Interpretation of time-to-event outcomes in randomized trials: an online randomized experiment

Weir, Isabelle R; Marshall, Gareth D.; Schneider, Jeffrey I.; Sherer, Jason A.; Lord, Emily; Gyawali, Bishal; Paasche‐Orlow, Michael K.; Benjamin, Emelia J.; Trinquart, Ludovic

doi:10.1093/annonc/mdy462

Cited by 37 publications

(31 citation statements)

References 24 publications

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“…If the RMSTs are reported as complementary to the HR, they would offer a tool to compare the potential of different exposures in similar target populations. 41 This will assist researchers and clinicians in interpreting the impact of such exposures on outcomes.…”

Section: Discussionmentioning

confidence: 99%

Adjusted restricted mean survival times in observational studies

Conner

Sullivan

Benjamin

et al. 2019

Statistics in Medicine

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In observational studies with censored data, exposure-outcome associations are commonly measured with adjusted hazard ratios from multivariable Cox proportional hazards models. The difference in restricted mean survival times (RMSTs) up to a pre-specified time point is an alternative measure that offers a clinically meaningful interpretation. Several regression-based methods exist to estimate an adjusted difference in RMSTs, but they digress from the model-free method of taking the area under the survival function. We derive the adjusted RMST by integrating an adjusted Kaplan-Meier estimator with inverse probability weighting (IPW). The adjusted difference in RMSTs is the area between the two IPW-adjusted survival functions. In a Monte Carlo-type simulation study, we demonstrate that the proposed estimator performs as well as two regression-based approaches: the ANCOVA-type method of Tian et al and the pseudo-observation method of Andersen et al. We illustrate the methods by reexamining the association between total cholesterol and the 10-year risk of coronary heart disease in the Framingham Heart Study. KEYWORDS inverse probability weighting, observational studies, propensity score, restricted mean survival time, survival analysis, time-to-event data BACKGROUNDIn observational studies with time-to-event outcomes, the adjusted hazard ratio (HR) has become the effect measure of choice to quantify exposure-outcome associations when adjustment for confounders is required. The adjusted HR is commonly estimated by a multivariable Cox proportional hazards model. There are, however, two limitations of the adjusted HR. First, the interpretation of survival benefit using HRs is challenging. 1,2 The HR is a relative measure and does not communicate any information about the absolute effect. It is well established that absolute measures are valuable for public health decision-making. [3][4][5] Second, the HR depends on follow-up duration if the proportional hazards assumption is violated. In the case of non-proportional hazards, reporting only the HR may result in incorrect conclusions. 2,6 3832

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Section: Discussionmentioning

confidence: 99%

Adjusted restricted mean survival times in observational studies

Conner

Sullivan

Benjamin

et al. 2019

Statistics in Medicine

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“…In a randomized experiment, Weir et al 2 demonstrated how clinicians may reach different conclusions when hazard ratios or differences in RMST are reported. Similarly, reporting the difference in RMTL as a complement to subdistribution and cause‐specific hazard ratios may give additional insight to the data and offer a clinically meaningful interpretation.…”

Section: Discussionmentioning

confidence: 99%

“…The difference in restricted mean survival times (RMST) has recently become of great interest as an alternative measure of association to the hazard ratio in time-to-event analyses. 1,2 Marginal between-group differences in RMST up to a given timepoint can be estimated nonparametrically by integration of the survival function or, in the presence of covariates, of the inverse probability weighted (IPW) survival function. 3 Moreover, regression models have been introduced to estimate the difference in RMST conditional on covariates.…”

Section: Introductionmentioning

confidence: 99%

Estimation and modeling of the restricted mean time lost in the presence of competing risks

Conner

Trinquart

2021

Statistics in Medicine

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Survival data with competing or semi-competing risks are common in observational studies. As an alternative to cause-specific and subdistribution hazard ratios, the between-group difference in cause-specific restricted mean times lost (RMTL) gives the mean difference in life expectancy lost to a specific cause of death or in disease-free time lost, in the case of a nonfatal outcome, over a prespecified period. To adjust for covariates, we introduce an inverse probability weighted estimator and its variance for the marginal difference in RMTL. We also introduce an inverse probability of censoring weighted regression model for the RMTL. In simulation studies, we examined the finite sample performance of the proposed methods under proportional and nonproportional subdistribution hazards scenarios. We illustrated both methods with competing risks data from the Framingham Heart Study. We estimated sex differences in atrial fibrillation (AF)-free times lost over 40 years. We also estimated sex differences in mean lifetime lost to cardiovascular disease (CVD) and non-CVD death over 10 years among individuals with AF.

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“…In addition, a critical advantage of the pseudo-observation approach is that the estimates are not sensitive to the assumption of proportional hazards during follow-up, as in Cox proportional hazard regression [25]. RRs and RMST differences are also more easily interpreted by patients and providers than hazard ratios, which could facilitate shared decision-making [25][26][27][28].…”

Section: Discussionmentioning

confidence: 99%

Smoking cessation and survival among people diagnosed with non-metastatic cancer

Barnett

Gehr

et al. 2020

BMC Cancer

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Background: We aimed to estimate the effects of smoking cessation on survival among people diagnosed with cancer. Methods: We used data from a Comprehensive Community Cancer Program that is part of a large urban safety-net hospital system. Eligible patients were diagnosed with primary invasive solid tumors between 2013 and 2015, and were current smokers at time of diagnosis. Our exposure of interest was initiation of smoking cessation within 6 months of cancer diagnosis. We estimated inverse probability weighted restricted mean survival time (RMST) differences and risk ratio (RR) for all cause 3-year mortality. Results: Our study population comprised 369 patients, of whom 42% were aged < 55 years, 59% were male, 44% were racial/ethnic minorities, and 59% were uninsured. The 3-year RMST was 1.8 (95% CL: − 1.5, 5.1) months longer for individuals who initiated smoking cessation within 6 months of cancer diagnosis. The point estimate for risk of 3-year mortality was lower for initiation of smoking cessation within 6 months of diagnosis compared with no initiation within 6 months (RR = 0.72, 95% CL: 0.37, 1.4). Conclusions: Our point estimates suggest longer 3-year survival, but the results are compatible with 1.5 month shorter or 5.1 longer 3-year overall survival after smoking cessation within 6 months of cancer diagnosis. Future studies with larger sample sizes that test the comparative effectiveness of different smoking cessation strategies are needed for more detailed evidence to inform decision-making about the effect of smoking cessation on survival among cancer patients. Implications for Cancer survivors: The benefits of smoking cessation after cancer diagnosis may include longer survival, but the magnitude of benefit is unclear.

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Interpretation of time-to-event outcomes in randomized trials: an online randomized experiment

Cited by 37 publications

References 24 publications

Adjusted restricted mean survival times in observational studies

Adjusted restricted mean survival times in observational studies

Estimation and modeling of the restricted mean time lost in the presence of competing risks

Smoking cessation and survival among people diagnosed with non-metastatic cancer

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