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1.  Association Between Unprotected Ultraviolet Radiation Exposure and Recurrence of Ocular Herpes Simplex Virus 
American Journal of Epidemiology  2013;179(2):208-215.
Studies have suggested that exposure to ultraviolet (UV) light may increase risk of herpes simplex virus (HSV) recurrence. Between 1993 and 1997, the Herpetic Eye Disease Study (HEDS) randomized 703 participants with ocular HSV to receipt of acyclovir or placebo for prevention of ocular HSV recurrence. Of these, 308 HEDS participants (48% female and 85% white; median age, 49 years) were included in a nested study of exposures thought to cause recurrence and were followed for up to 15 months. We matched weekly UV index values from the National Oceanic and Atmospheric Administration to each participant's study center and used marginal structural Cox models to account for time-varying psychological stress and contact lens use and selection bias from dropout. There were 44 recurrences of ocular HSV, yielding an incidence of 4.3 events per 1,000 person-weeks. Weighted hazard ratios comparing persons with ≥8 hours of time outdoors to those with less exposure were 0.84 (95% confidence interval (CI): 0.27, 2.63) and 3.10 (95% CI: 1.14, 8.48) for weeks with a UV index of <4 and ≥4, respectively (ratio of hazard ratios = 3.68, 95% CI: 0.43, 31.4). Though results were imprecise, when the UV index was higher (i.e., ≥4), spending 8 or more hours per week outdoors was associated with increased risk of ocular HSV recurrence.
PMCID: PMC3873108  PMID: 24142918
cohort studies; herpes simplex virus; recurrence; sunlight; ultraviolet light; UV index
2.  Maximum Likelihood, Profile Likelihood, and Penalized Likelihood: A Primer 
American Journal of Epidemiology  2013;179(2):252-260.
The method of maximum likelihood is widely used in epidemiology, yet many epidemiologists receive little or no education in the conceptual underpinnings of the approach. Here we provide a primer on maximum likelihood and some important extensions which have proven useful in epidemiologic research, and which reveal connections between maximum likelihood and Bayesian methods. For a given data set and probability model, maximum likelihood finds values of the model parameters that give the observed data the highest probability. As with all inferential statistical methods, maximum likelihood is based on an assumed model and cannot account for bias sources that are not controlled by the model or the study design. Maximum likelihood is nonetheless popular, because it is computationally straightforward and intuitive and because maximum likelihood estimators have desirable large-sample properties in the (largely fictitious) case in which the model has been correctly specified. Here, we work through an example to illustrate the mechanics of maximum likelihood estimation and indicate how improvements can be made easily with commercial software. We then describe recent extensions and generalizations which are better suited to observational health research and which should arguably replace standard maximum likelihood as the default method.
PMCID: PMC3873110  PMID: 24173548
epidemiologic methods; maximum likelihood; modeling; penalized estimation; regression; statistics
3.  Viral Suppression in HIV Studies: Combining Times to Suppression and Rebound 
Biometrics  2014;70(2):441-448.
In HIV-1 clinical trials the interest is often to compare how well treatments suppress the HIV-1 RNA viral load. The current practice in statistical analysis of such trials is to define a single ad hoc composite event which combines information about both the viral load suppression and the subsequent viral rebound, and then analyze the data using standard univariate survival analysis techniques. The main weakness of this approach is that the results of the analysis can be easily influenced by minor details in the definition of the composite event. We propose a straightforward alternative endpoint based on the probability of being suppressed over time, and suggest that treatment differences be summarized using the restricted mean time a patient spends in the state of viral suppression. A nonparametric analysis is based on methods for multiple endpoint studies. We demonstrate the utility of our analytic strategy using a recent therapeutic trial, in which the protocol specified a primary analysis using a composite endpoint approach.
PMCID: PMC4319678  PMID: 24446693
AIDS; Clinical trial endpoint; Counting processes; Multistate models; Survival analysis
4.  The parametric G-formula for time-to-event data: towards intuition with a worked example 
Epidemiology (Cambridge, Mass.)  2014;25(6):889-897.
The parametric g-formula can be used to estimate the effect of a policy, intervention, or treatment. Unlike standard regression approaches, the parametric g-formula can be used to adjust for time-varying confounders that are affected by prior exposures. To date, there are few published examples in which the method has been applied.
We provide a simple introduction to the parametric g-formula and illustrate its application in analysis of a small cohort study of bone marrow transplant patients in which the effect of treatment on mortality is subject to time-varying confounding.
Standard regression adjustment yields a biased estimate of the effect of treatment on mortality relative to the estimate obtained by the g-formula.
The g-formula allows estimation of a relevant parameter for public health officials: the change in the hazard of mortality under a hypothetical intervention, such as reduction of exposure to a harmful agent or introduction of a beneficial new treatment. We present a simple approach to implement the parametric g-formula that is sufficiently general to allow easy adaptation to many settings of public health relevance.
PMCID: PMC4310506  PMID: 25140837
5.  Sodium Intake and Incident Hypertension among Chinese Adults: Estimated Effects Across Three Different Exposure Periods 
Epidemiology (Cambridge, Mass.)  2013;24(3):410-418.
Although it is clear that there are short-term effects of sodium intake on blood pressure, little is known about the most relevant timing of sodium exposure for the onset of hypertension. This question can only be addressed in cohorts with repeated measures of sodium intake.
Using up to 7 measures of dietary sodium intake and blood pressure between 1991 and 2009, we compared baseline, the mean of all measures, and the most recent sodium intake in association with incident hypertension, in 6578 adults enrolled in the China Health and Nutrition Survey aged 18 to 65 free of hypertension at baseline. We used survival methods that account for the interval-censored nature of this study, and inverse probability weights to generate adjusted survival curves and time-specific cumulative risk differences; hazard ratios were also estimated.
For mean and most recent measures, the probability of hypertension-free survival was the lowest among those in the highest intake sodium group compared to all other intake groups across the entire follow-up. In addition, the most recent sodium intake measure had a positive dose-response association with incident hypertension [Risk Difference at 11 years of follow-up = 0.04 (95%CI −0.01, 0.09), 0.06 (0.00, 0.13), 0.18 (0.12, 0.24) and 0.20 (0.12, 0.27) for the second to fifth sodium intake groups compared to the lowest group respectively]. Baseline sodium intake was not associated with incident hypertension.
These results suggest caution when using baseline sodium intake measures with long-term follow up.
PMCID: PMC3909658  PMID: 23466527
China; sodium intake; incident hypertension; interval-censored; adjusted survival curves
6.  A prospective study of alcohol consumption and HIV acquisition among injection drug users 
AIDS (London, England)  2011;25(2):221-228.
Estimate the effect of alcohol consumption on HIV acquisition while appropriately accounting for confounding by time-varying risk factors.
African American injection drug users in the AIDS Link to Intravenous Experience cohort study. Participants were recruited and followed with semiannual visits in Baltimore, Maryland between 1988 and 2008.
Marginal structural models were used to estimate the effect of alcohol consumption on HIV acquisition.
At entry, 28% of 1,525 participants were female with a median (quartiles) age of 37 (32; 42) years and 10 (10; 12) years of formal education. During follow up, 155 participants acquired HIV and alcohol consumption was 24%, 24%, 26%, 17%, and 9% for 0, 1–5, 6–20, 21–50 and 51–140 drinks/week over the prior two years, respectively. In analyses accounting for socio-demographic factors, drug use, and sexual activity, hazard ratios for participants reporting 1–5, 6–20, 21–50, and 51–140 drinks/week in the prior two years compared to participants who reported 0 drinks/week were 1.09 (0.60, 1.98), 1.18 (0.66, 2.09), 1.66 (0.94, 2.93) and 2.12 (1.15, 3.90), respectively. A trend test indicated a dose-response relationship between alcohol consumption and HIV acquisition (P value for trend = 9.7×10−4).
A dose-response relationship between alcohol consumption and subsequent HIV acquisition is indicated, independent of measured known risk factors.
PMCID: PMC3006640  PMID: 21099668
Alcohol consumption; HIV infection; Bias; Cohort studies; Injection drug users
7.  Breast cancer subtypes and previously established genetic risk factors: A Bayesian approach 
Gene expression analyses indicate that breast cancer is a heterogeneous disease with at least 5 immunohistologic subtypes. Despite growing evidence that these subtypes are etiologically and prognostically distinct, few studies have investigated whether they have divergent genetic risk factors. To help fill in this gap in our understanding, we examined associations between breast cancer subtypes and previously established susceptibility loci among white and African-American women in the Carolina Breast Cancer Study.
We used Bayesian polytomous logistic regression to estimate odds ratios (ORs) and 95% posterior intervals (PIs) for the association between each of 78 single nucleotide polymorphisms (SNPs) and 5 breast cancer subtypes. Subtypes were defined using 5 immunohistochemical markers: estrogen receptors (ER), progesterone receptors (PR), human epidermal growth factor receptors 1 and 2 (HER1/2) and cytokeratin (CK) 5/6.
Several SNPs in TNRC9/TOX3 were associated with luminal A (ER/PR+, HER2−) or basal-like breast cancer (ER−, PR−, HER2−, HER1 or CK 5/6+), and one SNP (rs3104746) was associated with both. SNPs in FGFR2 were associated with luminal A, luminal B (ER/PR+, HER2+), or HER2+/ER− disease, but none were associated with basal-like disease. We also observed subtype differences in the effects of SNPs in 2q35, 4p, TLR1, MAP3K1, ESR1, CDKN2A/B, ANKRD16, and ZM1Z1.
Conclusion and Impact
We found evidence that genetic risk factors for breast cancer vary by subtype and further clarified the role of several key susceptibility genes.
PMCID: PMC3947131  PMID: 24177593
breast cancer; single nucleotide polymorphisms; breast cancer subtypes; GWAS; Bayesian analysis
8.  Statistical Methods for Multivariate Meta-analysis of Diagnostic Tests: An Overview and Tutorial 
Statistical methods in medical research  2013;10.1177/0962280213492588.
In this article, we present an overview and tutorial of statistical methods for meta-analysis of diagnostic tests under two scenarios: 1) when the reference test can be considered a gold standard; and 2) when the reference test cannot be considered a gold standard. In the first scenario, we first review the conventional summary receiver operating characteristics (ROC) approach and a bivariate approach using linear mixed models (BLMM). Both approaches require direct calculations of study-specific sensitivities and specificities. We next discuss the hierarchical summary ROC curve approach for jointly modeling positivity criteria and accuracy parameters, and the bivariate generalized linear mixed models (GLMM) for jointly modeling sensitivities and specificities. We further discuss the trivariate GLMM for jointly modeling prevalence, sensitivities and specificities, which allows us to assess the correlations among the three parameters. These approaches are based on the exact binomial distribution and thus do not require an ad hoc continuity correction. Last, we discuss a latent class random effects model for meta-analysis of diagnostic tests when the reference test itself is imperfect for the second scenario. A number of case studies with detailed annotated SAS code in procedures MIXED and NLMIXED are presented to facilitate the implementation of these approaches.
PMCID: PMC3883791  PMID: 23804970
meta-analysis; diagnostic test; gold standard; generalized linear mixed models
9.  Causal Inference in Occupational Epidemiology: Accounting for the Healthy Worker Effect by Using Structural Nested Models 
American Journal of Epidemiology  2013;178(12):1681-1686.
In a recent issue of the Journal, Kirkeleit et al. (Am J Epidemiol. 2013;177(11):1218–1224) provided empirical evidence for the potential of the healthy worker effect in a large cohort of Norwegian workers across a range of occupations. In this commentary, we provide some historical context, define the healthy worker effect by using causal diagrams, and use simulated data to illustrate how structural nested models can be used to estimate exposure effects while accounting for the healthy worker survivor effect in 4 simple steps. We provide technical details and annotated SAS software (SAS Institute, Inc., Cary, North Carolina) code corresponding to the example analysis in the Web Appendices, available at
PMCID: PMC3858107  PMID: 24077092
causal inference; healthy worker effect; marginal structural models; occupational epidemiology; structural nested models
10.  Joint effects of alcohol consumption and high-risk sexual behavior on HIV seroconversion among men who have sex with men 
AIDS (London, England)  2013;27(5):815-823.
PMCID: PMC3746520  PMID: 23719351
Alcohol Drinking; HIV Seropositivity; Men who Have Sex with Men; Prospective Studies; Sexual Behavior
11.  A Bayesian approach to strengthen inference for case-control studies with multiple error-prone exposure assessments 
Statistics in medicine  2013;32(25):4426-4437.
In case-control studies, exposure assessments are almost always error-prone. In the absence of a gold standard, two or more assessment approaches are often used to classify people with respect to exposure. Each imperfect assessment tool may lead to misclassification of exposure assignment; the exposure misclassification may be differential with respect to case status or not; and, the errors in exposure classification under the different approaches may be independent (conditional upon the true exposure status) or not. Although methods have been proposed to study diagnostic accuracy in the absence of a gold standard, these methods are infrequently used in case-control studies to correct exposure misclassification that is simultaneously differential and dependent. In this paper, we proposed a Bayesian method to estimate the measurement-error corrected exposure-disease association, accounting for both differential and dependent misclassification. The performance of the proposed method is investigated using simulations, which show that the proposed approach works well, as well as an application to a case-control study assessing the association between asbestos exposure and mesothelioma.
PMCID: PMC3788843  PMID: 23661263
Case-control study; gold standard; misclassification; dependent; differential
12.  An information criterion for marginal structural models 
Statistics in medicine  2012;32(8):1383-1393.
Marginal structural models were developed as a semiparametric alternative to the G-computation formula to estimate causal effects of exposures. In practice, these models are often specified using parametric regression models. As such, the usual conventions regarding regression model specification apply. This paper outlines strategies for marginal structural model specification, and considerations for the functional form of the exposure metric in the final structural model. We propose a quasi-likelihood information criterion adapted from use in generalized estimating equations. We evaluate the properties of our proposed information criterion using a limited simulation study. We illustrate our approach using two empirical examples. In the first example, we use data from a randomized breastfeeding promotion trial to estimate the effect of breastfeeding duration on infant weight at one year. In the second example, we use data from two prospective cohorts studies to estimate the effect of highly active antiretroviral therapy on CD4 count in an observational cohort of HIV-infected men and women. The marginal structural model specified should reflect the scientific question being addressed, but can also assist in exploration of other plausible and closely related questions. In marginal structural models, as in any regression setting, correct inference depends on correct model specification. Our proposed information criterion provides a formal method for comparing model fit for different specifications.
PMCID: PMC4180061  PMID: 22972662
Bias; Causal inference; Marginal structural model; Regression analysis; Model specification
13.  Marginal Structural Models for Case-Cohort Study Designs to Estimate the Association of Antiretroviral Therapy Initiation With Incident AIDS or Death 
American Journal of Epidemiology  2012;175(5):381-390.
To estimate the association of antiretroviral therapy initiation with incident acquired immunodeficiency syndrome (AIDS) or death while accounting for time-varying confounding in a cost-efficient manner, the authors combined a case-cohort study design with inverse probability-weighted estimation of a marginal structural Cox proportional hazards model. A total of 950 adults who were positive for human immunodeficiency virus type 1 were followed in 2 US cohort studies between 1995 and 2007. In the full cohort, 211 AIDS cases or deaths occurred during 4,456 person-years. In an illustrative 20% random subcohort of 190 participants, 41 AIDS cases or deaths occurred during 861 person-years. Accounting for measured confounders and determinants of dropout by inverse probability weighting, the full cohort hazard ratio was 0.41 (95% confidence interval: 0.26, 0.65) and the case-cohort hazard ratio was 0.47 (95% confidence interval: 0.26, 0.83). Standard multivariable-adjusted hazard ratios were closer to the null, regardless of study design. The precision lost with the case-cohort design was modest given the cost savings. Results from Monte Carlo simulations demonstrated that the proposed approach yields approximately unbiased estimates of the hazard ratio with appropriate confidence interval coverage. Marginal structural model analysis of case-cohort study designs provides a cost-efficient design coupled with an accurate analytic method for research settings in which there is time-varying confounding.
PMCID: PMC3282878  PMID: 22302074
acquired immunodeficiency syndrome; case-cohort studies; cohort studies; confounding bias; HIV; pharmacoepidemiology; selection bias
14.  Bayesian Posterior Distributions Without Markov Chains 
American Journal of Epidemiology  2012;175(5):368-375.
Bayesian posterior parameter distributions are often simulated using Markov chain Monte Carlo (MCMC) methods. However, MCMC methods are not always necessary and do not help the uninitiated understand Bayesian inference. As a bridge to understanding Bayesian inference, the authors illustrate a transparent rejection sampling method. In example 1, they illustrate rejection sampling using 36 cases and 198 controls from a case-control study (1976–1983) assessing the relation between residential exposure to magnetic fields and the development of childhood cancer. Results from rejection sampling (odds ratio (OR) = 1.69, 95% posterior interval (PI): 0.57, 5.00) were similar to MCMC results (OR = 1.69, 95% PI: 0.58, 4.95) and approximations from data-augmentation priors (OR = 1.74, 95% PI: 0.60, 5.06). In example 2, the authors apply rejection sampling to a cohort study of 315 human immunodeficiency virus seroconverters (1984–1998) to assess the relation between viral load after infection and 5-year incidence of acquired immunodeficiency syndrome, adjusting for (continuous) age at seroconversion and race. In this more complex example, rejection sampling required a notably longer run time than MCMC sampling but remained feasible and again yielded similar results. The transparency of the proposed approach comes at a price of being less broadly applicable than MCMC.
PMCID: PMC3282880  PMID: 22306565
Bayes theorem; epidemiologic methods; inference; Monte Carlo method; posterior distribution; simulation
15.  Incidence and Timing of Cancer in HIV-Infected Individuals Following Initiation of Combination Antiretroviral Therapy 
Kaposi sarcoma and lymphoma rates were highest immediately after antiretroviral therapy (ART) initiation, particularly among patients with low CD4 cell counts, whereas other cancers increased with time on ART. Calendar year of ART initiation was not associated with subsequent cancer incidence.
Cancer is an important cause of morbidity and mortality in individuals infected with human immunodeficiency virus (HIV), but patterns of cancer incidence after combination antiretroviral therapy (ART) initiation remain poorly characterized.
We evaluated the incidence and timing of cancer diagnoses among patients initiating ART between 1996 and 2011 in a collaboration of 8 US clinical HIV cohorts. Poisson regression was used to estimate incidence rates. Cox regression was used to identify demographic and clinical characteristics associated with cancer incidence after ART initiation.
At initiation of first combination ART among 11 485 patients, median year was 2004 (interquartile range [IQR], 2000–2007) and median CD4 count was 202 cells/mm3 (IQR, 61–338). Incidence rates for Kaposi sarcoma (KS) and lymphomas were highest in the first 6 months after ART initiation (P < .001) and plateaued thereafter, while incidence rates for all other cancers combined increased from 416 to 615 cases per 100 000 person-years from 1 to 10 years after ART initiation (average 7% increase per year; 95% confidence interval, 2%–13%). Lower CD4 count at ART initiation was associated with greater risk of KS, lymphoma, and human papillomavirus–related cancer. Calendar year of ART initiation was not associated with cancer incidence.
KS and lymphoma rates were highest immediately following ART initiation, particularly among patients with low CD4 cell counts, whereas other cancers increased with time on ART, likely reflecting increased cancer risk with aging. Our results underscore recommendations for earlier HIV diagnosis followed by prompt ART initiation along with ongoing aggressive cancer screening and prevention efforts throughout the course of HIV care.
PMCID: PMC3739467  PMID: 23735330
HIV-associated malignancies; AIDS-defining cancer; non-AIDS-defining cancer; combination antiretroviral therapy
16.  Temporal Trends in Presentation and Survival for HIV-Associated Lymphoma in the Antiretroviral Therapy Era 
Lymphoma is the leading cause of cancer-related death among HIV-infected patients in the antiretroviral therapy (ART) era.
We studied lymphoma patients in the Centers for AIDS Research Network of Integrated Clinical Systems from 1996 until 2010. We examined differences stratified by histology and diagnosis year. Mortality and predictors of death were analyzed using Kaplan–Meier curves and Cox proportional hazards.
Of 23 050 HIV-infected individuals, 476 (2.1%) developed lymphoma (79 [16.6%] Hodgkin lymphoma [HL]; 201 [42.2%] diffuse large B-cell lymphoma [DLBCL]; 56 [11.8%] Burkitt lymphoma [BL]; 54 [11.3%] primary central nervous system lymphoma [PCNSL]; and 86 [18.1%] other non-Hodgkin lymphoma [NHL]). At diagnosis, HL patients had higher CD4 counts and lower HIV RNA than NHL patients. PCNSL patients had the lowest and BL patients had the highest CD4 counts among NHL categories. During the study period, CD4 count at lymphoma diagnosis progressively increased and HIV RNA decreased. Five-year survival was 61.6% for HL, 50.0% for BL, 44.1% for DLBCL, 43.3% for other NHL, and 22.8% for PCNSL. Mortality was associated with age (adjusted hazard ratio [AHR] = 1.28 per decade increase, 95% confidence interval [CI] = 1.06 to 1.54), lymphoma occurrence on ART (AHR = 2.21, 95% CI = 1.53 to 3.20), CD4 count (AHR = 0.81 per 100 cell/µL increase, 95% CI = 0.72 to 0.90), HIV RNA (AHR = 1.13 per log10copies/mL, 95% CI = 1.00 to 1.27), and histology but not earlier diagnosis year.
HIV-associated lymphoma is heterogeneous and changing, with less immunosuppression and greater HIV control at diagnosis. Stable survival and increased mortality for lymphoma occurring on ART call for greater biologic insights to improve outcomes.
PMCID: PMC3748003  PMID: 23892362
17.  A comparison of methods to estimate the hazard ratio under conditions of time-varying confounding and nonpositivity 
Epidemiology (Cambridge, Mass.)  2011;22(5):718-723.
In occupational epidemiologic studies, the healthy-worker survivor effect refers to a process that leads to bias in the estimates of an association between cumulative exposure and a health outcome. In these settings, work status acts both as an intermediate and confounding variable, and may violate the positivity assumption (the presence of exposed and unexposed observations in all strata of the confounder). Using Monte Carlo simulation, we assess the degree to which crude, work-status adjusted, and weighted (marginal structural) Cox proportional hazards models are biased in the presence of time-varying confounding and nonpositivity. We simulate data representing time-varying occupational exposure, work status, and mortality. Bias, coverage, and root mean squared error (MSE) were calculated relative to the true marginal exposure effect in a range of scenarios. For a base-case scenario, using crude, adjusted, and weighted Cox models, respectively, the hazard ratio was biased downward 19%, 9%, and 6%; 95% confidence interval coverage was 48%, 85%, and 91%; and root MSE was 0.20, 0.13, and 0.11. Although marginal structural models were less biased in most scenarios studied, neither standard nor marginal structural Cox proportional hazards models fully resolve the bias encountered under conditions of time-varying confounding and nonpositivity.
PMCID: PMC3155387  PMID: 21747286
18.  Loss to Clinic and Five-Year Mortality among HIV-Infected Antiretroviral Therapy Initiators 
PLoS ONE  2014;9(7):e102305.
Missing outcome data due to loss to follow-up occurs frequently in clinical cohort studies of HIV-infected patients. Censoring patients when they become lost can produce inaccurate results if the risk of the outcome among the censored patients differs from the risk of the outcome among patients remaining under observation. We examine whether patients who are considered lost to follow up are at increased risk of mortality compared to those who remain under observation. Patients from the US Centers for AIDS Research Network of Integrated Clinical Systems (CNICS) who newly initiated combination antiretroviral therapy between January 1, 1998 and December 31, 2009 and survived for at least one year were included in the study. Mortality information was available for all participants regardless of continued observation in the CNICS. We compare mortality between patients retained in the cohort and those lost-to-clinic, as commonly defined by a 12-month gap in care. Patients who were considered lost-to-clinic had modestly elevated mortality compared to patients who remained under observation after 5 years (risk ratio (RR): 1.2; 95% CI: 0.9, 1.5). Results were similar after redefining loss-to-clinic as 6 months (RR: 1.0; 95% CI: 0.8, 1.3) or 18 months (RR: 1.2; 95% CI: 0.8, 1.6) without a documented clinic visit. The small increase in mortality associated with becoming lost to clinic suggests that these patients were not lost to care, rather they likely transitioned to care at a facility outside the study. The modestly higher mortality among patients who were lost-to-clinic implies that when we necessarily censor these patients in studies of time-varying exposures, we are likely to incur at most a modest selection bias.
PMCID: PMC4092142  PMID: 25010739
19.  Evaluating influenza vaccine effectiveness among hemodialysis patients using a natural experiment 
Archives of internal medicine  2012;172(7):548-554.
Although the influenza vaccine is recommended for end-stage renal disease (ESRD) patients, little is known about its effectiveness. Observational studies of vaccine effectiveness (VE) are challenging because vaccinated persons may be healthier than unvaccinated persons.
Using United States Renal Data System data, we estimated VE for influenza-like illness (ILI), influenza/pneumonia hospitalization, and mortality in adult, hemodialysis patients using a natural experiment created by year-to-year variation in the match of the influenza vaccine to the circulating virus. Matched (1998, 1999, 2001) and unmatched (1997) years among vaccinated patients were compared using Cox proportional hazards models. Ratios of hazard ratios compared vaccinated patients between two years and unvaccinated patients between two years. VE was calculated as 1 - effect measure.
Vaccination rates were <50% each year. Conventional analysis comparing vaccinated to unvaccinated patients produced average VE estimates of 13%, 16%, and 30% for ILI, influenza/pneumonia hospitalization and mortality respectively. When restricted to the pre-influenza period, results were even stronger, indicating bias. The pooled ratio of HRs comparing matched seasons to a placebo season resulted in a VE of 0% (95% CI: −3,2%) for ILI, 2% (95% CI: −2,5%) for hospitalization, and 0% (95% CI: −3,3%) for death.
Relative to a mismatched year, we found little evidence of increased VE in subsequent, well-matched years, suggesting that the current influenza vaccine strategy may have a smaller effect on morbidity and mortality in the ESRD population than previously thought. Alternate strategies (high dose vaccine, adjuvanted vaccine, multiple doses) should be investigated.
PMCID: PMC4082376  PMID: 22493462
Influenza vaccines; vaccine effectiveness; bias (epidemiology); renal dialysis; cohort studies
20.  Serum uric acid in relation to endogenous reproductive hormones during the menstrual cycle: findings from the BioCycle study 
Human Reproduction (Oxford, England)  2013;28(7):1853-1862.
Do uric acid levels across the menstrual cycle show associations with endogenous estradiol (E2) and reproductive hormone concentrations in regularly menstruating women?
Mean uric acid concentrations were highest during the follicular phase, and were inversely associated with E2 and progesterone, and positively associated with FSH.
E2 may decrease serum levels of uric acid in post-menopausal women; however, the interplay between endogenous reproductive hormones and uric acid levels among regularly menstruating women has not been elucidated.
The BioCycle study was a prospective cohort study conducted at the University at Buffalo research centre from 2005 to 2007, which followed healthy women for one (n = 9) or 2 (n = 250) menstrual cycle(s).
Participants were healthy women aged 18–44 years. Hormones and uric acid were measured in serum eight times each cycle for up to two cycles. Marginal structural models with inverse probability of exposure weights were used to evaluate the associations between endogenous hormones and uric acid concentrations.
Uric acid levels were observed to vary across the menstrual cycle, with the lowest levels observed during the luteal phase. Every log-unit increase in E2 was associated with a decrease in uric acid of 1.1% (β = −0.011; 95% confidence interval (CI): −0.019, −0.004; persistent-effects model), and for every log-unit increase in progesterone, uric acid decreased by ∼0.8% (β = −0.008; 95% CI: −0.012, −0.004; persistent-effects model). FSH was positively associated with uric acid concentrations, such that each log-unit increase was associated with a 1.6% increase in uric acid (β = 0.016; 95% CI: 0.005, 0.026; persistent-effects model). Progesterone and FSH were also associated with uric acid levels in acute-effects models. Of 509 cycles, 42 were anovulatory (8.3%). Higher uric acid levels were associated with increased odds of anovulation (odds ratio 2.39, 95% CI: 1.25, 4.56).
The change in uric acid levels among this cohort of healthy women was modest, and analysis was limited to two menstrual cycles. The women in this study were healthy and regularly menstruating, and as such there were few women with high uric acid levels and anovulatory cycles.
These findings demonstrate the importance of taking menstrual cycle phase into account when measuring uric acid in premenopausal women, and confirm the hypothesized beneficial lowering effects of endogenous E2 on uric acid levels. These findings suggest that there could be an underlying association affecting both sporadic anovulation and high uric acid levels among young, regularly menstruating women. Further studies are needed to confirm these findings and elucidate the connection between uric acid and reproductive and later cardiovascular health.
This work was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (contract # HHSN275200403394C). No competing interests declared.
PMCID: PMC3685334  PMID: 23562957
anovulation; estradiol; menstrual cycle; premenopausal women; uric acid
21.  The use of propensity scores to assess the generalizability of results from randomized trials 
Randomized trials remain the most accepted design for estimating the effects of interventions, but they do not necessarily answer a question of primary interest: Will the program be effective in a target population in which it may be implemented? In other words, are the results generalizable? There has been very little statistical research on how to assess the generalizability, or “external validity,” of randomized trials. We propose the use of propensity-score-based metrics to quantify the similarity of the participants in a randomized trial and a target population. In this setting the propensity score model predicts participation in the randomized trial, given a set of covariates. The resulting propensity scores are used first to quantify the difference between the trial participants and the target population, and then to match, subclassify, or weight the control group outcomes to the population, assessing how well the propensity score-adjusted outcomes track the outcomes actually observed in the population. These metrics can serve as a first step in assessing the generalizability of results from randomized trials to target populations. This paper lays out these ideas, discusses the assumptions underlying the approach, and illustrates the metrics using data on the evaluation of a schoolwide prevention program called Positive Behavioral Interventions and Supports.
PMCID: PMC4051511  PMID: 24926156
Causal inference; External validity; Positive Behavioral Interventions and Supports; Research synthesis
22.  Alcohol consumption trajectory patterns in adult women with HIV infection 
AIDS and behavior  2013;17(5):1705-1712.
HIV-infected women with excessive alcohol consumption are at risk for adverse health outcomes, but little is known about their long-term drinking trajectories. This analysis included longitudinal data, obtained from 1996–2006, from 2791 women with HIV from the Women’s Interagency HIV Study. Among these women, the proportion in each of five distinct drinking trajectories was: continued heavy drinking (3%), reduction from heavy to non-heavy drinking (4%), increase from non-heavy to heavy drinking (8%), continued non-heavy drinking (36%), and continued non-drinking (49%). Depressive symptoms, other substance use (crack/cocaine, marijuana, and tobacco), co-infection with HCV, and heavy drinking prior to enrollment were associated with trajectories involving future heavy drinking. In conclusion, many women with HIV change their drinking patterns over time. Clinicians and those providing alcohol-related interventions might target those with depression, current use of tobacco or illicit drugs, HCV infection, or a previous history of drinking problems.
PMCID: PMC3534826  PMID: 22836592
Alcohol consumption; women; HIV-infection; trajectories
23.  Hematologic, Hepatic, Renal and Lipid Laboratory Monitoring Following Initiation of Combination Antiretroviral Therapy in the United States, 2000–2010 
We assessed laboratory monitoring following combination antiretroviral therapy (cART) initiation among 3,678 patients in a large US multi-site clinical cohort, censoring participants at last clinic visit, cART change, or three years. Median days (interquartile range) to first hematologic, hepatic, renal and lipid tests were 30 (18–53), 31 (19–56), 33 (20–59) and 350 (96–1106), respectively. At one year, approximately 80% received more than two hematologic, hepatic, and renal tests consistent with guidelines. However, only 40% received one or more lipid tests. Monitoring was more frequent in specific subgroups, likely reflecting better clinic attendance or clinician perception of higher susceptibility to toxicities.
PMCID: PMC3654034  PMID: 23446495
Laboratory Monitoring; Antiretroviral Therapy; Antiretroviral Toxicity
24.  Assessing the component associations of the healthy worker survivor bias: occupational asbestos exposure and lung cancer mortality 
Annals of epidemiology  2013;23(6):334-341.
The healthy worker survivor bias is well-recognized in occupational epidemiology. Three component associations are necessary for this bias to occur: i) prior exposure and employment status; ii) employment status and subsequent exposure; and iii) employment status and mortality. Together, these associations result in time-varying confounding affected by prior exposure. We illustrate how these associations can be assessed using standard regression methods.
We use data from 2975 asbestos textile factory workers hired between January 1940 and December 1965 and followed for lung cancer mortality through December 2001.
At entry, median age was 24 years, with 42% female and 19% non-Caucasian. Over follow-up, 21% and 17% of person-years were classified as at work and exposed to any asbestos, respectively. For a 100 fiber-year/mL increase in cumulative asbestos, the covariate-adjusted hazard of leaving work decreased by 52% (95% confidence interval [CI], 46–58). The association between employment status and subsequent asbestos exposure was strong due to nonpositivity: 88.3% of person-years at work (95% CI, 87.0–89.5) were classified as exposed to any asbestos; no person-years were classified as exposed to asbestos after leaving work. Finally, leaving active employment was associated with a 48% (95% CI, 9–71) decrease in the covariate-adjusted hazard of lung cancer mortality.
We found strong associations for the components of the healthy worker survivor bias in these data. Standard methods, which fail to properly account for time-varying confounding affected by prior exposure, may provide biased estimates of the effect of asbestos on lung cancer mortality under these conditions.
PMCID: PMC3773512  PMID: 23683709
Epidemiologic methods; Occupational health; Healthy worker effect; Bias; Lung cancer; Mortality
25.  Depressive Symptoms, HIV Medication Adherence, and HIV Clinical Outcomes in Tanzania: A Prospective, Observational Study 
PLoS ONE  2014;9(5):e95469.
Depressive symptoms have been shown to independently affect both antiretroviral therapy (ART) adherence and HIV clinical outcomes in high-income countries. We examined the prospective relationship between depressive symptoms and adherence, virologic failure, and suppressed immune function in people living with HIV/AIDS in Tanzania. Data from 403 study participants who were on stable ART and engaged in HIV clinical care were analyzed. We assessed crude and adjusted associations of depressive symptoms and ART adherence, both at baseline and at 12 months, using logistic regression. We used logistic generalized estimating equations to assess the association and 95% confidence intervals (CI) between depressive symptoms and both virologic failure and suppressed immune function. Ten percent of participants reported moderate or severe depressive symptoms at baseline and 31% of participants experienced virologic failure (>150 copies/ml) over two years. Depressive symptoms were associated with greater odds of reported medication nonadherence at both baseline (Odds Ratio [OR] per 1-unit increase  = 1.18, 95% CI [1.12, 1.24]) and 12 months (OR  = 1.08, 95% CI [1.03, 1.14]). By contrast, increases in depressive symptom score were inversely related to both virologic failure (OR = 0.93, 95% CI [0.87, 1.00]) and immune system suppression (OR = 0.88, 95% CI [0.79, 0.99]), though the association between depressive symptoms and clinical outcomes was less precise than for the association with nonadherence. Findings indicate a positive association between depressive symptoms and nonadherence, and also an inverse relationship between depressive symptoms and clinical outcomes, possibly due to informative loss to follow-up.
PMCID: PMC4010413  PMID: 24798428

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