Synthesis Analysis of Regression Models with a Continuous Outcome

Synthesis Analysis of Regression Models with a Continuous Outcome Xiao-Hua Zhou 1,2, Nan Hu 2, Guizhou Hu3, and Martin Root3 1 HSR&D Center of Excellence, VA Puget Sound Health Care System, Seattle, WA 98101. 2 Department of Biostatistics, University of Washington, Seattle, WA 98195. 3 BioSignia, Inc., 1822 East NC Highway 54, Suite 350, Durham, NC 27713 To estimate the multivariate regression model from multiple individual studies, it would be challenging to obtain results if the input from individual studies only provide univariate or incomplete multivariate regression information. Samsa et al [1] proposed a simple method to combine coefficients from univariate linear regression models into a multivariate linear regression model, a method known as synthesis analysis. However, the validity of this method relies on the normality assumption of the data, and it does not provide variance estimates. In this paper we propose a new synthesis method that improves on the existing synthesis method by eliminating the normality assumption, reducing bias, and allowing for the variance estimation of the estimated parameters

Combining Information From Multiple Data Sources to Create Multivariable Risk Models: Illustration and Preliminary Assessment of a New Method

A common practice of metanalysis is combining the results of numerous studies on the effects of a risk factor on a disease outcome. If several of these composite relative risks are estimated from the medical literature for a specific disease, they cannot be combined in a multivariate risk model, as is often done in individual studies, because methods are not available to overcome the issues of risk factor colinearity and heterogeneity of the different cohorts. We propose a solution to these problems for general linear regression of continuous outcomes using a simple example of combining two independent variables from two sources in estimating a joint outcome. We demonstrate that when explicitly modifying the underlying data characteristics (correlation coefficients, standard deviations, and univariate betas) over a wide range, the predicted outcomes remain reasonable estimates of empirically derived outcomes (gold standard). This method shows the most promise in situations where the primary interest is in generating predicted values as when identifying a high-risk group of individuals. The resulting partial regression coefficients are less robust than the predicted values

Statistical evaluation of adding multiple risk factors improves Framingham stroke risk score

Abstract Background Framingham Stroke Risk Score (FSRS) is the most well-regarded risk appraisal tools for evaluating an individual’s absolute risk on stroke onset. However, several widely accepted risk factors for stroke were not included in the original Framingham model. This study proposed a new model which combines an existing risk models with new risk factors using synthesis analysis, and applied it to the longitudinal Atherosclerosis Risk in Communities (ARIC) data set. Methods Risk factors in original prediction models and new risk factors in proposed model had been discussed. Three measures, like discrimination, calibration and reclassification, were used to evaluate the performance of the original Framingham model and new risk prediction model. Results Modified C-statistics, Hosmer-Lemeshow Test and classless NRI, class NRI were the statistical indices which, respectively, denoted the performance of discrimination, calibration and reclassification for evaluating the newly developed risk prediction model on stroke onset. It showed that the NEW-STROKE (new stroke risk score prediction model) model had higher modified C-statistics, smaller Hosmer-Lemeshow chi-square values after recalibration than original FSRS model, and the classless NRI and class NRI of the NEW-STROKE model over the original FSRS model were all significantly positive in overall group. Conclusion The NEW-STROKE integrated with seven literature-derived risk factors outperformed the original FSRS model in predicting the risk score of stroke. It illustrated that seven literature-derived risk factors contributed significantly to stroke risk prediction

Indoor coal combustion emissions, GSTM1 and GSTT1 genotypes, and lung cancer risk: A case-control study in Xuan Wei, China

The lung cancer mortality rate in Xuan Wei County, China is among the highest in the country and has been associated with exposure to indoor smoky coal emissions that contain high levels of polycyclic aromatic hydrocarbons. This risk may be modified by variation in metabolism genes, including GSTM1, which encodes an enzyme known to detoxify polycyclic aromatic hydrocarbons. To investigate the relationship between GST genotypes and lung cancer risk in Xuan Wei County, we analyzed GSTM1 and GSTT1 genotypes in a population-based case-control study. A total of 122 lung cancer patients and 122 controls, individually matched by age, sex, and home fuel type, were studied. Compared to subjects who used less than 130 tons of smoky coal during their lifetime, heavier users (≥130 tons) had a 2.4-fold (95% confidence interval, 1.3-4.4) increased risk of lung cancer. The GSTM1-null genotype was associated with a 2.3-fold (95% confidence interval, 1.3-4.2) increased risk of lung cancer. Furthermore, there was some evidence that smoky coal use was more strongly associated with lung cancer risk among GSTM1-null versus GSTM1-positive individuals. In contrast, the GSTT1 genotype was not significantly associated with lung cancer risk. Our data suggest that the GSTM1-null genotype may enhance susceptibility to air pollution from indoor coal combustion emissions.link_to_subscribed_fulltex

Data from: Silver spoon effects of hatching order in an asynchronous hatching bird

The silver spoon hypothesis proposes that individuals which develop under favourable conditions will gain fitness benefits throughout their lifetime. Hatching order may create a considerable size hierarchy within a brood and lead to earlier-hatched nestlings having a competitive advantage over their siblings, which has been illustrated in some studies. However, there have been few explorations into the effect on subsequent generations. Here, using a 15-year-long study, we investigated the long-term fitness consequence of hatching order in the endangered crested ibis, Nipponia nippon, a species with complete hatching asynchrony. In this study, we found strong support for silver spoon effects acting on hatching order. Compared to later-hatched nestlings, first-hatched nestlings begin reproduction at an earlier age, have higher adult survival rates, possess a longer breeding life span and achieve higher lifetime reproductive success. Interestingly, we found carry-over effects of hatching order into the next generation. Nestlings which hatched earlier and became breeders in turn also produced nestlings with larger tarsus and better body condition. Additionally, we found a positive correlation among life-history traits in crested ibis. Individuals which started reproduction at a younger age were shown to possess a longer breeding life span. And the annual brood size increased with an individual’s breeding life span. This suggests that the earlier-hatched nestlings are of better quality and the ‘silver spoon’ effects of hatching order cover all life-history stages and next generation effects.,CI.allnestSUBLifetime breeding performance and breeding life-span of crested ibisCI.nest_eachyear_SUBBreeding performance for each years of crested ibisCI.chick_bodytraitsThe effects of parental hatching order on chicks body traitsCI.chick_Bodytraits.xlsxCI_survival rateAdult survival rate of crested ibis