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Robins, James

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Robins

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Robins, James
Author's Publications: search.filters.isAuthorOfPublication.f64b3217-2802-4ed6-8750-078275b1fe80

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Now showing 1 - 7 of 7
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    Comparative Effectiveness Research Using Observational Data: Active Comparators to Emulate Target Trials with Inactive Comparators
    (AcademyHealth, 2016) Huitfeldt, Anders; Hernan, Miguel; Kalager, Mette; Robins, James
    Introduction: Because a comparison of noninitiators and initiators of treatment may be hopelessly confounded, guidelines for the conduct of observational research often recommend using an “active” comparator group consisting of people who initiate a treatment other than the medication of interest. In this paper, we discuss the conditions under which this approach is valid if the goal is to emulate a trial with an inactive comparator. Identification of Effects: We provide conditions under which a target trial in a subpopulation can be validly emulated from observational data, using an active comparator that is known or believed to be inactive for the outcome of interest. The average treatment effect in the population as a whole is not identified, but under certain conditions this approach can be used to emulate a trial in the subset of individuals who were treated with the treatment of interest, in the subset of individuals who were treated with the treatment of interest but not with the comparator, or in the subset of individuals who were treated with both the treatment of interest and the active comparator. The Plausibility of the Comparability Conditions: We discuss whether the required conditions can be expected to hold in pharmacoepidemiologic research, with a particular focus on whether the conditions are plausible in situations where the standard analysis fails due to unmeasured confounding by access to health care or health seeking behaviors. Discussion: The conditions discussed in this paper may at best be approximately true. Investigators using active comparator designs to emulate trials with inactive comparators should exercise caution.
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    Pandemic Influenza: Risk of Multiple Introductions and the Need to Prepare for Them
    (Public Library of Science (PLoS), 2006) Mills, Christina E; Robins, James; Bergstrom, Carl T; Lipsitch, Marc
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    Efavirenz versus boosted atazanavir-containing regimens and immunologic, virologic, and clinical outcomes: A prospective study of HIV-positive individuals
    (Wolters Kluwer Health, 2016) Cain, Lauren; Caniglia, Ellen; Phillips, Andrew; Olson, Ashley; Muga, Roberto; Pérez-Hoyos, Santiago; Abgrall, Sophie; Costagliola, Dominique; Rubio, Rafael; Jarrín, Inma; Bucher, Heiner; Fehr, Jan; van Sighem, Ard; Reiss, Peter; Dabis, François; Vandenhende, Marie-Anne; Logan, Roger; Robins, James; Sterne, Jonathan A. C.; Justice, Amy; Tate, Janet; Touloumi, Giota; Paparizos, Vasilis; Esteve, Anna; Casabona, Jordi; Seng, Rémonie; Meyer, Laurence; Jose, Sophie; Sabin, Caroline; Hernan, Miguel
    Abstract Objective: To compare regimens consisting of either ritonavir-boosted atazanavir or efavirenz and a nucleoside reverse transcriptase inhibitor (NRTI) backbone with respect to clinical, immunologic, and virologic outcomes. Design: Prospective studies of human immunodeficiency virus (HIV)-infected individuals in Europe and the United States included in the HIV-CAUSAL Collaboration. Methods: HIV-positive, antiretroviral therapy-naive, and acquired immune deficiency syndrome (AIDS)-free individuals were followed from the time they started an atazanavir or efavirenz regimen. We estimated an analog of the “intention-to-treat” effect for efavirenz versus atazanavir regimens on clinical, immunologic, and virologic outcomes with adjustment via inverse probability weighting for time-varying covariates. Results: A total of 4301 individuals started an atazanavir regimen (83 deaths, 157 AIDS-defining illnesses or deaths) and 18,786 individuals started an efavirenz regimen (389 deaths, 825 AIDS-defining illnesses or deaths). During a median follow-up of 31 months, the hazard ratios (95% confidence intervals) were 0.98 (0.77, 1.24) for death and 1.09 (0.91, 1.30) for AIDS-defining illness or death comparing efavirenz with atazanavir regimens. The 5-year survival difference was 0.1% (95% confidence interval: −0.7%, 0.8%) and the AIDS-free survival difference was −0.3% (−1.2%, 0.6%). After 12 months, the mean change in CD4 cell count was 20.8 (95% confidence interval: 13.9, 27.8) cells/mm3 lower and the risk of virologic failure was 20% (14%, 26%) lower in the efavirenz regimens. Conclusion: Our estimates are consistent with a smaller 12-month increase in CD4 cell count, and a smaller risk of virologic failure at 12 months for efavirenz compared with atazanavir regimens. No overall differences could be detected with respect to 5-year survival or AIDS-free survival.
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    When to Monitor CD4 Cell Count and HIV RNA to Reduce Mortality and AIDS-Defining Illness in Virologically Suppressed HIV-Positive Persons on Antiretroviral Therapy in High-Income Countries: A Prospective Observational Study
    (JAIDS Journal of Acquired Immune Deficiency Syndromes, 2016) Caniglia, Ellen; Sabin, Caroline; Robins, James; Logan, Roger; Cain, Lauren; Abgrall, Sophie; Mugavero, Michael J.; Hernandez-Diaz, Sonia; Meyer, Laurence; Seng, Remonie; Drozd, Daniel R.; Seage, George; Bonnet, Fabrice; Dabis, Francois; Moore, Richard R.; Reiss, Peter; van Sighem, Ard; Mathews, William C.; del Amo, Julia; Moreno, Santiago; Deeks, Steven G.; Muga, Roberto; Boswell, Stephen L.; Ferrer, Elena; Eron, Joseph J.; Napravnik, Sonia; Jose, Sophie; Phillips, Andrew; Olson, Ashley; Justice, Amy C.; Tate, Janet P.; Bucher, Heiner C.; Egger, Matthias; Touloumi, Giota; Sterne, Jonathan A.; Costagliola, Dominique; Saag, Michael; Hernan, Miguel
    Objective: To illustrate an approach to compare CD4 cell count and HIV-RNA monitoring strategies in HIV-positive individuals on antiretroviral therapy (ART). Design: Prospective studies of HIV-positive individuals in Europe and the USA in the HIV-CAUSAL Collaboration and The Center for AIDS Research Network of Integrated Clinical Systems. Methods: Antiretroviral-naive individuals who initiated ART and became virologically suppressed within 12 months were followed from the date of suppression. We compared 3 CD4 cell count and HIV-RNA monitoring strategies: once every (1) 3 ± 1 months, (2) 6 ± 1 months, and (3) 9–12 ± 1 months. We used inverse-probability weighted models to compare these strategies with respect to clinical, immunologic, and virologic outcomes. Results: In 39,029 eligible individuals, there were 265 deaths and 690 AIDS-defining illnesses or deaths. Compared with the 3-month strategy, the mortality hazard ratios (95% CIs) were 0.86 (0.42 to 1.78) for the 6 months and 0.82 (0.46 to 1.47) for the 9–12 month strategy. The respective 18-month risk ratios (95% CIs) of virologic failure (RNA >200) were 0.74 (0.46 to 1.19) and 2.35 (1.56 to 3.54) and 18-month mean CD4 differences (95% CIs) were −5.3 (−18.6 to 7.9) and −31.7 (−52.0 to −11.3). The estimates for the 2-year risk of AIDS-defining illness or death were similar across strategies. Conclusions: Our findings suggest that monitoring frequency of virologically suppressed individuals can be decreased from every 3 months to every 6, 9, or 12 months with respect to clinical outcomes. Because effects of different monitoring strategies could take years to materialize, longer follow-up is needed to fully evaluate this question.
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    Identifiability, Exchangeability and Confounding revisited
    (BioMed Central, 2009) Greenland, Sander; Robins, James
    In 1986 the International Journal of Epidemiology published "Identifiability, Exchangeability and Epidemiological Confounding". We review the article from the perspective of a quarter century after it was first drafted and relate it to subsequent developments on confounding, ignorability, and collapsibility.
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    Oseltamivir for treatment and prevention of pandemic influenza A/H1N1 virus infection in households, Milwaukee, 2009
    (BioMed Central, 2010) Cowling, Benjamin J; Fang, Vicky J; Hagy, Angela; Reshef, David; Biedrzycki, Paul; Goldstein, Edward; O'Hagan, Justin; Danon, Leon; Miller, Joel C.; Robins, James; Lipsitch, Marc
    Background: During an influenza pandemic, a substantial proportion of transmission is thought to occur in households. We used data on influenza progression in individuals and their contacts collected by the City of Milwaukee Health Department (MHD) to study the transmission of pandemic influenza A/H1N1 virus in 362 households in Milwaukee, WI, and the effects of oseltamivir treatment and chemoprophylaxis. Methods: 135 households had chronological information on symptoms and oseltamivir usage for all household members. The effect of oseltamivir treatment and other factors on the household secondary attack rate was estimated using univariate and multivariate logistic regression with households as the unit of analysis. The effect of oseltamivir treatment and other factors on the individual secondary attack rate was estimated using univariate and multivariate logistic regression with individual household contacts as the unit of analysis, and a generalized estimating equations approach was used to fit the model to allow for clustering within households. Results: Oseltamivir index treatment on onset day or the following day (early treatment) was associated with a 42% reduction (OR: 0.58, 95% CI: 0.19, 1.73) in the odds of one or more secondary infections in a household and a 50% reduction (OR: 0.5, 95% CI: 0.17, 1.46) in the odds of a secondary infection in individual contacts. The confidence bounds are wide due to a small sample of households with early oseltamivir index usage - in 29 such households, 5 had a secondary attack. Younger household contacts were at higher risk of infection (OR: 2.79, 95% CI: 1.50-5.20). Conclusions: Early oseltamivir treatment may be beneficial in preventing H1N1pdm influenza transmission; this may have relevance to future control measures for influenza pandemics. Larger randomized trials are needed to confirm this finding statistically.
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    Using Observational Data to Calibrate Simulation Models
    (SAGE Publications, 2017) Murray, Eleanor; Robins, James; Seage, George; Lodi, Sara; Hyle, Emily; Reddy, Krishna; Freedberg, Kenneth; Hernan, Miguel
    BACKGROUND: Individual-level simulation models are valuable tools for comparing the impact of clinical or public health interventions on population health and cost outcomes over time. However, a key challenge is ensuring that outcome estimates correctly reflect real-world impacts. Calibration to targets obtained from randomized trials may be insufficient if trials do not exist for populations, time periods, or interventions of interest. Observational data can provide a wider range of calibration targets but requires methods to adjust for treatment-confounder feedback. We propose the use of the parametric g-formula to estimate calibration targets and present a case-study to demonstrate its application. METHODS: We used the parametric g-formula applied to data from the HIV-CAUSAL Collaboration to estimate calibration targets for 7-y risks of AIDS and/or death (AIDS/death), as defined by the Center for Disease Control and Prevention under 3 treatment initiation strategies. We compared these targets to projections from the Cost-Effectiveness of Preventing AIDS Complications (CEPAC) model for treatment-naïve individuals presenting to care in the following year ranges: 1996 to 1999, 2000 to 2002, or 2003 onwards. RESULTS: The parametric g-formula estimated a decreased risk of AIDS/death over time and with earlier treatment. The uncalibrated CEPAC model successfully reproduced targets obtained via the g-formula for baseline 1996 to 1999, but over-estimated calibration targets in contemporary populations and failed to reproduce time trends in AIDS/death risk. Calibration to g-formula targets improved CEPAC model fit for contemporary populations. CONCLUSION: Individual-level simulation models are developed based on best available information about disease processes in one or more populations of interest, but these processes can change over time or between populations. The parametric g-formula provides a method for using observational data to obtain valid calibration targets and enables updating of simulation model inputs when randomized trials are not available.