The Impact of Delayed Switch to Second-Line Antiretroviral Therapy on Mortality, Depending on Definition of Failure Time and CD4 Count at Failure.

Little is known about the functional relationship of delaying second-line treatment initiation for human immunodeficiency virus-positive patients and mortality, given a patient's immune status. We included 7,255 patients starting antiretroviral therapy during 2004-2017, from 9 South African cohorts, with virological failure and complete baseline data. We estimated the impact of switch time on the hazard of death using inverse probability of treatment weighting of marginal structural models. The nonlinear relationship between month of switch and the 5-year survival probability, stratified by CD4 count at failure, was estimated with targeted maximum likelihood estimation. We adjusted for measured time-varying confounding by CD4 count, viral load, and visit frequency. Five-year mortality was estimated to be 10.5% (95% CI: 2.2, 18.8) for immediate switch and to be 26.6% (95% CI: 20.9, 32.3) for no switch (51.1% if CD4 count was <100 cells/mm3). The hazard of death was estimated to be 0.37 (95% CI: 0.30, 0.46) times lower if everyone had been switched immediately compared with never. The shorter the delay in switching, the lower the hazard of death-delaying 30-59 days reduced the hazard by 0.53 (95% CI: 0.43, 0.65) times and 60-119 days by 0.58 (95% CI: 0.49, 0.69) times, compared with no switch. Early treatment switch is particularly important for patients with low CD4 counts at failure.

Increased Mortality With Delayed and Missed Switch to Second-Line Antiretroviral Therapy in South Africa.

BACKGROUND: After failure of first-line antiretroviral therapy (ART) in the public sector, delayed or missed second-line ART switch is linked with poor outcomes in patients with advanced HIV. SETTING: We investigated delayed or missed second-line ART switch after confirmed virologic failure in the largest private sector HIV cohort in Africa. METHODS: We included HIV-infected adults with confirmed virologic failure after 6 months of nonnucleoside reverse-transcriptase inhibitor-based ART. We estimated the effect of timing of switch on the hazard of death using inverse probability of treatment weighting of marginal structural models. We adjusted for time-dependent confounding of CD4 count, viral load, and visit frequency. RESULTS: Five thousand seven hundred forty-eight patients (53% female) with confirmed virologic failure met inclusion criteria; the median age was 40 [interquartile range (IQR): 35-47], advanced HIV was present in 48% and the prior duration of nonnucleoside reverse-transcriptase inhibitor-based ART was 1083 days (IQR: 665-1770). Median time to confirmation of virologic failure and to second-line switch was 196 (IQR: 136-316) and 220 days (IQR: 65-542), respectively. Switching to second-line ART after confirmed failure compared with remaining on first-line ART reduced risk of subsequent death [adjusted hazard ratio: 0.47 (95% confidence interval: 0.36 to 0.63)]. Compared with patients who experienced delayed switch, those switched immediately had a lower risk of death, regardless of CD4 cell count. CONCLUSIONS: Delayed or missed switch to second-line ART after confirmed first-line ART failure is common in the South African private sector and associated with mortality. Novel interventions to minimize switch delay should be tested and not limited to those with advanced disease at treatment failure.

Assessing the Long-Term Effectiveness of Cladribine vs. Placebo in the Relapsing-Remitting Multiple Sclerosis CLARITY Randomized Controlled Trial and CLARITY Extension Using Treatment Switching Adjustment Methods.

OBJECTIVES: Treatment switching adjustment methods are often used to adjust for switching in oncology randomized controlled trials (RCTs). In this exploratory analysis, we apply these methods to adjust for treatment changes in the setting of an RCT followed by an extension study in relapsing-remitting multiple sclerosis. METHODS: The CLARITY trial evaluated cladribine tablets versus placebo over 96 weeks. In the 96-week CLARITY Extension, patients who received placebo in CLARITY received cladribine tablets; patients who received cladribine tablets in CLARITY were re-randomized to placebo or cladribine tablets. End points were time to first qualifying relapse (FQR) and time to 3- and 6-month confirmed disability progression (3mCDP, 6mCDP). We aimed to compare the effectiveness of cladribine tablets with placebo over CLARITY and the extension. The rank-preserving structural failure time model (RPSFTM) and iterative parameter estimation (IPE) were used to estimate what would have happened if patients had received placebo in CLARITY and the extension versus patients that received cladribine tablets and switched to placebo. To gauge whether treatment effect waned after the 96 weeks of CLARITY, we compared hazard ratios (HRs) from the adjustment analysis with HRs from CLARITY. RESULTS: The RPSFTM resulted in an HR of 0.48 [95% confidence interval (CI) 0.36-0.62] for FQR, 0.62 (95% CI 0.46-0.84) for 3mCDP and 0.62 (95% CI 0.44-0.88) for 6mCDP. IPE algorithm results were similar. CLARITY HRs were 0.44 (95% CI 0.34-0.58), 0.60 (95% CI 0.41-0.87) and 0.58 (95% CI 0.40-0.83) for FQR, 3mCDP and 6mCDP, respectively. CONCLUSIONS: Treatment switching adjustment methods are applicable in non-oncology settings. Adjusted CLARITY plus CLARITY Extension HRs were similar to the CLARITY HRs, demonstrating significant treatment benefits associated with cladribine tablets versus placebo. FUNDING: EMD Serono, Inc. (a business of Merck KGaA, Darmstadt, Germany).

A Review of Survival Analysis Methods Used in NICE Technology Appraisals of Cancer Treatments: Consistency, Limitations, and Areas for Improvement.

Objectives. In June 2011, the National Institute for Health and Care Excellence (NICE) Decision Support Unit published a Technical Support Document (TSD) providing recommendations on survival analysis for NICE technology appraisals (TAs). Survival analysis outputs are influential inputs into economic models estimating the cost-effectiveness of new cancer treatments. Hence, it is important that systematic and justifiable model selection approaches are used. This study investigates the extent to which the TSD recommendations have been followed since its publication. Methods. We reviewed NICE cancer TAs completed between July 2011 and July 2017. Information on survival analyses undertaken and associated critiques for overall survival (OS) and progression-free survival were extracted from the company submissions, Evidence Review Group (ERG) reports, and final appraisal determination documents. Results. Information was extracted from 58 TAs. Only 4 (7%) followed all TSD recommendations for OS outcomes. The vast majority (91%) compared a range of common parametric models and assessed their fit to the data (86%). Only a minority of TAs included an assessment of the shape of the hazard function (38%) or proportional hazards assumption (40%). Validation of the extrapolated portion of the survival function using external data was attempted in a minority of TAs (40%). Extrapolated survival functions were frequently criticized by ERGs (71%). Conclusions. Survival analysis within NICE TAs remains suboptimal, despite publication of the TSD. Model selection is not undertaken in a systematic way, resulting in inconsistencies between TAs. More attention needs to be given to assessing hazard functions and validation of extrapolated survival functions. Novel methods not described in the TSD have been used, particularly in the context of immuno-oncology, suggesting that an updated TSD may be of value.

Survival Extrapolation in Cancer Immunotherapy: A Validation-Based Case Study.

BACKGROUND: Immune-checkpoint inhibitors may provide long-term survival benefits via a cured proportion, yet data are usually insufficient to prove this upon submission to health technology assessment bodies. OBJECTIVE: We revisited the National Institute for Health and Care Excellence assessment of ipilimumab in melanoma (TA319). We used updated data from the pivotal trial to assess the accuracy of the extrapolation methods used and compared these to previously unused techniques to establish whether an alternative extrapolation may have provided more accurate survival projections. METHODS: We compared projections from the piecewise survival model used in TA319 and those produced by alternative models (fit to trial data with minimum follow-up of 3 years) to a longer-term data cut (5-year follow-up). We also compared projections to external data to help assess validity. Alternative approaches considered were parametric, spline-based, mixture, and mixture-cure models. RESULTS: Only the survival model used in TA319 and a mixture-cure model provided 5-year survival predictions close to those observed in the 5-year follow-up data set. Standard parametric, spline, and non-curative-mixture models substantially underestimated 5-year survival. Survival estimates from the TA319 model and the mixture-cure model diverge considerably after 5 years and remain unvalidated. CONCLUSIONS: In our case study, only models that incorporated an element of external information (through a cure fraction combined with background mortality rates or using registry data) provided accurate estimates of 5-year survival. Flexible models that were able to capture the complex hazard functions observed during the trial, but which did not incorporate external information, extrapolated poorly.