Gender representation in authorship in later-phase systemic clinical trials in biliary tract cancer (BTC).

2021 ◽  
Vol 39 (3_suppl) ◽  
pp. 348-348
Author(s):  
Mairead Geraldine McNamara ◽  
John A. Bridgewater ◽  
Lipika Goyal ◽  
David Goldstein ◽  
Rachna T. Shroff ◽  
...  

348 Background: The proportion of females in medicine is increasing (approx. 50% in medical school/workforce), but disparities in female authorship in oncology research publications exist; female corresponding authorship reportedly ranges from 7.2-39.1% in oncology clinical trials (Ludmir et al 2019). This study aimed to describe and assess factors associated with female first and senior authorship in later phase systemic clinical trials in BTC and to identify any changes over time. Methods: Embase/Medline were used to identify final primary trial publications in BTC (2000-2020) (excluding phase I (PI) (expected to move to later phase), mixed tumour site trials, reviews, editorials and trial-in-progress publications). Gender was determined by inspection of names, google search and author communication. Chi-square tests and log regression were used to assess factors associated with female first and senior authorship, including changes over time (STATA16). Results: Of 501 publications, 163 met inclusion criteria; 80% single-arm PII and 15% and 5% randomised PII and PIII respectively; 73% enrolled ≤50 patients. Tumour primary sites were all BTC: 86%, cholangiocarcinoma: 8%, gallbladder cancer: 6%; 80% involved chemotherapy, 13% targeted therapy and 5% localised/systemic combinations; 65% were in first-line (1L) advanced setting, 17% post 1L, 13% advanced non-specified and 5% neo-adjuvant/adjuvant. Forty-eight percent received industry funding and 65% met primary end-point. Sixty-four percent were published post ABC-02 (Valle et al 2010). Publication impact factor (IF) was ≤5 in 50% and >20 in 12%. Median number of authors in all publications was 11. Geographic location of all first and senior authors were Asia (42%/42%), Europe (29%/29%), USA (24%/22%) and other (4%/6%), respectively. Median individual trial female author representation was 25%; there were no female authors in 12% of trials. Overall, female first and senior author representation was 21% and 11%, respectively. Median position of first female author was second. In publications with IF ≤20 and >20, there were 22% and 16% female first and 13% and 0% female senior authors, respectively. The phase of trial, journal IF, industry funding, or whether met primary end-point did not impact female first or senior author representation (all P>.05). There were more female senior authors associated with “other” geographic locations (40% in 10 trials) (P=.016) vs Asia (7%), Europe (8%) and USA (14%). There were no significant changes in female first or senior author representation over time (‘00-05: 21%/18%, ‘06-10: 27%/5%, ‘11-15: 15%/15%, ‘16-20: 22%/9%, P=.738, and P=.508 respectively). Conclusions: Female first and senior author representation in later phase systemic clinical trial publications in BTC is low and has not changed significantly over time. The underlying reasons for this imbalance need to be better understood and addressed.

Oncology ◽  
2021 ◽  
pp. 1-10
Author(s):  
Robert L. Coleman ◽  
J. Thaddeus Beck ◽  
Joaquina C. Baranda ◽  
Ira Jacobs ◽  
Karen E. Smoyer ◽  
...  

<b><i>Objective:</i></b> To investigate patient-reported outcome (PRO) usage in phase I oncology clinical trials, including types of PRO measures and changes over time. <b><i>Methods:</i></b> We analyzed ClinicalTrials.gov records of phase I oncology clinical trials completed by December 2019. <b><i>Results:</i></b> Of all eligible trials, 2.3% (129/5,515) reported ≥1 PRO, totaling 181 instances of PRO usage. PRO usage increased over time, from 0.6% (trials initiated before 2000) to 3.4% (trials starting between 2015 and 2019). The most common PRO measures were unspecified (29%), tumor-specific (24%), and generic cancer (19%). <b><i>Conclusion:</i></b> Although uncommon in phase I oncology clinical trials, PRO usage is increasing over time. PRO measures were often unspecified on ClinicalTrials.gov, suggesting that more precise reporting and standardization are needed.


2020 ◽  
Author(s):  
Torfinn S. Madssen ◽  
Guro F. Giskeødegård ◽  
Age K. Smilde ◽  
Johan A. Westerhuis

AbstractLongitudinal intervention studies with repeated measurements over time are an important type of experimental design in biomedical research. Due to the advent of “omics”-sciences (genomics, transcriptomics, proteomics, metabolomics), longitudinal studies generate increasingly multivariate outcome data. Analysis of such data must take both the longitudinal intervention structure and multivariate nature of the data into account. The ASCA+-framework combines general linear models with principal component analysis, and can be used to separate and visualize the multivariate effect of different experimental factors. However, this methodology has not yet been developed for the more complex designs often found in longitudinal intervention studies, which may be unbalanced, involve randomized interventions, and have substantial missing data. Here we describe a new methodology, repeated measures ASCA+ (RM-ASCA+), and show how it can be used to model metabolic changes over time, and compare metabolic changes between groups, in both randomized and non-randomized intervention studies. Tools for both visualization and model validation are discussed. This approach can facilitate easier interpretation of data from longitudinal clinical trials with multivariate outcomes.Author summaryClinical trials are increasingly generating large amounts of complex biological data. Examples can include measuring metabolism or gene expression in tissue or blood sampled repeatedly over the course of a treatment. In such cases, one might wish to compare changes in not one, but hundreds, or thousands of variables simultaneously. In order to effectively analyze such data, both the study design and the multivariate nature of the data should be considered during data analysis. ANOVA simultaneous component analysis+ (ASCA+) is a statistical method which combines general linear models with principal component analysis, and provides a way to separate and visualize the effects of different factors on complex biological data. In this work, we describe how repeated measures linear mixed models, a class of models commonly used when analyzing changes over time and treatment effects in longitudinal studies, can be used together with ASCA+ for analyzing clinical trials in a novel method called repeated measures-ASCA+ (RM-ASCA+).


Author(s):  
Michelle S. Phelps ◽  
Devah Pager

After decades of steady expansion, state prison populations declined in recent years for the first time since 1972. Though the size of the decrease was small, it masks substantial state heterogeneity. This article investigates variation in state-level incarceration rates from 1980 through 2013, examining the factors associated with the rise and decline in prison populations. We find evidence for four key stories in explaining the prison decline: crime, budgets, politics, and inequality. Many of these relationships are consistent across decades, including the role of racial composition, violent crime, and Republican political dominance. In contrast, states’ fiscal capacity and economic inequality became more important after 2000. This research emphasizes the importance of examining changes over time in the correlates of incarceration growth and decline and represents the first effort to systematically understand the recent reversal in the trajectory of incarceration practices in the United States.


Author(s):  
W.J. Becker

Background:The place of health-related quality of life (HRQoL) instruments in clinical research trials and clinical practice as compared to more traditional clinical outcome measures such as headache intensity and frequency is unclear.Objectives:To review the current status of HRQoL measurement in migraine.Methods:A literature search was done for HRQoL and migraine. Selected articles dealing with migraine and commonly used HRQoL instruments and HRQoL measures used in recent clinical trials were reviewed.Results:Several general and migraine specific HRQoLinstruments can detect changes over time in response to at least major changes in migraine therapy. Both also show a correlation with clinical headache features. However, their sensitivity to detect clinically significant changes over time is not clear.Conclusion:The SF-36, a general HRQoLmeasure and several migraine-specific HRQoL instruments are useful endpoints for migraine clinical trials. Their role in clinical practice is yet to be established.


2011 ◽  
Vol 38 (9) ◽  
pp. 2023-2030 ◽  
Author(s):  
CHARLES G. PETERFY ◽  
PETER COUNTRYMAN ◽  
ANNARITA GABRIELE ◽  
TIM SHAW ◽  
ANDREW ANISFELD ◽  
...  

Objective.The current validated magnetic resonance imaging (MRI) scoring method for rheumatoid arthritis (RA) in clinical trials, RA MRI Score (RAMRIS), incorporates all metacarpophalangeal (MCP) and wrist joints except MCP-1. The experience with radiographic scoring, however, was that excluding certain bones in the wrist improved the discriminative power for changes over time. In this study, we pool MRI data from randomized controlled clinical trails (RCT) to determine which combination of MCP and wrist joints are most sensitive and discriminative for structural changes over time.Methods.MR images from 4 multicenter RCT, including 522 RA patients, were read by 2 radiologists, using the RAMRIS scoring system for erosion, osteitis, and synovitis. In one RCT, joint-space narrowing (JSN) was assessed cross-sectionally by one radiologist using a previously validated method. Baseline frequencies of erosion, JSN, osteitis, and synovitis of different bones and joints in the hand and wrist were compared. Intraclass correlation coefficients between readers were determined for each location. Finally, 7 different combinations of bone/joint locations were compared for their ability to discriminate subjects showing increases or decreases from baseline greater than or equal to smallest detectable changes (SDC) at Weeks 12 or 24.Results.Frequency of involvement and reliability for assessing change varied by location. As in earlier analyses, excluding certain wrist bones increased the percentage of subjects showing changes greater than or equal to SDC.Conclusion.These findings suggest that excluding wrist bones that do not frequently or reliably demonstrate structural changes improves the discriminative power of the RAMRIS scoring system.


2021 ◽  
Author(s):  
Dan-Yu Lin ◽  
Yu Gu ◽  
Donglin Zeng ◽  
Holly E. Janes ◽  
Peter B. Gilbert

AbstractAlthough interim results from several large placebo-controlled phase 3 trials demonstrated high vaccine efficacy (VE) against symptomatic COVID-19, it is unknown how effective the vaccines are in preventing people from becoming asymptomatically infected and potentially spreading the virus unwittingly. It is more difficult to evaluate VE against SARS-CoV-2 infection than against symptomatic COVID-19 because infection is not observed directly but rather is known to occur between two antibody or RT-PCR tests. Additional challenges arise as community transmission changes over time and as participants are vaccinated on different dates because of staggered enrollment or crossover before the end of the study. Here, we provide valid and efficient statistical methods for estimating potentially waning VE against SARS-CoV-2 infection with blood or nasal samples under time-varying community transmission, staggered enrollment, and blinded or unblinded crossover. We demonstrate the usefulness of the proposed methods through numerical studies mimicking the BNT162b2 phase 3 trial and the Prevent COVID U study. In addition, we assess how crossover and the frequency of diagnostic tests affect the precision of VE estimates.SummaryWe show how to estimate potentially waning efficacy of COVID-19 vaccines against SARS-CoV-2 infection using blood or nasal samples collected periodically from clinical trials with staggered enrollment of participants and crossover of placebo recipients.


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