Application of Statistical Methods for Central Statistical Monitoring and Implementations on the German Multiple Sclerosis Registry

Monitoring of clinical trials is a fundamental process required by regulatory agencies. It assures the compliance of a center to the required regulations and the trial protocol. Traditionally, monitoring teams relied on extensive on-site visits and source data verification. However, this is costly, and the outcome is limited. Thus, central statistical monitoring (CSM) is an additional approach recently embraced by the International Council for Harmonisation (ICH) to detect problematic or erroneous data by using visualizations and statistical control measures. Existing implementations have been primarily focused on detecting inlier and outlier data. Other approaches include principal component analysis and distribution of the data. Here we focus on the utilization of comparisons of centers to the Grand mean for different model types and assumptions for common data types, such as binomial, ordinal, and continuous response variables. We implement the usage of multiple comparisons of single centers to the Grand mean of all centers. This approach is also available for various non-normal data types that are abundant in clinical trials. Further, using confidence intervals, an assessment of equivalence to the Grand mean can be applied. In a Monte Carlo simulation study, the applied statistical approaches have been investigated for their ability to control type I error and the assessment of their respective power for balanced and unbalanced designs which are common in registry data and clinical trials. Data from the German Multiple Sclerosis Registry (GMSR) including proportions of missing data, adverse events and disease severity scores were used to verify the results on Real-World-Data (RWD)

Quality control measures in clinical trials. risk-based monitoring and central statistical monitoring

Regulatory authorities have encouraged the usage of a risk-based monitoring (RBM) system in clinical trials. In addition to the identification of possible risks, risk-based monitoring also includes their evaluation to enable targeted monitoring. Risks are defined as conditions that could affect patient safety and the integrity of the study. Various studies demonstrated the increasing usage of RBM in practice. The application of the many RBM tools available has not been investigated. Central statistical monitoring (CSM) which falls under the remote monitoring of the RBM system has also been gaining more attention due to the recognition of its efficiency in monitoring clinical trials. This dissertation is dedicated to improving the quality assessments in risk-based monitoring and central statistical monitoring. The first chapter of the thesis provides an overview of clinical research and the types of clinical studies. Furthermore, it specifically focuses on clinical research structure, management, and activities in clinical trials. The different types of clinical trials are illustrated, followed by the management process of the trial and monitoring activities. Section 2.1 highlights the limitations of the current RBM tools. It shows how different an outcome risk assessment of a clinical trial can be when assessed with different RBM tools. Furthermore, this section shows the different risks covered within RBM tools. It shows the need for a risk assessment tool that can cover any risk in a clinical trial. Hence section 2.3 proposes a new risk methodology assessment (RMA) that can be applied to any clinical trial with the ability to add additional risks to the assessment. It presents a scoring method that allows stakeholders to visualize and quantify a risk size. This would guide stakeholders and assist them in the decision plan for mitigating a certain risk by an effective measure and monitoring degree in the monitoring plan. The theoretical RMA approach is presented in a shiny web app with a user-friendly interface to ease its implementation in practice. Section 2.4 proposes a new approach for the benefit of CSM. It presents multiple comparisons of individual center means to the Grand Mean of all centers. The approach is available and has been applied in different contexts. Here its implementation to detect a deviating center is recommended. As it is available for different data types, it shows specifically the comparison for continuous, binomial, and ordinal data types. In a Monte-Carlo simulation study, different model types estimating GM comparisons were tested for the control of Type I error and the highest power for balanced scenarios and unbalanced scenarios observed in clinical trials and observational studies. It also shows the validation of the approach on Real-world data (RWD) from the German Multiple Sclerosis Registry (GMSR). Finally, the approach is presented in shiny web apps to facilitate a common graphical conclusion style for different endpoints

Bivariate copula additive models for location, scale and shape

In generalized additive models for location, scale and shape (GAMLSS), the response distribution is not restricted to belong to the exponential family and all the model’s parameters can be made dependent on additive predictors that allow for several types of covariate effects (such as linear, non-linear, random and spatial effects). In many empirical situations, however, modeling simultaneously two or more responses conditional on some covariates can be of considerable relevance. The scope of GAMLSS is extended by introducing bivariate copula models with continuous margins for the GAMLSS class. The proposed computational tool permits the copula dependence and marginal distribution parameters to be estimated simultaneously, and each parameter to be modeled using an additive predictor. Simultaneous parameter estimation is achieved within a penalized likelihood framework using a trust region algorithm with integrated automatic multiple smoothing parameter selection. The introduced approach allows for straightforward inclusion of potentially any parametric marginal distribution and copula function. The models can be easily used via the copulaReg() function in the R package SemiParBIVProbit. The proposal is illustrated through two case studies and simulated data

Sample selection models for count data in R

We provide a detailed hands-on tutorial for the R package SemiParSampleSel (version 1.5). The package implements selection models for count responses fitted by penalized maximum likelihood estimation. The approach can deal with non-random sample selection, flexible covariate effects, heterogeneous selection mechanisms and varying distributional parameters. We provide an overview of the theoretical background and then demonstrate how SemiParSampleSel can be used to fit interpretable models of different complexity. We use data from the German Socio-Economic Panel survey (SOEP v28, 2012. doi: 10.5684/soep.v28) throughout the tutorial

Evaluation of interaction effects in two-factorial designs by simultaneous confidence intervals in the cell means model

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Copula regression spline models for binary outcomes

We introduce a framework for estimating the effect that a binary treatment has on a binary outcome in the presence of unobserved confounding. The methodology is applied to a case study which uses data from the Medical Expenditure Panel Survey and whose aim is to estimate the effect of private health insurance on health care utilization. Unobserved confounding arises when variables which are associated with both treatment and outcome are not available (in economics this issue is known as endogeneity). Also, treatment and outcome may exhibit a dependence which cannot be modeled using a linear measure of association, and observed confounders may have a non-linear impact on the treatment and outcome variables. The problem of unobserved confounding is addressed using a two-equation structural latent variable framework, where one equation essentially describes a binary outcome as a function of a binary treatment whereas the other equation determines whether the treatment is received. Non-linear dependence between treatment and outcome is dealt using copula functions, whereas covariate-response relationships are flexibly modeled using a spline approach. Related model fitting and inferential procedures are developed, and asymptotic arguments presented