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ABSTRACT: The joint effects of multiple exposures on an outcome are frequently of interest in epidemiologic research. In 2001, Hernán et al (J Am Stat Assoc. 2001;96:440-448) presented methods for estimating the joint effects of multiple time-varying exposures subject to time-varying confounding affected by prior exposure using joint marginal structural models. Nonetheless, the use of these joint models is rare in the applied literature. Minimal uptake of these joint models, in contrast to the now widely used standard marginal structural model, is due in part to a lack of examples demonstrating the method. In this paper, we review the assumptions necessary for unbiased estimation of joint effects as well as the distinction between interaction and effect measure modification. We demonstrate the use of marginal structural models for estimating the joint effects of alcohol consumption and injection drug use on HIV acquisition, using data from 1525 injection drug users in the AIDS Link to Intravenous Experience cohort study. In the joint model, the hazard ratio (HR) for heavy drinking in the absence of any drug injections was 1.58 (95% confidence interval = 0.67-3.73). The HR for any drug injections in the absence of heavy drinking was 1.78 (1.10-2.89). The HR for heavy drinking and any drug injections was 2.45 (1.45-4.12). The P values for multiplicative and additive interaction were 0.7620 and 0.9200, respectively, indicating a lack of departure from effects that multiply or add. We could not rule out interaction on either scale due to imprecision.
Epidemiology (Cambridge, Mass.) 04/2012; 23(4):574-82. · 5.51 Impact Factor
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Epidemiology (Cambridge, Mass.) 11/2011; 22(6):874-5. · 5.51 Impact Factor
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Epidemiology 10/2011; 22(6):874-875. · 5.57 Impact Factor
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American Journal of Clinical Nutrition 08/2011; 94(2):614-6. · 6.67 Impact Factor
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ABSTRACT: In time-to-event analyses, artificial censoring with correction for induced selection bias using inverse probability-of-censoring weights can be used to 1) examine the natural history of a disease after effective interventions are widely available, 2) correct bias due to noncompliance with fixed or dynamic treatment regimens, and 3) estimate survival in the presence of competing risks. Artificial censoring entails censoring participants when they meet a predefined study criterion, such as exposure to an intervention, failure to comply, or the occurrence of a competing outcome. Inverse probability-of-censoring weights use measured common predictors of the artificial censoring mechanism and the outcome of interest to determine what the survival experience of the artificially censored participants would be had they never been exposed to the intervention, complied with their treatment regimen, or not developed the competing outcome. Even if all common predictors are appropriately measured and taken into account, in the context of small sample size and strong selection bias, inverse probability-of-censoring weights could fail because of violations in assumptions necessary to correct selection bias. The authors used an example from the Multicenter AIDS Cohort Study, 1984-2008, regarding estimation of long-term acquired immunodeficiency syndrome-free survival to demonstrate the impact of violations in necessary assumptions. Approaches to improve correction methods are discussed.
American journal of epidemiology 02/2011; 173(5):569-77. · 5.59 Impact Factor
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ABSTRACT: to 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 1525 participants were women 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 per week over the prior 2 years, respectively. In analyses accounting for sociodemographic factors, drug use, and sexual activity, hazard ratios for participants reporting 1-5, 6-20, 21-50, and 51-140 drinks per week in the prior 2 years compared to participants who reported 0 drinks per 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).
a dose-response relationship between alcohol consumption and subsequent HIV acquisition is indicated, independent of measured known risk factors.
AIDS (London, England) 01/2011; 25(2):221-8. · 4.91 Impact Factor
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ABSTRACT: Predictors of study retention and scheduled visit attendance in the University of North Carolina Center for AIDS Research (UNC CFAR) prospective clinical cohort of HIV-infected patients enrolled between 1 January 2001 and 1 January 2008 are reported. At study entry, 1636 participants were 32% female, 58% were African-American, 49% had not received HIV care elsewhere, 71% were receiving or initiated combination antiretroviral therapy, and 26% were diagnosed with AIDS, with median (quartiles) age of 40 (34; 47) years, distance to clinic of 45 (21; 70) miles, HIV-1 RNA of 1396 (200; 26,750) copies/ml, and CD4 of 374 (182; 602) cells/mm(3). Participants contributed a median of 7 (4; 13) scheduled visits and 2.25 (1.0; 3.9) years alive under follow-up. During 6134 person-years of follow-up, 414 participants dropped out and 145 died. Accounting for differences in death by participant characteristics, the 6-year cumulative probability of retention was 67% [95% confidence limits (CL): 65, 70%], with 6.75 (95% CL: 6.13, 7.43) drop outs per 100 person-years. In a multivariable Cox proportional hazards model, retention was higher among participants who were insured, had not received HIV care elsewhere, had controlled HIV viremia, and were living in nonurban areas or proximate to the clinic. In a multivariable modified Poisson regression model that accounted for differences in drop out and death by participant characteristics, visit attendance was higher among older, AIDS-diagnosed, immune compromised, and cART-initiated participants. The UNC CFAR clinical cohort has ample enrollment with retention and visit attendance modestly influenced by factors such as disease severity.
AIDS research and human retroviruses 08/2010; 26(8):875-81. · 2.18 Impact Factor
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ABSTRACT: Discuss issues related to time-varying exposures using as an example the recently meta-analyzed literature (Baliunas et al. in Int J Public Health, 2009) on alcohol consumption and risk of HIV infection.
Cataloged sources of bias and imprecision in the context of time-varying exposures.
Confounding, selection, or measurement bias may occur when standard regression approaches are used to estimate effects of time-varying exposures. The reviewed literature on alcohol consumption and HIV infection suffer from one or more of these biases.
Detailed prospective data and thoughtful implementation of appropriate statistical methods are needed to obtain unbiased estimates of time-varying exposures, such as alcohol consumption.
International Journal of Public Health 02/2010; 55(3):227-8. · 2.54 Impact Factor
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ABSTRACT: Hepatitis C virus (HCV) rates are higher in non-injecting drug users (NIDUs) than general population estimates. Whether this elevated HCV rate is due to drug use or other putative risk behaviors remains unclear.
Recent non-injection drug users of heroin, crack and/or cocaine were street-recruited from 2000 to 2003 and underwent an interview and venipuncture for HCV antibody assays. Multiple logistic regression analyses were used to assess correlates for HCV infection.
Of 740 enrollees, 3.9% were HCV positive. The median age (intraquartile range) was 30 (35-24) years, 70% were male and 90% were Black or Hispanic. After adjustment, HCV seropositives were significantly more likely than seronegatives to be older than 30 [adjusted odds ratio (AOR)=5.71], tattooed by a friend/relative/acquaintance [AOR=3.61] and know someone with HCV [AOR=4.29], but were less likely to have shared nail or hair clippers, razors or a toothbrush [AOR=0.32].
Non-commercial tattooing may be a mode of HCV transmission among NIDUs and education on the potential risk in using non-sterile tattooing equipment should be targeted toward this population. While no evidence was found for HCV transmission through NIDU equipment sharing or sexual risk behavior, further research is still warranted.
Drug and Alcohol Dependence 10/2005; 79(3):389-95. · 3.38 Impact Factor
