Search Results

You are looking at 1 - 5 of 5 items for

  • Author: Matthew C. Cheung x
  • Refine by Access: All x
Clear All Modify Search
Full access

Quantifying the Survival Benefits of Oncology Drugs With a Focus on Immunotherapy Using Restricted Mean Survival Time

Amanda Putri Rahmadian, Seanthel Delos Santos, Shruti Parshad, Louis Everest, Matthew C. Cheung, and Kelvin K. Chan

Background: Restricted mean survival time (RMST) overcomes limitations of current measures of survival benefits because it directly captures information of the entire area under Kaplan-Meier survival curves. Using RMST difference (absolute survival benefit) and RMST ratio (relative survival benefit), we quantified the magnitude of survival benefits of recent oncology drugs and compared immunotherapies with nonimmunotherapies. Methods: Kaplan-Meier curves were extracted from phase II/III randomized controlled trials used by the FDA for oncology drug approvals from January 2011 through November 2017 with overall survival (OS) or progression-free survival (PFS) as primary endpoints. RMST differences, ratios, and their 95% confidence intervals were meta-analyzed to estimate absolute and relative survival benefits of contemporary oncology drugs and to compare immunotherapies with nonimmunotherapies. Meta-regression was conducted to adjust for potential confounders. Results: Ninety-four trials with a total of 51,639 patients were included. Overall absolute survival benefits (RMST differences) were 1.55 months for OS (95% CI, 1.32–1.77) and 2.99 months for PFS (95% CI, 2.65–3.33). Overall relative survival benefits (RMST ratios) were 1.11 for OS (95% CI, 1.09–1.13) and 1.42 for PFS (95% CI, 1.36–1.48). Immunotherapy absolute PFS benefit was less than that of nonimmunotherapy (1.56 vs 3.23 months), whereas immunotherapy absolute OS benefit was larger than that of nonimmunotherapy by 0.59 months (2.02 vs 1.43 months). Adjusted OS RMST difference was 0.91 months greater for immunotherapy than for nonimmunotherapy after adjusting for confounders. Conclusions: Absolute survival benefits of recent oncology drugs are modest. Survival benefits of immunotherapies are not dramatically superior to those of nonimmunotherapies. Routine reporting and use of RMST may help patients, physicians, and payers make more informed and responsible decisions regarding the care of patients with cancer.

Full access

Shorter Diagnosis-to-Treatment Interval in Diffuse Large B-Cell Lymphoma is Associated With Inferior Overall Survival in a Large, Population-Based Registry

Danielle N. Blunt, Liam Smyth, Chenthila Nagamuthu, Evgenia Gatov, Ruth Croxford, Lee Mozessohn, and Matthew C. Cheung

Background: Because of prolonged screening requirements, patient and time-dependent selection have been proposed as potential biases in clinical trials. The screening process may exclude patients with a need for emergent treatment (and a short period from diagnosis to treatment initiation [DTI]). We explored the impact of DTI on overall survival (OS) in a population-based cohort of patients with diffuse large B-cell lymphoma (DLBCL). Patients and Methods: Using population-based administrative databases in Ontario, Canada, we identified adults aged ≥18 years with DLBCL treated with rituximab-based chemotherapy for curative intent between January 2005 and December 2015. Cox regression and multivariable analyses were presented to evaluate the impact of time from DTI on OS, controlling for relevant covariates. Results: We identified 9,441 patients with DLBCL in Ontario; median age was 66 years, 53.6% were male, median number of comorbidities (Johns Hopkins aggregated diagnosis groups) was 10 (interquartile range [IQR], 8–13), and median DTI was 37 days (IQR, 22–61). Between treatment initiation and study end, 43% of patients died (median OS, 1 year; IQR, 0.4–2.8 years). Shorter DTI was a significant predictor of mortality (P<.001). Compared with the shortest DTI period of 0–18 days, those who commenced therapy at 19–29 days (hazard ratio [HR], 0.75; 95% CI, 0.68–0.84), 30–41 days (HR, 0.70; 95% CI, 0.63–0.78), 42–57 days (HR, 0.52; 95% CI, 0.46–0.58), and 58–180 days (HR, 0.52; 95% CI, 0.47–0.58) had improved survival. Increasing age (HR, 1.03; 95% CI, 1.03–1.04), male sex (HR, 1.23; 95% CI, 1.14–1.32), and increasing number of comorbidities (HR, 1.12; 95% CI, 1.11–1.13) were associated with inferior survival. Conclusions: Among patients with DLBCL, shorter DTI was associated with inferior OS. Therefore, DTI may represent a surrogate marker for aggressive biology. Clinical trials with lengthy screening periods are likely creating a time-dependent patient selection bias.

Full access

Frailty in Patients With Newly Diagnosed Diffuse Large B-Cell Lymphoma Receiving Curative-Intent Therapy: A Population-Based Study

Abi Vijenthira, Lee Mozessohn, Chenthila Nagamuthu, Ning Liu, Danielle Blunt, Shabbir Alibhai, Anca Prica, and Matthew C. Cheung

Background: The objectives of this study were to determine whether frailty is associated with survival in a population-based sample of patients with diffuse large B-cell lymphoma (DLBCL) and to describe the healthcare utilization patterns of frail versus nonfrail patients during treatment. Methods: A retrospective cohort study was conducted using population-based data in Ontario, Canada. Patients aged ≥66 years diagnosed between 2006 and 2017 with DLBCL or transformed follicular lymphoma who received first-line curative-intent chemoimmunotherapy were included. Frailty was defined using a modified version of a generalizable frailty index developed for use with Ontario administrative data. Cox regression was performed to examine the association between frailty and 1-year mortality. Results: A total of 5,527 patients were included (median age, 75 years [interquartile range, 70–80 years]; 48% female), of whom 2,699 (49%) were classified as frail. Within 1 year of first-line treatment, 32% (n=868) of frail patients had died compared with 20% (n=553) of nonfrail patients (unadjusted hazard ratio, 1.8; 95% CI, 1.6–2.0; P<.0001). Frail patients had higher healthcare utilization during treatment, with most hospitalizations related to infection and/or lymphoma. In multivariable modeling controlling for age, inpatient diagnosis, number of chemoimmunotherapy cycles received, comorbidity burden, and healthcare utilization, frailty remained independently associated with 1-year mortality (adjusted hazard ratio, 1.5; 95% CI, 1.3–1.7; P<.0001). Conclusions: In a population-based sample of older adult patients with DLBCL receiving front-line curative-intent therapy, half were classified as frail, and their adjusted relative rate of death in the first year after starting treatment was 50% higher than that of nonfrail patients. Frailty seems to be associated with poor treatment tolerance and a higher likelihood of requiring acute hospital-based care.

Full access

Reassessing the Net Benefit of Cancer Drugs With Evolution of Evidence Using the ASCO Value Framework

Seanthel Delos Santos, Noah Witzke, Bishal Gyawali, Vanessa Sarah Arciero, Amanda Putri Rahmadian, Louis Everest, Matthew C. Cheung, and Kelvin K. Chan

Background: Regulatory approval of oncology drugs is often based on interim data or surrogate endpoints. However, clinically relevant data, such as long-term overall survival and quality of life (QoL), are often reported in subsequent publications. This study evaluated the ASCO-Value Framework (ASCO-VF) net health benefit (NHB) at the time of approval and over time as further evidence arose. Methods: FDA-approved oncology drug indications from January 2006 to December 2016 were reviewed to identify clinical trials scorable using the ASCO-VF. Subsequent publications of clinical trials relevant for scoring were identified (until December 2019). Using ASCO-defined thresholds (≤40 for low and ≥45 for substantial benefit), we assessed changes in classification of benefit at 3 years postapproval. Results: Fifty-five eligible indications were included. At FDA approval, 40.0% were substantial, 10.9% were intermediate, and 49.1% were low benefit. We then identified 90 subsequent publications relevant to scoring, including primary (28.9%) and secondary endpoint updates (47.8%), safety updates (31.1%), and QoL reporting (47.8%). There was a change from initial classification of benefit in 27.3% of trials (10.9% became substantial, 9.1% became low, and 7.3% became intermediate). These changes were mainly due to updated hazard ratios (36.4%), toxicities (56.4%), new tail-of-the-curve bonus (9.1%), palliation bonus (14.5%), or QoL bonus (18.2%). Overall, at 3 years postapproval, 40.0% were substantial, 9.1% were intermediate, and 50.9% were low benefit. Conclusions: Because there were changes in classification for more than one-quarter of indications, in either direction, reassessing the ASCO-VF NHB as more evidence becomes available may be beneficial to inform clinical shared decision-making. On average, there was no overall improvement in the ASCO-VF NHB with longer follow-up and evolution of evidence.

Full access

Are Surrogate Endpoints Unbiased Metrics in Clinical Benefit Scores of the ASCO Value Framework?

Sierra Cheng, Matthew C. Cheung, Di Maria Jiang, Erica McDonald, Vanessa S. Arciero, Doreen Anuli Ezeife, Amanda Rahmadian, Alexandra Chambers, Kelley-Anne Sabarre, Ambika Parmar, and Kelvin K.W. Chan

Background: Clinical benefit scores (CBS) are key elements of the ASCO Value Framework (ASCO-VF) and are weighted based on a hierarchy of efficacy endpoints: hazard ratio for death (HR OS), median overall survival (mOS), HR for disease progression (HR PFS), median progression-free survival (mPFS), and response rate (RR). When HR OS is unavailable, the other endpoints serve as “surrogates” to calculate CBS. CBS are computed from PFS or RR in 39.6% of randomized controlled trials. This study examined whether surrogate-derived CBS offer unbiased scoring compared with HR OS–derived CBS. Methods: Using the ASCO-VF, CBS for advanced disease settings were computed for randomized controlled trials of oncology drug approvals by the FDA, European Medicines Agency, and Health Canada in January 2006 through December 2017. Mean differences of surrogate-derived CBS minus HR OS–derived CBS assessed the tendency of surrogate-derived CBS to overestimate or underestimate clinical benefit. Spearman’s correlation evaluated the association between surrogate- and HR OS–derived CBS. Mean absolute error assessed the average difference between surrogate-derived CBS relative to HR OS–derived CBS. Results: CBS derived from mOS, HR PFS, mPFS, and RR overestimated HR OS–derived CBS in 58%, 68%, 77%, and 55% of pairs and overall by an average of 5.62 (n=90), 6.86 (n=110), 29.81 (n=101), and 3.58 (n=108), respectively. Correlation coefficients were 0.80 (95% CI, 0.70–0.86), 0.38 (0.20–0.53), 0.20 (0.00–0.38), and 0.01 (–0.18 to 0.19) for mOS-, HR PFS–, mPFS-, and RR-derived CBS, respectively, and mean absolute errors were 11.32, 12.34, 40.40, and 18.63, respectively. Conclusions: Based on the ASCO-VF algorithm, HR PFS–, mPFS-, and RR-derived CBS are suboptimal surrogates, because they were shown to be biased and poorly correlated to HR OS–derived CBS. Despite lower weighting than OS in the ASCO-VF algorithm, PFS still overestimated CBS. Simple rescaling of surrogate endpoints may not improve their validity within the ASCO-VF given their poor correlations with HR OS–derived CBS.