Early career researchers face a hypercompetitive funding environment. To help identify effective intervention strategies for early career researchers, we examined whether first-time NIH R01 ...applicants who resubmitted their original, unfunded R01 application were more successful at obtaining any R01 funding within 3 and 5 years than original, unfunded applicants who submitted new NIH applications, and we examined whether underrepresented minority (URM) applicants differentially benefited from resubmission. Our observational study is consistent with an NIH working group's recommendations to develop interventions to encourage resubmission.
First-time applicants with US medical school academic faculty appointments who submitted an unfunded R01 application between 2000-2014 yielded 4,789 discussed and 7,019 not discussed applications. We then created comparable groups of first-time R01 applicants (resubmitted original R01 application or submitted new NIH applications) using optimal full matching that included applicant and application characteristics. Primary and subgroup analyses used generalized mixed models with obtaining any NIH R01 funding within 3 and 5 years as the two outcomes. A gamma sensitivity analysis was performed. URM applicants represented 11% and 12% of discussed and not discussed applications, respectively. First-time R01 applicants resubmitting their original, unfunded R01 application were more successful obtaining R01 funding within 3 and 5 years than applicants submitting new applications-for both discussed and not discussed applications: discussed within 3 years (OR 4.17 95 CI 3.53, 4.93) and 5 years (3.33 2.82-3.92); and not discussed within 3 years (2.81 2.52, 3.13) and 5 years (2.47 2.22-2.74). URM applicants additionally benefited within 5 years for not discussed applications.
Encouraging early career researchers applying as faculty at a school of medicine to resubmit R01 applications is a promising potential modifiable factor and intervention strategy. First-time R01 applicants who resubmitted their original, unfunded R01 application had log-odds of obtaining downstream R01 funding within 3 and 5 years 2-4 times higher than applicants who did not resubmit their original application and submitted new NIH applications instead. Findings held for both discussed and not discussed applications.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Current standards for methodological rigor and trial reporting underscore the critical issue of statistical power. Still, the chance of detecting most effects reported in randomized controlled trials ...in medicine and other disciplines is currently lower than winning a toss of a fair coin. Here we propose that investigators who retain a practical understanding of how statistical power works can proactively avoid the potentially devastating consequences of underpowered trials. We first offer a vivid, carefully constructed analogy that illuminates the underlying relationships among 3 of the 5 essential parameters-namely, statistical power, effect size, and sample size-while holding the remaining 2 parameters constant (type of statistical test and significance level). Second, we extend the analogy to a set of critical scenarios in which investigators commonly miss detecting intervention effects due to insufficient statistical power. Third, we highlight effective pragmatic strategies for the design and conduct of sufficiently powered trials, without increasing sample size.
Valve-replacement outcomes were examined with statewide data in California. Bioprostheses were associated with higher long-term mortality than mechanical valves among patients up to 55 years of age ...for aortic-valve replacement and up to 70 years of age for mitral-valve replacement.
This study examines the prevalence of depression, anxiety, and post-traumatic stress disorder (PTSD) among adolescents attending schools in several informal settlements of Nairobi, Kenya. Primary ...aims were estimating prevalence of these mental health conditions, understanding their relationship to gender-based violence (GBV), and assessing changes in response to an empowerment intervention.
Mental health measures were added to the final data collection point of a two-year randomized controlled trial (RCT) evaluating an empowerment self-defense intervention. Statistical models evaluated how past sexual violence, access to money to pay for a needed hospital visit, alcohol use, and self-efficacy affect both mental health outcomes as well as how the intervention affected female students' mental health.
Population prevalence of mental health conditions for combined male and female adolescents was estimated as: PTSD 12.2% (95% confidence interval 10.5-15.4), depression 9.2% (95% confidence interval 6.6-10.1) and anxiety 17.6% (95% confidence interval 11.2% - 18.7%). Female students who reported rape before and during the study-period reported significantly higher incidence of all mental health outcomes than the study population. No significant differences in outcomes were found between female students in the intervention and standard-of-care (SOC) groups. Prior rape and low ability to pay for a needed hospital visit were associated with higher prevalence of mental health conditions. The female students whose log-PTSD scores were most lowered by the intervention (effects between -0.23 and -0.07) were characterized by high ability to pay for a hospital visit, low agreement with gender normative statements, larger homes, and lower academic self-efficacy.
These data illustrate a need for research and interventions related to (1) mental health conditions among the young urban poor in low-income settings, and (2) sexual violence as a driver of poor mental health, leading to a myriad of negative long-term outcomes.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Because greater percentages of women deliver at hospitals without high-level NICUs, there is little information on the effect of delivery hospital on the outcomes of premature infants in the past 2 ...decades, or how these effects differ across states with different perinatal regionalization systems.
A retrospective population-based cohort study was constructed of all hospital-based deliveries in Pennsylvania and California between 1995 and 2005 and Missouri between 1995 and 2003 with a gestational age between 23 and 37 weeks (N = 1328132). The effect of delivery at a high-level NICU on in-hospital death and 5 complications of premature birth was calculated by using an instrumental variables approach to control for measured and unmeasured differences between hospitals.
Infants who were delivered at a high-level NICU had significantly fewer in-hospital deaths in Pennsylvania (7.8 fewer deaths/1000 deliveries, 95% confidence interval CI 4.1-11.5), California (2.7 fewer deaths/1000 deliveries, 95% CI 0.9-4.5), and Missouri (12.6 fewer deaths/1000 deliveries, 95% CI 2.6-22.6). Deliveries at high-level NICUs had similar rates of most complications, with the exception of lower bronchopulmonary dysplasia rates at Missouri high-level NICUs (9.5 fewer cases/1000 deliveries, 95% CI 0.7-18.4) and higher infection rates at high-level NICUs in Pennsylvania and California. The association between delivery hospital, in-hospital mortality, and complications differed across the 3 states.
There is benefit to neonatal outcomes when high-risk infants are delivered at high-level NICUs that is larger than previously reported, although the effects differ between states, which may be attributable to different methods of regionalization.
There is some initial evidence suggesting that mindsets about the adequacy and health consequences of one's physical activity (activity adequacy mindsets AAMs) can shape physical activity behavior, ...health, and well-being. However, it is unknown how to leverage these mindsets using wearable technology and other interventions.
This research examined how wearable fitness trackers and meta-mindset interventions influence AAMs, affect, behavior, and health.
A total of 162 community-dwelling adults were recruited via flyers and web-based platforms (ie, Craigslist and Nextdoor; final sample size after attrition or exclusion of 45 participants). Participants received an Apple Watch (Apple Inc) to wear for 5 weeks, which was equipped with an app that recorded step count and could display a (potentially manipulated) step count on the watch face. After a baseline week of receiving no feedback about step count, participants were randomly assigned to 1 of 4 experimental groups: they received either accurate step count (reference group; 41/162, 25.3%), 40% deflated step count (40/162, 24.7%), 40% inflated step count (40/162, 24.7%), or accurate step count+a web-based meta-mindset intervention teaching participants the value of adopting more positive AAMs (41/162, 25.3%). Participants were blinded to the condition. Outcome measures were taken in the laboratory by an experimenter at the beginning and end of participation and via web-based surveys in between. Longitudinal analysis examined changes within the accurate step count condition from baseline to treatment and compared them with changes in the deflated step count, inflated step count, and meta-mindset conditions.
Participants receiving accurate step counts perceived their activity as more adequate and healthier, adopted a healthier diet, and experienced improved mental health (Patient-Reported Outcomes Measurement Information System PROMIS-29) and aerobic capacity but also reduced functional health (PROMIS-29; compared with their no-step-count baseline). Participants exposed to deflated step counts perceived their activity as more inadequate; ate more unhealthily; and experienced more negative affect, reduced self-esteem and mental health, and increased blood pressure and heart rate (compared with participants receiving accurate step counts). Inflated step counts did not change AAM or most other outcomes (compared with accurate step counts). Participants receiving the meta-mindset intervention experienced improved AAM, affect, functional health, and self-reported physical activity (compared with participants receiving accurate step counts only). Actual step count did not change in either condition.
AAMs--induced by trackers or adopted deliberately--can influence affect, behavior, and health independently of actual physical activity.
ClinicalTrials.gov NCT03939572; https://www.clinicaltrials.gov/ct2/show/NCT03939572.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Background US veterans report lower health-related quality of life (HRQoL) relative to the general population. Identifying behavioral factors related to HRQoL that are malleable to change may inform ...interventions to improve well-being in this vulnerable group. Purpose The current study sought to characterize HRQoL in a largely male sample of veterans in addictions treatment, both in relation to US norms and in association with five recommended health behavior practices: regularly exercising, managing stress, having good sleep hygiene, consuming fruits and vegetables, and being tobacco free. Methods We assessed HRQoL with 250 veterans in addictions treatment (96 % male, mean age 53, range 24-77) using scales from four validated measures. Data reduction methods identified two principal components reflecting physical and mental HRQoL. Model testing of HRQoL associations with health behaviors adjusted for relevant demographic and treatment-related covariates. Results Compared to US norms, the sample had lower HRQoL scores. Better psychological HRQoL was associated with higher subjective social standing, absence of pain or trauma, lower alcohol severity, and monotonically with the sum of health behaviors (all p < 0.05). Specifically, psychological HRQoL was associated with regular exercise, stress management, and sleep hygiene. Regular exercise also related to better physical HRQoL. The models explained >40 % of the variance in HRQoL. Conclusions Exercise, sleep hygiene, and stress management are strongly associated with HRQoL among veterans in addictions treatment. Future research is needed to test the effect of interventions for improving well-being in this high-risk group.
BACKGROUND:Whether a second arterial conduit improves outcomes after multivessel coronary artery bypass grafting remains unclear. Consequently, arterial conduits other than the left internal thoracic ...artery are seldom used in the United States.
METHODS:Using a state-maintained clinical registry including all 126 nonfederal hospitals in California, we compared all-cause mortality and rates of stroke, myocardial infarction, repeat revascularization, and sternal wound infection between propensity score–matched cohorts who underwent primary, isolated multivessel coronary artery bypass grafting with the left internal thoracic artery, and who received a second arterial conduit (right internal thoracic artery or radial artery, n=5866) or a venous conduit (n=53 566) between 2006 and 2011. Propensity score matching using 34 preoperative characteristics yielded 5813 matched sets. A subgroup analysis compared outcomes between propensity score–matched recipients of a right internal thoracic artery (n=1576) or a radial artery (n=4290).
RESULTS:Second arterial conduit use decreased from 10.7% in 2006 to 9.1% in 2011 (P<0.0001). However, receipt of a second arterial conduit was associated with significantly lower mortality (13.1% versus 10.6% at 7 years; hazard ratio, 0.79; 95% confidence interval CI, 0.72–0.87), and lower risks of myocardial infarction (hazard ratio, 0.78; 95% CI, 0.70–0.87) and repeat revascularization (hazard ratio, 0.82; 95% CI, 0.76–0.88). In comparison with radial artery grafts, right internal thoracic artery grafts were associated with similar mortality rates (right internal thoracic artery 10.3% versus radial artery 10.7% at 7 years; hazard ratio, 1.10; 95% CI, 0.89–1.37) and individual risks of cardiovascular events, but the risk of sternal wound infection was increased (risk difference, 1.07%; 95% CI, 0.15–2.07).
CONCLUSIONS:Second arterial conduit use in California is low and declining, but arterial grafts were associated with significantly lower mortality and fewer cardiovascular events. A right internal thoracic artery graft offered no benefit over that of a radial artery, but did increase risk of sternal wound infection. These findings suggest surgeons should consider lowering their threshold for using arterial grafts, and the radial artery may be the preferred second conduit.
Heterogeneous treatment effects (HTEs), or systematic differences in treatment effectiveness among participants with different observable features, may be important when applying trial results to ...clinical practice. Current methods suffer from a potential for false detection of HTEs due to imbalances in covariates between candidate subgroups.
We introduce a new method, matching plus classification and regression trees (mCART), that yields balance in covariates in identified HTE subgroups. We compared mCART to a classical method (logistic regression LR with backwards covariate selection using the Akaike information criterion ) and two machine-learning approaches increasingly applied to HTE detection (random forest RF and gradient RF) in simulations with a binary outcome with known HTE subgroups. We considered an N = 200 phase II oncology trial where there were either no HTEs (1A) or two HTE subgroups (1B) and an N = 6000 phase III cardiovascular disease trial where there were either no HTEs (2A) or four HTE subgroups (2B). Additionally, we considered an N = 6000 phase III cardiovascular disease trial where there was no average treatment effect but there were four HTE subgroups (2C).
In simulations 1A and 2A (no HTEs), mCART did not identify any HTE subgroups, whereas LR found 2 and 448, RF 5 and 2, and gradient RF 5 and 24, respectively (all false positives). In simulation 1B, mCART failed to identify the two true HTE subgroups whereas LR found 4, RF 6, and gradient RF 10 (half or more of which were false positives). In simulations 2B and 2C, mCART captured the four true HTE subgroups, whereas the other methods found only false positives. All HTE subgroups identified by mCART had acceptable treated vs. control covariate balance with absolute standardized differences less than 0.2, whereas the absolute standardized differences for the other methods typically exceeded 0.2. The imbalance in covariates in identified subgroups for LR, RF, and gradient RF indicates the false HTE detection may have been due to confounding.
Covariate imbalances may be producing false positives in subgroup analyses. mCART could be a useful tool to help prevent the false discovery of HTE subgroups in secondary analyses of randomized trial data.
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•Patterns from clinical order entry data can yield relevant decision support content.•Automatic patterns outperform manually-authored order sets by multiple metrics.•Deviation in ...observed from expected patient outcomes can stratify clinicians.•Patterns from all clinicians prove more robust than those from “preferred” clinicians.
Evaluate the quality of clinical order practice patterns machine-learned from clinician cohorts stratified by patient mortality outcomes.
Inpatient electronic health records from 2010 to 2013 were extracted from a tertiary academic hospital. Clinicians (n = 1822) were stratified into low-mortality (21.8%, n = 397) and high-mortality (6.0%, n = 110) extremes using a two-sided P-value score quantifying deviation of observed vs. expected 30-day patient mortality rates. Three patient cohorts were assembled: patients seen by low-mortality clinicians, high-mortality clinicians, and an unfiltered crowd of all clinicians (n = 1046, 1046, and 5230 post-propensity score matching, respectively). Predicted order lists were automatically generated from recommender system algorithms trained on each patient cohort and evaluated against (i) real-world practice patterns reflected in patient cases with better-than-expected mortality outcomes and (ii) reference standards derived from clinical practice guidelines.
Across six common admission diagnoses, order lists learned from the crowd demonstrated the greatest alignment with guideline references (AUROC range = 0.86–0.91), performing on par or better than those learned from low-mortality clinicians (0.79–0.84, P < 10−5) or manually-authored hospital order sets (0.65–0.77, P < 10−3). The same trend was observed in evaluating model predictions against better-than-expected patient cases, with the crowd model (AUROC mean = 0.91) outperforming the low-mortality model (0.87, P < 10−16) and order set benchmarks (0.78, P < 10−35).
Whether machine-learning models are trained on all clinicians or a subset of experts illustrates a bias-variance tradeoff in data usage. Defining robust metrics to assess quality based on internal (e.g. practice patterns from better-than-expected patient cases) or external reference standards (e.g. clinical practice guidelines) is critical to assess decision support content.
Learning relevant decision support content from all clinicians is as, if not more, robust than learning from a select subgroup of clinicians favored by patient outcomes.
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