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Tan NQP, Ma GX, Maxwell AE, Brown RL, Zhou K, Loh A, Young L, Volk RJ, Lu Q, Wang JHY. The impact of a small-group mammography video discussion on promoting screening uptake among nonadherent Chinese American immigrant women: A randomized controlled trial. Cancer 2024. [PMID: 39257218 DOI: 10.1002/cncr.35524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 06/25/2024] [Accepted: 07/10/2024] [Indexed: 09/12/2024]
Abstract
BACKGROUND The objective of this study was to evaluate the efficacy of an in-person, small-group mammography video discussion (SMVD) intervention on mammography uptake among nonadherent Chinese American immigrant women. METHODS Women (N = 956) were randomized into either an SMVD group, where Chinese-speaking community health workers (CHWs) used an effective, culturally appropriate video to discuss mammography, or a video-only group, which viewed the cultural video sent by mail. Outcomes were mammography uptake at 6 months and 21 months postintervention. RESULTS Women in both groups increased mammography uptake, and an outcome analysis revealed no group differences (adjusted odds ratio [AOR], 1.18; 95% confidence interval [CI], .68-2.06). Overall, 61.2% of the SMVD group and 55.3% of the video-only group had at least one mammogram during the 21-month follow-up period. When considering attendance to the SMVD, SMVD attendees had higher mammography uptake than the video-only group (AOR, 1.51; 95% CI, 1.19-1.92), and SMVD nonattendees had lower mammography uptake than the video-only group (AOR, .33; 95% CI, .22-.50). CONCLUSIONS Both intervention strategies were associated with increased mammography uptake. The authors observed that the increase in use was greater among women who participated in the SMVD session compared with those who viewed the cultural video only. Future research may explore a virtual SMVD intervention for higher session attendance and increased mammography uptake (ClinicalTrials.gov identifier NCT01292200).
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Affiliation(s)
- Naomi Q P Tan
- Rutgers Cancer Institute, Rutgers University, New Brunswick, New Jersey, USA
- Division of Medical Oncology, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, New Jersey, USA
| | - Grace X Ma
- Center for Asian Health, Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania, USA
| | - Annette E Maxwell
- Center for Cancer Prevention and Control Research, University of California Los Angeles, Los Angeles, California, USA
| | - Roger L Brown
- Schools of Nursing Medicine and Public Health, University of Wisconsin, Madison, Wisconsin, USA
| | - Kathy Zhou
- Center for Asian Health, Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania, USA
| | - Alice Loh
- Herald Cancer Association, San Gabriel, California, USA
| | - Lucy Young
- Herald Cancer Association, San Gabriel, California, USA
| | - Robert J Volk
- Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Qian Lu
- Department of Health Disparities Research, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Judy Huei-Yu Wang
- Department of Oncology, Cancer Prevention and Control Program of Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, District of Columbia, USA
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2
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Zeng S, Li F, Wang R, Li F. Correction to "Propensity score weighting for covariate adjustment in randomized clinical trials". Stat Med 2024; 43:3759. [PMID: 38881284 DOI: 10.1002/sim.10080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 04/02/2024] [Indexed: 06/18/2024]
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3
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Schein J, Cloutier M, Gauthier-Loiselle M, Catillon M, Xu C, Qu A, Childress A. Assessment of centanafadine in adults with ADHD: a matching adjusted indirect comparison versus methylphenidate hydrochloride extended release (Concerta). Curr Med Res Opin 2024; 40:1397-1406. [PMID: 38958732 DOI: 10.1080/03007995.2024.2373883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 06/19/2024] [Accepted: 06/25/2024] [Indexed: 07/04/2024]
Abstract
OBJECTIVE To compare safety and efficacy of centanafadine versus methylphenidate hydrochloride extended release (ER; Concerta) in adults with ADHD. METHODS Without head-to-head trials, anchored matching-adjusted indirect comparisons (MAIC) of adverse event rates reported across trials and mean change from baseline in Adult ADHD Investigator Symptom Rating Scale (AISRS) score between centanafadine and methylphenidate hydrochloride ER were conducted. Pooled patient-level data from two centanafadine trials (NCT03605680/NCT03605836) and aggregate data from one published methylphenidate hydrochloride ER trial (NCT00937040) were used. Characteristics of individual patients from the centanafadine trials were matched to aggregate baseline characteristics from the methylphenidate hydrochloride ER trial using propensity score weighting. A sensitivity analysis assessed the robustness of the results to the capping of extreme weights (i.e. >99th percentile). RESULTS Compared with methylphenidate hydrochloride ER, centanafadine was associated with significantly lower risk of dry mouth (risk difference [RD] in percentage points: -11.95), initial insomnia (-11.10), decreased appetite (-8.05), anxiety (-5.39), palpitations (-5.25), and feeling jittery (-4.73) though a significantly smaller reduction in AISRS score (4.16-point). In the sensitivity analysis, the safety results were consistent with the primary analysis but there was no significant difference in efficacy between centanafadine and methylphenidate hydrochloride ER. CONCLUSION In this MAIC, centanafadine had better safety and possibly lower efficacy than methylphenidate hydrochloride ER. While safety results were robust across analyses, there was no efficacy difference between centanafadine and methylphenidate hydrochloride ER in the sensitivity analysis. Considering its favorable safety profile, centanafadine may be preferred among patients for whom treatment-related adverse events are a concern.
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Affiliation(s)
- Jeff Schein
- Otsuka Pharmaceutical Development & Commercialization, Inc, Princeton, NJ, USA
| | | | | | | | - Chunyi Xu
- Analysis Group, Inc, New York, NY, USA
| | - Alice Qu
- Analysis Group, Inc, New York, NY, USA
| | - Ann Childress
- Psychiatry and Behavioral Medicine, Center for Psychiatry and Behavioral Medicine, Las Vegas, NV, USA
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Tong G, Tong J, Jiang Y, Esserman D, Harhay MO, Warren JL. Hierarchical Bayesian modeling of heterogeneous outcome variance in cluster randomized trials. Clin Trials 2024; 21:451-460. [PMID: 38197388 PMCID: PMC11233424 DOI: 10.1177/17407745231222018] [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] [Indexed: 01/11/2024]
Abstract
BACKGROUND Heterogeneous outcome correlations across treatment arms and clusters have been increasingly acknowledged in cluster randomized trials with binary endpoints, where analytical methods have been developed to study such heterogeneity. However, cluster-specific outcome variances and correlations have yet to be studied for cluster randomized trials with continuous outcomes. METHODS This article proposes models fitted in the Bayesian setting with hierarchical variance structure to quantify heterogeneous variances across clusters and explain it with cluster-level covariates when the outcome is continuous. The models can also be extended to analyzing heterogeneous variances in individually randomized group treatment trials, with arm-specific cluster-level covariates, or in partially nested designs. Simulation studies are carried out to validate the performance of the newly introduced models across different settings. RESULTS Simulations showed that overall the newly introduced models have good performance, reporting low bias and approximately 95% coverage for the intraclass correlation coefficients and regression parameters in the variance model. When variances are heterogeneous, our proposed models had improved model fit over models with homogeneous variances. When used to analyze data from the Kerala Diabetes Prevention Program study, our models identified heterogeneous variances and intraclass correlation coefficients across clusters and examined cluster-level characteristics associated with such heterogeneity. CONCLUSION We proposed new hierarchical Bayesian variance models to accommodate cluster-specific variances in cluster randomized trials. The newly developed methods inform the understanding of how an intervention strategy is implemented and disseminated differently across clusters and can help improve future trial design.
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Affiliation(s)
- Guangyu Tong
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
- Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, Connecticut, USA
| | - Jiaqi Tong
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
- Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, Connecticut, USA
| | - Yi Jiang
- Department of Biostatistics, Penn State College of Medicine, Hershey, Pennsylvania, USA
| | - Denise Esserman
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
- Yale Center for Analytical Science, Yale School of Public Health, New Haven, Connecticut, USA
| | - Michael O Harhay
- Clinical Trials Methods and Outcomes Lab, Palliative and Advanced Illness Research (PAIR) Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Joshua L Warren
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
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Lelutiu-Weinberger C, Filimon ML, Zavodszky AM, Lixandru M, Hanu L, Fierbinteanu C, Patrascu R, Streinu-Cercel A, Luculescu S, Bora M, Filipescu I, Jianu C, Heightow-Weidman LB, Rochelle A, Yi B, Buckner N, Golub SA, van Dyk IS, Burger J, Li F, Pachankis JE. Prepare Romania: study protocol for a randomized controlled trial of an intervention to promote pre-exposure prophylaxis adherence and persistence among gay, bisexual, and other men who have sex with men. Trials 2024; 25:470. [PMID: 38987812 PMCID: PMC11238350 DOI: 10.1186/s13063-024-08313-4] [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: 04/12/2024] [Accepted: 07/03/2024] [Indexed: 07/12/2024] Open
Abstract
BACKGROUND Gay, bisexual, and other men who have sex with men (GBMSM) represent a high-risk group for HIV transmission in Romania, yet they possess few resources for prevention. Despite having no formal access to pre-exposure prophylaxis (PrEP) through the health system, GBMSM in Romania demonstrate a high need for and interest in this medication. In anticipation of a national rollout of PrEP, this study tests the efficacy of a novel strategy, Prepare Romania, that combines two evidence-based PrEP promotion interventions for GBMSM living in Romania. METHODS This study uses a randomized controlled trial design to examine whether GBMSM living in Romania receiving Prepare Romania, a culturally adapted counseling and mobile health intervention (expected n = 60), demonstrate greater PrEP adherence and persistence than those assigned to a PrEP education control arm (expected n = 60). Participants from two main cities in Romania are prescribed PrEP and followed-up at 3 and 6 months post-randomization. PrEP adherence data are obtained through weekly self-report surveys and dried blood spot testing at follow-up visits. Potential mediators (e.g., PrEP use motivation) of intervention efficacy are also assessed. Furthermore, Prepare Romania's implementation (e.g., proportion of enrolled participants attending medical visits, intervention experience) will be examined through interviews with participants, study implementers, and healthcare officials. DISCUSSION The knowledge gained from this study will be utilized for further refinement and scale-up of Prepare Romania for a future multi-city effectiveness trial. By studying the efficacy of tools to support PrEP adherence and persistence, this research has the potential to lay the groundwork for PrEP rollout in Romania and similar contexts. Trial registration This study was registered on ClinicalTrials.gov, identifier NCT05323123 , on March 25, 2022.
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Affiliation(s)
| | - Mircea L Filimon
- School of Nursing, Columbia University, 560 West 168th Street, New York, NY, 10032, USA
| | - Anna M Zavodszky
- School of Nursing, Columbia University, 560 West 168th Street, New York, NY, 10032, USA
| | - Mihai Lixandru
- The Romanian Association Against AIDS, Bulevardul Eroilor Sanitari 49, 050471, Bucharest, Romania
| | - Lucian Hanu
- The Romanian Association Against AIDS, Bulevardul Eroilor Sanitari 49, 050471, Bucharest, Romania
| | - Cristina Fierbinteanu
- The Romanian Association Against AIDS, Bulevardul Eroilor Sanitari 49, 050471, Bucharest, Romania
| | - Raluca Patrascu
- The National Institute of Infectious Diseases "Professor Dr. Matei Bals", Strada Doctor Calistrat Grozovici 1, 021105, Bucharest, Romania
| | - Adrian Streinu-Cercel
- The National Institute of Infectious Diseases "Professor Dr. Matei Bals", Strada Doctor Calistrat Grozovici 1, 021105, Bucharest, Romania
| | - Sergiu Luculescu
- The Romanian Association Against AIDS, Bulevardul Eroilor Sanitari 49, 050471, Bucharest, Romania
| | - Maria Bora
- The Clinical Hospital of Infectious Diseases, Str. Iuliu Moldovan, nr. 23, 400000, Cluj-Napoca, Romania
| | - Irina Filipescu
- The Clinical Hospital of Infectious Diseases, Str. Iuliu Moldovan, nr. 23, 400000, Cluj-Napoca, Romania
| | - Cristian Jianu
- The Clinical Hospital of Infectious Diseases, Str. Iuliu Moldovan, nr. 23, 400000, Cluj-Napoca, Romania
| | | | - Aimee Rochelle
- College of Nursing, Florida State University, 98 Varsity Way, Tallahassee, FL, 32305, USA
| | - Brian Yi
- One Cow Standing, 300 W Morgan St Ste 1425, Durham, NC, 27701, USA
| | - Nickie Buckner
- One Cow Standing, 300 W Morgan St Ste 1425, Durham, NC, 27701, USA
| | - Sarit A Golub
- Department of Psychology, Hunter College of the City University of New York, 695 Park Avenue, New York, 10065, NY, USA
| | - Ilana Seager van Dyk
- School of Psychology, Massey University, PO Box 756, Wellington, 6140, New Zealand
| | - Julian Burger
- Department of Social and Behavioral Sciences, Yale School of Public Health, 60 College Street, New Haven, CT, 06610, USA
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, 06520, USA
- Social and Behavioral Sciences, Yale School of Public Health, 135 College Street, New Haven, 06520, CT, USA
| | - John E Pachankis
- Department of Social and Behavioral Sciences, Yale School of Public Health, 60 College Street, New Haven, CT, 06610, USA
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Cao Z, Cho Y, Li F. Transporting randomized trial results to estimate counterfactual survival functions in target populations. Pharm Stat 2024; 23:442-465. [PMID: 38233102 DOI: 10.1002/pst.2354] [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: 01/29/2023] [Revised: 08/27/2023] [Accepted: 11/30/2023] [Indexed: 01/19/2024]
Abstract
When the distributions of treatment effect modifiers differ between a randomized trial and an external target population, the sample average treatment effect in the trial may be substantially different from the target population average treatment, and accurate estimation of the latter requires adjusting for the differential distribution of effect modifiers. Despite the increasingly rich literature on transportability, little attention has been devoted to methods for transporting trial results to estimate counterfactual survival functions in target populations, when the primary outcome is time to event and subject to right censoring. In this article, we study inverse probability weighting and doubly robust estimators to estimate counterfactual survival functions and the target average survival treatment effect in the target population, and provide their respective approximate variance estimators. We focus on a common scenario where the target population information is observed only through a complex survey, and elucidate how the survey weights can be incorporated into each estimator we considered. Simulation studies are conducted to examine the finite-sample performances of the proposed estimators in terms of bias, efficiency and coverage, under both correct and incorrect model specifications. Finally, we apply the proposed method to assess transportability of the results in the Action to Control Cardiovascular Risk in Diabetes-Blood Pressure (ACCORD-BP) trial to all adults with Diabetes in the United States.
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Affiliation(s)
- Zhiqiang Cao
- Department of Mathematics, College of Big Data and Internet, Shenzhen Technology University, Shenzhen, People's Republic of China
| | - Youngjoo Cho
- Department of Applied Statistics, Konkuk University, Seoul, Republic of Korea
| | - Fan Li
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
- Center for Methods in Implementation and Prevention Science, Yale University School of Public Health, New Haven, Connecticut, USA
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7
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Fay MP, Li F. Causal interpretation of the hazard ratio in randomized clinical trials. Clin Trials 2024:17407745241243308. [PMID: 38679930 DOI: 10.1177/17407745241243308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/01/2024]
Abstract
BACKGROUND Although the hazard ratio has no straightforward causal interpretation, clinical trialists commonly use it as a measure of treatment effect. METHODS We review the definition and examples of causal estimands. We discuss the causal interpretation of the hazard ratio from a two-arm randomized clinical trial, and the implications of proportional hazards assumptions in the context of potential outcomes. We illustrate the application of these concepts in a synthetic model and in a model of the time-varying effects of COVID-19 vaccination. RESULTS We define causal estimands as having either an individual-level or population-level interpretation. Difference-in-expectation estimands are both individual-level and population-level estimands, whereas without strong untestable assumptions the causal rate ratio and hazard ratio have only population-level interpretations. We caution users against making an incorrect individual-level interpretation, emphasizing that in general a hazard ratio does not on average change each individual's hazard by a factor. We discuss a potentially valid interpretation of the constant hazard ratio as a population-level causal effect under the proportional hazards assumption. CONCLUSION We conclude that the population-level hazard ratio remains a useful estimand, but one must interpret it with appropriate attention to the underlying causal model. This is especially important for interpreting hazard ratios over time.
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Affiliation(s)
- Michael P Fay
- Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, USA
| | - Fan Li
- Department of Biostatistics and Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, CT, USA
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8
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Heo J, Lee H, Lee IH, Lim IH, Hong SH, Shin J, Nam HS, Kim YD. Combined use of anticoagulant and antiplatelet on outcome after stroke in patients with nonvalvular atrial fibrillation and systemic atherosclerosis. Sci Rep 2024; 14:304. [PMID: 38172278 PMCID: PMC10764735 DOI: 10.1038/s41598-023-51013-3] [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: 04/04/2023] [Accepted: 12/29/2023] [Indexed: 01/05/2024] Open
Abstract
This study aimed to investigate whether there was a difference in one-year outcome after stroke between patients treated with antiplatelet and anticoagulation (OAC + antiplatelet) and those with anticoagulation only (OAC), when comorbid atherosclerotic disease was present with non-valvular atrial fibrillation (NVAF). This was a retrospective study using a prospective cohort of consecutive patients with ischemic stroke. Patients with NVAF and comorbid atherosclerotic disease were assigned to the OAC + antiplatelet or OAC group based on discharge medication. All-cause mortality, recurrent ischemic stroke, hemorrhagic stroke, myocardial infarction, and bleeding events within 1 year after the index stroke were compared. Of the 445 patients included in this study, 149 (33.5%) were treated with OAC + antiplatelet. There were no significant differences in all outcomes between groups. After inverse probability of treatment weighting, OAC + antiplatelet was associated with a lower risk of all-cause mortality (hazard ratio 0.48; 95% confidence interval 0.23-0.98; P = 0.045) and myocardial infarction (0% vs. 3.0%, P < 0.001). The risk of hemorrhagic stroke was not significantly different (P = 0.123). OAC + antiplatelet was associated with a decreased risk of all-cause mortality and myocardial infarction but an increased risk of ischemic stroke among patients with NVAF and systemic atherosclerotic diseases.
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Affiliation(s)
- JoonNyung Heo
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
- Department of Radiology, Yonsei University College of Medicine, Seoul, South Korea
| | - Hyungwoo Lee
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
- Department of Radiology, Yonsei University College of Medicine, Seoul, South Korea
| | - Il Hyung Lee
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
- Department of Radiology, Yonsei University College of Medicine, Seoul, South Korea
| | - In Hwan Lim
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
- Department of Radiology, Yonsei University College of Medicine, Seoul, South Korea
| | - Soon-Ho Hong
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
- Department of Radiology, Yonsei University College of Medicine, Seoul, South Korea
| | - Joonggyeong Shin
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Hyo Suk Nam
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Young Dae Kim
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea.
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9
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Zhu AY, Mitra N, Hemming K, Harhay MO, Li F. Leveraging baseline covariates to analyze small cluster-randomized trials with a rare binary outcome. Biom J 2024; 66:e2200135. [PMID: 37035941 PMCID: PMC10562517 DOI: 10.1002/bimj.202200135] [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: 05/06/2022] [Revised: 11/20/2022] [Accepted: 02/08/2023] [Indexed: 04/11/2023]
Abstract
Cluster-randomized trials (CRTs) involve randomizing entire groups of participants-called clusters-to treatment arms but are often comprised of a limited or fixed number of available clusters. While covariate adjustment can account for chance imbalances between treatment arms and increase statistical efficiency in individually randomized trials, analytical methods for individual-level covariate adjustment in small CRTs have received little attention to date. In this paper, we systematically investigate, through extensive simulations, the operating characteristics of propensity score weighting and multivariable regression as two individual-level covariate adjustment strategies for estimating the participant-average causal effect in small CRTs with a rare binary outcome and identify scenarios where each adjustment strategy has a relative efficiency advantage over the other to make practical recommendations. We also examine the finite-sample performance of the bias-corrected sandwich variance estimators associated with propensity score weighting and multivariable regression for quantifying the uncertainty in estimating the participant-average treatment effect. To illustrate the methods for individual-level covariate adjustment, we reanalyze a recent CRT testing a sedation protocol in 31 pediatric intensive care units.
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Affiliation(s)
- Angela Y. Zhu
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States of America
| | - Nandita Mitra
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States of America
| | - Karla Hemming
- Department of Public Health, Epidemiology, and Biostatistics, University of Birmingham Institute of Applied Health Research, Birmingham B15 2TT, United Kingdom
| | - Michael O. Harhay
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States of America
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, United States of America
- Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, CT 06510, United States of America
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10
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Wang D, Zheng S, Cui Y, He N, Chen T, Huang B. Adjusted win ratio using the inverse probability of treatment weighting. J Biopharm Stat 2023:1-16. [PMID: 37947400 DOI: 10.1080/10543406.2023.2275759] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 10/22/2023] [Indexed: 11/12/2023]
Abstract
The win ratio method has been increasingly applied in the design and analysis of clinical trials. However, the win ratio method is a univariate approach that does not allow for adjusting for baseline imbalances in covariates, although a stratified win ratio can be calculated when the number of strata is small. This paper proposes an adjusted win ratio to control for such imbalances by inverse probability of treatment weighting (IPTW) method. We derive the adjusted win ratio with its variance and suggest three IPTW adjustments: IPTW-average treatment effect (IPTW-ATE), stabilized IPTW-ATE (SIPTW-ATE) and IPTW-average treatment effect in the treated (IPTW-ATT). The proposed adjusted methods are applied to analyse a composite outcome in the CHARM trial. The statistical properties of the methods are assessed through simulations. Results show that adjusted win ratio methods can correct the win ratio for covariate imbalances at baseline. Simulation results show that the three proposed adjusted win ratios have similar power to detect the treatment difference and have slightly lower power than the corresponding adjusted Cox models when the assumption of proportional hazards holds true but have consistently higher power than adjusted Cox models when the proportional hazard assumption is violated.
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Affiliation(s)
- Duolao Wang
- Biostatistics Unit, Department of Clinical Sciences, Liverpool School of Tropical Medicine, Pembroke, UK
| | - Sirui Zheng
- Biostatistics Unit, Department of Clinical Sciences, Liverpool School of Tropical Medicine, Pembroke, UK
| | - Ying Cui
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, USA
| | - Nengjie He
- Biostatistics Unit, Department of Clinical Sciences, Liverpool School of Tropical Medicine, Pembroke, UK
| | - Tao Chen
- Centre for Health Economics, University of York, York, UK
| | - Bo Huang
- Pfizer Research & Development, Pfizer Inc, Groton, Connecticut, USA
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11
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Chang CR, Song Y, Li F, Wang R. Covariate adjustment in randomized clinical trials with missing covariate and outcome data. Stat Med 2023; 42:3919-3935. [PMID: 37394874 DOI: 10.1002/sim.9840] [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: 07/10/2022] [Revised: 04/27/2023] [Accepted: 06/15/2023] [Indexed: 07/04/2023]
Abstract
When analyzing data from randomized clinical trials, covariate adjustment can be used to account for chance imbalance in baseline covariates and to increase precision of the treatment effect estimate. A practical barrier to covariate adjustment is the presence of missing data. In this article, in the light of recent theoretical advancement, we first review several covariate adjustment methods with incomplete covariate data. We investigate the implications of the missing data mechanism on estimating the average treatment effect in randomized clinical trials with continuous or binary outcomes. In parallel, we consider settings where the outcome data are fully observed or are missing at random; in the latter setting, we propose a full weighting approach that combines inverse probability weighting for adjusting missing outcomes and overlap weighting for covariate adjustment. We highlight the importance of including the interaction terms between the missingness indicators and covariates as predictors in the models. We conduct comprehensive simulation studies to examine the finite-sample performance of the proposed methods and compare with a range of common alternatives. We find that conducting the proposed adjustment methods generally improves the precision of treatment effect estimates regardless of the imputation methods when the adjusted covariate is associated with the outcome. We apply the methods to the Childhood Adenotonsillectomy Trial to assess the effect of adenotonsillectomy on neurocognitive functioning scores.
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Affiliation(s)
- Chia-Rui Chang
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Yue Song
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Fan Li
- Department of Statistical Science, Duke University, Durham, North Carolina, USA
| | - Rui Wang
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
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12
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Varga AN, Guevara Morel AE, Lokkerbol J, van Dongen JM, van Tulder MW, Bosmans JE. Dealing with confounding in observational studies: A scoping review of methods evaluated in simulation studies with single-point exposure. Stat Med 2023; 42:487-516. [PMID: 36562408 PMCID: PMC10107671 DOI: 10.1002/sim.9628] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 11/22/2022] [Accepted: 12/01/2022] [Indexed: 12/24/2022]
Abstract
The aim of this article was to perform a scoping review of methods available for dealing with confounding when analyzing the effect of health care treatments with single-point exposure in observational data. We aim to provide an overview of methods and their performance assessed by simulation studies indexed in PubMed. We searched PubMed for simulation studies published until January 2021. Our search was restricted to studies evaluating binary treatments and binary and/or continuous outcomes. Information was extracted on the methods' assumptions, performance, and technical properties. Of 28,548 identified references, 127 studies were eligible for inclusion. Of them, 84 assessed 14 different methods (ie, groups of estimators that share assumptions and implementation) for dealing with measured confounding, and 43 assessed 10 different methods for dealing with unmeasured confounding. Results suggest that there are large differences in performance between methods and that the performance of a specific method is highly dependent on the estimator. Furthermore, the methods' assumptions regarding the specific data features also substantially influence the methods' performance. Finally, the methods result in different estimands (ie, target of inference), which can even vary within methods. In conclusion, when choosing a method to adjust for measured or unmeasured confounding it is important to choose the most appropriate estimand, while considering the population of interest, data structure, and whether the plausibility of the methods' required assumptions hold.
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Affiliation(s)
- Anita Natalia Varga
- Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute, The Netherlands
| | - Alejandra Elizabeth Guevara Morel
- Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute, The Netherlands
| | - Joran Lokkerbol
- Centre of Economic Evaluation, Trimbos Institute (Netherlands Institute of Mental Health), Utrecht, The Netherlands
| | - Johanna Maria van Dongen
- Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute, The Netherlands
| | - Maurits Willem van Tulder
- Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute, The Netherlands.,Department Physiotherapy and Occupational Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Judith Ekkina Bosmans
- Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute, The Netherlands
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13
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Tackney MS, Morris T, White I, Leyrat C, Diaz-Ordaz K, Williamson E. A comparison of covariate adjustment approaches under model misspecification in individually randomized trials. Trials 2023; 24:14. [PMID: 36609282 PMCID: PMC9817411 DOI: 10.1186/s13063-022-06967-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 11/28/2022] [Indexed: 01/09/2023] Open
Abstract
Adjustment for baseline covariates in randomized trials has been shown to lead to gains in power and can protect against chance imbalances in covariates. For continuous covariates, there is a risk that the the form of the relationship between the covariate and outcome is misspecified when taking an adjusted approach. Using a simulation study focusing on individually randomized trials with small sample sizes, we explore whether a range of adjustment methods are robust to misspecification, either in the covariate-outcome relationship or through an omitted covariate-treatment interaction. Specifically, we aim to identify potential settings where G-computation, inverse probability of treatment weighting (IPTW), augmented inverse probability of treatment weighting (AIPTW) and targeted maximum likelihood estimation (TMLE) offer improvement over the commonly used analysis of covariance (ANCOVA). Our simulations show that all adjustment methods are generally robust to model misspecification if adjusting for a few covariates, sample size is 100 or larger, and there are no covariate-treatment interactions. When there is a non-linear interaction of treatment with a skewed covariate and sample size is small, all adjustment methods can suffer from bias; however, methods that allow for interactions (such as G-computation with interaction and IPTW) show improved results compared to ANCOVA. When there are a high number of covariates to adjust for, ANCOVA retains good properties while other methods suffer from under- or over-coverage. An outstanding issue for G-computation, IPTW and AIPTW in small samples is that standard errors are underestimated; they should be used with caution without the availability of small-sample corrections, development of which is needed. These findings are relevant for covariate adjustment in interim analyses of larger trials.
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Affiliation(s)
- Mia S. Tackney
- grid.8991.90000 0004 0425 469XDepartment of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK ,grid.5335.00000000121885934MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Tim Morris
- grid.415052.70000 0004 0606 323XMRC Clinical Trials Unit at UCL, London, UK
| | - Ian White
- grid.415052.70000 0004 0606 323XMRC Clinical Trials Unit at UCL, London, UK
| | - Clemence Leyrat
- grid.8991.90000 0004 0425 469XDepartment of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Karla Diaz-Ordaz
- grid.8991.90000 0004 0425 469XDepartment of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK ,grid.83440.3b0000000121901201Department of Statistical Science, UCL, London, United Kingdom
| | - Elizabeth Williamson
- grid.8991.90000 0004 0425 469XDepartment of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
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14
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Gettel CJ, Yiadom MYA, Bernstein SL, Grudzen CR, Nath B, Li F, Hwang U, Hess EP, Melnick ER. Pragmatic clinical trial design in emergency medicine: Study considerations and design types. Acad Emerg Med 2022; 29:1247-1257. [PMID: 35475533 PMCID: PMC9790188 DOI: 10.1111/acem.14513] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 04/04/2022] [Accepted: 04/25/2022] [Indexed: 01/25/2023]
Abstract
Pragmatic clinical trials (PCTs) focus on correlation between treatment and outcomes in real-world clinical practice, yet a guide highlighting key study considerations and design types for emergency medicine investigators pursuing this important study type is not available. Investigators conducting emergency department (ED)-based PCTs face multiple decisions within the planning phase to ensure robust and meaningful study findings. The PRagmatic Explanatory Continuum Indicator Summary 2 (PRECIS-2) tool allows trialists to consider both pragmatic and explanatory components across nine domains, shaping the trial design to the purpose intended by the investigators. Aside from the PRECIS-2 tool domains, ED-based investigators conducting PCTs should also consider randomization techniques, human subjects concerns, and integration of trial components within the electronic health record. The authors additionally highlight the advantages, disadvantages, and rationale for the use of four common randomized study design types to be considered in PCTs: parallel, crossover, factorial, and stepped-wedge. With increasing emphasis on the conduct of PCTs, emergency medicine investigators will benefit from a rigorous approach to clinical trial design.
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Affiliation(s)
- Cameron J. Gettel
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale School of Medicine, New Haven, CT, USA
| | - Maame Yaa A.B. Yiadom
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Corita R. Grudzen
- Ronald O. Perelman Department of Emergency Medicine and Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Bidisha Nath
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Ula Hwang
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA
- Geriatrics Research, Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY, USA
| | - Erik P. Hess
- Department of Emergency Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Edward R. Melnick
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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15
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Remiro-Azócar A. Two-stage matching-adjusted indirect comparison. BMC Med Res Methodol 2022; 22:217. [PMID: 35941551 PMCID: PMC9358807 DOI: 10.1186/s12874-022-01692-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 07/19/2022] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Anchored covariate-adjusted indirect comparisons inform reimbursement decisions where there are no head-to-head trials between the treatments of interest, there is a common comparator arm shared by the studies, and there are patient-level data limitations. Matching-adjusted indirect comparison (MAIC), based on propensity score weighting, is the most widely used covariate-adjusted indirect comparison method in health technology assessment. MAIC has poor precision and is inefficient when the effective sample size after weighting is small. METHODS A modular extension to MAIC, termed two-stage matching-adjusted indirect comparison (2SMAIC), is proposed. This uses two parametric models. One estimates the treatment assignment mechanism in the study with individual patient data (IPD), the other estimates the trial assignment mechanism. The first model produces inverse probability weights that are combined with the odds weights produced by the second model. The resulting weights seek to balance covariates between treatment arms and across studies. A simulation study provides proof-of-principle in an indirect comparison performed across two randomized trials. Nevertheless, 2SMAIC can be applied in situations where the IPD trial is observational, by including potential confounders in the treatment assignment model. The simulation study also explores the use of weight truncation in combination with MAIC for the first time. RESULTS Despite enforcing randomization and knowing the true treatment assignment mechanism in the IPD trial, 2SMAIC yields improved precision and efficiency with respect to MAIC in all scenarios, while maintaining similarly low levels of bias. The two-stage approach is effective when sample sizes in the IPD trial are low, as it controls for chance imbalances in prognostic baseline covariates between study arms. It is not as effective when overlap between the trials' target populations is poor and the extremity of the weights is high. In these scenarios, truncation leads to substantial precision and efficiency gains but induces considerable bias. The combination of a two-stage approach with truncation produces the highest precision and efficiency improvements. CONCLUSIONS Two-stage approaches to MAIC can increase precision and efficiency with respect to the standard approach by adjusting for empirical imbalances in prognostic covariates in the IPD trial. Further modules could be incorporated for additional variance reduction or to account for missingness and non-compliance in the IPD trial.
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Affiliation(s)
- Antonio Remiro-Azócar
- Medical Affairs Statistics, Bayer plc, 400 South Oak Way, Reading, UK.
- Department of Statistical Science, University College London, 1-19 Torrington Place, London, UK.
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16
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Austin PC. Bootstrap vs asymptotic variance estimation when using propensity score weighting with continuous and binary outcomes. Stat Med 2022; 41:4426-4443. [PMID: 35841200 PMCID: PMC9544125 DOI: 10.1002/sim.9519] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 06/15/2022] [Accepted: 06/20/2022] [Indexed: 11/07/2022]
Abstract
We used Monte Carlo simulations to compare the performance of asymptotic variance estimators to that of the bootstrap when estimating standard errors of differences in means, risk differences, and relative risks using propensity score weighting. We considered four different sets of weights: conventional inverse probability of treatment weights with the average treatment effect (ATE) as the target estimand, weights for estimating the average treatment effect in the treated (ATT), matching weights, and overlap weights. We considered sample sizes ranging from 250 to 10 000 and allowed the prevalence of treatment to range from 0.1 to 0.9. We found that, when using ATE weights and sample sizes were ≤ 1000, then the use of the bootstrap resulted in estimates of SE that were more accurate than the asymptotic estimates. A similar finding was observed when using ATT weights and sample sizes were ≤ 1000 and the prevalence of treatment was moderate to high. When using matching weights and overlap weights, both the asymptotic estimator and the bootstrap resulted in accurate estimates of SE across all sample sizes and prevalences of treatment. Even when using the bootstrap with ATE weights, empirical coverage rates of confidence intervals were suboptimal when sample sizes were low to moderate and the prevalence of treatment was either very low or very high. A similar finding was observed when using the bootstrap with ATT weights when sample sizes were low to moderate and the prevalence of treatment was very high.
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Affiliation(s)
- Peter C Austin
- ICES, Toronto, Ontario, Canada.,Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.,Sunnybrook Research Institute, Toronto, Ontario, Canada
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17
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Li F, Buchanan AL, Cole SR. Generalizing trial evidence to target populations in non-nested designs: Applications to AIDS clinical trials. J R Stat Soc Ser C Appl Stat 2022; 71:669-697. [PMID: 35968541 PMCID: PMC9367209 DOI: 10.1111/rssc.12550] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Comparative effectiveness evidence from randomized trials may not be directly generalizable to a target population of substantive interest when, as in most cases, trial participants are not randomly sampled from the target population. Motivated by the need to generalize evidence from two trials conducted in the AIDS Clinical Trials Group (ACTG), we consider weighting, regression and doubly robust estimators to estimate the causal effects of HIV interventions in a specified population of people living with HIV in the USA. We focus on a non-nested trial design and discuss strategies for both point and variance estimation of the target population average treatment effect. Specifically in the generalizability context, we demonstrate both analytically and empirically that estimating the known propensity score in trials does not increase the variance for each of the weighting, regression and doubly robust estimators. We apply these methods to generalize the average treatment effects from two ACTG trials to specified target populations and operationalize key practical considerations. Finally, we report on a simulation study that investigates the finite-sample operating characteristics of the generalizability estimators and their sandwich variance estimators.
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Affiliation(s)
- Fan Li
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
- Center for Methods in Implementation and Prevention Science, Yale University, New Haven, Connecticut, USA
| | - Ashley L. Buchanan
- Department of Pharmacy Practice, College of Pharmacy, University of Rhode Island, Kingston, Rhode Island, USA
| | - Stephen R. Cole
- Department of Epidemiology, Gillings School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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18
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Propensity Score Matching Underestimates Real Treatment Effect, in a Simulated Theoretical Multivariate Model. MATHEMATICS 2022. [DOI: 10.3390/math10091547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Propensity Score Matching (PSM) is a useful method to reduce the impact of Treatment-Selection Bias in the estimation of causal effects in observational studies. After matching, the PSM significantly reduces the sample under investigation, which may lead to other possible biases (due to overfitting, excess of covariation or a reduced number of observations). In this sense, we wanted to analyze the behavior of this PSM compared with other widely used methods to deal with non-comparable groups, such as the Multivariate Regression Model (MRM). Monte Carlo Simulations are made to construct groups with different effects in order to compare the behavior of PSM and MRM estimating these effects. In addition, the Treatment Selection Bias reduction for the PSM is calculated. With the PSM a reduction in the Treatment Selection Bias is achieved (0.983 [0.982, 0.984]), with a reduction in the Relative Real Treatment Effect Estimation Error (0.216 [0.2, 0.232]), but despite this bias reduction and estimation error reduction, the MRM reduces this estimation error significantly more than the PSM (0.539 [0.522, 0.556], p < 0.001). In addition, the PSM leads to a 30% reduction in the sample. This loss of information derived from the matching process may lead to another not known bias and thus to the inaccuracy of the effect estimation compared with the MRM.
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19
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Morris TP, Walker AS, Williamson EJ, White IR. Planning a method for covariate adjustment in individually randomised trials: a practical guide. Trials 2022; 23:328. [PMID: 35436970 PMCID: PMC9014627 DOI: 10.1186/s13063-022-06097-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 02/10/2022] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND It has long been advised to account for baseline covariates in the analysis of confirmatory randomised trials, with the main statistical justifications being that this increases power and, when a randomisation scheme balanced covariates, permits a valid estimate of experimental error. There are various methods available to account for covariates but it is not clear how to choose among them. METHODS Taking the perspective of writing a statistical analysis plan, we consider how to choose between the three most promising broad approaches: direct adjustment, standardisation and inverse-probability-of-treatment weighting. RESULTS The three approaches are similar in being asymptotically efficient, in losing efficiency with mis-specified covariate functions and in handling designed balance. If a marginal estimand is targeted (for example, a risk difference or survival difference), then direct adjustment should be avoided because it involves fitting non-standard models that are subject to convergence issues. Convergence is most likely with IPTW. Robust standard errors used by IPTW are anti-conservative at small sample sizes. All approaches can use similar methods to handle missing covariate data. With missing outcome data, each method has its own way to estimate a treatment effect in the all-randomised population. We illustrate some issues in a reanalysis of GetTested, a randomised trial designed to assess the effectiveness of an electonic sexually transmitted infection testing and results service. CONCLUSIONS No single approach is always best: the choice will depend on the trial context. We encourage trialists to consider all three methods more routinely.
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Affiliation(s)
- Tim P. Morris
- MRC Clinical Trials Unit at UCL, London, UK
- Department of Medical Statistics, LSHTM, London, UK
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20
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Luo L, Liu X, Yu H, Luo M, Jia W, Dong W, Lei X. Red blood cell transfusions post diagnosis of necrotizing enterocolitis and the deterioration of necrotizing enterocolitis in full-term and near-term infants: a propensity score adjustment retrospective cohort study. BMC Pediatr 2022; 22:211. [PMID: 35428277 PMCID: PMC9012001 DOI: 10.1186/s12887-022-03276-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 04/08/2022] [Indexed: 11/29/2022] Open
Abstract
Background Necrotizing enterocolitis (NEC) is one of serious gastrointestinal inflammatory diseases in newborn infants, with a high morbidity and mortality. Red blood cell transfusion (RBCT) plays a controversial and doubtful role in the treatment of NEC. In present study, we aim to analyze the association between RBCT and the deterioration of NEC. Methods This was a retrospective cohort study of near-term and full-term infants with a confirmed diagnosis of Bell’s stage II NEC between Jan 1, 2010 and Jan 31, 2020. The maternal and infant baseline characteristics, treatment information and laboratory test for each case were collected. The eligible subjects were divided into two groups based on receiving RBCT post NEC diagnosis or not. The propensity score was used to eliminate potential bias and baseline differences. A multivariate logistic regression model was used to adjust the propensity score and calculate the odds ratio (OR) and 95% confidential interval (CI) of RBCT for the deterioration of NEC. Results A total of 242 infants were included in this study, 60 infants had a history of RBCT post NEC diagnosis, and 40 infants deteriorated from Bell’s stage II to stage III. By adjusting the propensity score, RBCT post NEC diagnosis was associated with an increased risk for NEC deteriorating from stage II to III (adjusted OR 6.06, 95%CI 2.94–12.50, P = 0.000). Conclusions NEC infants who required RBCT post NEC diagnosis were more likely to deteriorate from stage II to III in full-term and near-term infants. Supplementary Information The online version contains supplementary material available at 10.1186/s12887-022-03276-4.
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21
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Su L, Seaman SR, Yiu S. Sensitivity analysis for calibrated inverse probability-of-censoring weighted estimators under non-ignorable dropout. Stat Methods Med Res 2022; 31:1374-1391. [PMID: 35410545 PMCID: PMC9253927 DOI: 10.1177/09622802221090763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Inverse probability of censoring weighting is a popular approach to handling
dropout in longitudinal studies. However, inverse probability-of-censoring
weighted estimators (IPCWEs) can be inefficient and unstable if the weights are
estimated by maximum likelihood. To alleviate these problems, calibrated IPCWEs
have been proposed, which use calibrated weights that directly optimize
covariate balance in finite samples rather than the weights from maximum
likelihood. However, the existing calibrated IPCWEs are all based on the
unverifiable assumption of sequential ignorability and sensitivity analysis
strategies under non-ignorable dropout are lacking. In this paper, we fill this
gap by developing an approach to sensitivity analysis for calibrated IPCWEs
under non-ignorable dropout. A simple technique is proposed to speed up the
computation of bootstrap and jackknife confidence intervals and thus facilitate
sensitivity analyses. We evaluate the finite-sample performance of the proposed
methods using simulations and apply our methods to data from an international
inception cohort study of systemic lupus erythematosus. An R Markdown tutorial
to demonstrate the implementation of the proposed methods is provided.
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Affiliation(s)
- Li Su
- MRC Biostatistics Unit, School of Clinical Medicine, 12204University of Cambridge, UK
| | - Shaun R Seaman
- MRC Biostatistics Unit, School of Clinical Medicine, 12204University of Cambridge, UK
| | - Sean Yiu
- MRC Biostatistics Unit, School of Clinical Medicine, 12204University of Cambridge, UK
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22
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Zhou Y, Turner EL, Simmons RA, Li F. Constrained randomization and statistical inference for multi‐arm parallel cluster randomized controlled trials. Stat Med 2022; 41:1862-1883. [PMID: 35146788 PMCID: PMC9007899 DOI: 10.1002/sim.9333] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 01/12/2022] [Accepted: 01/14/2022] [Indexed: 12/17/2022]
Abstract
A practical limitation of cluster randomized controlled trials (cRCTs) is that the number of available clusters may be small, resulting in an increased risk of baseline imbalance under simple randomization. Constrained randomization overcomes this issue by restricting the allocation to a subset of randomization schemes where sufficient overall covariate balance across comparison arms is achieved. However, for multi-arm cRCTs, several design and analysis issues pertaining to constrained randomization have not been fully investigated. Motivated by an ongoing multi-arm cRCT, we elaborate the method of constrained randomization and provide a comprehensive evaluation of the statistical properties of model-based and randomization-based tests under both simple and constrained randomization designs in multi-arm cRCTs, with varying combinations of design and analysis-based covariate adjustment strategies. In particular, as randomization-based tests have not been extensively studied in multi-arm cRCTs, we additionally develop most-powerful randomization tests under the linear mixed model framework for our comparisons. Our results indicate that under constrained randomization, both model-based and randomization-based analyses could gain power while preserving nominal type I error rate, given proper analysis-based adjustment for the baseline covariates. Randomization-based analyses, however, are more robust against violations of distributional assumptions. The choice of balance metrics and candidate set sizes and their implications on the testing of the pairwise and global hypotheses are also discussed. Finally, we caution against the design and analysis of multi-arm cRCTs with an extremely small number of clusters, due to insufficient degrees of freedom and the tendency to obtain an overly restricted randomization space.
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Affiliation(s)
- Yunji Zhou
- Department of Biostatistics and Bioinformatics Duke University Durham North Carolina USA
- Duke Global Health Institute Duke University Durham North Carolina USA
| | - Elizabeth L. Turner
- Department of Biostatistics and Bioinformatics Duke University Durham North Carolina USA
- Duke Global Health Institute Duke University Durham North Carolina USA
| | - Ryan A. Simmons
- Department of Biostatistics and Bioinformatics Duke University Durham North Carolina USA
- Duke Global Health Institute Duke University Durham North Carolina USA
| | - Fan Li
- Department of Biostatistics Yale School of Public Health New Haven Connecticut USA
- Center for Methods in Implementation and Prevention Science Yale School of Public Health New Haven Connecticut USA
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23
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Li F, Tian Z, Bobb J, Papadogeorgou G, Li F. Clarifying selection bias in cluster randomized trials. Clin Trials 2021; 19:33-41. [PMID: 34894795 DOI: 10.1177/17407745211056875] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND In cluster randomized trials, patients are typically recruited after clusters are randomized, and the recruiters and patients may not be blinded to the assignment. This often leads to differential recruitment and consequently systematic differences in baseline characteristics of the recruited patients between intervention and control arms, inducing post-randomization selection bias. We aim to rigorously define causal estimands in the presence of selection bias. We elucidate the conditions under which standard covariate adjustment methods can validly estimate these estimands. We further discuss the additional data and assumptions necessary for estimating causal effects when such conditions are not met. METHODS Adopting the principal stratification framework in causal inference, we clarify there are two average treatment effect (ATE) estimands in cluster randomized trials: one for the overall population and one for the recruited population. We derive analytical formula of the two estimands in terms of principal-stratum-specific causal effects. Furthermore, using simulation studies, we assess the empirical performance of the multivariable regression adjustment method under different data generating processes leading to selection bias. RESULTS When treatment effects are heterogeneous across principal strata, the average treatment effect on the overall population generally differs from the average treatment effect on the recruited population. A naïve intention-to-treat analysis of the recruited sample leads to biased estimates of both average treatment effects. In the presence of post-randomization selection and without additional data on the non-recruited subjects, the average treatment effect on the recruited population is estimable only when the treatment effects are homogeneous between principal strata, and the average treatment effect on the overall population is generally not estimable. The extent to which covariate adjustment can remove selection bias depends on the degree of effect heterogeneity across principal strata. CONCLUSION There is a need and opportunity to improve the analysis of cluster randomized trials that are subject to post-randomization selection bias. For studies prone to selection bias, it is important to explicitly specify the target population that the causal estimands are defined on and adopt design and estimation strategies accordingly. To draw valid inferences about treatment effects, investigators should (1) assess the possibility of heterogeneous treatment effects, and (2) consider collecting data on covariates that are predictive of the recruitment process, and on the non-recruited population from external sources such as electronic health records.
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Affiliation(s)
- Fan Li
- Department of Statistical Science, Duke University, Durham, NC, USA
| | - Zizhong Tian
- Department of Public Health Sciences, Pennsylvania State University, Hershey, PA, USA
| | - Jennifer Bobb
- Kaiser Permanente Washington Health Research Institute, and Department of Biostatistics, University of Washington, Seattle, WA, USA
| | | | - Fan Li
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA
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24
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Yang S, Li F, Thomas LE, Li F. Covariate adjustment in subgroup analyses of randomized clinical trials: A propensity score approach. Clin Trials 2021; 18:570-581. [PMID: 34269087 DOI: 10.1177/17407745211028588] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Subgroup analyses are frequently conducted in randomized clinical trials to assess evidence of heterogeneous treatment effect across patient subpopulations. Although randomization balances covariates within subgroups in expectation, chance imbalance may be amplified in small subgroups and adversely impact the precision of subgroup analyses. Covariate adjustment in overall analysis of randomized clinical trial is often conducted, via either analysis of covariance or propensity score weighting, but covariate adjustment for subgroup analysis has been rarely discussed. In this article, we develop propensity score weighting methodology for covariate adjustment to improve the precision and power of subgroup analyses in randomized clinical trials. METHODS We extend the propensity score weighting methodology to subgroup analyses by fitting a logistic regression propensity model with pre-specified covariate-subgroup interactions. We show that, by construction, overlap weighting exactly balances the covariates with interaction terms in each subgroup. Extensive simulations were performed to compare the operating characteristics of unadjusted estimator, different propensity score weighting estimators and the analysis of covariance estimator. We apply these methods to the Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training trial to evaluate the effect of exercise training on 6-min walk test in several pre-specified subgroups. RESULTS Standard errors of the adjusted estimators are smaller than those of the unadjusted estimator. The propensity score weighting estimator is as efficient as analysis of covariance, and is often more efficient when subgroup sample size is small (e.g. <125), and/or when outcome model is misspecified. The weighting estimators with full-interaction propensity model consistently outperform the standard main-effect propensity model. CONCLUSION Propensity score weighting is a transparent and objective method to adjust chance imbalance of important covariates in subgroup analyses of randomized clinical trials. It is crucial to include the full covariate-subgroup interactions in the propensity score model.
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Affiliation(s)
- Siyun Yang
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Fan Li
- Department of Statistical Science, Duke University, Durham, NC, USA
| | - Laine E Thomas
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.,Duke Clinical Research Institute, Durham, NC, USA
| | - Fan Li
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA
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