Estimating the treatment effect in a clinical trial using difference in restricted mean survival time
Patrick Royston
MRC Clinical Trials Unit
University College London
London, UK
[email protected]
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Abstract. The causal effect of a new medical treatment compared with a standard regimen
is best assessed in a randomized controlled trial setting. When the main
outcome is time to some event of interest, such as death, studies often use the
hazard ratio to describe the treatment effect. Typically, proportional hazards
are assumed. Here I discuss several significant disadvantages of using the
hazard ratio, including its vulnerability to the proportionality assumption,
its relative nature, and its lack of relationship with time-to-event or
survival probabilities. I describe the use of restricted mean survival time as
an alternative outcome measure in time-to-event trials. With this method, the
treatment effect is defined as the difference in restricted mean between the
trial arms. I suggest the use of Royston and Parmar's (2002, Statistics in
Medicine 21: 2175–2197) class of flexible parametric models,
implemented through the command stpm2 (Lambert and Royston [2009,
Stata Journal 9: 265–290] and Andersson and Lambert [2012,
Stata Journal 12: 623–638]), to estimate the required quantities.
With this approach, proportional hazards are not assumed. I describe a new
command, strmst, for implementing these calculations. This method
supports "direct" adjustment for covariates by using marginalization over their
observed distribution, and it supports estimation of treatment effects
conditional on fixed values of covariates. I illustrate the methodology using
data from a trial in primary biliary cirrhosis. I provide an example that
demonstrates the importance of understanding the relationship between the
treatment effect, the prognosis of the disease outcome, and the often-neglected
time domain.
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Patrick Royston
View all articles with these keywords:
strmst, randomized trial, time-to-event data, treatment effect, restricted mean, flexible parametric model, nonproportional hazards
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