Analysis Of Clinical Trial Data Research Articles

Post hoc analysis of SUSTAIN 6 and PIONEER 6 trials suggests that people with type 2 diabetes at high cardiovascular risk treated with semaglutide experience more stable kidney function compared with placebo

Glucagon-like peptide-1 receptor agonists reduce albuminuria and may stabilize the estimated glomerular filtration rate (eGFR) in people with type 2 diabetes (T2D). In this post hoc analysis of the SUSTAIN 6/PIONEER 6 trials encompassing 6480 participants at high cardiovascular risk (semaglutide, 3239 participants; placebo, 3241 participants), we investigated the effects of semaglutide versus placebo on eGFR decline. Pooled data by treatment were evaluated for annual eGFR change (total annual eGFR slope in ml/min per 1.73 m2) from baseline to end of treatment and time to persistent eGFR reductions of 30%, 40%, 50% and 57% or more, including subgroup analyses by baseline eGFR (30 to under 60 or 60 and over ml/min per 1.73 m2). In the overall population, the estimated treatment difference (ETD; semaglutide versus placebo) in annual eGFR slope was significant at 0.59 ml/min per 1.73 m2 (95% confidence interval 0.29; 0.89). The ETD was numerically largest in the 30 to under 60 ml/min per 1.73 m2 eGFR subgroup, 1.06 ml/min per 1.73 m2(0.45; 1.67), but no significant interaction was observed for treatment effect by subgroup. Hazard ratios (semaglutide versus placebo) for time to persistent eGFR decline were under 1.0 for all eGFR thresholds in the overall population; and were numerically lower in the baseline eGFR 30 to under 60 ml/min per 1.73 m2 subgroup versus the overall population, although no significant interaction was observed for treatment effect by subgroup. Thus, pooled analyses of clinical trial data in patients with T2D suggest that semaglutide may reduce the rate of eGFR decline.

Predictive biomarkers for PD-1/PD-L1 checkpoint inhibitor response in NSCLC: an analysis of clinical trial and real-world data

BackgroundMany biomarkers have been proposed to be predictive of response to anti-programmed cell death protein-1 (PD-1)/anti-programmed death ligand-1 (PD-L1) checkpoint inhibitors (CPI). However, conflicting observations and lack of consensus call...

PP01.78 Brain Metastasis in Patients With KRAS Mutant Advanced NSCLC Receiving Docetaxel: Pooled Clinical Trial Data Analysis

PP01.78 Brain Metastasis in Patients With KRAS Mutant Advanced NSCLC Receiving Docetaxel: Pooled Clinical Trial Data Analysis

Bayesian Design Study of the TGN1412 Trial

Objective: This study considers the Bayesian design of clinical trials from the perspective of a comparative study of the Bayesian approach for the TGN1412 clinical trial. The Bayesian setting analyses the statistical issues of the TGN1412 trials’ first in man studies, which include the details of the Northwick Park trial, and the clinical study design recommendations are implemented. Methods: The clinical trial data analysis was carried out to screen the structural evidence of the trial outcomes. Hierarchical modeling is implemented with structural priors to get the most efficient Bayesian outcomes. Findings: We concentrated on determining the best model for the clinical pathway of randomization and Bayesian design of experiments. Novelty: The Bayesian design of experiments is widely used for outcome prediction under various treatments in order to validate the trial’s dosing periods. The purpose of this paper is to compare the growth models for Phase I trial results. Current research on Bayesian designs incorporates simulation studies on the effects of design variables. Keywords: Clinical Trial; Bayesian; Efficacy; Dose Limiting Event; Desirable Outcome; Treatment Effects; Cohort Effects; Dose Response

Opportunities and challenges in application of artificial intelligence in pharmacology.

Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research.

Comparing translational success rates across medical research fields - A combined analysis of literature and clinical trial data.

Many interventions that show promising results in preclinical development do not pass clinical tests. Part of this may be explained by poor animal-to-human translation. Using animal models with low predictability for humans is neither ethical nor efficient. If translational success shows variation between medical research fields, analyses of common practices in these fields could identify factors contributing to successful translation. We assessed translational success rates in medical research fields using two approaches: through literature and clinical trial registers. Literature: We comprehensively searched PubMed for pharmacology, neuroscience, cancer research, animal models, clinical trials, and translation. After screening, 117 review papers were included in this scoping review. Translational success rates were not different within pharmacology (72%), neuroscience (62%), and cancer research (69%). Clinical trials: The fraction of phase-2 clinical trials with a positive outcome was used as a proxy (i.e., an indirect resemblance measure) for translational success. Trials were retrieved from the WHO trial register and categorized into medical research fields following the international classification of disease (ICD-10). Of the phase-2 trials analyzed, 65.2% were successful. Fields with the highest success rates were disorders of lipoprotein metabolism (86.0%) and epilepsy (85.0%). Fields with the lowest success rates were schizophrenia (45.4%) and pancreatic cancer (46.0%). Our combined analyses suggest relevant differences in success rates between medical research fields. Based on the clinical trials, comparisons of practice, e.g., between epilepsy and schizophrenia, might identify factors that influence translational success.

Comparing translational success rates across medical research fields - A combined analysis of literature and clinical trial data_suppl1

Comparing translational success rates across medical research fields - A combined analysis of literature and clinical trial data_suppl1

Predicting risk factors for pediatric mortality in clinical trial research: A retrospective, cross-sectional study using a Healthcare Cost and Utilization Project database.

Incorporating real-world data using "big data" analysis in healthcare are useful to extract specific information for healthcare delivery system improvement. All-cause mortality is an essential measure to enhance patient safety in clinical trial research, especially for underrepresented pediatric participants. This study aimed to determine the associations between pediatric mortality and patient-specific factors using the Healthcare Cost and Utilization Project (HCUP) database. Data from the 2019 the HCUP Kids' Inpatient Database (KID) were used to conduct a logistic regression analysis to determine associations between pediatric patients' the chance of survival and their demographic and socioeconomic background, discharge records, and hospital information. Total number of diagnoses (OR = 0.84), total number of procedures (OR = 0.86), length of stay (OR = 1.04), age intervals greater than 1 year (OR > 1.0), transfer into the hospital from a different acute care (OR = 0.34), major diagnoses of multiple significant trauma (OR = 0.03) or hepatobiliary system and pancreas (OR = 0.10), region of hospital - west and midwest (OR > 1.0), and medium or larger hospital bed size (OR > 1.0) were all significantly associated with the chance of survival for patients participating in pediatric clinical trials (p < 0.05). Real-world clinical trial data analysis showed the potential improvement area including reallocating trial resources to promote trial quality and safe participation for pediatric patients. Pediatric trials need tools that are developed using user-centered design approaches to satisfy the unique needs and requirements of pediatric patients and their caregivers. Safe intrahospital transfer procedures and active dissemination of successful trial best practices are crucial to trial management, adherence, quality, and safety.

Introducing the fragility index-A case study using the Term Breech Trial.

The fragility index (FI) is a sensitivity analysis of the statistically significant result of a clinical study. It is the number of hypothetical changes in the primary event of one of the two cohorts in a 1-to-1 comparative trial to render the statistically significant result non-significant (ie, to alter the P-value from ≤0.05 to >0.05). The FI can be compared with the patient drop-out rates and protocol violations, which, if much higher than the FI, may arguably suggest less robustness/stability of the trial's results. To illustrate the concept, we have chosen the Term Breech Trial (TBT) as a case study. The TBT results favor planned cesarean birth, as opposed to planned vaginal delivery, in the term singleton fetus with breech presentation. Our analysis shows that the FI of the TBT is 21, which is small in comparison to the number (hundreds) of protocol violations present. Some experts have suggested the inclusion of the FI in data analysis and subsequent discussion of clinical trial data. Routine use of such a metric may be valuable in encouraging readers to maintain a healthy degree of skepticism, especially when interpreting trial results which may directly influence clinical practice.

Bayesian tests of two proportions: A tutorial with R and JASP

The need for a comparison between two proportions (sometimes called an A/B test) often arises in business, psychology, and the analysis of clinical trial data. Here we discuss two Bayesian A/B tests that allow users to monitor the uncertainty about a difference in two proportions as data accumulate over time. We emphasize the advantage of assigning a dependent prior distribution to the proportions (i.e., assigning a prior to the log odds ratio). This dependent-prior approach has been implemented in the open-source statistical software programs R and JASP. Several examples demonstrate how JASP can be used to apply this Bayesian test and interpret the results.

The Hidden Pandemic of COVID-19-Induced Organizing Pneumonia.

Since the beginning of the COVID-19 pandemic, clinical, radiological, and histopathological studies have provided evidence that organizing pneumonia is a possible consequence of the SARS-CoV2 infection. This post-COVID-19 organizing pneumonia (PCOP) causes persisting dyspnea, impaired pulmonary function, and produces radiological abnormalities for at least 5 weeks after onset of symptoms. While most patients with PCOP recover within a year after acute COVID-19, 5-25% of cases need specialized treatment. However, despite substantial resources allocated worldwide to finding a solution to this problem, there are no approved treatments for PCOP. Oral corticosteroids produce a therapeutic response in a majority of such PCOP patients, but their application is limited by the anticipated high-relapse frequency and the risk of severe adverse effects. Herein, we conduct a systematic comparison of the epidemiology, pathogenesis, and clinical presentation of the organizing pneumonias caused by COVID-19 as well as other viral infections. We also use the clinical efficacy of corticosteroids in other postinfection OPs (PIOPs) to predict the therapeutic response in the treatment of PCOP. Finally, we discuss the potential application of a candidate anti-inflammatory and antifibrotic therapy for the treatment of PCOP based on the analysis of the latest clinical trials data.

Post hoc analysis of clinical trial data and pharmacokinetic data to assess wearing-off of erenumab within monthly treatment cycle.

Treatment wearing-off has been reported for calcitonin gene-related peptide-pathway monoclonal antibodies, including erenumab, specifically in the last week of the monthly dosing cycle. We sought to determine the consistency of erenumab effect throughout the monthly treatment cycle. In this post hoc analysis of four pivotal double-blind, randomized controlled studies of erenumab in episodic and chronic migraine, we assessed wearing-off based on change in weekly migraine days at week 4 versus average over weeks 1-3 in each monthly dosing cycle. Analyses were conducted at each monthly dosing cycle in all patients, in responders (≥50% reduction in weekly migraine days), and in consistent responders (response in ≥2monthly cycles). There was no evidence of wearing-off in the full study populations of two global studies (N=946 and N=656) and two Japan studies (N=475 and N=261). In the full study population, mean change in weekly migraine days at week 4 compared with the average over week 1-3 ranged from 0.15 days improvement to 0.19 days worsening in the placebo group and 0.08 days improvement to 0.20 days worsening in the erenumab groups. A subgroup of responders experienced wearing-off, but the extent of wearing-off did not differ between erenumab and placebo groups. The mean change in weekly migraine days at week 4 compared with the average over weeks 1-3 ranged from 0.34 to 0.61 days worsening in the placebo group and 0.27 to 0.78 days worsening in the erenumab groups. Few patients had persistent wearing-off in ≥2 consecutive monthly treatment cycles. For erenumab-treated responders, serum erenumab concentrations were similar among patients experiencing wearing-off and those maintaining response. No systematic wearing-off with erenumab was identified. Further research is needed to determine if wearing-off reported for some patients in clinical practice reflects a true treatment response pattern or normal fluctuations in migraine frequency.

Bicycle ergometer exercise during hemodialysis and its impact on quality of life, aerobic fitness and dialysis adequacy: A pilot study

BackgroundChronic kidney disease patients on hemodialysis commonly have a worse quality of life (QoL) due to complications of the disease and dialysis procedure. Physical exercise has emerged as a strategy to improve this scenario. The objective of this study was to evaluate the effect of an intradialytic aerobic exercise program on QoL and aerobic fitness in hemodialysis patients. Material and methodsThese are a secondary analysis of clinical trial data previously published in which hemodialysis patients were randomized into “bike group” (using an adapted exercise bicycle) or “control group” (usual care). The exercise sessions lasted 45 min (5 min of warm-up, 35 min of moderate-intensity and 5 min of cool-down) three times/week for three months. The QoL domains were assessed using the SF-36 QoL questionnaire. Aerobic fitness was evaluated using the 6-min walk test (6MWT). Circulating cytokines, biochemical parameters and Kt/V were also assessed. ResultsNine patients completed three months of exercise (5 men, 44 ± 11 years), and nine were in the control group (6 men, 44 ± 14 years). In the bike group, there was a trend to improve the physical role domain (p = 0.06) regarding QoL, an improvement in the 6MWT (p = 0.02), and in the Kt/V (p = 0.03) after three months. There was a positive correlation between the general health domain and Kt/V (r = 0.691; p = 0.003) and an inverse correlation between the physical functioning domain and plasma TNF-α levels (r = −0.514; p = 0.04). Conclusions12 weeks of intradialytic aerobic exercise was enough to benefit hemodialysis patients' quality of life, aerobic fitness, and quality of dialysis.Clinicaltrials.gov id: NCT04375553.

Randomization-based Joint Central Limit Theorem and Efficient Covariate Adjustment in Randomized Block 2 K Factorial Experiments

Randomized block factorial experiments are widely used in industrial engineering, clinical trials, and social science. Researchers often use a linear model and analysis of covariance to analyze experimental results; however, limited studies have addressed the validity and robustness of the resulting inferences because assumptions for a linear model might not be justified by randomization in randomized block factorial experiments. In this article, we establish a new finite population joint central limit theorem for usual (unadjusted) factorial effect estimators in randomized block 2 K factorial experiments. Our theorem is obtained under a randomization-based inference framework, making use of an extension of the vector form of the Wald–Wolfowitz–Hoeffding theorem for a linear rank statistic. It is robust to model misspecification, numbers of blocks, block sizes, and propensity scores across blocks. To improve the estimation and inference efficiency, we propose four covariate adjustment methods. We show that under mild conditions, the resulting covariate-adjusted factorial effect estimators are consistent, jointly asymptotically normal, and generally more efficient than the unadjusted estimator. In addition, we propose Neyman-type conservative estimators for the asymptotic covariances to facilitate valid inferences. Simulation studies and a clinical trial data analysis demonstrate the benefits of the covariate adjustment methods. Supplementary materials for this article are available online.

Ther-O-14 - A post-hoc analysis of clinical trial data shows that prior phototherapy does not affect response to chlormethine gel in patients with mycosis fungoides

Ther-O-14 - A post-hoc analysis of clinical trial data shows that prior phototherapy does not affect response to chlormethine gel in patients with mycosis fungoides

Infrastructure platform for privacy-preserving distributed machine learning development of computer-assisted theragnostics in cancer

IntroductionEmerging evidence suggests that data-driven support tools have found their way into clinical decision-making in a number of areas, including cancer care. Improving them and widening their scope of availability in various differing clinical scenarios, including for prognostic models derived from retrospective data, requires co-ordinated data sharing between clinical centres, secondary analyses of large multi-institutional clinical trial data, or distributed (federated) learning infrastructures. A systematic approach to utilizing routinely collected data across cancer care clinics remains a significant challenge due to privacy, administrative and political barriers. MethodsAn information technology infrastructure and web service software was developed and implemented which uses machine learning to construct clinical decision support systems in a privacy-preserving manner across datasets geographically distributed in different hospitals. The infrastructure was deployed in a network of Australian hospitals. A harmonized, international ontology-linked, set of lung cancer databases were built with the routine clinical and imaging data at each centre. The infrastructure was demonstrated with the development of logistic regression models to predict major cardiovascular events following radiation therapy. ResultsThe infrastructure implemented forms the basis of the Australian computer-assisted theragnostics (AusCAT) network for radiation oncology data extraction, reporting and distributed learning. Four radiation oncology departments (across seven hospitals) in New South Wales (NSW) participated in this demonstration study. Infrastructure was deployed at each centre and used to develop a model predicting for cardiovascular admission within a year of receiving curative radiotherapy for non-small cell lung cancer. A total of 10,417 lung cancer patients were identified with 802 being eligible for the model. Twenty features were chosen for analysis from the clinical record and linked registries. After selection, 8 features were included and a logistic regression model achieved an area under the receiver operating characteristic (AUROC) curve of 0.70 and C-index of 0.65 on out-of-sample data. ConclusionThe infrastructure developed was demonstrated to be usable in practice between clinical centres to harmonize routinely collected oncology data and develop models with federated learning. It provides a promising approach to enable further research studies in radiation oncology using real world clinical data.

Clinical Efficacy and Safety of Psoriasis Treatments in Patients with Concomitant Metabolic Syndrome: A Narrative Review.

Metabolic syndrome (MetS) is well recognized as a frequent comorbidity of psoriasis with important implications for efficacy and safety of psoriasis treatment. The presence of concomitant MetS is associated with decreased efficacy response to biologic treatment for psoriasis in observational studies. In post hoc analyses of clinical trial data, the anti–IL-23p19 antibody tildrakizumab appears to maintain efficacy in patients compared to those without MetS; no published subgroup analyses by MetS status are yet available for other biologics. However, there is some evidence that obese patients have decreased psoriasis treatment efficacy with biologics with certain mechanisms of action relative to overweight patients. This confounds interpretation of the effect of MetS due to the association between MetS and body weight. Because of the association between MetS and cardiovascular risk, treatment of psoriasis in patients with concomitant MetS requires special consideration for cardiovascular safety and attention to potential for exacerbation of MetS and related conditions, including nonalcoholic fatty liver disease. Additional studies are needed to clarify the risks for treatment failure and cardiovascular safety concerns in patients with psoriasis and concomitant MetS.

Predictors of Placebo Induction Response and Remission in Ulcerative Colitis

High placebo response rates in clinical trials of ulcerative colitis (UC) have been reported previously. However, data from patient-level analyses are lacking. We assessed factors associated with clinical and endoscopic placebo response among placebo-treated patients in clinical trials of UC. We performed a post hoc analysis of pooled clinical trial data from GEMINI-1, ACT-1, ACT-2, PURSUIT, ULTRA-2, OCTAVE-1, and OCTAVE-2. Predictors were assessed in placebo-treated patients for their association with end of induction (week 6 of 8) clinical response (reduction in total Mayo score of ≥3 and ≥30% from baseline with ≥1 point decrease in rectal bleeding subscore [RBS] or absolute RBS ≤1); clinical remission (total Mayo score ≤2 and no subscore >1); endoscopic healing (Mayo endoscopic subscore ≤1); partial Mayo score of 0; patient-reported outcome 2-item remission (RBS of 0 and stool frequency ≤1), resolution of rectal bleeding, and stool frequency normalization. Predictors on univariate analyses with P < .05 were included in multivariate logistic regression models. Placebo-treated patients with normal serum C-reactive protein and albumin levels were more likely to attain clinical response (71 of 437 [16.3%] vs 49 of 660 [7.4%]; adjusted odds ratio, 2.76; 95% confidence interval, 1.19-5.41; P= .018). Compared with patients with a Mayo endoscopic score of 2, patients with a Mayo endoscopic score of 3 were less likely to attain clinical response (105 of 556 [18.8%] vs 179 of 675 [25.9%]; adjusted odds ratio, 0.33; 95% confidence interval, 0.16-0.68; P= .003). Similar findings were observed for clinical remission and resolution of rectal bleeding. Biomarkers such as normal serum C-reactive protein and albumin and baseline endoscopic severity were found to affect placebo response rates in clinical trials of UC. These findings have implications for clinical trial design in UC.

16. Likelihood of achieving physiological range of motion after cervical disc arthroplasty: analysis of IDE clinical trial data

16. Likelihood of achieving physiological range of motion after cervical disc arthroplasty: analysis of IDE clinical trial data

Generalization error bounds of dynamic treatment regimes in penalized regression-based learning

A dynamic treatment regime (DTR) is a sequence of decision rules, one per stage of intervention, that maps up-to-date patient information to a recommended treatment. Discovering an appropriate DTR for a given disease is a challenging issue especially when a large set of prognostic variables are observed. To address this problem, we propose penalized regression-based learning methods with l1 penalty to estimate the optimal DTR that would maximize the expected outcome if implemented. We also provide generalization error bounds of the estimated DTR in the setting of finite number of stages with multiple treatment options. We first examine the relationship between value and Q-functions and derive a finite sample upper bound on the difference in values between the optimal and the estimated DTRs. For practical implementation, we develop an algorithm with partial regularization via orthogonality to construct the optimal DTR. The advantages of the proposed methods are demonstrated with extensive simulation studies and data analysis of depression clinical trials.