scholarly journals SLE clinical trials: impact of missing data on estimating treatment effects

2019 ◽  
Vol 6 (1) ◽  
pp. e000348 ◽  
Author(s):  
Mimi Kim ◽  
Joan T Merrill ◽  
Cuiling Wang ◽  
Shankar Viswanathan ◽  
Ken Kalunian ◽  
...  

ObjectiveA common problem in clinical trials is missing data due to participant dropout and loss to follow-up, an issue which continues to receive considerable attention in the clinical research community. Our objective was to examine and compare current and alternative methods for handling missing data in SLE trials with a particular focus on multiple imputation, a flexible technique that has been applied in different disease settings but not to address missing data in the primary outcome of an SLE trial.MethodsData on 279 patients with SLE randomised to standard of care (SoC) and also receiving mycophenolate mofetil (MMF), azathioprine or methotrexate were obtained from the Lupus Foundation of America-Collective Data Analysis Initiative Database. Complete case analysis (CC), last observation carried forward (LOCF), non-responder imputation (NRI) and multiple imputation (MI) were applied to handle missing data in an analysis to assess differences in SLE Responder Index-5 (SRI-5) response rates at 52 weeks between patients on SoC treated with MMF versus other immunosuppressants (non-MMF).ResultsThe rates of missing data were 32% in the MMF and 23% in the non-MMF groups. As expected, the NRI missing data approach yielded the lowest estimated response rates. The smallest and least significant estimates of differences between groups were observed with LOCF, and precision was lowest with the CC method. Estimated between-group differences were magnified with the MI approach, and imputing SRI-5 directly versus deriving SRI-5 after separately imputing its individual components yielded similar results.ConclusionThe potential advantages of applying MI to address missing data in an SLE trial include reduced bias when estimating treatment effects, and measures of precision that properly reflect uncertainty in the imputations. However, results can vary depending on the imputation model used, and the underlying assumptions should be plausible. Sensitivity analysis should be conducted to demonstrate robustness of results, especially when missing data proportions are high.

2019 ◽  
Vol 24 (8) ◽  
pp. 649-660 ◽  
Author(s):  
Jane Frances Ndyetukira ◽  
Richard Kwizera ◽  
Florence Kugonza ◽  
Cynthia Ahimbisibwe ◽  
Carol Namujju ◽  
...  

Background Nurses form a very important part of the health workforce in sub-Saharan Africa. Research nurses are critical to the implementation of clinical trials. The duties and responsibilities of a research nurse are complex and continue to evolve as new practices and guidelines are formulated. Aims In this paper, we have highlighted the major contributions of research nurses in HIV clinical trials in sub-Saharan Africa from the unique perspective of Ugandan nurses. Methods The requirements and challenges of two multi-site, randomised cryptococcal meningitis clinical trials in Uganda were assessed from the perspective of research nurses conducting complex research in resource-limited settings. Results Over the course of 8 years, approximately 1739 participants were screened and 934 people were enrolled into the two trials. The nurses found that patient education and engagement were among the most important predictors of success in minimising loss to follow-up. Conclusions Research nurses played a key role in communicating clinical research goals to patients, obtaining informed consent, minimising loss to follow-up, and ensuring that research practices are translated and implemented into standard of care. However, there remains a need to integrate the same level of care provided in clinical research studies to non-study patients.


Author(s):  
Sean Wharton ◽  
Arne Astrup ◽  
Lars Endahl ◽  
Michael E. J. Lean ◽  
Altynai Satylganova ◽  
...  

AbstractIn the approval process for new weight management therapies, regulators typically require estimates of effect size. Usually, as with other drug evaluations, the placebo-adjusted treatment effect (i.e., the difference between weight losses with pharmacotherapy and placebo, when given as an adjunct to lifestyle intervention) is provided from data in randomized clinical trials (RCTs). At first glance, this may seem appropriate and straightforward. However, weight loss is not a simple direct drug effect, but is also mediated by other factors such as changes in diet and physical activity. Interpreting observed differences between treatment arms in weight management RCTs can be challenging; intercurrent events that occur after treatment initiation may affect the interpretation of results at the end of treatment. Utilizing estimands helps to address these uncertainties and improve transparency in clinical trial reporting by better matching the treatment-effect estimates to the scientific and/or clinical questions of interest. Estimands aim to provide an indication of trial outcomes that might be expected in the same patients under different conditions. This article reviews how intercurrent events during weight management trials can influence placebo-adjusted treatment effects, depending on how they are accounted for and how missing data are handled. The most appropriate method for statistical analysis is also discussed, including assessment of the last observation carried forward approach, and more recent methods, such as multiple imputation and mixed models for repeated measures. The use of each of these approaches, and that of estimands, is discussed in the context of the SCALE phase 3a and 3b RCTs evaluating the effect of liraglutide 3.0 mg for the treatment of obesity.


2020 ◽  
Vol 3 ◽  
pp. 82
Author(s):  
Robert Murphy ◽  
Emer McGrath ◽  
Aoife Nolan ◽  
Andrew Smyth ◽  
Michelle Canavan ◽  
...  

Background: A run-in period is often employed in randomised controlled trials to increase adherence to the intervention and reduce participant loss to follow-up in the trial population. However, it is uncertain whether use of a run-in period affects the magnitude of treatment effect. Methods: We will conduct a sensitive search for systematic reviews of cardiovascular preventative trials and a complete meta-analysis of treatment effects comparing cardiovascular prevention trials using a run-in period (“run-in trials”) with matched cardiovascular prevention trials that did not use a run-in period (“non-run-in trials”). We describe a comprehensive matching process which will match run-in trials with non-run-in trials by patient populations, interventions, and outcomes. For each pair of run-in trial and matched non-run-in trial(s), we will estimate the ratio of relative risks and 95% confidence interval. We will evaluate differences in treatment effect between run-in and non-run-in trials and our and our priamry outcome will be the ratio of relative risks for matched run-in and non-run-in trials for their reported cardiovascular composite outcome. Our secondary outcomes are comparisons of mortality, loss to follow up, frequency of adverse events and methodological quality of trials. Conclusions: This study will answer a key question about what influence a run-in period has on the magnitude of treatment effects in randomised controlled trials for cardiovascular prevention therapies.


Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 2800-2800
Author(s):  
Emily J. Vannorsdall ◽  
Vu H. Duong ◽  
Xinyi Ng ◽  
Dan P. Zandberg ◽  
Michael L. Tidwell ◽  
...  

Abstract Abstract 2800 Background: Chronic myelomonocytic leukemia (CMML) is a clonal hematopoietic stem cell disorder categorized as a mixed myeloproliferative/myelodysplastic disorder in the World Health Organization classification system. Diagnostic criteria include a persistent peripheral blood monocytosis >1 × 109/L and bone marrow dysplasia. Our recent review of SEER Medicare data (ASH 2011 abstract 2784) demonstrated that CMML has a shorter overall survival (OS) and more frequent progression to acute myeloid leukemia (AML), compared to myelodysplastic syndromes (MDS). Due to the heterogeneity of this disease and its differences from MDS, efforts to identify prognostic factors have been ongoing. The MD Anderson prognostic score was previously validated, but was derived from patients treated prior to the availability of the hypomethylating agents (HMAs) azacitidine and decitabine. HMAs have now emerged as standard therapy, with reported response rates of 37–69%, but their impact on survival and AML transformation is unclear. The OS of CMML patients has been reported at 12–18 months and transformation rates have varied between 15–52%. We reviewed our own single-center experience with CMML over the past 12 years. Methods: We conducted a retrospective review of CMML patients evaluated at the University of Maryland Greenebaum Cancer Center between January 2000 and August 2012. Patient and disease characteristics, treatments, complications, progression to AML, and OS were recorded and analyzed. Descriptive statistics were used for baseline characteristics and Kaplan-Meier analysis was performed for all time-to-event data. Statistical analyses were performed using SPSS version 20.0. Results: We identified 35 patients with CMML, 71% were male and 71% white, with a median age of 69 (range 34–86) years; 75% had <10% bone marrow (BM) blasts and 68% had low-risk cytogenetic findings (normal karyotype or -Y). Most patients treated prior to 2005 received hydroxyurea and/or erythropoiesis-stimulating agents or were enrolled on clinical trials, while patients treated since 2005 received HMAs as primary therapy. The median OS of the entire cohort was 19.5 months, with 49% of patients progressing to AML with a median time to progression (TTP) of 16.9 months. Of the entire cohort, patients with <10% and ≥10% BM blasts had an estimated OS of 19.4 and 11.7 months respectively (p=.021). Patients with low-, intermediate-, and high-risk (complex karyotype, +8, or chromosome 7 abnormalities) cytogenetic findings had an estimated OS of 23.3, 16.5, and 12.0 months respectively (p<0.001). Twenty-two patients received HMAs. Their estimated OS was 16.5 months, compared to 23.0 months for patients who did not receive HMAs (p =.683); 50% of patients treated with HMAs had known progression to AML, with TTP varying from 3–28 months. AML-free-survival was 16 months in patients receiving HMAs, compared to 14 months in patients not treated with HMAs (p=0.960). The majority of patients receiving HMA therapy (63%) were treated with ≥ 6 cycles; 57% of these patients transformed to AML despite initial response, often in a sudden and unpredictable manner. Conclusions: Published trials using HMAs in CMML have been limited by small patient numbers, short median follow-up, and paucity of data on AML transformation. Our study had a median follow-up period of 41.1 months. We found a high rate of AML transformation and short OS even in patients who received HMAs. HMA treatment had no statistically significant impact on AML-free survival or OS. Although the results may be confounded by some selection bias, treatment with HMAs was largely based on the date of diagnosis rather than prognostic variables or performance status. Therefore, the favorable response rates previously reported with these agents, and also seen in our patients, do not appear to translate into an OS or AML-free-survival advantage. Our study underscores the continued need for novel agents and the need to prioritize clinical trials for this group of patients. Additionally, based on our data, early bone marrow transplantation should be strongly considered for CMML patients when feasible. Disclosures: Davidoff: Novartis: Research Funding; Celgene: Research Funding; GlaskoSmithKline: Research Funding. Baer:Novartis, Inc.: Research Funding; Celgene, Inc.: Research Funding.


2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 610-610 ◽  
Author(s):  
Stefan Michiels ◽  
Lina Pugliano ◽  
Delphine Grun ◽  
Jana Barinoff ◽  
David A. Cameron ◽  
...  

610 Background: The gold standard endpoint in randomized clinical trials (RCTs) in MBC is OS, which has the disadvantage of requiring extended follow-up and being confounded by subsequent anti-cancer therapies. Although therapeutics have been approved based on PFS, its use as a primary endpoint is controversial. This study, the first IPD meta-analysis of targeted agents in MBC, aimed to collect data from RCTs of HER2-targeted agents in HER2+ MBC, assessing to what extent PFS correlates with, and may be used as, a surrogate for OS. Methods: A search was conducted in April 2011. Eligible RCTs accrued HER2+ MBC patients (pts) in 1992-2008. Collaboration was obtained from industrial partners (Roche, GSK) for industry-led studies. Investigator-assessed PFS was defined as the time from randomization to clinical or radiological progression, or death. A correlation approach was used: at the individual level, to estimate the association between PFS and OS using a bivariate survival model and at the trial level, to estimate the association between treatment effects on PFS and OS. Squared correlation values close to 1.0 would indicate strong surrogacy. Results: The search strategy resulted in 2137 eligible pts in 13 RCTs testing trastuzumab or lapatinib. We collected IPD data from 1963 pts in 9 RCTs. One phase II RCT did not have sufficient follow-up data so that 1839 pts in 8 RCTs were retained (5 evaluating trastuzumab, 3 lapatinib); 6 out of 8 RCTs were first-line. At the individual level, the Spearman rank correlation using Hougaard copula was equal to r=0.66 (95% CI 0.65 to 0.66) corresponding to an r2 of 0.42. At the trial level, the squared correlation between treatment effects on PFS and OS was provided by R2=0.33 (95% CI -0.22 to 0.86) using Hougaard copula and R2=0.53 (95% CI 0.22 to 0.83) using log hazard ratios from Cox models. Conclusions: In RCTs of HER2-targeted agents in HER2+ MBC, PFS is moderately correlated with OS and treatment effects on PFS are modestly correlated with treatment effects on OS, similarly to first-line chemotherapy in MBC (Burzykowski et al JCO 2008). PFS does not completely substitute for OS.


2009 ◽  
Vol 37 (1) ◽  
pp. 54-63 ◽  
Author(s):  
Sarra L. Hedden ◽  
Robert F. Woolson ◽  
Rickey E. Carter ◽  
Yuko Palesch ◽  
Himanshu P. Upadhyaya ◽  
...  

Biometrics ◽  
2012 ◽  
Vol 68 (4) ◽  
pp. 1250-1259 ◽  
Author(s):  
Devan V. Mehrotra ◽  
Xiaoming Li ◽  
Jiajun Liu ◽  
Kaifeng Lu

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