Multistate quantile regression models

A mark-specific quantile regression model

Quantile regression has become a widely used tool for analysing competing risk data. However, quantile regression for competing risk data with a continuous mark is still scarce. The mark variable is an extension of cause of failure in a classical competing risk model where cause of failure is replaced by a continuous mark only observed at uncensored failure times. An example of the continuous mark variable is the genetic distance that measures dissimilarity between the infecting virus and the virus contained in the vaccine construct. In this article, we propose a novel mark-specific quantile regression model. The proposed estimation method borrows strength from data in a neighbourhood of a mark and is based on an induced smoothed estimation equation, which is very different from the existing methods for competing risk data with discrete causes. The asymptotic properties of the resulting estimators are established across mark and quantile continuums. In addition, a mark-specific quantile-type vaccine efficacy is proposed and its statistical inference procedures are developed. Simulation studies are conducted to evaluate the finite sample performances of the proposed estimation and hypothesis testing procedures. An application to the first HIV vaccine efficacy trial is provided.

Global Epidemiological Patterns in the Burden of Main Non-Communicable Diseases, 1990-2019: Relationships With Socio-Demographic Index

Objectives: This study aimed to analyze spatio-temporal patterns of the global burden caused by main NCDs along the socio-economic development. Methods: We extracted relevant data from GBD 2019. The estimated annual percentage changes, quantile regression and limited cubic splines were adopted to estimate temporal trends and relationships with socio-demographic index. Results: NCDs accounted for 74.36% of global all-cause deaths in 2019. The main NCDs diseases were estimated for cardiovascular diseases, neoplasms, and chronic respiratory diseases, with deaths of 18.56 (17.08-19.72) million, 10.08 (9.41-10.66) million and 3.97 (3.58-4.30) million, respectively. The death burden of three diseases gradually decreased globally over time. Regional and sex variations existed worldwide. Besides, the death burden of CVD showed the inverted U-shaped associations with SDI, while neoplasms were positively correlated with SDI, and CRD showed the negative association. Conclusion: NCDs remain a crucial public health issue worldwide, though several favorable trends of CVD, neoplasms and CRD were observed. Regional and sex disparities still existed. Public health managers should execute more targeted programs to lessen NCDs burden, predominantly among lower SDI countries.

Burden of diabetes and kidney disease attributable to non-optimal temperature from 1990 to 2019: A systematic analysis from the Global Burden of Disease Study 2019

INTRODUCTION

This study quantitatively described the disease burden of diabetes and kidney disease attributable to non-optimal temperatures and explored the influencing factors.

METHODS

We quantitatively described the mortality burden of diabetes and kidney disease attributable to non-optimal temperatures in six countries (China, USA, South Africa, Australia, Iraq, Portugal), and compare trends in mortality in six countries from 1990 to 2019. We used the APC model to analyse age, period, and cohort effects on mortality in six countries. We used restricted cubic splines and quantile regression to analyse the association of SDI with mortality and YLL using data from 21 regions in the world.

RESULTS

The mortality rates of diabetes and kidney disease in the six countries in 2019 were 1.72% (Australia), 1.83% (China), 2.99% (USA), 3% (Portugal), 7.45% (South Africa) and 8.71% (Iraq) attributable to non-optimal temperatures. Cold was more harmful than heat. The mortality, YLLs of diabetes and kidney disease of male were higher than females. The mortality rate showed an upwards trend with age. The period effect had little changes or showed a slight upwards trend. The cohort effect showed a downwards trend. The regions with higher mortality or YLLs rates were mainly had SDI values of 0.45-0.80.

CONCLUSIONS

Among the death burdens of diabetes and kidney disease attributed to non-optimal temperatures, cold had a greater burden than heat. The burden of death was affected by sex, age, period, cohort, and SDI.

Model-robust designs for nonlinear quantile regression

We construct robust designs for nonlinear quantile regression, in the presence of both a possibly misspecified nonlinear quantile function and heteroscedasticity of an unknown form. The asymptotic mean-squared error of the quantile estimate is evaluated and maximized over a neighbourhood of the fitted quantile regression model. This maximum depends on the scale function and on the design. We entertain two methods to find designs that minimize the maximum loss. The first is local - we minimize for given values of the parameters and the scale function, using a sequential approach, whereby each new design point minimizes the subsequent loss, given the current design. The second is adaptive - at each stage, the maximized loss is evaluated at quantile estimates of the parameters, and a kernel estimate of scale, and then the next design point is obtained as in the sequential method. In the context of a Michaelis-Menten response model for an estrogen/hormone study, and a variety of scale functions, we demonstrate that the adaptive approach performs as well, in large study sizes, as if the parameter values and scale function were known beforehand and the sequential method applied. When the sequential method uses an incorrectly specified scale function, the adaptive method yields an, often substantial, improvement. The performance of the adaptive designs for smaller study sizes is assessed and seen to still be very favourable, especially so since the prior information required to design sequentially is rarely available.