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Background: Investigation of infection risk with subcutaneous versus intravenous trastuzumab and rituximab administration in an individual patient data (IPD) and published data meta-analysis of randomised controlled trials (RCTs). Methods: Databases were searched to September 2021. Primary outcomes were serious and high-grade infection. Relative-risk (RR) and 95% confidence intervals (95%CI) were calculated using random-effects models. Results: IPD meta-analysis (6 RCTs, 2971 participants, 2320 infections) demonstrated higher infection incidence with subcutaneous versus intravenous administration, without reaching statistical significance (serious: 12.2% versus 9.3%, RR 1.28, 95%CI 0.93to1.77, P = 0.13; high-grade: 12.2% versus 9.9%, RR 1.32, 95%CI 0.98to1.77, P = 0.07). With exclusion of an outlying study in post-hoc analysis, increased risks were statistically significant (serious: 13.1% versus 8.4%, RR 1.53, 95%CI 1.14to2.06, P = 0.01; high-grade: 13.2% versus 9.3%, RR 1.56, 95%CI 1.16to2.11, P < 0.01). Published data meta-analysis (8 RCTs, 3745 participants, 648 infections) demonstrated higher incidence of serious (HR 1.31, 95%CI 1.02to1.68, P = 0.04) and high-grade (HR 1.52, 95%CI 1.17to1.98, P < 0.01) infection with subcutaneous versus intravenous administration. Conclusions: Results suggest increased infection risk with subcutaneous versus intravenous administration, although IPD findings are sensitive to exclusion of one trial with inconsistent results and identified risk-of-bias. Ongoing trials may confirm findings. Clinical surveillance should be considered when switching to subcutaneous administration. PROSPERO registration CRD42020221866/CRD42020125376. [ABSTRACT FROM AUTHOR]

Background: For the design and analysis of clinical trials with time-to-event outcomes, the Cox proportional hazards model and the logrank test have been the cornerstone methods for many decades. Increasingly, the key assumption of proportionality-or time-fixed effects-that underpins these methods has been called into question. The availability of novel therapies with new mechanisms of action and clinical trials of longer duration mean that non-proportional hazards are now more frequently encountered.Methods: We compared several regression-based methods to model time-dependent treatment effects. For illustration purposes, we used selected endpoints from a large, community-based clinical trial of low dose daily aspirin in older persons. Relative and absolute estimands were defined, and analyses were conducted in all participants. Additional exploratory analyses were undertaken by selected subgroups of interest using interaction terms in the regression models.Discussion: In the trial with median 4.7 years follow-up, we found evidence for non-proportionality and a time-dependent treatment effect of aspirin on cancer mortality not previously reported in trial findings. We also found some evidence of time-dependence to an aspirin by age interaction for major adverse cardiovascular events. For other endpoints, time-fixed treatment effect estimates were confirmed as appropriate.Conclusions: The consideration of treatment effects using both absolute and relative estimands enhanced clinical insights into potential dynamic treatment effects. We recommend these analytical approaches as an adjunct to primary analyses to fully explore findings from clinical trials. [ABSTRACT FROM AUTHOR]

Background: Non-proportional hazards are common with time-to-event data but the majority of randomised clinical trials (RCTs) are designed and analysed using approaches which assume the treatment effect follows proportional hazards (PH). Recent advances in oncology treatments have identified two forms of non-PH of particular importance - a time lag until treatment becomes effective, and an early effect of treatment that ceases after a period of time. In sample size calculations for treatment effects on time-to-event outcomes where information is based on the number of events rather than the number of participants, there is crucial importance in correct specification of the baseline hazard rate amongst other considerations. Under PH, the shape of the baseline hazard has no effect on the resultant power and magnitude of treatment effects using standard analytical approaches. However, in a non-PH context the appropriateness of analytical approaches can depend on the shape of the underlying hazard.Methods: A simulation study was undertaken to assess the impact of clinically plausible non-constant baseline hazard rates on the power, magnitude and coverage of commonly utilized regression-based measures of treatment effect and tests of survival curve difference for these two forms of non-PH used in RCTs with time-to-event outcomes.Results: In the presence of even mild departures from PH, the power, average treatment effect size and coverage were adversely affected. Depending on the nature of the non-proportionality, non-constant event rates could further exacerbate or somewhat ameliorate the losses in power, treatment effect magnitude and coverage observed. No single summary measure of treatment effect was able to adequately describe the full extent of a potentially time-limited treatment benefit whilst maintaining power at nominal levels.Conclusions: Our results show the increased importance of considering plausible potentially non-constant event rates when non-proportionality of treatment effects could be anticipated. In planning clinical trials with the potential for non-PH, even modest departures from an assumed constant baseline hazard could appreciably impact the power to detect treatment effects depending on the nature of the non-PH. Comprehensive analysis plans may be required to accommodate the description of time-dependent treatment effects. [ABSTRACT FROM AUTHOR]