Publications

Scholarly Journals--Published

  • The immediate effects of cervical spine manipulation on pain and biochemical markers in females with acute non-specific mechanical neck pain: a randomized clinical trial E.B. Lohman, G.R. Pacheco, L. Gharibvand, N. Daher, K. Devore, G. Bains, M. AlAmeri & L.S. BerkJournal of Manual & Manipulative Therapy - Published online: 11 Dec 2018 DOI: 10.1080/10669817.2018.1553696 ABSTRACTStudy Design: Randomized clinical trial with pre-test, post-test control group design.   Objectives: To examine the immediate effects of cervical spinal manipulation (CSM) on serum concentration of biochemical markers (oxytocin, neurotensin, orexin A, and cortisol). Background: Several studies have found an association between spinal manipulation (SM) and pain perception. However, the mechanism by which SM modulates pain remains undefined. Methods: Twenty-eight female subjects with non-specific mechanical neck pain were randomly assigned to one of two interventions (CSM versus sham CSM). Blood samples were drawn before and immediately after the respective interventions. Oxytocin, neurotensin, orexin A, and cortisol were measured from the blood and serum using the Milliplex Map Magnetic Bead Panel Immunoassay on the Luminex 200 Platform. Results: In the CSM group, there were significant increases in pre- versus post-manipulation mean oxytocin (154.5 ± 60.1 vs. 185.1 ± 75.6, p = .012); neurotensin (116.0 ± 26.5 vs.136.4 ± 34.1, p < . 001); orexin A (52.2 ± 31.1 vs. 73.8 ± 38.8, p < .01) serum concentration; but no significant differences in mean cortisol (p = .052) serum concentration. In the sham group, there were no significant differences in any of the biomarkers (p > .05). Conclusion: The results of the current study suggest that the mechanical stimuli provided through a CSM may modify neuropeptide expression by immediately increasing the serum concentration of nociception-related biomarkers (oxytocin, neurotensin, orexin A, but not cortisol) in the blood of female subjects with non-specific mechanical neck pain. KEYWORDS: Spinal manipulation, oxytocin, neurotensin, orexin A, cortisol, neck pain (12/2018) (link)
  • The association between ambient fine particulate matter and incident adenocarcinoma subtype of lung cancerLida Gharibvand, W. Lawrence Beeson, David Shavlik, Raymond Knutsen, Mark Ghamsary, Samuel Soret, Synnove F. KnutsenEnvironmental Health 2017; 16: 71. Published online 2017 Jun 24. doi: 10.1186/s12940-017-0268-7 ABSTRACTBackground: Adenocarcinoma (AC) is the most common lung cancer among non-smokers, but few studies have assessed the effect of PM2.5 on AC among never smokers. The purpose of this study was to assess the association between ambient PM2.5 and incident lung AC in the Adventist Health and Smog Study-2 (AHSMOG-2), a cohort of 80,044 non-smokers (81% never smokers) followed for 7.5 years (597,177 person-years) (2002–2011). Methods: Incident lung AC was identified through linkage with U.S. state cancer registries. Ambient PM2.5 levels at subjects’ residences were estimated for the years 2000 and 2001, immediately prior to study start. Results: A total of 164 incident lung AC occurred during follow-up. Each 10 μg/m3 increment in PM2.5 was associated with an increase in the hazard rate of lung AC [HR = 1.31 (95% confidence interval (CI) 0.87–1.97)] in the single-pollutant model. Excluding those with prevalent non-melanoma skin cancer (NMSC) strengthened the association with lung AC (HR = 1.62 (95% CI, 1.11–2.36) for each 10 μg/m3 PM2.5 increment. Also, limiting the analyses to subjects who spent more than 1 h/day outdoors, increased the estimate (HR = 1.55, 95% CI: 1.05, 2.30). Conclusions: Increased risk of AC was observed for each 10 μg/m3 increment in ambient PM2.5 concentrations. The risk was higher among those without prevalent NMSC and those who spent more than 1 h/day outdoors. Keywords: Air pollution, Lung adenocarcinoma, Lung cancer, Particulate matter, Adventists, Non-smokers, Non-melanoma skin cancer (06/2017) (link)
  • The association between ambient fine particulate air pollution and lung cancer incidence: Results from the AHSMOG-2 studyLida Gharibvand, David Shavlik, Mark Ghamsary, W. Lawrence Beeson, Samuel Soret, Raymond Knutsen, Synnove F. KnutsenEnvironmental Health Perspectives 2017 Mar; 125(3): 378–384. Published online 2016 Aug 12. doi: 10.1289/EHP124 ABSTRACTBackground: There is a positive association between ambient fine particulate matter ≤ 2.5 μm in aerodynamic diameter (PM2.5) and incidence and mortality of lung cancer (LC), but few studies have assessed the relationship between ambient PM2.5 and LC among never smokers.Objectives: We assessed the association between PM2.5 and risk of LC using the Adventist Health and Smog Study-2 (AHSMOG-2), a cohort of health conscious nonsmokers where 81% have never smoked. Results: A total of 250 incident LC cases occurred during 598,927 person-years of follow-up. For each 10-μg/m3 increment in PM2.5, adjusted hazard ratio (HR) with 95% confidence interval (CI) for LC incidence was 1.43 (95% CI: 1.11, 1.84) in the two-pollutant multivariable model with ozone. Among those who spent > 1 hr/day outdoors or who had lived 5 or more years at their enrollment address, the HR was 1.68 (95% CI: 1.28, 2.22) and 1.54 (95% CI: 1.17, 2.04), respectively. Conclusion: Increased risk estimates of LC were observed for each 10-μg/m3 increment in ambient PM2.5 concentration. The estimate was higher among those with longer residence at enrollment address and those who spent > 1 hr/day outdoors. (03/2017) (link)

Abstract

  • (NON-PEER REVIEWED) LLU GRASP Grant: The Association between Air Pollution and Biological Aging and Cognition Outdoor air pollution is known to be associated with certain adverse health conditions and malignancies, but very few studies have assessed the effect of air pollution on biological aging. More importantly, our extensive literature search has not found any previous longitudinal study that investigated the association of air pollution with the human aging process. The Adventist Health Study-2 (AHS-2) represents a unique opportunity to determine the independent effect of ambient particulate air pollution on aging based on a longitudinal follow-up period.  Our goal is to determine whether long-term exposure to ambient air pollution is associated with two outcomes related to aging: 1) change, over time, in epigenetic mechanisms measured as DNA methylation (DNAm) (biologic clock) and 2) cognitive function, assessed by the California Verbal Learning Test (CVLT), at two different points in time. We further want to determine whether diet, physical activity and various psycho-social variables might have a modifying effect on an association between air pollution and aging measured by DNAm (biologic clock). (01/2018 - Present)

Non-Scholarly Journals

  • Financial Analysis Using SAS ® Procs, SAS Global Forum Conference Financial services industry is interested in analyzing vast financial data including price trends from stock exchanges around the world. SAS analytical and graphical tools are extremely useful to enable statistical analysis of various financial data including stock price information. Time Series statistical method is a valuable approach for analysis of financial data. Time Series modeling involves trends, seasonality, cyclical behavior, and forecasting future trends using historical data collected at regular time intervals. Many advanced Time Series analysis procedures are available in SAS/ETS module. In this paper, three PROCs are shown: PROC TIMESERIES, PROC FORECASTING, and PROC UCM. The advanced features of SAS procedures were used to analyze Texas Instruments stock with TXN symbol. It will be demonstrated that PROC UCM (Unobserved Components Model) is better suited to analyze the stock price movements. (04/2010) (link)
  • Analysis of Survival Data with Clustered Events, SAS Global Forum Conference Two methods to analyzing survival data with clustered events are presented. The first method is a proportional hazards model which adopts a marginal approach with a working independence assumption. This model can be fitted by SAS PROC PHREG with the robust sandwich estimate option. The second method is a likelihood-based random effects (frailty) model. In the second model, the baseline hazard could be either a priori determined (e.g., Weibull) or approximated by piecewise constant counterpart. The estimation could be carried out by adaptive Gaussian quadrature method which is implemented in SAS PROC NLMIXED. The advantages, disadvantages, and relevant situations for proper application of each model are demonstrated using the published diabetic retinopathy data. (03/2009) (link)
  • Using Unobserved Components Model (UCM) for a Stock Price Fluctuation, Western Users of SAS Software (WUSS) Time Series modeling involves trends, seasonality, cyclical behavior, and forecasting future trends using historical data collected at regular time intervals. Many advanced Time Series analysis procedures are available in SAS/ETS module. The advanced features of SAS procedures were used to analyze Texas Instruments stock with TXN symbol. In this paper, three PROCs are shown: PROC TIMESERIES, PROC FORECASTING, and PROC UCM. It will be demonstrated that PROC UCM (Unobserved Components Model) is better suited to analyze the stock price movements. (11/2008) (link)
  • A Step-by-Step Guide to Survival Analysis, Western Users of SAS Software (WUSS) Survival analysis involves the modeling of time-to-event data whereby death or failure is considered an "event". The graphical presentation of survival analysis is a significant tool to facilitate a clear understanding of the underlying events. In particular, the graphical presentation of Cox’s proportional hazards model using SAS PHREG is important for data exploration in survival analysis. In this paper, we will present a comprehensive set of tools and plots to implement survival analysis and Cox’s proportional hazard functions in a step-by-step manner. We will demonstrate the features of SAS ® PROC LIFEREG, PROC LIFETEST, PROC PHREG, PROC BPHREG, estimated hazard function, survival function, advanced features of PHREG, and selecting the best candidate models in model selection. A method will be outlined to perform all possible subset model selection within user-defined subsets using AIC information criterion. The new user-friendly features of BPHREG, an experimental upgrade to PHREG procedure, such as ‘class’, ‘hazards ratio’, and ‘strata’ statements will be covered. The cumulative residuals from PROC PHREG are used to investigate the model specification error of covariate and validate the proportion hazard function. Finally, the methods to identify outliers are commonly based on Cox regression residuals such as Martingale and deviance residuals which will be demonstrated using PROC GPLOT in SAS/GRAPH. (11/2008) (link)
  • Evaluation of a Hospice Care Referral Program Using Cox Proportional Hazards Model, Western Users of SAS Software (WUSS) This paper will discuss the data collected about patients discharged from a geropsychiatry unit and their post-discharge survival rates. First, we will show methods to analyze the data arising from studies where the response variable is the length of time taken to reach a certain end-point, often death. The Kaplan-Meier methods, log rank test and Cox's proportional hazards model will be demonstrated. We will attempt to identify a small number of key risk factors with all possible two-factor interactions associated with the survival of patients after discharge and referral to hospice. We will also estimate the survival function for each individual with given significant factors. This will be accomplished by analyzing censored survival data using Cox's proportional hazards model and through implementation of the PROC PHREG procedure in SAS. Also, we will attempt to identify the significant risk factors among the group that was not referred to hospice. Schoenfeld residuals and the cumulative residuals from PROC PHREG will be used to investigate the model specification error of covariates and validate the proportional hazard function. As is the case in clinical trials, potential outlier individuals who 'died far too early' or 'lived far too long' will be identified and compared to what the fitted model predicts. Cox regression residuals such as Martingale and Deviance residuals will be used to identify the outlier patients. (11/2008) (link)
  • Use of SAS® PROCS in Survival Analysis, SAS LA Forum - LABSUG Survival analysis involves the modeling of time-to-event data whereby death or failure is considered an "event". The graphic presentation of survival analysis is a significant tool which facilitates clear understanding of the underlying events. Two SAS ® PROCS, LIFETEST and PHREG can generate some of the survival analysis plots using the ODS graphics option in version 9.1.3. In this paper, we will demonstrate the features of estimated hazard function, survival function, and cumulative martingale residual plots using ODS Graphics. In clinical trials, identifying potential outlier individuals who ‘died far too early’ or ‘lived far too long’ as compared to what the fitted model predicts. The cumulative residuals from PROC PHREG are used to investigate functional form error of covariate and validates of the proportion hazard function. (06/2008) (link)
  • Advanced Statistical and Graphical features of SAS, SAS Global Forum Conference Survival analysis involves the modeling of time-to-event data whereby death or failure is considered an "event". The graphic presentation of Cox proportional hazards model using SAS PHREG is a significant tool which facilitates effective data exploration in survival analysis. The SAS PROC PHREG can generate some of the useful survival analysis plots using the ODS graphics option in version 9.1.3. In this paper, we will demonstrate the advanced features of PHREG for investigating the cumulative martingale residual plots and for selecting best candidate models in model selection. In clinical trials, potential outlier individuals who ‘died far too early’ or ‘lived far too long’ are identified and compared to what the fitted model predicts. The cumulative residuals from PROC PHREG are used to investigate the model specification error of covariate and validate the proportion hazard function. Methods to identify outliers are commonly based on Cox regression residuals such as martingale and deviance residuals. We will use PROC GPLOT in SAS/GRAPH to generate these two residual plots and to detect influential outliers. We will outline a method to perform all possible subset model selection within user-defined subsets using AIC information criterion. Also we will discus the new and improved features of the BPHREG, an experimental upgrade to PHREG procedure that has some user-friendly options such as ‘class’, the ‘hazards ratio’, and ‘strata’ statements which can be used to fit Cox proportional hazards model more efficiently. (03/2008) (link)
  • Survival Analysis Plots Using SAS ® ODS Graphics, Western Users of SAS Software (WUSS) Survival analysis involves the modeling of time-to-event data whereby death or failure is considered an "event". The graphic presentation of survival analysis is a significant tool which facilitates clear understanding of the underlying events. Two SAS procedures, LIFETEST and PHREG can generate survival analysis plots using the ODS graphics option in version 9.1.3. In this paper, we will demonstrate the features of estimated hazard function, survival function, and cumulative martingale residual plots using ODS graphics. In clinical trials, identifying potential outlier individuals who "died far too early" or "lived far too long" as compared to what the fitted model predicts is useful for successful model development. The cumulative residuals from PROC PHREG are used to investigate functional form error of covariate and validates of the proportion hazard function. Methods to identify outliers are commonly based on Cox regression residuals such as martingale and deviance residuals. We will use PROC GPLOT in SAS/GRAPH to generate these two residual plots and to detect influential outliers. (10/2007) (link)