  Meta-analyses of survival studies aim to reveal the variation of an effect
measure of interest over different studies and present a meaningful summary.
They must address between study heterogeneity in several dimensions and
eliminate spurious sources of variation. Forest plots of the usual (adjusted)
hazard ratios are fraught with difficulties from this perspective since both
the magnitude and interpretation of these hazard ratios depend on factors
ancillary to the true study-specific exposure effect. These factors generally
include the study duration, the censoring patterns within studies, the
covariates adjusted for and their distribution over exposure groups. Ignoring
these mentioned features and accepting implausible hidden assumptions may
critically affect interpretation of the pooled effect measure.
  Risk differences or restricted mean effects over a common follow-up interval
and balanced distribution of a covariate set are natural candidates for
exposure evaluation and possible treatment choice. In this paper, we propose
differently standardized survival curves over a fitting time horizon, targeting
various estimands with their own transportability. With each type of
standardization comes a given interpretation within studies and overall, under
stated assumptions. These curves can in turn be summarized by standardized
study-specific contrasts, including hazard ratios with more consistent meaning.
We prefer forest plots of risk differences at well chosen time points. Our case
study examines overall survival among anal squamous cell carcinoma patients,
expressing the tumor marker $p16^{INK4a}$ or not, based on the individual
patient data of six studies.
