cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25934/00006494
title: Implications of body mass index on the biological and clinical effects of endocrine therapy and abemaciclib in the neoadjuvant setting

description: Obesity is a risk factor for the development of hormone receptor-positive breast cancer (e.g. Breast cells that contain special proteins called hormone receptors) [1]. These receptors ‘receive’ messages from hormones in the body and respond by telling the cells what to do. Cancer cells grow in response to the hormones estrogen or progesterone binding to the cells.)  
 
In addition, overweight and obesity are factors associated with a worse prognosis when a patient is diagnosed with breast cancer.[2] As obesity is a global public health problem that is increasing worldwide [3, 4] it is extremely important to understand whether the treatments available to treat breast cancer work differently in the context of overweight and obesity and what are the mechanisms in which the body mass index can interfere with the response to available treatments.  

Recently, in a research conducted by our group, we observed that, for patients with hormone receptor-positive metastatic breast cancer, abemaciclib (a type of targeted treatment used for the treatment of breast cancer which inhibits the cell cycle regulators cyclin 4 and 6) plus endocrine as first or second-line therapy is superior to endocrine therapy alone regardless of body mass index.[5] 

Interestingly, we demonstrated that overweight and obese patients presented inferior response rates (percentage of patients whose cancer shrinks or disappears after treatment) and inferior rates of neutropenia (a blood condition characterized by low levels of neutrophils, which are white blood cells that protect our body from infections) when treated with abemaciclib plus endocrine therapy.[5]

It has already been shown that metastatic samples differ from primary breast cancer in several aspects, such as the amount of immune cells that have moved from the blood into the tumor (tumor infiltrating lymphocytes), acquired mutations, and activated signaling pathways (which refers to a series of chemical reactions in which a group of molecules in a cell work together to control a cell function, in this case the division and multiplication of breast cancer cells) [6–8]. 

Thus, it is important to investigate the impact of body mass index also in patients with early breast cancer.
Our objective is to use the data available from the NEOMONARCH study [9] to compare the biological and clinical effects of endocrine therapy with or without abemaciclib given before surgery in overweight and obese patients vs. patients with normal weight. More precisely, we will compare the reduction of tumor proliferation rates, reduction of tumor size, and activation of the immune response towards the cancer cells after treatment according to the body mass index. 

This research project can generate hypotheses for future studies that may improve the curative treatment of hormone receptor-positive breast cancer in overweight and obese patients.
DOI: 10.25934/00006496
title: Endoscopic Disease Severity and Patient Reported Outcomes in Crohn’s Disease

description: Crohn’s disease (CD) is a type of inflammatory bowel disease that is characterized by periods of relapse and remission. CD affects any area of the gastrointestinal tract but is more common in the ileum and colon (1). Symptoms of CD include abdominal pain, diarrhea, fever, fatigue and weight loss, which are considered as part of disease assessment. The Crohn’s Disease Activity Index (CDAI) is a tool that combines laboratory values, findings from physical examination, and three patient-reported outcomes (PROs): abdominal pain, number of daily soft/liquid stools and general well-being. 

PRO-based endpoints are now required for clinical trials for CD in place of CDAI-based endpoints (2).  However, several studies have identified a disconnect between the CDAI, PROs and objective measures of disease such as endoscopy (3). Endoscopic remission (ER) remains an important goal of treatment in CD. Recent post-hoc analyses have suggested that PROs at baseline and after the induction phase of treatment are not associated with one-year ER (4). Further, it has been shown that ulcer size and location at baseline play an integral role in the ability to achieve one-year ER (5). 

In the context of these findings, there is a need to better understand how ulcer size at baseline and post-induction impact the ability to achieve one-year ER. The proposed study aims to analyze data from the UNITI-1, UNITI-2, and IM-UNITI trials (provided by the YODA Project) and the EXTEND trial (provided by Vivli).
DOI: 10.25934/00006521
title: Bayesian probability of success for multivariate generalized linear models

description: The project will enable clinicians to power studies with possibly mixed outcome types (e.g., binary, continuous, and count). Because trials typically have primary and multiple secondary endpoints, methods to determine sample size to achieve clinical success across multiple outcomes are necessary. However, such methods are scarce. In earlier work, we have developed a Bayesian approach to multiplicity that is uniformly more powerful than frequentist adjustment procedures, such as the Holm-Bonferroni method, for multiple continuous outcomes (see https://arxiv.org/abs/2010.13774). In this work, we aim to extend this previous work to the case of the multivariate generalized linear model.
DOI: 10.25934/00006543
title: Reproducible Machine Learning (ML) for Tumor Growth Inhibition (TGI) - can ML methods support the validity of conventional TGI metrics?

description: Reproducibility is a cornerstone of science and provides the basis for successful replication; reproducible results under the same conditions support the methodology and design to be applied to an independent setting. This is all the more important for machine learning (ML) techniques that are often considered as a "black box". Here, it is fundamentally necessary to prove reproducibility and consistency with established approaches before such advanced methods can be validly applied to study further questions and potentially improve prediction of overall survival (OS) in model-informed drug development. Only then, medical science and patient care can profit from such analyses.
In order to do so, this project aims to reproduce conventional approaches to model tumor growth inhibition (TGI) and ML methods as already been published. In particular, conventional (maximum-likelihood-based) regression analyses will be complemented by the four ML methods of lasso, boosting, random forest, and kernel machine to investigate their (comparative) predictive performance, to explore the interactions between predictor variables, and to incorporate nonlinear relationships for the prediction of OS. 
The project is an exploratory analysis of the OAK randomized controlled trial (RCT) comparing OS with atezolizumab versus docetaxel. All patients included in the primary population (each 425 patients randomly assigned to receive atezolizumab or docetaxel, respectively) will be considered for predictive models for the co-primary endpoint OS. Patient characteristics at study baseline will be considered as (candidate) predictors in each methodological approach (conventional regression, lasso, boosting, random forest, and kernel machine)


• What design and methods you have you chosen and why (in brief); 
The prediction models are based on analyses of event times (i.e., overall survival). Among standard procedures, semi-parametric Cox regression and parametric, so-called Accelerated Failure Time (AFT) models with corresponding link functions for the event times are used here (e.g., transformations corresponding to the Weibull, exponential or lognormal distribution). Performance measures for these classes of models include prediction error curves or the Brier score. 
Lasso procedures, also considered, are intermediate between standard procedures and machine-learning procedures because the so-called shrinkage parameter for effect estimation of independent variables comes from a (data-driven) cross-validation. The Lasso procedure is applicable to Cox and AFT models. 
Among the pure machine-learning procedures, the so-called Cox boosting and random survival forests are implemented, while the latter require tuning various hyperparameters, including the number of variables randomly selected as candidates for splitting a node and the number of random splits considered for each candidate splitting variable.
DOI: 10.25934/00006544
title: Investigating the association between smoking cessation and mental health in people with and without psychiatric disorders

description: In many countries, such as the United Kingdom, the prevalence of smoking in the general population has decreased from approximately 46% in 1970s to 14.9% in 2018. The decrease in prevalence is not to the same extent among people with mental health conditions, with the prevalence remaining at around 40% from 1993 to 2013. Many individuals who smoke say that they wish to quit; however, many continue to believe that smoking provides them with mental health benefits. This belief is shared among individuals with and without psychiatric disorders. Smokers, therefore, may be less likely to cease smoking if they believe that their mental health will decline as a result. Additionally, health professionals may also thereby be reluctant to suggest smoking cessation to certain smokers due to impact on mental health. However, there is evidence suggesting that there is an association between stopping smoking and improved mental health. Given that the rate of smoking in individuals diagnosed with psychiatric disorders is not subsiding to the same level as the general population, it is, therefore, essential to update the evidence exploring the association between mental health and smoking for populations with and without psychiatric disorders.

This project's focus is to assess the strength of the relationship between quitting smoking and increased positive mental health for people with and without psychiatric disorders. It is not feasible to randomly assign participants to continue or quit smoking; therefore, to study the association between smoking cessation on mental health, observational analysis strategies must be used. The primary issue with traditional observational epidemiology is teasing out whether associations are causal. This study will have a unique opportunity to use three analytical approaches in a large dataset with populations with and without psychiatric disorders in order to derive results with higher causal inference confidence. We will triangulate results derived from statistical methods that differ in their ability to produce causal estimates: multivariable regression models, propensity score-adjusted models, and instrumental variable regressions.
DOI: 10.25934/00006601
title: Adaptation and validation of the Mucosal Inflammation Non-invasive (MINI) Index for Crohn's disease in adults using the SERENE clinical trial data

description: The Mucosal Inflammation Non-invasive (MINI) index is a clinical score used to assess disease activity (inflammation) in pediatric Crohn’s disease (CD). It has been previously tested using different groups of pediatric CD patients. In this scoring tool, a score of under 8 has been shown to be a good predictor of bowel tissue healing (Mucosal healing - MH). This scoring system uses the following items: blood inflammatory markers ( c-reactive protein - CRP and erythrocyte sedimentation rate - ESR), fecal inflammatory markers (Calprotectin), and stool frequency reported by the patient. As the MINI index is a useful clinical tool, we now aim to test this tool on an adult population of CD patients to potentially expand the use of this score.
 
Crohn's Disease is a condition that causes chronic (long-lasting) inflammation (soreness, swelling or redness) along the digestive tract (the path that food takes through the body) and is part of a group of diseases called Inflammatory Bowel Disease (IBD). Crohn's disease affects an increasing portion of the population. According to the center for disease control and prevention (CDC),IBD prevalence was 1.3% of the USA adult population in 2015. 

Mucosal healing (MH) – is one of the targets of treatment in IBD patients. This term refers to complete improvement (healing) of the bowel tissue (mucosa) and can be assessed by a number of different tests including fecal tests and invasive procedures. 

The Pediatric Crohn Disease Activity Index (PCDAI) – is a scoring system that gives patients a score based on the  disease severity. This is a useful tool to measure response to treatment.  This score includes questions about the patients history (abdominal pain, stools per day, general well-being), the physical examination (abdominal exam), laboratory tests (hematocrit, ESR, albumin) and height, weight, age and sex. 


The Simple endoscopic score for Crohn's disease- (SES-CD) is a simple tool that helps doctors describe the degree of bowel disease (inflammation) in a standardized way. It includes scores for each anatomical location. Each location is given a different score according to the number of ulcers (tissue wounds), the size of the ulcers and the kind of ulcers (deep or superficial). A SES-CD score of less than 3 is considered to represent healing of the bowel tissue termed Mucosal healing. 

Fecal calprotectin (FC) – is a biochemical test to measure the amount of a protein named “Calprotectin” in the stool. In a healthy person calprotectin in the stool should be minimal to none. In disease conditions such as IBD, FC is elevated. This test is often used to asses the degree of inflammation or healing and is sometimes used instead of invasive procedures (e.g., colonoscopy).
DOI: 10.4231/d36h4cp9j
title: Dataset for Investigating Computational Efficiency of Alternative Arterial Offset Optimization Algorithms

description: This dataset contains 16 hours of coordinated signal operations (vehicle arrival times and coordinated phase status) for a four-intersection signalized arterial in Noblesville, Indiana in 2009. The dataset was used to test alternative algorithms for optimizing signal offsets. The dataset accompanies the paper, "Computational Efficiency of Alternative Arterial Offset Optimization Algorithms," by C.M. Day and D.M. Bullock, to appear in an issue of Transportation Research Record in 2011.
DOI: 10.25345/c5v980285
title: MassIVE MSV000093035 - Untargeted UHPLC-MS/MS screen of human gut microbiome commensal isolate bacteria from healthy human donor feces and validated standards from 'Microbially-catalyzed conjugation of GABA and tyramine to bile acids'
title: Untargeted UPLC-MS/MS data from a screen of human gut microbiome commensal isolate bacteria from healthy human donor feces. Isolates include Mediterraneibacter gnavus MSK15.77 (NCBI accession NZ_JAAIRR010000000), Bacteroides ovatus MSK22.29 (NCBI accession NZ_JAHOCX010000000), Bifidobacterium longum DFI.2.45 (NCBI accession NZ_JAJCNS010000000), and Lachnoclostridium scindens SL.1.22 (NCBI accession GCA_020555615.1). The dataset includes validated bile acid standards. All using positive ionization.
DOI: 10.25934/00006642
title: Modified Multiplier SES-CD (MM-SES-CD) on Predicting Endoscopic Remission in Crohn’s Disease

description: Crohn's disease is a type of inflammatory bowel disease characterized by periods of relapse and remission. Disease severity is assessed by endoscopy and is important to inform treatment decision-making. For example, patients with more severe disease (e.g. large bleeding ulcers in the colon) may require more advanced therapies to help heal the colon. Gastroenterologists often use scoring tools to help them quantify disease severity. One such tool is the Simple Endoscopic Score for Crohn’s disease (SES-CD). This tool scores the ileum, right colon, transverse colon, descending colon and rectum on four parameters: size of ulcers, extent of ulcers, surface affected by ulcers and any narrowings observed throughout the ileum and colon.

An important goal of treatment in Crohn’s disease is mucosal healing (MH). MH is a term used to describe healing of the ileum and colon. MH can be defined in many ways, including absence of ulcers in the ileum and colon, SES-CD score less than 3 and SES-CD score of 0. In clinical trials, patients often undergo endoscopy after the induction period of therapy (e.g. week 8-12) and at one year to assess if MH was achieved.

The SES-CD assumes each of these four parameters across all ileocolonic segments are equally important for predicting MH. However, there is growing evidence to suggest some components of the SES-CD are more important for predicting who will achieve MH in the future. As published in Gut, the Modified Multiplier SES-CD (MM-SES-CD) is a novel tool that differentially weights components of the SES-CD to reflect the relative importance of each baseline parameter on disease prognosis at one year (5). However, it is unknown what short-term reduction in MM-SES-CD can reliably predict long-term healing.

The primary objective of this study is to determine the minimally clinically important reduction in MM-SES-CD from baseline to post-induction endoscopy that can predict MH at week 52. Week 52 will be used as the endpoint as it was used as the endpoint in the initial MM-SES-CD publication (5). After further validation, this reduction in MM-SES-CD can potentially be used as a target cut-off of endoscopic response.

Patients with Crohn’s disease may benefit from this study as the MM-SES-CD can help them understand how likely they are to achieve healing in the future based on their improvement in MM-SES-CD score. This may help patients and clinicians in deciding if their therapy is helping them achieve healing, or if an earlier switch in therapy is warranted. In the future, patients may also benefit from alternative stratification criteria in clinical trials based on evidence generated from this study on the MM-SES-CD.
DOI: 10.25934/00006710
title: Identifying Patients in the PROWESS SHOCK Clinical Trial Among Whom Drotrecogin Alfa (DrottAA) Reduced Mortality

description: For nearly 40 years, more than 100 randomized clinical trials (RCT), from the original Solu-Medrol RCT through the recent SCARLET RCT, have tested novel interventions targeting the host response to infection in sepsis, organ failure and septic shock on thousands of patients. (1, 2, 3) All of those molecules and regimens were grounded firmly in basic science, both in-vitro and in-vivo. After 40 years of sepsis clinical trials, worldwide sepsis still claims 11 million lives each year.  Many new drugs targeting the detrimental effects of sepsis performed well in the laboratory but none reduced septic mortality in clinical trials.  Hypothesis:  Non-specific clinical sepsis definitions entry criteria enroll so many patients whose bodies cannot benefit from the study drug that the true treatment effects of effective drugs are masked.  The present study proposes to determine whether or not it is possible to predict from pre-randomization RCT data which septic patients are able to benefit from Drotrecogin Alfa (DrotAA), a drug from Eli Lilly & Co. If this investigation identifies such patients within the PROWESS SHOCK RCT, then it may be possible to identify drug-responsive patients in other sepsis RCT’s, possibly facilitating new life-saving treatments for sepsis.
DOI: 10.25934/00006712
title: Development and validation of a prediction model for the indication of inhaled corticosteroid (ICS) in patients with chronic obstructive pulmonary disease (COPD)

description: Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. COPD is well known as a clinical syndrome characterized by chronic respiratory symptoms, structural pulmonary abnormalities, lung function impairment, or any combination of these. Inhaled drugs, including long-acting bronchodilators and steroids, are mainly used for COPD pharmacotherapy. Among them, the role of inhaled corticosteroid (ICS) for COPD patients are less known than bronchodilators. Pneumonia, which can be detrimental for COPD patients, is the well-known side effect for ICS. Therefore, when prescribing ICS to COPD patients, its positive effects and side effects must be fully considered. In this study, we intend to create a model that predicts COPD patients who can expect a positive effect when using ICS. Through this study, we expect to improve the quality of life and provide better therapeutic effects for patients requiring ICS in COPD patients.
DOI: 10.25934/00006716
title: Applying machine learning tools to personalize dabigatran treatment decisions

description: Atrial fibrillation—a condition in which the top of the heart beats irregularly—affects 35.5 million people worldwide. People with atrial fibrillation experience a heightened risk of stroke and are therefore often prescribed “blood thinners”, or anti-clot medications. Unfortunately, the most commonly-used drug called warfarin presents risks and burdens to patients, including the need for laboratory monitoring, and interactions with many other medications. 

The newer medication dabigatran does not pose these limitations. In the RELY trial, dabigatran 150 mg twice daily was superior to warfarin in preventing a stroke or major clot, while dabigatran 110 mg twice daily was similar to warfarin for these outcomes. Both doses greatly reduced strokes caused by bleeding in the brain, and dabigatran 110 mg twice daily significantly reduced major bleeding anywhere in the body in comparison with warfarin.  

The investigators of RELY commented that because the risk of clot was lower with the 150-mg dose, but the risk of major bleeding was lower with the 100mg dose, it may be possible to tailor the choice of dosage to an individual patient, based on their unique risk of clotting or bleeding. There were variations in the absolute risk reduction in clotting and absolute risk increase in bleeding within each group of participants on dabigatran in RELY and the subsequent RELY-ABLE trial. Traditional statistical analyses (called univariate subgroup analyses) have low power for detecting what features contribute to greater benefit or greater harm from a medication, but newer “machine learning” methods have greater power for accomplishing this aim. 

Our objective is to create a personalized dabigatran treatment decision tool, which is a calculator that estimates the personalized estimated reduction in risk in stroke or major clot, and increase in risk of major bleeding, from either dose of dabigatran. The tool would be developed using a novel machine learning method called gradient forest analysis, which analyzes participant data from trials to assess how multiple combinations of patient features predict benefit or harm from a drug. Our research will help improve patient care by assisting clinicians to determine the potential risks and benefits of dabigatran, and optimal dose of dabigatran, before prescribing it. We have chosen the gradient forest method because it has been shown to have benefits over previous methods, particularly reducing the rate of wrong conclusions (“false positive” findings), accounting for complex interactions between multiple factors (e.g., a patient who is both lower weight and of South Asian ethnicity may have more benefit than a patient with just one or the other feature), and producing correct (“unbiased”) estimates of the change in the probability of an event on the drug.  

The findings will be interpreted using a pre-specified protocol for assessing significance of the results, and communicated through a peer-reviewed journal.
DOI: 10.7294/17092283.v1
title: Longitudinal Survey of Cybercriminology – November 2019

description: This survey is part of a series of cross-sectional online polls. The series begins in 2019. Questions focus around three main areas, cyberoffending, cybervictimization, and criminology. Demographic and other computer use questions are also included.