1
|
Ramanathan S, Hochstedler KA, Laucis AM, Movsas B, Stevens CW, Kestin LL, Dominello MM, Grills IS, Matuszak M, Hayman J, Paximadis PA, Schipper MJ, Jolly S, Boike TP. Predictors of Early Hospice or Death in Patients With Inoperable Lung Cancer Treated With Curative Intent. Clin Lung Cancer 2023:S1525-7304(23)00283-8. [PMID: 38290875 DOI: 10.1016/j.cllc.2023.12.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 12/11/2023] [Accepted: 12/23/2023] [Indexed: 02/01/2024]
Abstract
INTRODUCTION Treatment for inoperable stage II to III non-small cell lung cancer (NSCLC) involves chemo-radiotherapy (CRT). However, some patients transition to hospice or die early during their treatment course. We present a model to prognosticate early poor outcomes in NSCLC patients treated with curative-intent CRT. METHODS AND MATERIALS Across a statewide consortium, data was prospectively collected on stage II to III NSCLC patients who received CRT between 2012 and 2019. Early poor outcomes included hospice enrollment or death within 3 months of completing CRT. Logistic regression models were used to assess predictors in prognostic models. LASSO regression with multiple imputation were used to build a final multivariate model, accounting for missing covariates. RESULTS Of the 2267 included patients, 128 experienced early poor outcomes. Mean age was 71 years and 59% received concurrent chemotherapy. The best predictive model, created parsimoniously from statistically significant univariate predictors, included age, ECOG, planning target volume (PTV), mean heart dose, pretreatment lack of energy, and cough. The estimated area under the ROC curve for this multivariable model was 0.71, with a negative predictive value of 95%, specificity of 97%, positive predictive value of 23%, and sensitivity of 16% at a predicted risk threshold of 20%. CONCLUSIONS This multivariate model identified a combination of clinical variables and patient reported factors that may identify individuals with inoperable NSCLC undergoing curative intent chemo-radiotherapy who are at higher risk for early poor outcomes.
Collapse
Affiliation(s)
| | | | - Anna M Laucis
- Department of Radiation Oncology, University of Michigan Health System, Ann Arbor, MI
| | | | | | - Larry L Kestin
- Genesis Care / Michigan Healthcare Professionals, Troy, MI
| | | | | | - Martha Matuszak
- Department of Radiation Oncology, University of Michigan Health System, Ann Arbor, MI
| | - James Hayman
- Department of Radiation Oncology, University of Michigan Health System, Ann Arbor, MI
| | | | - Matthew J Schipper
- Department of Radiation Oncology, University of Michigan Health System, Ann Arbor, MI
| | - Shruti Jolly
- Department of Radiation Oncology, University of Michigan Health System, Ann Arbor, MI.
| | - Thomas P Boike
- Genesis Care / Michigan Healthcare Professionals, Troy, MI
| |
Collapse
|
2
|
Laucis AMB, Hochstedler KA, Schipper MJ, Paximadis PA, Boike TP, Bergsma DP, Movsas B, Kretzler A, Spratt DE, Dess RT, Mietzel MA, Dominello MM, Matuszak MM, Jagsi R, Hayman JA, Pierce LJ, Jolly S. Racial Differences in Treatments and Toxicity in Patients With Non-Small-Cell Lung Cancer Treated With Thoracic Radiation Therapy. JCO Oncol Pract 2022; 18:e1034-e1044. [PMID: 35167337 DOI: 10.1200/op.21.00224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Historical racial disparities in lung cancer surgery rates resulted in lower survival in Black patients. Our objective was to examine racial differences in thoracic radiation treatments and toxicities in patients with non-small-cell lung cancer. METHODS AND MATERIALS A large institutional review board-approved statewide patient-level database of patients with stage II-III non-small-cell lung cancer who received definitive thoracic radiation from March 2012 to November 2019 was analyzed to assess associations between race and other variables. Race (White or Black) was defined by patient self-report. Provider-reported toxicity was defined by Common Terminology Criteria for Adverse Events version 4.0. Patient-reported toxicity was determined by the Functional Assessment of Cancer Therapy-Lung quality-of-life instrument. Univariable and multivariable regression models were fitted to assess relationships between race and variables of interest. Spearman rank-correlation coefficients were calculated between provider-reported toxicity and similar patient-reported outcomes. RESULTS One thousand four hundred forty-one patients from 24 institutions with mean age 68 years (range, 38-94 years) were evaluated. Race was not significantly associated with radiation or chemotherapy approach. There was significantly increased patient-reported general pain in Black patients at the preradiation and end-of-radiation time points. Black patients were significantly less likely to have provider-reported grade 2+ pneumonitis (odds ratio 0.36, P = .03), even after controlling for known patient and treatment factors. Correlation coefficients between provider- and patient-reported toxicities were generally similar across race groups except for a stronger correlation between patient- and provider-reported esophagitis in White patients. CONCLUSION In this large multi-institutional study, we found no evidence of racial differences in radiation treatment or chemotherapy approaches. We did, however, unexpectedly find that Black race was associated with lower odds of provider-reported grade 2+ radiation pneumonitis. The stronger correlation between patient- and provider-reported esophagitis and swallowing symptoms for White patients also suggests possible under-recognition of symptoms in Black patients. Further research is needed to study the implications for Black patients.
Collapse
Affiliation(s)
- Anna Mary Brown Laucis
- Department of Radiation Oncology, Rogel Comprehensive Cancer Center at the University of Michigan, Ann Arbor, MI
| | | | - Matthew J Schipper
- Department of Radiation Oncology, Rogel Comprehensive Cancer Center at the University of Michigan, Ann Arbor, MI.,Department of Biostatistics, University of Michigan, Ann Arbor, MI
| | | | | | - Derek P Bergsma
- Department of Radiation Oncology, Mercy Health Saint Mary's, Grand Rapids, MI
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, MI
| | - Annette Kretzler
- Department of Radiation Oncology, Henry Ford Allegiance, Jackson, MI
| | - Daniel E Spratt
- Department of Radiation Oncology, Rogel Comprehensive Cancer Center at the University of Michigan, Ann Arbor, MI
| | - Robert T Dess
- Department of Radiation Oncology, Rogel Comprehensive Cancer Center at the University of Michigan, Ann Arbor, MI
| | - Melissa A Mietzel
- Department of Radiation Oncology, Rogel Comprehensive Cancer Center at the University of Michigan, Ann Arbor, MI
| | - Michael M Dominello
- Department of Radiation Oncology, Barbara Ann Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI
| | - Martha M Matuszak
- Department of Radiation Oncology, Rogel Comprehensive Cancer Center at the University of Michigan, Ann Arbor, MI
| | - Reshma Jagsi
- Department of Radiation Oncology, Rogel Comprehensive Cancer Center at the University of Michigan, Ann Arbor, MI
| | - James A Hayman
- Department of Radiation Oncology, Rogel Comprehensive Cancer Center at the University of Michigan, Ann Arbor, MI
| | - Lori J Pierce
- Department of Radiation Oncology, Rogel Comprehensive Cancer Center at the University of Michigan, Ann Arbor, MI
| | - Shruti Jolly
- Department of Radiation Oncology, Rogel Comprehensive Cancer Center at the University of Michigan, Ann Arbor, MI
| |
Collapse
|
3
|
Hochstedler KA, Bell G, Park H, Ghassabian A, Bell EM, Sundaram R, Grantz KL, Yeung EH. Gestational Age at Birth and Risk of Developmental Delay: The Upstate KIDS Study. Am J Perinatol 2021; 38:1088-1095. [PMID: 32143225 PMCID: PMC7507972 DOI: 10.1055/s-0040-1702937] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVE The aim of this study is to model the association between gestational age at birth and early child development through 3 years of age. STUDY DESIGN Development of 5,868 children in Upstate KIDS (New York State; 2008-2014) was assessed at 7 time points using the Ages and Stages Questionnaire (ASQ). The ASQ was implemented using gestational age corrected dates of birth at 4, 8, 12, 18, 24, 30, and 36 months. Whether children were eligible for developmental services from the Early Intervention Program was determined through linkage. Gestational age was based on vital records. Statistical models adjusted for covariates including sociodemographic factors, maternal smoking, and plurality. RESULTS Compared with gestational age of 39 weeks, adjusted odds ratios (aOR) and 95% confidence intervals of failing the ASQ for children delivered at <32, 32-34, 35-36, 37, 38, and 40 weeks of gestational age were 5.32 (3.42-8.28), 2.43 (1.60-3.69), 1.38 (1.00-1.90), 1.37 (0.98-1.90), 1.29 (0.99-1.67), 0.73 (0.55-0.96), and 0.51 (0.32-0.82). Similar risks of being eligible for Early Intervention Program services were observed (aOR: 4.19, 2.10, 1.29, 1.20, 1.01, 1.00 [ref], 0.92, and 0.78 respectively for <32, 32-34, 37, 38, 39 [ref], 40, and 41 weeks). CONCLUSION Gestational age was inversely associated with developmental delays for all gestational ages. Evidence from our study is potentially informative for low-risk deliveries at 39 weeks, but it is notable that deliveries at 40 weeks exhibited further lower risk.
Collapse
Affiliation(s)
- Kimberly A Hochstedler
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan
| | - Griffith Bell
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland
| | - Hyojun Park
- Department of Sociology, Utah State University, Logan, Utah
| | - Akhgar Ghassabian
- Departments of Pediatrics, Environmental Medicine, and Population Health, New York University School of Medicine, New York, New York
| | - Erin M Bell
- Departments of Environmental Health Sciences and Epidemiology and Biostatistics, University at Albany School of Public Health, Albany, New York
| | - Rajeshwari Sundaram
- Biostatistics & Bioinformatics Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland
| | - Katherine L Grantz
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland
| | - Edwina H Yeung
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland
| |
Collapse
|
4
|
McFarlane MR, Hochstedler KA, Laucis AM, Sun Y, Chowdhury A, Matuszak MM, Hayman J, Bergsma D, Boike T, Kestin L, Movsas B, Grills I, Dominello M, Dess RT, Schonewolf C, Spratt DE, Pierce L, Paximadis P, Jolly S, Schipper M. Predictors of Pneumonitis After Conventionally Fractionated Radiotherapy for Locally Advanced Lung Cancer. Int J Radiat Oncol Biol Phys 2021; 111:1176-1185. [PMID: 34314815 DOI: 10.1016/j.ijrobp.2021.07.1691] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 07/14/2021] [Accepted: 07/19/2021] [Indexed: 12/25/2022]
Abstract
PURPOSE Multiple factors influence the risk of developing pneumonitis after radiation therapy (RT) for lung cancer, but few resources exist to guide clinicians in predicting risk in an individual patient treated with modern techniques. We analyzed toxicity data from a state-wide consortium to develop an integrated pneumonitis risk model. METHODS AND MATERIALS All patients (N = 1302) received conventionally fractionated RT for stage II-III non-small cell lung cancer between April 2012 and July 2019. Pneumonitis occurring within 6 months of treatment was graded by local practitioners and collected prospectively from 27 academic and community clinics participating in a state-wide quality consortium. Pneumonitis was modeled as either grade ≥2 (G2+) or grade ≥3 (G3+). Logistic regression models were fit to quantify univariable associations with dose and clinical factors, and stepwise Akaike information criterion-based modeling was used to build multivariable prediction models. RESULTS The overall rate of pneumonitis of any grade in the 6 months following RT was 16% (208 cases). Seven percent of cases (n = 94) were G2+ and <1% (n = 11) were G3+. Adjusting for incomplete follow-up, estimated rates for G2+ and G3+ were 14% and 2%, respectively. In univariate analyses, gEUD, V5, V10, V20, V30, and mean lung dose (MLD) were positively associated with G2+ pneumonitis risk, whereas current smoking status was associated with lower odds of pneumonitis. G2+ pneumonitis risk of ≥22% was independently predicted by MLD of ≥20 Gy, V20 of ≥35%, and V5 of ≥75%. In multivariate analyses, the lung V5 metric remained a significant predictor of G2+ pneumonitis, even when controlling for MLD, despite their close correlation. For G3+ pneumonitis, MLD and V20 were statistically significant predictors. Number of patient comorbidities was an independent predictor of G3+, but not G2+ pneumonitis. CONCLUSIONS We present an analysis of pneumonitis risk after definitive RT for lung cancer using a large, prospective dataset. We incorporate comorbidity burden, smoking status, and dosimetric parameters in an integrated risk model. These data may guide clinicians in assessing pneumonitis risk in individual patients.
Collapse
Affiliation(s)
- Matthew R McFarlane
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | | | - Anna M Laucis
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Yilun Sun
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Aulina Chowdhury
- College of Osteopathic Medicine, Kansas City University of Medicine and Biosciences, Kansas City, Missouri
| | - Martha M Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - James Hayman
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Derek Bergsma
- Department of Radiation Oncology, Lacks Cancer Center, University of Michigan, Grand Rapids, Michigan
| | - Thomas Boike
- MHP Radiation Oncology/21st Century Oncology, Multiple Sites, Michigan
| | - Larry Kestin
- MHP Radiation Oncology/21st Century Oncology, Multiple Sites, Michigan
| | | | - Inga Grills
- Beaumont Radiation Oncology, Royal Oak, Michigan
| | - Michael Dominello
- Department of Radiation Oncology, Wayne State University, Detroit, Michigan
| | - Robert T Dess
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Caitlin Schonewolf
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Daniel E Spratt
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Lori Pierce
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | | | - Shruti Jolly
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
| | - Matthew Schipper
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan; Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | | |
Collapse
|
5
|
Laucis AMB, Hochstedler KA, Boike TP, Movsas B, Stevens CW, Kestin LL, Dominello MM, Wilkie J, Grills IS, Matuszak M, Hayman J, Paximadis PA, Schipper MJ, Jolly S. Predictors of early hospice or death in patients with inoperable lung cancer treated with curative intent. J Clin Oncol 2021. [DOI: 10.1200/jco.2021.39.15_suppl.e20525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
e20525 Background: Treatment for inoperable stage II-III non-small cell lung cancer (NSCLC) involves aggressive chemo-radiotherapy (CRT). While outcomes have improved with immunotherapy, some patients transition to hospice or die early in their treatment course. To help identify these patients, we developed a predictive model for early poor outcomes in NSCLC patients treated with curative intent. Methods: In a statewide consortium involving 27 sites, information was collected prospectively on stage II-III NSCLC patients who received curative CRT from April 2012 to November 2019. We defined an early poor outcome as termination of treatment due to hospice enrollment or death within 5 months of initiating radiation therapy. Potential predictors included clinical characteristics and patient reported outcomes (PROs) from validated questionnaires. Logistic regression models were used to assess potential predictors and build predictive models. Multiple imputation was used to handle missing data. We used Lasso regularized logistic regression to build a predictive model with multiple predictor variables. Results: Of the total of 2267 included patients, 128 patients discontinued treatment early due to hospice enrollment or death. The mean age of the 128 patients was 71 years old (range 48-91) and 59% received concurrent chemotherapy. Significant uni-variable predictors of early hospice or death were advanced age, worse ECOG performance status, high PTV volume, short distance to normal tissue critical structures, high mean heart dose, uninsured status, lower scores on the Functional and Physical Well-Being scale and the Lung Cancer Symptoms sub-scale of the FACT-L quality of life instrument, as well as higher levels of patient-reported lack of energy, cough, and shortness of breath. The best predictive model included age, ECOG performance status, PTV volume, mean heart dose, patient insurance status, and patient-reported lack of energy and cough. The pooled estimate of area under the curve (AUC) for this multivariable model was 0.71, with a negative predictive value of 95%, specificity of 97%, positive predictive value of 23%, and sensitivity of 16% at a predicted risk threshold of 20%. Conclusions: Our models identified a combination of clinical variables and PROs that may help identify individuals with inoperable NSCLC undergoing curative intent chemo-radiotherapy who are at a high risk of early hospice enrollment or death. These preliminary results are encouraging and warrant further evaluation in a larger cohort of patients.
Collapse
|
6
|
Hartman HE, Sun Y, Devasia TP, Chase EC, Jairath NK, Dess RT, Jackson WC, Morris E, Li P, Hochstedler KA, Abbott MR, Kidwell KM, Walter V, Wang M, Wang X, Zaorsky NG, Schipper MJ, Spratt DE. Integrated Survival Estimates for Cancer Treatment Delay Among Adults With Cancer During the COVID-19 Pandemic. JAMA Oncol 2020; 6:1881-1889. [PMID: 33119036 PMCID: PMC7596687 DOI: 10.1001/jamaoncol.2020.5403] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 08/04/2020] [Indexed: 12/18/2022]
Abstract
Importance Cancer treatment delay has been reported to variably impact cancer-specific survival and coronavirus disease 2019 (COVID-19)-specific mortality during the severe acute respiratory syndrome coronavirus 2 pandemic. During the pandemic, treatment delay is being recommended in a nonquantitative, nonobjective, and nonpersonalized manner, and this approach may be associated with suboptimal outcomes. Quantitative integration of cancer mortality estimates and data on the consequences of treatment delay is needed to aid treatment decisions and improve patient outcomes. Objective To obtain quantitative integration of cancer-specific and COVID-19-specific mortality estimates that can be used to make optimal decisions for individual patients and optimize resource allocation. Design, Setting, and Participants In this decision analytical model, age-specific and stage-specific estimates of overall survival pre-COVID-19 were adjusted by the probability of COVID-19 (individualized by county, treatment-specific variables, hospital exposure frequency, and COVID-19 infectivity estimates), COVID-19 mortality (individualized by age-specific, comorbidity-specific, and treatment-specific variables), and delay of cancer treatment (impact and duration). These model estimates were integrated into a web application (OncCOVID) to calculate estimates of the cumulative overall survival and restricted mean survival time of patients who received immediate vs delayed cancer treatment. Using currently available information about COVID-19, a susceptible-infected-recovered model that accounted for the increased risk among patients at health care treatment centers was developed. This model integrated the data on cancer mortality and the consequences of treatment delay to aid treatment decisions. Age-specific and cancer stage-specific estimates of overall survival pre-COVID-19 were extracted from the Surveillance, Epidemiology, and End Results database for 691 854 individuals with 25 cancer types who received cancer diagnoses in 2005 to 2006. Data from 5 436 896 individuals in the National Cancer Database were used to estimate the independent impact of treatment delay by cancer type and stage. In addition, data from 275 patients in a nested case-control study were used to estimate the COVID-19 mortality rate by age group and number of comorbidities. Data were analyzed from March 17 to May 21, 2020. Exposures COVID-19 and cancer. Main Outcomes and Measures Estimates of restricted mean survival time after the receipt of immediate vs delayed cancer treatment. Results At the time of the study, the OncCOVID web application allowed for the selection of up to 47 individualized variables to assess net survival for an individual patient with cancer. Substantial heterogeneity was found regarding the association between delayed cancer treatment and net survival among patients with a given cancer type and stage, and these 2 variables were insufficient to discriminate the net impact of immediate vs delayed treatment. Individualized overall survival estimates were associated with patient age, number of comorbidities, treatment received, and specific local community estimates of COVID-19 risk. Conclusions and Relevance This decision analytical modeling study found that the OncCOVID web-based application can quantitatively aid in the resource allocation of individualized treatment for patients with cancer during the COVID-19 global pandemic.
Collapse
Affiliation(s)
| | - Yilun Sun
- Department of Biostatistics, University of Michigan, Ann Arbor
- Department of Radiation Oncology, University of Michigan, Ann Arbor
| | | | | | - Neil K. Jairath
- Department of Radiation Oncology, University of Michigan, Ann Arbor
| | - Robert T. Dess
- Department of Radiation Oncology, University of Michigan, Ann Arbor
| | | | - Emily Morris
- Department of Biostatistics, University of Michigan, Ann Arbor
| | - Pin Li
- Department of Biostatistics, University of Michigan, Ann Arbor
| | | | | | | | - Vonn Walter
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Penn State University College of Medicine, Hershey, Pennsylvania
| | - Ming Wang
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Penn State University College of Medicine, Hershey, Pennsylvania
| | - Xi Wang
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Penn State University College of Medicine, Hershey, Pennsylvania
| | - Nicholas G. Zaorsky
- Department of Radiation Oncology, Penn State Cancer Institute, Hershey, Pennsylvania
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania
| | - Matthew J. Schipper
- Department of Biostatistics, University of Michigan, Ann Arbor
- Department of Radiation Oncology, University of Michigan, Ann Arbor
| | - Daniel E. Spratt
- Department of Radiation Oncology, University of Michigan, Ann Arbor
| |
Collapse
|
7
|
Fang F, Hochstedler KA, Tamura RN, Braun TM, Kidwell KM. Bayesian methods to compare dose levels with placebo in a small n, sequential, multiple assignment, randomized trial. Stat Med 2020; 40:963-977. [PMID: 33216360 DOI: 10.1002/sim.8813] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 10/15/2020] [Accepted: 10/30/2020] [Indexed: 11/11/2022]
Abstract
Clinical trials studying treatments for rare diseases are challenging to design and conduct due to the limited number of patients eligible for the trial. One design used to address this challenge is the small n, sequential, multiple assignment, randomized trial (snSMART). We propose a new snSMART design that investigates the response rates of a drug tested at a low and high dose compared with placebo. Patients are randomized to an initial treatment (stage 1). In stage 2, patients are rerandomized, depending on their initial treatment and their response to that treatment in stage 1, to either the same or a different dose of treatment. Data from both stages are used to determine the efficacy of the active treatment. We present a Bayesian approach where information is borrowed between stage 1 and stage 2. We compare our approach to standard methods using only stage 1 data and a log-linear Poisson model that uses data from both stages where parameters are estimated using generalized estimating equations. We observe that the Bayesian method has smaller root-mean-square-error and 95% credible interval widths than standard methods in the tested scenarios. We conclude that it is advantageous to utilize data from both stages for a primary efficacy analysis and that the specific snSMART design shown here can be used in the registration of a drug for the treatment of rare diseases.
Collapse
Affiliation(s)
- Fang Fang
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Kimberly A Hochstedler
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Roy N Tamura
- Health Informatics Institute, University of South Florida, Tampa, Florida, USA
| | - Thomas M Braun
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Kelley M Kidwell
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| |
Collapse
|