Ten years of NIHR research training: who got an award? A retrospective cohort study

ObjectiveIn 2017, the National Institute for Health Research (NIHR) academy produced a strategic review of training, which reported the variation in application characteristics associated with success rates. It was noted that variation in applicant characteristic was not independent of one another. Therefore, the aim of this secondary analysis was to investigate the inter-relationships in order to identify factors (or groups of factors) most associated with application numbers and success rates.DesignRetrospective data were gathered from 4388 applications to NIHR Academy between 2007 and 2016. Multinominal logistic regression models quantified the likelihood of success depending on changes in the explanatory factors; relative risk ratios with 95% CIs. A classification tree analysis was built using exhaustive χ2 automatic interaction detection to better understand the effect of interactions between explanatory variables on application success rates.Results936 (21.3%) applications were awarded. Applications from males and females were equally likely to be successful (p=0.71). There was an overall reduction in numbers of applications from females as award seniority increased from predoctoral to professorship. Applications from institutions with a medical school had a 2.6-fold increase in likelihood of success (p<0.001). Classification tree analysis revealed key predictors of application success: award level, type of programme, previous NIHR award experience and applying form a medical school.ConclusionSuccess rates did not differ according to gender, and doctors were not more likely to be successful than applications from other professions. Taken together, these findings suggest an essential fairness in how the quality of a submitted application is assessed, but they also raise questions about variation in the opportunity to submit a high-quality application. The companion qualitative study (Burkshaw et al. (2021) BMJ Open) provides valuable insight into potential candidate mechanisms and discusses how research capacity development initiatives might be targeted in the future.

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Diminished ovarian reserve is the major cause of infertility among women undergoing their first assisted reproductive technology treatment: a retrospective observational study

10.21203/rs.3.rs-124361/v1 ◽

2020 ◽

Author(s):

Mengqian Zhang ◽

Hongxian Zhang ◽

Rui Yang ◽

Guoshuang Feng ◽

Huiyu Xu ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Ovarian Reserve ◽

Cox Regression ◽

Classification Tree ◽

Diminished Ovarian Reserve ◽

Classification Tree Analysis ◽

Success Rates ◽

Tree Analysis ◽

Cox Regression Analysis

Abstract Background This study aims to investigate the effects of various factors on treatment outcomes in women undergoing in vitro fertilization or intracytoplasmic sperm injection (IVF/ICSI) with embryo transfer (ET). Methods Of the 8993 eligible women who underwent their first IVF/ICSI–ET cycles, and met our inclusion and exclusion criteria, 2742(30.5%) achieved clinical pregnancy while 6251(69.5%) did not. Multivariable Cox regression analysis, multiple logistic regression analysis, and classification tree analysis were used sequentially to screen key predictors among predictors of various infertility causes and ovarian stimulation protocols through the best subset technique. Results Multivariate Cox regression analysis showed that the main factor affecting fertility in first attempts at IVF/ICSI–ET is diminished ovarian reserve (DOR), with a hazard ratio (HR) of 0.406 and 95% confidence interval (CI) of 0.353–0.466. Multiple forward logistic regression with 5-fold cross-validation also found that, with an odds ratio (OR) of 2.522 (95% CI = 2.167–2.937), DOR affects fertility. The classification tree analysis was further used to better visualize the model. Conclusions DOR is the major factor affecting success rates in couples undergoing their first attempt at IVF/ICSI-ET. The selection of the most appropriate pairs for IVF/ICSI treatment can not only increase the success rates but also the cumulative cost-effectiveness.

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Classifying and prioritizing the success and failure factors of technology commercialization of public R&D in South Korea: using classification tree analysis

The Journal of Technology Transfer ◽

10.1007/s10961-014-9376-5 ◽

2014 ◽

Vol 40 (5) ◽

pp. 877-898 ◽

Cited By ~ 4

Author(s):

Mijung Jung ◽

Yi-beck Lee ◽

Heesang Lee

Keyword(s):

South Korea ◽

Classification Tree ◽

Technology Commercialization ◽

Classification Tree Analysis ◽

Tree Analysis ◽

Failure Factors

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Early prediction of poor outcome in extremely low birth weight infants by classification tree analysis

The Journal of Pediatrics ◽

10.1016/j.jpeds.2005.11.042 ◽

2006 ◽

Vol 148 (4) ◽

pp. 438-444.e1 ◽

Cited By ~ 29

Author(s):

N. Ambalavanan ◽

A. Baibergenova ◽

W.A. Carlo ◽

S. Saigal ◽

B. Schmidt ◽

...

Keyword(s):

Birth Weight ◽

Low Birth Weight ◽

Classification Tree ◽

Extremely Low Birth Weight ◽

Early Prediction ◽

Classification Tree Analysis ◽

Low Birth Weight Infants ◽

Tree Analysis ◽

Poor Outcome

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Classification tree analysis to examine influences on colorectal cancer screening

Cancer Causes & Control ◽

10.1007/s10552-015-0523-6 ◽

2015 ◽

Vol 26 (3) ◽

pp. 443-454 ◽

Cited By ~ 7

Author(s):

Gregory M. Dominick ◽

Mia A. Papas ◽

Michelle L. Rogers ◽

William Rakowski

Keyword(s):

Colorectal Cancer ◽

Cancer Screening ◽

Colorectal Cancer Screening ◽

Classification Tree ◽

Classification Tree Analysis ◽

Tree Analysis

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Substance Use Relapse Among Veterans at Termination of Treatment for Substance Use Disorders

Military Medicine ◽

10.1093/milmed/usab280 ◽

2021 ◽

Author(s):

Christian A Betancourt ◽

Panagiota Kitsantas ◽

Deborah G Goldberg ◽

Beth A Hawks

Keyword(s):

Risk Factors ◽

Substance Use ◽

Logistic Regression ◽

Substance Use Disorders ◽

Classification Tree ◽

Treatment Completion ◽

Classification Tree Analysis ◽

Data Set ◽

Tree Analysis ◽

Substance Addiction

ABSTRACT Introduction Military veterans continue to struggle with addiction even after receiving treatment for substance use disorders (SUDs). Identifying factors that may influence SUD relapse upon receiving treatment in veteran populations is crucial for intervention and prevention efforts. The purpose of this study was to examine risk factors that contribute to SUD relapse upon treatment completion in a sample of U.S. veterans using logistic regression and classification tree analysis. Materials and Methods Data from the 2017 Treatment Episode Data Set—Discharge (TEDS-D) included 40,909 veteran episode observations. Descriptive statistics and multivariable logistic regression analysis were conducted to determine factors associated with SUD relapse after treatment discharge. Classification trees were constructed to identify high-risk subgroups for substance use after discharge from treatment for SUDs. Results Approximately 94% of the veterans relapsed upon discharge from outpatient or residential SUD treatment. Veterans aged 18-34 years old were significantly less likely to relapse than the 35-64 age group (odds ratio [OR] 0.73, 95% confidence interval [CI]: 0.66, 0.82), while males were more likely than females to relapse (OR 1.55, 95% CI: 1.34, 1.79). Unemployed veterans (OR 1.92, 95% CI: 1.67, 2.22) or veterans not in the labor force (OR 1.29, 95% CI: 1.13, 1.47) were more likely to relapse than employed veterans. Homeless vs. independently housed veterans had 3.26 (95% CI: 2.55, 4.17) higher odds of relapse after treatment. Veterans with one arrest vs. none were more likely to relapse (OR 1.52, 95% CI: 1.19, 1.95). Treatment completion was critical to maintain sobriety, as every other type of discharge led to more than double the odds of relapse. Veterans who received care at 24-hour detox facilities were 1.49 (95% CI: 1.23, 1.80) times more likely to relapse than those at rehabilitative/residential treatment facilities. Classification tree analysis indicated that homelessness upon discharge was the most important predictor in SUD relapse among veterans. Conclusion Aside from numerous challenges that veterans face after leaving military service, SUD relapse is intensified by risk factors such as homelessness, unemployment, and insufficient SUD treatment. As treatment and preventive care for SUD relapse is an active field of study, further research on SUD relapse among homeless veterans is necessary to better understand the epidemiology of substance addiction among this vulnerable population. The findings of this study can inform healthcare policy and practices targeting veteran-tailored treatment programs to improve SUD treatment completion and lower substance use after treatment.

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