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Zivich PN, Cole SR, Edwards JK, Glidden DV, Das M, Shook-Sa BE, Shao Y, Mehrotra ML, Adimora AA, Eron JJ. HIV Prevention Among Men Who Have Sex With Men: Tenofovir Alafenamide Combination Preexposure Prophylaxis Versus Placebo. J Infect Dis 2024; 229:1123-1130. [PMID: 37969014 PMCID: PMC11011171 DOI: 10.1093/infdis/jiad507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 11/06/2023] [Accepted: 11/13/2023] [Indexed: 11/17/2023] Open
Abstract
BACKGROUND While noninferiority of tenofovir alafenamide and emtricitabine (TAF/FTC) as preexposure prophylaxis (PrEP) for the prevention of human immunodeficiency virus (HIV) has been shown, interest remains in its efficacy relative to placebo. We estimate the efficacy of TAF/FTC PrEP versus placebo for the prevention of HIV infection. METHODS We used data from the DISCOVER and iPrEx trials to compare TAF/FTC to placebo. DISCOVER was a noninferiority trial conducted from 2016 to 2017. iPrEx was a placebo-controlled trial conducted from 2007 to 2009. Inverse probability weights were used to standardize the iPrEx participants to the distribution of demographics and risk factors in the DISCOVER trial. To check the comparison, we evaluated whether risk of HIV infection in the shared tenofovir disoproxil fumarate and emtricitabine (TDF/FTC) arms was similar. RESULTS Notable differences in demographics and risk factors occurred between trials. After standardization, the difference in risk of HIV infection between the TDF/FTC arms was near zero. The risk of HIV with TAF/FTC was 5.8 percentage points lower (95% confidence interval [CI], -2.0% to -9.6%) or 12.5-fold lower (95% CI, .02 to .31) than placebo standardized to the DISCOVER population. CONCLUSIONS There was a reduction in HIV infection with TAF/FTC versus placebo across 96 weeks of follow-up. CLINICAL TRIALS REGISTRATION NCT02842086 and NCT00458393.
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Affiliation(s)
- Paul N Zivich
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Institute of Global Health and Infectious Diseases, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Stephen R Cole
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Jessie K Edwards
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - David V Glidden
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA
| | - Moupali Das
- Gilead Sciences, Foster City, California, USA
| | - Bonnie E Shook-Sa
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Yongwu Shao
- Gilead Sciences, Foster City, California, USA
| | - Megha L Mehrotra
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA
- Gilead Sciences, Foster City, California, USA
| | - Adaora A Adimora
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Division of Infectious Diseases, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Joseph J Eron
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Division of Infectious Diseases, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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Shook-Sa BE, Zivich PN, Rosin SP, Edwards JK, Adimora AA, Hudgens MG, Cole SR. Fusing trial data for treatment comparisons: Single vs multi-span bridging. Stat Med 2024; 43:793-815. [PMID: 38110289 PMCID: PMC10843571 DOI: 10.1002/sim.9989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 10/23/2023] [Accepted: 11/30/2023] [Indexed: 12/20/2023]
Abstract
While randomized controlled trials (RCTs) are critical for establishing the efficacy of new therapies, there are limitations regarding what comparisons can be made directly from trial data. RCTs are limited to a small number of comparator arms and often compare a new therapeutic to a standard of care which has already proven efficacious. It is sometimes of interest to estimate the efficacy of the new therapy relative to a treatment that was not evaluated in the same trial, such as a placebo or an alternative therapy that was evaluated in a different trial. Such dual-study comparisons are challenging because of potential differences between trial populations that can affect the outcome. In this article, two bridging estimators are considered that allow for comparisons of treatments evaluated in different trials, accounting for measured differences in trial populations. A "multi-span" estimator leverages a shared arm between two trials, while a "single-span" estimator does not require a shared arm. A diagnostic statistic that compares the outcome in the standardized shared arms is provided. The two estimators are compared in simulations, where both estimators demonstrate minimal empirical bias and nominal confidence interval coverage when the identification assumptions are met. The estimators are applied to data from the AIDS Clinical Trials Group 320 and 388 to compare the efficacy of two-drug vs four-drug antiretroviral therapy on CD4 cell counts among persons with advanced HIV. The single-span approach requires weaker identification assumptions and was more efficient in simulations and the application.
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Affiliation(s)
- Bonnie E. Shook-Sa
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Paul N. Zivich
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Institute of Global Health and Infectious Diseases, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Samuel P. Rosin
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jessie K. Edwards
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Adaora A. Adimora
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Michael G. Hudgens
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Stephen R. Cole
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Dang LE, Fong E, Tarp JM, Clemmensen KKB, Ravn H, Kvist K, Buse JB, van der Laan M, Petersen M. Case study of semaglutide and cardiovascular outcomes: An application of the C ausal Roadmap to a hybrid design for augmenting an RCT control arm with real-world data. J Clin Transl Sci 2023; 7:e231. [PMID: 38028337 PMCID: PMC10643919 DOI: 10.1017/cts.2023.656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 09/09/2023] [Accepted: 10/14/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction Increasing interest in real-world evidence has fueled the development of study designs incorporating real-world data (RWD). Using the Causal Roadmap, we specify three designs to evaluate the difference in risk of major adverse cardiovascular events (MACE) with oral semaglutide versus standard-of-care: (1) the actual sequence of non-inferiority and superiority randomized controlled trials (RCTs), (2) a single RCT, and (3) a hybrid randomized-external data study. Methods The hybrid design considers integration of the PIONEER 6 RCT with RWD controls using the experiment-selector cross-validated targeted maximum likelihood estimator. We evaluate 95% confidence interval coverage, power, and average patient time during which participants would be precluded from receiving a glucagon-like peptide-1 receptor agonist (GLP1-RA) for each design using simulations. Finally, we estimate the effect of oral semaglutide on MACE for the hybrid PIONEER 6-RWD analysis. Results In simulations, Designs 1 and 2 performed similarly. The tradeoff between decreased coverage and patient time without the possibility of a GLP1-RA for Designs 1 and 3 depended on the simulated bias. In real data analysis using Design 3, external controls were integrated in 84% of cross-validation folds, resulting in an estimated risk difference of -1.53%-points (95% CI -2.75%-points to -0.30%-points). Conclusions The Causal Roadmap helps investigators to minimize potential bias in studies using RWD and to quantify tradeoffs between study designs. The simulation results help to interpret the level of evidence provided by the real data analysis in support of the superiority of oral semaglutide versus standard-of-care for cardiovascular risk reduction.
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Affiliation(s)
- Lauren E. Dang
- Department of Biostatistics, University of California, Berkeley, CA, USA
| | | | | | | | | | | | - John B. Buse
- Division of Endocrinology, Department of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Mark van der Laan
- Department of Biostatistics, University of California, Berkeley, CA, USA
| | - Maya Petersen
- Department of Biostatistics, University of California, Berkeley, CA, USA
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Breskin A, Cole SR, Edwards JK, Brookmeyer R, Eron JJ, Adimora AA. Corrigendum to: Fusion designs and estimators for treatment effects. Stat Med 2023; 42:730. [PMID: 36688441 DOI: 10.1002/sim.9634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 12/12/2022] [Indexed: 01/24/2023]
Affiliation(s)
- Alexander Breskin
- NoviSci, Durham, North Carolina, USA.,Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Stephen R Cole
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Jessie K Edwards
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Ron Brookmeyer
- Department of Biostatistics, University of California - Los Angeles, Los Angeles, California, USA
| | - Joseph J Eron
- Institute of Global Health and Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Adimora A Adimora
- Institute of Global Health and Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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Cole SR, Edwards JK, Breskin A, Rosin S, Zivich PN, Shook-Sa BE, Hudgens MG. Illustration of 2 Fusion Designs and Estimators. Am J Epidemiol 2023; 192:467-474. [PMID: 35388406 PMCID: PMC10372880 DOI: 10.1093/aje/kwac067] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 03/25/2022] [Accepted: 03/31/2022] [Indexed: 11/12/2022] Open
Abstract
"Fusion" study designs combine data from different sources to answer questions that could not be answered (as well) by subsets of the data. Studies that augment main study data with validation data, as in measurement-error correction studies or generalizability studies, are examples of fusion designs. Fusion estimators, here solutions to stacked estimating functions, produce consistent answers to identified research questions using data from fusion designs. In this paper, we describe a pair of examples of fusion designs and estimators, one where we generalize a proportion to a target population and one where we correct measurement error in a proportion. For each case, we present an example motivated by human immunodeficiency virus research and summarize results from simulation studies. Simulations demonstrate that the fusion estimators provide approximately unbiased results with appropriate 95% confidence interval coverage. Fusion estimators can be used to appropriately combine data in answering important questions that benefit from multiple sources of information.
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Affiliation(s)
- Stephen R Cole
- Correspondence to Dr. Stephen Cole, Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Campus Box 7435, Chapel Hill, NC 27599-7435 (e-mail: )
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Msaouel P, Lee J, Karam JA, Thall PF. A Causal Framework for Making Individualized Treatment Decisions in Oncology. Cancers (Basel) 2022; 14:cancers14163923. [PMID: 36010916 PMCID: PMC9406391 DOI: 10.3390/cancers14163923] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/12/2022] [Accepted: 08/12/2022] [Indexed: 12/23/2022] Open
Abstract
Simple Summary Physicians routinely make individualized treatment decisions by accounting for the joint effects of patient prognostic covariates and treatments on clinical outcomes. Ideally, this is performed using historical randomized clinical trial (RCT) data. Randomization ensures that unbiased estimates of causal treatment effect parameters can be obtained from the historical RCT data and used to predict each new patient’s outcome based on the joint effect of their baseline covariates and each treatment being considered. However, this process becomes problematic if a patient seen in the clinic is very different from the patients who were enrolled in the RCT. That is, if a new patient does not satisfy the entry criteria of the RCT, then the patient does not belong to the population represented by the patients who were studied in the RCT. In such settings, it still may be possible to utilize the RCT data to help choose a new patient’s treatment. This may be achieved by combining the RCT data with data from other clinical trials, or possibly preclinical experiments, and using the combined dataset to predict the patient’s expected outcome for each treatment being considered. In such settings, combining data from multiple sources in a way that is statistically reliable is not entirely straightforward, and correctly identifying and estimating the effects of treatments and patient covariates on clinical outcomes can be complex. Causal diagrams provide a rational basis to guide this process. The first step is to construct a causal diagram that reflects the plausible relationships between treatment variables, patient covariates, and clinical outcomes. If the diagram is correct, it can be used to determine what additional data may be needed, how to combine data from multiple sources, how to formulate a statistical model for clinical outcomes as a function of treatment and covariates, and how to compute an unbiased treatment effect estimate for each new patient. We use adjuvant therapy of renal cell carcinoma to illustrate how causal diagrams may be used to guide these steps. Abstract We discuss how causal diagrams can be used by clinicians to make better individualized treatment decisions. Causal diagrams can distinguish between settings where clinical decisions can rely on a conventional additive regression model fit to data from a historical randomized clinical trial (RCT) to estimate treatment effects and settings where a different approach is needed. This may be because a new patient does not meet the RCT’s entry criteria, or a treatment’s effect is modified by biomarkers or other variables that act as mediators between treatment and outcome. In some settings, the problem can be addressed simply by including treatment–covariate interaction terms in the statistical regression model used to analyze the RCT dataset. However, if the RCT entry criteria exclude a new patient seen in the clinic, it may be necessary to combine the RCT data with external data from other RCTs, single-arm trials, or preclinical experiments evaluating biological treatment effects. For example, external data may show that treatment effects differ between histological subgroups not recorded in an RCT. A causal diagram may be used to decide whether external observational or experimental data should be obtained and combined with RCT data to compute statistical estimates for making individualized treatment decisions. We use adjuvant treatment of renal cell carcinoma as our motivating example to illustrate how to construct causal diagrams and apply them to guide clinical decisions.
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Affiliation(s)
- Pavlos Msaouel
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- David H. Koch Center for Applied Research of Genitourinary Cancers, The University of Texas, MD Anderson Cancer Center, Houston, TX 77030, USA
- Correspondence:
| | - Juhee Lee
- Department of Statistics, University of California, Santa Cruz, CA 95064, USA
| | - Jose A. Karam
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Urology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Peter F. Thall
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Zivich PN, Shook-Sa BE, Edwards JK, Westreich D, Cole SR. On the Use of Covariate Supersets for Identification Conditions. Epidemiology 2022; 33:559-562. [PMID: 35384912 PMCID: PMC9156549 DOI: 10.1097/ede.0000000000001493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The union of distinct covariate sets, or the superset, is often used in proofs for the identification or the statistical consistency of an estimator when multiple sources of bias are present. However, the use of a superset can obscure important nuances. Here, we provide two illustrative examples: one in the context of missing data on outcomes, and one in which the average causal effect is transported to another target population. As these examples demonstrate, the use of supersets may indicate a parameter is not identifiable when the parameter is indeed identified. Furthermore, a series of exchangeability conditions may lead to successively weaker conditions. Future work on approaches to address multiple biases can avoid these pitfalls by considering the more general case of nonoverlapping covariate sets.
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Affiliation(s)
- Paul N Zivich
- From the Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, NC
| | - Bonnie E Shook-Sa
- Department of Biostatistics, UNC Gillings School of Global Public Health, Chapel Hill, NC
| | - Jessie K Edwards
- From the Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, NC
| | - Daniel Westreich
- From the Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, NC
| | - Stephen R Cole
- From the Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, NC
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Msaouel P. Impervious to Randomness: Confounding and Selection Biases in Randomized Clinical Trials. Cancer Invest 2021; 39:783-788. [PMID: 34514927 DOI: 10.1080/07357907.2021.1974030] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
The random allocation of therapies in randomized clinical trials is a powerful tool that removes all confounding biases that can affect treatment assignment. However, confounders influencing mediators of the treatment effect are unaffected by randomization and should be considered during trial design and statistical modeling.Examples of such mediators include biomarkers predictive of response to targeted therapies in oncology. Patient selection for such biomarkers is prudent in clinical trials. Conversely, prognostic information on outcome heterogeneity can be derived from observational datasets that include more representative populations. The fusion of experimental and observational data can then allow patient-specific inferences.
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Affiliation(s)
- Pavlos Msaouel
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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