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ESTIMATING MEAN SURVIVAL TIME: WHEN IS IT POSSIBLE?

ABSTRACT: For right censored survival data, it is well known that the mean survival time can be consistently estimated when the support of the censoring time contains the support of the survival time. In practice, however, this condition can be easily violated because the follow-up of a study is usually within a finite window. In this article we show that the mean survival time is still estimable from a linear model when the support of some covariate(s) with nonzero coefficient(s) is unbounded regardless of the length of follow-up. This implies that the mean survival time can be well estimated when the support of linear predictor is wide in practice. The theoretical finding is further verified for finite samples by simulation studies. Simulations also show that, when both models are correctly specified, the linear model yields reasonable mean square prediction errors and outperforms the Cox model, particularly with heavy censoring and short follow-up time.

SUBMITTER: Ding Y

PROVIDER: S-EPMC4442028 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

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ESTIMATING MEAN SURVIVAL TIME: WHEN IS IT POSSIBLE?

Ding Ying Y Nan Bin B

Scandinavian journal of statistics, theory and applications 20150601 2

For right censored survival data, it is well known that the mean survival time can be consistently estimated when the support of the censoring time contains the support of the survival time. In practice, however, this condition can be easily violated because the follow-up of a study is usually within a finite window. In this article we show that the mean survival time is still estimable from a linear model when the support of some covariate(s) with nonzero coefficient(s) is unbounded regardless ...[more]

PMID: 26019387

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Project description:Background/objectivesRestricted mean survival time (RMST) summarizes treatment effect in terms of a gain or loss in the event-free days. It remains uncertain whether communicating treatment benefit and harm using RMST-based summary is more effective than conventional summary based on absolute and relative risk reduction. We compared the effect of RMST-based approach and conventional approach on decisional conflict using an example of intensive versus standard blood pressure-lowering strategies.DesignOn-line survey.SettingA convenience sample of patients in the United States.ParticipantsTwo hundred adults aged 65 and older with hypertension requiring anti-hypertensive treatment (response rate 85.5%).InterventionsParticipants were randomly assigned to either RMST-based summary or conventional summary about the benefit and harm of blood pressure-lowering strategies.MeasurementsDecisional Conflict Scale (DCS), ranging from 0 (no conflict) to 100 (high conflict), and preference for intensive blood pressure-lowering strategy.ResultsParticipants assigned to RMST-based approach (n = 100) and conventional approach (n = 100) had similar age (mean [standard deviation, SD]: 72.3 [5.6] vs 72.8 [5.5] years) and proportions of female (50 [50.0%] vs 61 [61.0%]) and white race (92 [92.0%] vs 92 [92.0%]). The mean (SD) DCS score was 25.2 (15.0) for RMST-based approach and 25.6 (14.1) for conventional approach (p = 0.84). The number (%) of participants who preferred intensive strategy was 10 (10.0%) for RMST-based approach and 14 (14.0%) for conventional approach (p = 0.52). The results were consistent in subgroups defined by age, sex, education level, cardiovascular disease status, and predicted mortality risk categories.ConclusionIn a sample of relatively healthy older adults with hypertension, RMST-based approach was as effective as conventional approach on decisional conflict about choosing a blood pressure-lowering strategy. This study provides proof-of-concept evidence that RMST-based approach can be used in conjunction with absolute and relative risk reduction for communicating treatment benefit and harm in a decision aid.

Dataset Information

ESTIMATING MEAN SURVIVAL TIME: WHEN IS IT POSSIBLE?

Publications

ESTIMATING MEAN SURVIVAL TIME: WHEN IS IT POSSIBLE?

Similar Datasets

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