Methods for Using Biomarker Information in Randomized Clinical Trials

Advances in high-throughput biological technologies have led to large numbers of potentially predictive biomarkers becoming routinely measured in modern clinical trials. Biomarkers which influence treatment efficacy may be used to find subgroups of patients who are most likely to benefit from a new treatment. Consequently, there is a growing interest in better approaches to identify biomarker signatures and utilize the biomarker information in clinical trials. The first focus of this thesis is on developing methods for detecting biomarker-treatment interactions in large-scale trials. Traditional interaction analysis, using regression models to test biomarker-treatment interactions one biomarker at a time, may suffer from poor power when there is a large multiple testing burden. I adapt recently proposed two-stage interaction detecting procedures for application in randomized clinical trials. I propose two new stage 1 multivariate screening strategies using lasso and ridge regressions to account for correlations among biomarkers. For these new multivariate screening strategies, I prove the asymptotic between-stage independence, required for family-wise error rate control. Simulation and real data application results are presented which demonstrate greater power of the new strategies compared with previously existing approaches. The second focus of this thesis is on developing methods for utilizing biomarker information during the course of a randomized clinical trial to improve the informativeness of results. Under the adaptive signature design (ASD) framework, I propose two new classifiers that more efficiently leverage biomarker signatures to select a subgroup of patients who are most likely to benefit from the new treatment. I provide analytical arguments and demonstrate through simulations that these two proposed classification criteria can provide at least as good, and sometimes significantly greater power than the originally proposed ASD classifier. Third, I focus on an important issue in the statistical analysis of interactions for binary outcomes, which is pertinent to both topics above. Testing for biomarker-treatment interactions with logistic regression can suffer from an elevated number of type I errors due to the asymptotic bias of the interaction regression coefficient under model misspecification. I analyze this problem in the randomized clinical trial setting and propose two new de-biasing procedures, which can offer improved family-wise error rate control in various simulated scenarios. Finally, I summarize the main contributions from the work above, discuss some practical limitations as well as their real world value, and prioritize future directions of research building upon the work in this thesis.Medical Research Council, grant ID: MR/R502303/

Fit Index Sensitivity in Multilevel Structural Equation Modeling

Multilevel Structural Equation Modeling (MSEM) is used to estimate latent variable models in the presence of multilevel data. A key feature of MSEM is its ability to quantify the extent to which a hypothesized model fits the observed data. Several test statistics and so-called fit indices can be calculated in MSEM as is done in single-level structural equation modeling. Accordingly, problems associated with these measures in the single-level case may apply to the multilevel case and new complications may arise. Few studies, however, have examined the performance of fit indices in MSEM. Furthermore, recent findings suggest that evaluating fit at each level separately is advantageous to evaluating fit for the overall model. Therefore, the purpose of the present study was to evaluate the sensitivity of several fit indices to misspecification in the cluster-level model under varying multilevel data conditions including the intraclass correlation coefficient, sample size configuration, and severity of model misspecification. Furthermore, three methods of level-specific fit evaluation were compared. Results from a Monte Carlo simulation study suggest that fit indices are affected by the ICC of model indicators and sample size configurations in MSEM. With the exception of the SRMR, all fit indices were less sensitive to cluster-level model misspecification at low indicator ICCs, large overall sample sizes, and smaller numbers of clusters. Discrepancies in fit information between two methods of level-specific fit were observed at low ICC values. Finally, two fit indices rarely used in SEM applications revealed desirable properties in certain simulation conditions. Implications of the simulation results are discussed and a program for implementing level-specific fit evaluation in the R statistical language is provided

Bayesian Markov-Switching Tensor Regression for Time-Varying Networks

Modeling time series of multilayer network data is challenging due to the peculiar characteristics of real-world networks, such as sparsity and abrupt structural changes. Moreover, the impact of external factors on the network edges is highly heterogeneous due to edge- and time-specific effects. Capturing all these features results in a very high-dimensional inference problem. A novel tensor-on-tensor regression model is proposed, which integrates zero-inflated logistic regression to deal with the sparsity, and Markov-switching coefficients to account for structural changes. A tensor representation and decomposition of the regression coefficients are used to tackle the high-dimensionality and account for the heterogeneous impact of the covariate tensor across the response variables. The inference is performed following a Bayesian approach, and an efficient Gibbs sampler is developed for posterior approximation. Our methodology applied to financial and email networks detects different connectivity regimes and uncovers the role of covariates in the edge-formation process, which are relevant in risk and resource management. Code is available on GitHub. Supplementary materials for this article are available online

Aid and Growth What Meta-Analysis Reveals

Some recent literature in the meta-analysis category where results from a range of studies are brought together throws doubt on the ability of foreign aid to foster economic growth and development. This paper assesses what meta-analysis has to say about the effectiveness of foreign aid in terms of the growth impact. We re-examine key hypotheses, and find that the effect of aid on growth is positive and statistically significant. This significant effect is genuine, and not an artefact of publication selection. We also show why our results differ from those published elsewhere.aid and growth, meta-analysis, heterogeneity and publication bias

Removing the influence of feature repetitions on the congruency sequence effect: why regressing out confounds from a nested design will often fall short

This article illustrates a shortcoming of using regression to control for confounds in nested designs. As an example, we consider the congruency sequence effect, which is the observation that the congruency effect in distractor interference (e.g., Stroop) tasks is smaller following incongruent as compared with congruent trials. The congruency sequence effect is often interpreted as indexing conflict adaptation: a relative increase of attention to the target following incongruent trials. However, feature repetitions across consecutive trials can complicate this interpretation. To control for this confound, the standard procedure is to delete all trials with a stimulus or response repetition and analyze the remaining trials. Notebaert and Verguts (2007) present an alternative method that allows researchers to use all trials. Specifically, they employ multiple regression to model conflict adaptation independent of feature repetitions. We show here that this approach fails to account for certain feature repetition effects. Furthermore, modeling these additional effects is typically not possible because of an upper bound on the number of degrees of freedom in the experiment. These findings have important implications for future investigations of conflict adaptation and, more broadly, for all researchers who attempt to regress out confounds in nested designs

Determinants of insurance companies' enviromental, social, and governance awareness

Environmental, social, and governance (ESG) criteria are increasingly important in all fields of economics. However, despite increasing interest from policy makers and financial regulators, literature relating to the insurance industry is still scarce. This paper aims to fill this gap by exploring the interaction between a set of financial ratios and environmental social governance scores of 107 large, listed US insurance companies for the period 2010–2018 for the purpose of identifying the determinants of ESG awareness. Larger, more profitable, and more solvent insurance companies show the highest level of ESG awareness. Our model contributes to shed light on the unfolding of ESG practices in the insurance industry

Perils and pitfalls of mixed-effects regression models in biology

This is the final version. Available on open access from PeerJ via the DOI in this recordData Availability: The following information was supplied regarding data availability: The R code used to conduct all simulations in the paper is available in the Supplemental Files.Biological systems, at all scales of organisation from nucleic acids to ecosystems, are inherently complex and variable. Biologists therefore use statistical analyses to detect signal among this systemic noise. Statistical models infer trends, find functional relationships and detect differences that exist among groups or are caused by experimental manipulations. They also use statistical relationships to help predict uncertain futures. All branches of the biological sciences now embrace the possibilities of mixed-effects modelling and its flexible toolkit for partitioning noise and signal. The mixed-effects model is not, however, a panacea for poor experimental design, and should be used with caution when inferring or deducing the importance of both fixed and random effects. Here we describe a selection of the perils and pitfalls that are widespread in the biological literature, but can be avoided by careful reflection, modelling and model-checking. We focus on situations where incautious modelling risks exposure to these pitfalls and the drawing of incorrect conclusions. Our stance is that statements of significance, information content or credibility all have their place in biological research, as long as these statements are cautious and well-informed by checks on the validity of assumptions. Our intention is to reveal potential perils and pitfalls in mixed model estimation so that researchers can use these powerful approaches with greater awareness and confidence. Our examples are ecological, but translate easily to all branches of biology.University of Exete