Sample size planning of two-arm superiority and noninferiority survival studies with discrete follow-up
In clinical trials using lifetime as primary outcome variable, it is more the rule than the exception that even for patients who are failing in the course of the study, survival time does not become known exactly since follow-up takes place according to a restricted schedule with fixed, possibly lon...
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| Main Author: | |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
12 June 2017
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| In: |
Statistics in medicine
Year: 2017, Volume: 36, Issue: 20, Pages: 3123-3136 |
| ISSN: | 1097-0258 |
| DOI: | 10.1002/sim.7360 |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.1002/sim.7360 Verlag, Volltext: http://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7360 
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| Author Notes: | Stefan Wellek |
MARC
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| 520 | |a In clinical trials using lifetime as primary outcome variable, it is more the rule than the exception that even for patients who are failing in the course of the study, survival time does not become known exactly since follow-up takes place according to a restricted schedule with fixed, possibly long intervals between successive visits. In practice, the discreteness of the data obtained under such circumstances is plainly ignored both in data analysis and in sample size planning of survival time studies. As a framework for analyzing the impact of making no difference between continuous and discrete recording of failure times, we use a scenario in which the partially observed times are assigned to the points of the grid of inspection times in the natural way. Evaluating the treatment effect in a two-arm trial fitting into this framework by means of ordinary methods based on Cox's relative risk model is shown to produce biased estimates and/or confidence bounds whose actual coverage exhibits marked discrepancies from the nominal confidence level. Not surprisingly, the amount of these distorting effects turns out to be the larger the coarser the grid of inspection times has been chosen. As a promising approach to correctly analyzing and planning studies generating discretely recorded failure times, we use large-sample likelihood theory for parametric models accommodating the key features of the scenario under consideration. The main result is an easily implementable representation of the expected information and hence of the asymptotic covariance matrix of the maximum likelihood estimators of all parameters contained in such a model. In two real examples of large-scale clinical trials, sample size calculation based on this result is contrasted with the traditional approach, which consists of applying the usual methods for exactly observed failure times. Copyright © 2017 John Wiley & Sons, Ltd. | ||
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