Robust Cox Regression as an Alternative Method to Estimate Adjusted Relative Risk in Prospective Studies with Common Outcomes

Objective: To demonstrate the use of robust Cox regression in estimating adjusted relative risks (and confidence intervals) when all participants with an identical follow-up time and when a common outcome is investigated.Methods: In this paper, we propose an alternative statistical method, robust Cox regression, to estimate adjusted relative risks in prospective studies. We use simulated cohort data to examine the suitability of robust Cox regression.Results: Robust Cox regression provides estimates that are equivalent to those of modified Poisson regression: regression coefficients, relative risks, 95% confidence intervals, P values. It also yields reasonable probabilities (bounded by 0 and 1). Unlike modified Poisson regression, robust Cox regression allows for four automatic variable selection methods, it directly computes adjusted relative risks for continuous variables, and is able to incorporate time-dependent covariates.Conclusion: Given the popularity of Cox regression in the medical and epidemiological literature, we believe that robust Cox regression may gain wider acceptance and application in the future. We recommend robust Cox regression as an alternative analytical tool to modified Poisson regression. In this study we demonstrated its utility to estimate adjusted relative risks for common outcomes in prospective studies with two or three waves of data collection (spaced similarly)

Cognitive decline among older adults with heart diseases before and during the COVID-19 pandemic: A longitudinal cohort study

BackgroundLittle is known about the impact induced by the COVID-19 pandemic on the cognitive function of older adults with heart diseases. This study aimed to examine whether older adults with heart diseases suffered larger cognitive deterioration during the COVID-19 pandemic.MethodsThis study leveraged longitudinal data from the Health and Retirement Study (HRS), a nationally representative U.S. aging cohort with objective cognitive assessments measured before and during the pandemic. The interval from HRS waves 13 to 14 (April 2016 to June 2019) was defined as the pre-pandemic period to control the pre-existed cognitive difference between participants with and without heart diseases, and the interval from waves 14 to 15 (June 2019 to June 2021) was defined as the pandemic period. The HRS wave 14 survey was considered the baseline. The heart disease status was defined by a self-reported diagnosis. Linear mixed models were performed to evaluate and compare the cognitive differences during different periods.ResultsA total of 9,304 participants (women: 5,655, 60.8%; mean age: 65.8 ± 10.8 years) were included, and 2,119 (22.8%) had heart diseases. During the pre-pandemic period, there was no significant difference (−0.03, 95% CI: −0.22 to 0.15, P = 0.716) in the changes in global cognitive scores between participants with and without heart disease. During the pandemic period, a larger decreased change in the global cognitive score was observed in the heart disease group compared with the non-heart disease group (−0.37, 95% CI: −0.55 to −0.19, P < 0.001). An enlarged difference in global cognitive score was observed during the pandemic period (−0.33, 95% CI: −0.65 to −0.02, P = 0.036).ConclusionThe findings demonstrated that the population with heart diseases suffered more cognitive decline related to the pandemic, underscoring the necessity to provide immediate cognitive monitoring and interventions for the population with heart diseases

Editorial: Epidemiology and clinical researches on neuropsychiatric disorders in aging

 With the rising aging population in a global range, related neuropsychiatric disorders such as depression and dementia, have emerged and caused a tremendous disease burden. Over the past decades, many risk factors have been identified (1–12), and advances have been made in developing prevention and intervention strategies. However, there still exist challenges to be addressed. These challenges include but are not limited to early detection and prediction of neuropsychiatric disorders, comorbidities of both neuropsychiatric and non-neuropsychiatric aspects, identifying novel indicators for disease progression and prognosis, as well as investigating potential mediating mechanisms. Facing unprecedented challenges, we launched this Research Topic to promote healthy aging and longevity from the neuropsychiatric perspective, via collaboration from a number of professional disciplines. </p

Predictive value for cardiovascular events of common carotid intima media thickness and its rate of change in individuals at high cardiovascular risk - Results from the PROG-IMT collaboration.

AIMS: Carotid intima media thickness (CIMT) predicts cardiovascular (CVD) events, but the predictive value of CIMT change is debated. We assessed the relation between CIMT change and events in individuals at high cardiovascular risk. METHODS AND RESULTS: From 31 cohorts with two CIMT scans (total n = 89070) on average 3.6 years apart and clinical follow-up, subcohorts were drawn: (A) individuals with at least 3 cardiovascular risk factors without previous CVD events, (B) individuals with carotid plaques without previous CVD events, and (C) individuals with previous CVD events. Cox regression models were fit to estimate the hazard ratio (HR) of the combined endpoint (myocardial infarction, stroke or vascular death) per standard deviation (SD) of CIMT change, adjusted for CVD risk factors. These HRs were pooled across studies. In groups A, B and C we observed 3483, 2845 and 1165 endpoint events, respectively. Average common CIMT was 0.79mm (SD 0.16mm), and annual common CIMT change was 0.01mm (SD 0.07mm), both in group A. The pooled HR per SD of annual common CIMT change (0.02 to 0.43mm) was 0.99 (95% confidence interval: 0.95-1.02) in group A, 0.98 (0.93-1.04) in group B, and 0.95 (0.89-1.04) in group C. The HR per SD of common CIMT (average of the first and the second CIMT scan, 0.09 to 0.75mm) was 1.15 (1.07-1.23) in group A, 1.13 (1.05-1.22) in group B, and 1.12 (1.05-1.20) in group C. CONCLUSIONS: We confirm that common CIMT is associated with future CVD events in individuals at high risk. CIMT change does not relate to future event risk in high-risk individuals

Automatic identification of variables in epidemiological datasets using logic regression

textabstractBackground: For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable. Methods: For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated. Results: In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34%, and a negative predictive value (NPV) of 95%. In the validation sample, PPV was 33%, whereas NPV remained at 94%. In the construction sample, PPV was 50% or less in 63% of all variables, in the validation sample in 71% of all variables. Conclusions: We demonstrated that the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies

Supplementary-2018-10-23

Supplementary Fil

Anticardiolipin antibody and anti-beta(2) glycoprotein I antibody are potential risk markers of ischaemic stroke in Chinese adults

Objectives. aCL and anti-beta(2) glycoprotein I antibody (a beta(2)GPI) are autoantibodies associated with thromboembolic diseases. Here we investigated whether they are correlated with ischaemic cardiovascular disease in a Chinese population. Methods. Serum total aCL and a beta(2)GPI isotypes (IgA, IgG or IgM, separately) were measured in 11 015 Chinese adults. Differences of antibody level between disease and non-disease groups were examined by t-test. The correlation between antibody and ischaemic cardiovascular disease was determined by logistic regression analysis. Performance of risk prediction models employed aCL or a beta(2)GPI isotypes was evaluated by C statistic, net reclassification improvement index and integrated discrimination improvement. Results. Total aCL and a beta(2)GPI isotypes maintained low levels and increased with increasing age except total aCL and a beta(2)GPI IgG in participants older than 70 years. When distinguishing ischaemic cardiovascular disease by coronary heart disease (CHD) and ischaemic stroke, the stroke group had higher levels of aCL and a beta(2)GPI isotypes than the non-stroke group, while the CHD group only had a slightly higher a beta(2)GPI IgG than non-CHD groups. aCL and a beta(2)GPI were positively correlated with stroke but not with CHD, and improved the performance of conventional risk factors for stroke risk prediction, with C statistic from 0.769 (95% CI 0.744, 0.793) to 0.777 (95% CI 0.754, 0.800) (a beta(2)GPI IgG, P = 0.0091), and 0.778 (95% CI 0.754, 0.801) (a beta(2)GPI IgA, P = 0.0793). Stroke risk could be better reclassified by aCL and a beta(2)GPI, in association with both net reclassification improvement and integrated discrimination improvement statistics (P<0.05). Conclusion. aCL and a beta(2)GPI are associated with ischaemic stroke and have added value for stroke risk prediction

Accuracy model, analysis, and adjustment in the context of multi-closed-loop planar deployable mechanisms

The links of the multi-closed-loop deployable mechanism are generally adjusted when being assembled, for the purpose of making the whole mechanism satisfy the requirements in fully deployed, folded, or the other key configurations. In order to calculate the appropriate adjustment amount, the complex adjustment amount of the whole mechanism is first defined by root mean square. And then, the adjustment model for planar mechanism involving different constraints is constructed according to the different situations. Through linearization of this model, it can be applied to calculate the adjustment sensitivity of this type of mechanisms. Second, with the factor of the practical adjustment in engineering, the three computation modes of adjustment (bidirectional adjustment, invariant adjustment, and unidirectional adjustment) have been developed. Furthermore, the multi-configuration adjustment approach has been proposed to make the assembled mechanism satisfy the requirements in different configurations. At last, all the above approaches in this article have been implemented in the scissor-like element mechanism and two-loop deployable mechanism. The achievement of this research provides a significant reference for assembling the multi-closed-loop mechanisms