This document summarizes current recommendations and gaps regarding extrapolation of time-to-event outcomes from clinical trials. It reviewed 11 methodological papers and 5 guidelines on extrapolating survival data. The guidelines, particularly from NICE, provide a detailed process for extrapolation including testing different survival models, validating the best fitting model, and using external data for validation. However, the guidelines need updating to apply to more disease areas beyond oncology and different time-to-event outcomes.

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EXTRAPOLATION OF TIME-TO-EVENT
DATA IN VARIOUS DISEASE AREAS:
CURRENT RECOMMENDATIONS AND GAPS
INTRODUCTION
• Extrapolation of time-to-event outcomes is required to evaluate long-term
effect of the new interventions in various disease areas particularly in oncology,
diabetes, rheumatoid arthritis, and cardiovascular diseases
• Application of incorrect extrapolation methods can lead to rejection of inter­
ventions by HTA bodies. Therefore, we have conducted a review of methodological
papers and guidelines detailing the extrapolation of time-to-event data
RESULTS
• Literature search using the search terms as
“extrapolation" AND "survival“ AND "time-to-
event"/“time to event“ resulted in 625 citations
• Of the 625 citations, 11 methodological papers and
five guidelines were included in this review (Figure 1)
• The methodological papers and guidelines
discussing extrapolation of time-to-event data were
only considered relevant for the review
• All the included methodological papers were
published in journals
Findings from methodological papers
• Review of the methodological papers emphasize
that generally, single and incorrect extrapolation
method is employed by different specialists for
time-to-event outcomes
• It highlights that a series of extrapolation methods
should be employed, which should be then
validated using statistical methods like AIC, BIC,
and goodness of fit
• In general, analysts fits one of the commonly used
distributions like exponential and Weibull. However,
it is also recommended to test other complex
distributions like log-logistic, log-normal, and
generalized gamma to get the true distribution in
order to avoid economic rejections
REFERENCES
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11. Latimer N. Medical Decision Making (2013) 33 (6): 743-754
12. Latimer N. NICE guidelines (DSU TSD 14). June 2011 (last updated
March 2013)
13. Lin B. Environ Toxicol Chem. 2009;28(7):1557-66
14. Mittmann et al. CADTH guidelines. Value in Health (2012): 580–585
15. NoMA guidelines. Valid from 13-04-2015, Norwegian Medicines
Agency
16. PBAC Guidelines for preparing submissions to the Pharmaceutical
Benefits Advisory Committee, version 4.4, June 2013
• Further, assumptions underlying these distributions
should also be tested. For example: Assumption of
proportional hazards
• However, the detailed procedure for extrapolation
explicitly has been discussed in the guidelines
Findings from the guidelines
• A total of five guidelines, NICE from England and
Wales, NoMA from Norway, CADTH from Canada,
PBAC and MSAC from Australia were included in
this review
• Across the included guidelines, only NICE
guidelines provided a detailed description of the
steps to be employed for extrapolation of survival
analysis (Figure 2)
• NICE guidelines suggest testing a range of survival
models including parametric, semi-parametric,
and non-parametric models
• Further, fit of the models should be checked using
visual inspection or various statistical tests like AIC,
BIC, and goodness of fit
• Same ‘type’ of model should be used in case
when parametric models are fitted separately to
individual treatment arms. This approach allows
using different parameters; however, treatment
arm will not follow drastically different distributions
• Following which, the methods should be validated
using internal and external methods on the basis of
clinical plausibility
• Source of external data is usually considered from
comparable population and longer-follow-up,
i.e. non-randomized clinical trials (as suggested
in PBAC) and long-term registry data (as
recommended in NICE)
• NoMA guidelines from Norway are brief of NICE
guidelines. In addition to the recommendations
from NICE guidelines, NoMA guidelines also
provide recommendations for treatment switching
• These guidelines suggest that switching data may
be contaminated and can commonly overestimate
the effect of standard arm compared to the
intervention arm
• Parameterization and extrapolation in the base
case analysis in presence of treatment switching
must be based on the KM data
• Different correction methods should be applied
for sensitivity analyses with correction methods
for treatment switching, preferably applying with
accompanying discussion on strengths, limitations
and assumptions for these methods
CONCLUSIONS
• Until now, NICE guidelines serve as a gold standard for survival extrapolation. However, there is an unmet need to update the guidelines for extrapolation of different time-
to-event outcomes across different therapeutic areas beyond oncology
METHODS
• Embase®
and MEDLINE®
databases for methodological paper and country
specific websites for guidelines were searched to identify relevant articles
• Inclusion criteria: English language studies published post 2005 and assessing
extrapolation of time-to-event data were included
• Each citation was reviewed by two independent reviewers; any disagreements
were resolved by a third reviewer
Kamra S1
, Kaur J1
,
Kartheek J1
, Sehgal M1
,
Siddiqui MK1
, Shukla, P1
1
PAREXEL International,
Chandigarh, India
Figure 2: Summary of NICE Recommendations
Patient level/digitised IPD data available
Compare log cumulative hazard plots, quantile-quantile plots or suitable residual plots to allow initial
selection of appropriate models
Plots are not straight line Plots are not parallel Plots are parallel
Consider piecewise or other
more flexible models
Fit individual model
Consider PH/AFT
model
Compare model fits to select the most appropriate model taking into account the completeness of
survival data
Complete survival data:
• AIC/BIC/Log-cumulative hazard plots/other
suitable test of internal validity
Incomplete survival data:
• Visual inspection/External data/Clinical
validity/AIC/BIC/Log-cumulative hazard
plots/Other suitable tests for internal
and external validity/Consider duration
of treatment effect
Choose most suitable model based on above analysis.
Complete sensitivity analysis using alternate plausible survival models, and taking into account
uncertainty in model parameter estimates
SOURCE: NICE DSU Technical Support Document 14
AFT: Accelerated Failure Time; AIC: Akaike's Information Criteria; BIC: Bayesian information criteria; IPD: Individual
Patient Data; PH: Proportional Hazards
Figure 1: Flow diagram
625 citations
First-pass
(excluded = 552)
Second-pass
(excluded = 62)
73 citations
11 methodological papers
+
Five guidelines
Country-specific
websites