Business experience and start-up size: buying more lottery tickets next time around?

This paper explores the determinants of start-up size by focusing on a cohort of 6247 businesses that started trading in 2004, using a unique dataset on customer records at Barclays Bank. Quantile regressions show that prior business experience is significantly related with start-up size, as are a number of other variables such as age, education and bank account activity. Quantile treatment effects (QTE) estimates show similar results, with the effect of business experience on (log) start-up size being roughly constant across the quantiles. Prior personal business experience leads to an increase in expected start-up size of about 50%. Instrumental variable QTE estimates are even higher, although there are concerns about the validity of the instrument

Stochastic Variability in Stress, Sleep Duration, and Sleep Quality Across the Distribution of Body Mass Index: Insights from Quantile Regression

[[abstract]]Background Obesity has become a problem in the USA and identifying modifiable factors at the individual level may help to address this public health concern. A burgeoning literature has suggested that sleep and stress may be associated with obesity; however, little is know about whether these two factors moderate each other and even less is known about whether their impacts on obesity differ by gender. Purpose This study investigates whether sleep and stress are associated with body mass index (BMI) respectively, explores whether the combination of stress and sleep is also related to BMI, and demonstrates how these associations vary across the distribution of BMI values. Methods We analyze the data from 3,318 men and 6,689 women in the Philadelphia area using quantile regression (QR) to evaluate the relationships between sleep, stress, and obesity by gender. Results Our substantive findings include: (1) high and/or extreme stress were related to roughly an increase of 1.2 in BMI after accounting for other covariates; (2) the pathways linking sleep and BMI differed by gender, with BMI for men increasing by 0.77–1 units with reduced sleep duration and BMI for women declining by 0.12 unit with 1 unit increase in sleep quality; (3) stress- and sleep-related variables were confounded, but there was little evidence for moderation between these two; (4) the QR results demonstrate that the association between high and/or extreme stress to BMI varied stochastically across the distribution of BMI values, with an upward trend, suggesting that stress played a more important role among adults with higher BMI (i.e., BMI > 26 for both genders); and (5) the QR plots of sleep-related variables show similar patterns, with stronger effects on BMI at the upper end of BMI distribution. Conclusions Our findings suggested that sleep and stress were two seemingly independent predictors for BMI and their relationships with BMI were not constant across the BMI distribution.[[incitationindex]]SSCI[[booktype]]紙

Women in Charnley class C fail to improve in mobility to a higher degree after total hip replacement

BACKGROUND: The Charnley comorbidity classification organizes patients into 3 classes: (A) 1 hip involved, (B) 2 hips involved, and (C) other severe comorbidities. Although this simple classification is a known predictor of health-related quality of life (HRQoL) after total hip replacement (THR), interactions between Charnley class, sex, and age have not been investigated and there is uncertainty regarding whether A and B should be grouped together. METHODS: We selected a nationwide cohort of patients from the Swedish Hip Arthroplasty Register operated with THR due to primary osteoarthritis between 2008 and 2010. For estimation of HRQoL, we used the generic health outcome questionnaire EQ-5D of the EuroQol group. This consists of 2 parts: the EQ-5D index and the EQ VAS estimates. We modeled the EQ-5D index and the EQ VAS against the self-administered Charnley classification. Confounding was controlled for using preoperative HRQoL values, pain, and previous contralateral hip surgery. RESULTS: We found that women in class C had a poorer EQ-5D outcome than men. This effect was mostly due to the fact that women failed to improve in the mobility dimension; only 40% improved, while about 50% of men improved. Age did not interact with Charnley class. We also found that the classification performed best without splitting or aggregating classes. INTERPRETATION: Our results suggests that the self-administered Charnley classification should be used in its full capacity and that it may be interesting to devote special attention to women in Charnley class C

From population to subject-specific reference intervals

In clinical practice, normal values or reference intervals are the main point of reference for interpreting a wide array of measurements, including biochemical laboratory tests, anthropometrical measurements, physiological or physical ability tests. They are historically defined to separate a healthy population from unhealthy and therefore serve a diagnostic purpose. Numerous cross-sectional studies use various classical parametric and nonparametric approaches to calculate reference intervals. Based on a large cross-sectional study (N = 60,799), we compute reference intervals for subpopulations (e.g. males and females) which illustrate that subpopulations may have their own specific and more narrow reference intervals. We further argue that each healthy subject may actually have its own reference interval (subject-specific reference intervals or SSRIs). However, for estimating such SSRIs longitudinal data are required, for which the traditional reference interval estimating methods cannot be used. In this study, a linear quantile mixed model (LQMM) is proposed for estimating SSRIs from longitudinal data. The SSRIs can help clinicians to give a more accurate diagnosis as they provide an interval for each individual patient. We conclude that it is worthwhile to develop a dedicated methodology to bring the idea of subject-specific reference intervals to the preventive healthcare landscape

Penalized least absolute deviations estimation for nonlinear model with change-points

Change-point, Parametric nonlinear model, SLAD estimator, Monte Carlo simulations, 62F12, 60J02, 62F40,