Simreg: a Software Including Some New Developments in Multiple Comparison and Simultaneous Confidence Bands for Linear Regression Models

The problem of simultaneous inference and multiple comparison for comparing means of k( >= 3) populations has been long studied in the statistics literature and is widely available in statistics literature. However to-date, the problem of multiple comparison of regression models has not found its way to the software. It is only recently that the computational aspects of this problem have been resolved in a general setting. SimReg employs this new methodology and provides users with software for multiple regression of several regression models. The comparisons can be among any set of pairs, and moreover any number of predictors can be included in the model. More importantly predictors can be constrained to their natural boundaries, if known. Computational methods for the problem of simultaneous confidence bands when predictors are constrained to intervals has also recently been addressed. SimReg utilizes this recent development to offer simultaneous confidence bands for regression models with any number of predictor variables. Again, the predictors can be constrained to their natural boundaries which results in narrower bands, as compared to the case where no restriction is imposed. A by-product of these confidence bands is a new method for comparing two regression surfaces, that is more informative than the usual partial F test.

Applications of Semi-parametric Estimation Methods in Causal Inference and Prediction

In this thesis, we argue for the use of loss-based semi-parametric estimation methods as an alternative to traditional parametric models in causal inference and prediction. We present a brief discussion on "black box" epidemiology in the first chapter and argue that risk factor epidemiology can be improved by using semi-parametric estimation methods. We demonstrate the use of semi-parametric methods by applying them to two different problems: one in causal inference and another in prediction. In each case, we demonstrate the process one would follow to define the question of interest, parameterize this question, and estimate it using semi-parametric methods. In the second chapter we introduce a formal concept of a perception effect, and define unmasking and placebo effects in the context of randomized trials. We employ modern tools from causal inference to derive semi-parametric estimators of such effects. The methods are illustrated on a motivating example from a recent pain trial where the occurrence of treatment-related side effects acts as a proxy for unmasking. In the third chapter, we redefine perception and unmasking effects for a longitudinal setting, and explore various causal graphs for the gabapentin trial. We demonstrate application of the semi-parametric methods in this more general setting by assuming a more complicated causal graph. To estimate the parameters, we use Maximum Likelihood Estimation and two different versions of Targeted Maximum Likelihood Estimation. Finally, in chapter four, we approach coronary heart disease risk prediction modeling from a semi-parametric perspective using data from the Framingham study. The "super learner" is used with a library of machine learning algorithms to create an ensemble risk prediction model for coronary heart disease. We define relative risk importance parameters for various risk factors and estimate them with semi-parametric methods used in earlier chapters. The results are compared to the Framingham study and those obtained by fitting a parametric model to the Framingham dataset

SimReg: a software including some new developments in multiple comparison and simultaneous confidence bands for linear regression models

The problem of simultaneous inference and multiple comparison for comparing means of k(?3) populations has been long studied in the statistics literature and is widely available in statistics literature. However to-date, the problem of multiple comparison of regression models has not found its way to the software. It is only recently that the computational aspects of this problem have been resolved in a general setting. SimReg employs this new methodology and provides users with software for multiple regression of several regression models. The comparisons can be among any set of pairs, and moreover any number of predictors can be included in the model. More importantly predictors can be constrained to their natural boundaries, if known.Computational methods for the problem of simultaneous confidence bands when predictors are constrained to intervals has also recently been addressed. SimReg utilizes this recent development to offer simultaneous confidence bands for regression models with any number of predictor variables. Again, the predictors can be constrained to their natural boundaries which results in narrower bands, as compared to the case where no restriction is imposed. A by-product of these confidence bands is a new method for comparing two regression surfaces, that is more informative than the usual partial F test

Performance evaluation and economics of a locally-made stand-alone hybrid photovoltaic-thermal brackish water reverse osmosis unit

The performance of a developed brackish water reverse osmosis (BWRO) desalination unit integrated with a stand-alone hybrid photovoltaic-thermal (PVT) module was evaluated under the climatic conditions of Tehran (35.68° N latitude 51.42° E longitude). From the experimental results, the maximum surface temperature of the PV module and thermal efficiency of the PVT module were recorded as 60.38 ​°C and 83.7%, respectively at the set-point temperature of 50.00 ​°C for the thermostat, and under the average solar radiation of 971.34 ​W/m2. During the evaluation, the maximum average productivity of the BWRO unit was obtained as 13.98 ​L/h when temperature values of the PV module, soft water, and brackish feed water (BFW) were recorded as 52.94 ​°C, 28.97 ​°C, and 36.60 ​°C, respectively. From the results, it was also found that the temperature of BFW positively affects the PVT module’s thermal performance and improves the whole unit’s productivity. Also, water quality parameters of pH, TDS, EC, and DO for treated water were evaluated. The economic analysis of the system revealed that the cost of the produced freshwater for the BFW’s salinity of 15,000 ​ppm is 18.96% higher in comparison with 5000 ​ppm for all cases, indicating a closer value to freshwater cost of conventional large-scale RO plants

First pregnancy events and future breast density: modification by age at first pregnancy and specific VEGF and IGF1R gene variants

PURPOSE: Pregnancy characteristics have been associated with breast cancer risk, but information is limited on their relationship with breast density. Our objective was to examine the relationship between first pregnancy characteristics and later life breast density, and whether the association is modified by genotype. METHODS: The Marin Women’s Study was initiated to examine breast cancer in a high-incidence mammography population (Marin County, CA). Reproductive characteristics and pregnancy information including pregnancy-induced hypertension (PIH) were self-reported at the time of mammography. Forty-seven candidate single nucleotide polymorphisms were obtained from saliva samples; seven were assessed in relation to PIH and percent fibroglandular volume (%FGV). Breast density assessed as %FGV was measured on full-field digital mammograms by the San Francisco Mammography Registry. RESULTS: A multivariable regression model including 2,440 parous women showed that PIH during first pregnancy was associated with a statistically significant decrease in %FGV (b = −0.31, 95 % CI −0.52, −0.11), while each month of breast-feeding after first birth was associated with a statistically significant increase in %FGV (b = 0.01, 95 % CI 0.003, 0.02). PIH and breast-feeding associations with %FGV were modified by age at first birth. In a subsample of 1,240 women, there was evidence of modification in the association between PIH and %FGV by specific vascular endothelial growth factor (VEGF) (rs3025039) and insulin growth factor receptor-1 (IGFR1) (rs2016347) gene variants. CONCLUSION: These findings suggest that first pregnancy characteristics may exert an influence on extent of breast density later in life and that this influence may vary depending on inherited IGFR1 and VEGF genotypes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10552-014-0386-2) contains supplementary material, which is available to authorized users

The renal-bone axis in older people living with HIV on stable antiretroviral therapy: A sub-analysis of the GS-US-104-0423 study.

peer reviewed[en] BACKGROUND: Data on low bone mineral density (BMD) in people living with HIV (PLWH) are mainly derived from younger adults; little is known about how antiretroviral therapy (ART) and alterations in the renal-bone axis relate to BMD in older PLWH. METHODS: Cross-sectional study of men > 50 years and post-menopausal women with HIV. Antiretroviral therapy exposure was stratified into four groups based on use of tenofovir disoproxil fumarate (TDF) and protease inhibitors (PI): non-TDF/non-PI, non-TDF/PI, TDF/non-PI, and TDF/PI. Bone mineral density was measured by dual X-ray absorptiometry (DXA). Bone turnover/regulatory markers and renal tubular function were analysed in stored plasma and urine samples. The association of ART exposure and bone/renal biomarkers on BMD was explored using logistic regression models. RESULTS: 247 individuals (median [IQR] age 57 [53, 65] years; 47% female; 13% of Black ethnicity; CD4 count 643 [473, 811] cells/mm3; and 98% with HIV RNA < 200 copies/mL) were included. Bone turnover and renal tubular function differed significantly by ART exposure. In analyses adjusted for demographic and traditional renal/bone risk factors, exposure to TDF and PI was associated with a fourfold greater risk of low BMD at the femoral neck and exposure to TDF and/or PI with a threefold greater risk of low BMD at the lumbar spine. The relationship between ART and low BMD was not altered by further adjustment for bone turnover or renal tubular function markers. CONCLUSIONS: The associations between low BMD and ART exposure (TDF vs. non-TDF and boosted vs. unboosted third agents) were minimally affected by adjustments for bone and kidney biomarkers

The renal-bone axis in older people living with HIV on stable antiretroviral therapy: A sub-analysis of the GS-US-104-0423 study.

BACKGROUND: Data on low bone mineral density (BMD) in people living with HIV (PLWH) are mainly derived from younger adults; little is known about how antiretroviral therapy (ART) and alterations in the renal-bone axis relate to BMD in older PLWH. METHODS: Cross-sectional study of men > 50 years and post-menopausal women with HIV. Antiretroviral therapy exposure was stratified into four groups based on use of tenofovir disoproxil fumarate (TDF) and protease inhibitors (PI): non-TDF/non-PI, non-TDF/PI, TDF/non-PI, and TDF/PI. Bone mineral density was measured by dual X-ray absorptiometry (DXA). Bone turnover/regulatory markers and renal tubular function were analysed in stored plasma and urine samples. The association of ART exposure and bone/renal biomarkers on BMD was explored using logistic regression models. RESULTS: 247 individuals (median [IQR] age 57 [53, 65] years; 47% female; 13% of Black ethnicity; CD4 count 643 [473, 811] cells/mm3; and 98% with HIV RNA < 200 copies/mL) were included. Bone turnover and renal tubular function differed significantly by ART exposure. In analyses adjusted for demographic and traditional renal/bone risk factors, exposure to TDF and PI was associated with a fourfold greater risk of low BMD at the femoral neck and exposure to TDF and/or PI with a threefold greater risk of low BMD at the lumbar spine. The relationship between ART and low BMD was not altered by further adjustment for bone turnover or renal tubular function markers. CONCLUSIONS: The associations between low BMD and ART exposure (TDF vs. non-TDF and boosted vs. unboosted third agents) were minimally affected by adjustments for bone and kidney biomarkers