinical Trials

description: Progression free survival is an increasingly popular end point in oncology clinical trials. A complete blinded independent central review (BICR) is often required by regulators in an attempt to reduce the bias in PFS assessment. In this paper, we propose a new methodology that uses a sample-based BICR as an audit tool to decide whether a complete BICR is needed. More specifically, we propose a new index, the differential risk, to measure the reading discordance pattern, and develop a corresponding hypothesis testing procedure to decide whether the bias in local evaluation is acceptable. Simulation results demonstrate that our new index is sensitive to the change of discordance pattern; type I error is well controlled in the hypothesis testing procedure, and the calculated sample size provides the desired power.
DOI: 10.6084/m9.figshare.5039386.v1
title: Dataset for: A marginal structural model for recurrent events in the presence of time-dependent confounding: non-specific effects of vaccines on child hospitalisations

description: Using a Danish register-based study on childhood vaccination and hospitalisation as motivation, a marginal structural model for recurrent events is studied. The model addresses a number of challenges which may be seen more generally in large register-based cohort studies. One is to adjust for a time-dependent confounder when studying the effect of a time-varying exposure on a recurrent event based on an analysis in continuous time. Another is to report results via a measure which is easy to interpret and communicate even though quite elaborate treatment regimes are considered. Lastly, the implementation of continuously updated weights implies a substantial computationally demanding workload.
DOI: 10.6084/m9.figshare.5039353.v1
title: Dataset for: Testing of Evaluation Bias for Progression Free Survival Endpoint in Oncology Clinical Trials

description: Progression free survival is an increasingly popular end point in oncology clinical trials. A complete blinded independent central review (BICR) is often required by regulators in an attempt to reduce the bias in PFS assessment. In this paper, we propose a new methodology that uses a sample-based BICR as an audit tool to decide whether a complete BICR is needed. More specifically, we propose a new index, the differential risk, to measure the reading discordance pattern, and develop a corresponding hypothesis testing procedure to decide whether the bias in local evaluation is acceptable. Simulation results demonstrate that our new index is sensitive to the change of discordance pattern; type I error is well controlled in the hypothesis testing procedure, and the calculated sample size provides the desired power.
DOI: 10.6084/m9.figshare.5039317
title: Dataset for: Parallel NMR Spectroscopy With Simultaneous Detection of 1H and 19F Nuclei

description: Recording NMR signals of several nuclear species simultaneously by using parallel receivers provides more information from a single measurement and at the same time increases the measurement sensitivity per unit time. Here we present a comprehensive series of the most frequently used NMR experiments modified for simultaneous direct detection of two of the most sensitive NMR nuclei – 1H and 19F. We hope that the presented material will stimulate interest in and further development of this technique.