 Methodology
 Open Access
 Published:
A technique for approximating transition rates from published survival analyses
Cost Effectiveness and Resource Allocation volume 17, Article number: 12 (2019)
Abstract
Background
Qualityadjustedlifeyears (QALYs) are used to concurrently quantify morbidity and mortality within a single parameter. For this reason, QALYs can facilitate the discussion of risks and benefits during patient counseling regarding treatment options. QALYs are often calculated using partitionedsurvival modelling. Alternatively, QALYs can be calculated using more flexible and informative statetransition models populated with transition rates estimated using multistate modelling (MSM) techniques. Unfortunately the latter approach is considered not possible when only progressionfree survival (PFS) and overall survival (OS) analyses are reported.
Methods
We have developed a method that can be used to estimate approximate transition rates from published PFS and OS analyses (we will refer to transition rates estimated using full multistate methods as true transition rates).
Results
The approximation method is more accurate for estimating the transition rates out of health than the transition rate out of illness. The method tends to underestimate true transition rates as censoring increases.
Conclusions
In this article we present the basis for and use of the transition rate approximation method. We then apply the method to a case study and evaluate the method in a simulation study.
Background
Chronic, progressive, and noncommunicable diseases (such as cancer, diabetes, cardiovascular disease and chronic respiratory disorders) are now the leading cause of morbidity and mortality around the world. More than 60% of global deaths are attributable to these types of diseases [1]; consequently these diseases now account for up to 50% of the total healthcare budget in some countries [2]. Many of these diseases can be conceptualized as consisting of three health states: healthy (h), ill (i), or dead (d) (Fig. 1).
Treatment decisions for chronic, progressive, and noncommunicable diseases are difficult because interventions can have distinct, and sometimes opposite, influences on the probability that a patient experiences a given health state. For example, a therapy (e.g. highrisk cancer surgery) may decrease the risk of death (by controlling cancer) but may increase the risk of becoming ill (if a postoperative complication occurs). Qualityadjustedlifeyears (QALYs) can be used to concurrently quantify morbidity and mortality within a single parameter [3]. For this reason, QALYs may facilitate the discussion of risks and benefits during patient counseling regarding treatment options [4]. QALY calculation requires knowledge of statemembership fractions. These are the proportion of patients from a defined cohort that are in a given health state at a given time t. Statemembership fractions can be calculated using partitionedsurvival modelling or statetransition modelling [5,6,7].
Partitionedsurvival modelling uses data abstracted from progressionfree survival (PFS) curves and overall survival (OS) curves reported in the literature [7]. PFS curves show the fraction of the cohort that is healthy over time t (PFS(t); OS curves show the fraction of the cohort that is alive (either healthy or ill) over time t (OS(t)). Since OS curves show the fraction of alive patients, the fraction of dead patients is simply \(1OS(t)\). The fraction of ill (but alive) patients is the difference between the fraction of alive and healthy patients \(OS(t)PFS(t)\). We will refer to statemembership fractions calculated in this way as partitionedsurvival fractions [5,6,7]. In contrast, statetransition modelling applies the results of a multistate analysis. For the disease shown in Fig. 1, these techniques would be used to estimate the transition rate (i.e. the instantaneous risk (or hazard) of moving from one state to another) from health to illness (\(h \rightarrow i\)), from health to death (\(h \rightarrow d\)), and from illness to death (\(i \rightarrow d\)) [6]. Transition rates can be used compute transition probability matrices to calculate statemembership fractions (“multistate fractions”). It is important to recognize that statetransition modelling is based on a set of mutually exclusive health states (health, illness, death), whereas partitioned survival modelling is based on nonmutually exclusive health states (health and illness or death for the PFS curve, and alive and dead for the OS curve). Partitionedsurvival modelling is used when sufficient data for statetransition modelling is unavailable.
QALY calculations based on partitionedsurvival fractions can suffer from two important limitations that result from the fact that (i) the OS analysis does not consider the survival of ill patients separate from healthy patients, and (ii) the risk of progressing to illness rather than death for healthy patients cannot be determined from PFS analysis. The first limitation of partitionedsurvival fractions stems from the difficultly of extrapolating partitionedsurvival fractions beyond the study’s observation period [6]. This is a significant deficiency because clinical studies often have a limited observation period that is of insufficient duration to characterize longterm clinical outcomes [8,9,10,11,12,13]. The second limitation of partitionedsurvival fractions is that computed QALYs are not generalizable to patient cohorts whose baseline fractions of healthy, ill and dead patients differs from those of the study cohort [6]. This is because the OS curve is a weighted average of OS curves for healthy and ill patients; therefore, the shape of the curve will change if the baseline ratio of healthy to ill patients differs. These two limitations restrict the use of partitioned survival fractions for decision analysis. These limitations can be avoided by calculating QALYs using multistate fractions. Because they are based on granular analyses of all transitions, multistate fractions have several advantages over partitionedsurvival fractions. First, they can be reliably extrapolated beyond the study observation period [6]. Second, they can be used for decision analysis in cohorts with baseline characteristics that differ from the original study cohort [14].
Unfortunately, one cannot usually calculate transition rates using data abstracted from PFS and OS analyses [6, 7]. Given the limitations of partitionedsurvival fractions and the advantages of multistate fractions, it would be helpful to obtain transition rates and calculate the latter when one only has access to PFS and OS analyses. We have developed a method that, under particular conditions, can be used to estimate approximate transition rates from published PFS and OS analyses (we will refer to transition rates estimated using full multistate methods as true transition rates).
This article organized as follows. We first present the basis for and use of the transition rate approximation method. A case study is then reported in which we apply transition rate approximation to data from a randomized controlled trial (RCT) of treatments for metastatic epidural spinal cord compression (MESCC). We then report a simulation study evaluating the accuracy of the approximation method. In the last section we summarize and discuss our findings.
Methods
The approximation technique is restricted to threestate progressive, timehomogenous Markov disease processes such as the one shown in Fig. 1 [15]. Progressive means that transitions are irreversible (i.e. cannot return to health from illness). Timehomogenous, means that transition rates do not change over time. Markov means that transition rates do not depend on disease history; in other words, the probability that a patient transitions from state x to state y during a particular time period is independent of their previous health state.
The data needed to use the approximation technique can be abstracted from most articles reporting PFS and OS analyses. The number of patients experiencing an event and number of censored patients in both the PFS (\(N_{pfs}^e\) and \(N_{pfs}^c\)) and OS (\(N_{os}^e\) and \(N_{os}^c\)) analyses can be determined from the article text or patientsatrisk risk table. To obtain the remaining data points, PFS and OS KM curves need to be digitized. Digitized KM curves can then be used to reconstruct individual patient data using validated algorithms to determine the event times in the PFS and OS analyses [16]. The approximation technique requires that we make note of the maximum observation time (event or censoring) in the PFS and OS analyses (\(\tau _{pfs}\) and \(\tau _{os}\) respectively). The area under the PFS and OS curves (\(AUC_{pfs}\) and \(AUC_{os}\) respectively) are calculated by summing the area under each step of the KM curve.
We denote \(h \rightarrow i\), \(h \rightarrow d\), and \(i \rightarrow d\) transition rates as \(\lambda _{hi}\), \(\lambda _{hd}\), and \(\lambda _{id}\). For the timehomogenous disease processes (i.e. constant transition rates), exit times from the (i) healthy state (i.e. \(h \rightarrow i\) or \(h \rightarrow d\) transition) and (ii) ill state (i.e. \(i \rightarrow d\) transition) are exponentially distributed. Furthermore, once a patient exits health, the probability that they make an \(h \rightarrow d\) transition is
We will refer to \(\rho\) as the risk of death for healthy patients. As there are only two possible transitions out of health, the probability that a transition out of the health state is an \(h \rightarrow i\) transition is \(1\rho\).
The mean time of exit from the healthy state (i.e. mean progressionfree survival time) is a biased measure in the presence of right censoring [17]. Instead we calculate the restricted mean progression freesurvival time (\({\mathrm {RMPFST}}^{\tau }\)) which is interpreted as the mean progressionfree survival time if observation is restricted to a truncation time \(\tau\) [18]. Since the exit time from health is exponentially distributed, the \({\mathrm {RMPFST}}^{\tau }\) can be calculated as
By definition, the area under the PFS curve is equal to \({\mathrm {RMPFST}}^{\tau }\) when \(\tau\) is set to the maximum observation time in the PFS analysis, \(\tau _{pfs}\) [19, 20]. Using Formula 1, we can then numerically solve for \(\lambda _{hi}+\lambda _{hd}\) using standard algorithmic methods [21]. Simultaneous events in the PFS and OS analyses indicate \(h \rightarrow d\) transitions. Therefore, we can approximate the risk of death for healthy patients as
To approximate \(\lambda _{id}\) we need to use information gathered from the OS analysis. It is more challenging to define an exact formula for the restricted mean overall survival time (\(\mathrm {RMOST}^{\tau }\)) than form the \({\mathrm {RMPFST}}^{\tau }\) because exit from the alive state (i.e. healthy or ill) is defined by a mixture of two exponential distributions: exit from health and exit from illness. However, if we know the death times \(o_i^e\) and censoring times \(o_j^c\) for a cohort of alive patients, \(N_{os}^e\) who had an observed event, and \(N_{os}^c\) who were right censored, we can approximate \({\mathrm {RMOST}}^{\tau }\) truncated to \(\tau _{os}\), \({\mathrm {RMOST}}^{\tau _{os}}\), using inverse probability weighting [22]
Next, we determine the total persontime of observation in the OS analysis
If censoring times are not denoted on the OS curve, it is not possible to determine \(o_j^c\). However, we can rearrange Formula 3 to yield
If we substitute this relationship into Formula 4 we obtain
We can repeat the same calculations using the corresponding data from the PFS analysis to approximate total persontime of observation in the OS analysis, \(E_{pfs}\). We then approximate the total persontime of observation in the ill state as
If we make the assumption that the number of \(i \rightarrow d\) transitions is
we can compute [23]
Results
MESCC case study
To evaluate whether the approximation method can generate reasonable results, we compared approximate transitions rates against a gold standard of true transition rates estimated from real study data.
Patchell et al. [24] conducted a randomized controlled trial (RCT) comparing modern surgery and radiotherapy (mS+RT) versus radiotherapy alone (RTalone) for the treatment of metastatic epidural spinal cord compression (MESCC). MESCC occurs when cancer metastasizes to the spine which and can lead to loss of ambulation from paralysis. MESCC can be modelled as in Fig. 1 if we consider ability to ambulate as the healthy state h and the inability to ambulate due to neurologic dysfunction as the ill state i. True transition rates were estimated using individual patient data provided by the study authors. To eliminate the potential for transcription error and inaccuracy in individual patient data reconstruction, we used actual individual patient data to generate the data listed in Table 1. We estimated true transition rates using the Bayesian modeling language Stan, [25] run through the statistical programming language R (Additional file 1: Appendix A) [26]. The effect of mS+RT was parametrized as a loghazard ratio for each RTalone transition rate.
Prior to comparing true and approximate transition rates, we conducted nonparametric multistate analysis to assess whether our assumed model (progressive, timehomogenous and Markov) was appropriate for MESCC. Nonparametric multistate fractions were estimated from individual patient data from the MESCC RCT using the etm library [27] run through the statistical programming language R [26]. We compared nonparametric multistate fractions and multistate fractions calculated from true transition rates. Goodnessoffit tests for true multistate analysis of data observed with exact transition times affected by right censoring have not been developed [5]. We therefore used informal graphical methods.
Plots comparing proper nonparametric multistate and proper parametric multistate fractions showed good agreement, and no evidence of systematic deviation (Figs. 2 and 3). Therefore, a progressive timehomogenous threestate Markov model is appropriate for the MESCC RCT data and true transition rates can serve as an appropriate comparator to evaluate approximate transition rates. Calculations for the mS+RT arm are shown in Additional file 1: Appendix B.
The true transition rates shown in Table 2 provides useful insights into the impact of treatment. mS+RT prolongs ambulation with a statistically significant hazard ratio of 0.53 (95% CrI: 0.30, 0.94) on the total transition rate for exit from the ambulatory state \(\left( \lambda _{hi}+\lambda _{hd}\right)\). For patients making a transition out of the ambulatory state, the risk of death was similar with both treatments: relative risk 1.07 (95% CrI, 0.65 – 1.75). mS+RT tended to increase the mortality rate for nonambulatory patients, hazard ratio for \(\lambda _{id}\) of 1.61 (95% CrI: 0.89, 2.66), but this effect was not statistically significant.
All approximate transition rates lay within the 95% credible intervals for true transition rates. There was no consistent direction of error indicating the approximation method does not consistently underor overestimate true transition rates.
Simulation study
To assess the validity of the approximation strategy in a wider set of conditions, we conducted a simulation study to assess the impact of censoring on the accuracy of the approximation method for \(\left( \lambda _{hi}+\lambda _{hd}\right)\), \(\rho\), and \(\lambda _{id}\).
Data were generated randomly for a threestate progressive, timehomogenous Markov disease process with parameters similar to those for the mS+RT arm from the MESCC trial. A simulated cohort of 100 patients, 75 of which were healthy at baseline, was created with \(\lambda _{hi}=0.33\), \(\lambda _{hd}=0.53\), \(\lambda _{id}=3.28\). Events times were independently censored using a uniform distribution to achieve all combinations of 0, 2, 5, and 10 patients censored from the OS and PFS analysis. 100 000 replications were generated for each set of simulation conditions.
We calculated the mean error (ME), mean absolute error (MAE), mean percentage error (%ME), and mean absolute percentage error (%MAE) for each set of simulation conditions (Tables 3, 4, and 5). ME and %ME are a measure of the direction of bias (systematic over or underestimation). MAE and %MAE are a measure of the magnitude of error, regardless of direction.
The approximation method tended to underestimate \(\left( \lambda _{hi}+\lambda _{hd}\right)\) and \(\rho\) as the censoring rate increased, however the bias was small with %ME under 3% in all censoring conditions. Even under no censoring, the approximation method was imprecise with a relatively high MAE and %MAE; increasing censoring did not significantly decrease precision.
The approximation method tended to underestimate \(\lambda _{id}\) as the censoring rate increased, however the bias was small with %ME under 3% in all censoring conditions. Even under no censoring, the approximation method was imprecise with a relatively high MAE and %MAE; increasing censoring did not significantly decrease precision.
The approximation method tended to underestimate \(\lambda _{id}\) as the censoring rate increased, however the bias was small with %ME under 3% in all censoring conditions. Even under no censoring, the approximation method was imprecise with a relatively high MAE and %MAE; increasing censoring did not significantly decrease precision.
Discussion
Although chronic, progressive, and noncommunicable diseases chronic diseases affect both patients’ survival and qualityoflife, interventions may impact on these two outcomes differentially. QALYs can simplify decisionmaking and counselling regarding treatment options [4]. For clinicians and decision makers, QALYs calculated using multistate fractions are useful because they can be used to extrapolate longterm qualityoflife and to conduct rich decision analysis. Unfortunately, one cannot usually calculate multistate fractions from PFS and OS curves [5,6,7].
In this paper, we presented a technique for approximating transition rates, which can be used to calculate multistate fractions, from PFS and OS analysis. Our technique requires that three elements be abstracted from each of the PFS and OS analyses: (i) total number of events, (ii) total number of censored patients, and (iii) event times.
Approximate transition rates provide a reasonable estimate of true transition rates estimated using full multistate methods. For the MESCC RCT case study, all approximate transition rates lay within the 95% Bayesian credible intervals for true transition rates. The simulation study indicates that the approximation method is relatively unbiased and precise for estimating the transition rate out of health \(\left( \lambda _{hi}+\lambda _{hd}\right)\) and the risk of death for healthy patients \(\rho\).
It is important to recognize that our techniques only apply to a timehomogenous progressive threestate irreversible disease process. Timehomogeneity is violated if the transition rates change with time (i.e. any parametric model aside from the exponential) or depend on the amount of time spent in the preceding health state (nonMarkov phenomenon) [28]. Irreversibility is violated if patients can become healthy after being ill [15]. Our approximation approach can be scaledup to more complex (e.g. reversible transitions, > 3 health states) disease models, however, the formulas will become more complex. Furthermore, as was done in this article, it would be necessary to validate the scaledup approximation approach to evaluate for bias.
Conclusions
In this paper, we have demonstrated that transition rates can be approximated from published PFS and OS analyses. The approximation method is more accurate for estimating the transition rates out of health than the transition rate out of illness. The method tends to underestimate true transition rates as censoring increases; therefore, approximate transition rates are not a substitute for true transition rates estimated with full multistate methods. However, when proper multistate analysis is not available, approximate transition rates can guide probabilistic modeling and enhance QALY analysis if one considers and accounts for the limitations of the approximation method.
Availability of data and materials
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Abbreviations
 MAE:

mean absolute
 %MAE:

mean absolute percentage error
 ME:

mean error
 %ME:

mean percentage error
 MESCC:

metastatic epidural spinal cord compression
 mS+RT:

modern surgery followed by radiotherapy
 OS:

overall survival
 PFS:

progressionfree survival
 QALY:

qualityadjusted lifeyears
 RCT:

randomized controlled trial
 RTalone:

radiotherapy alone
References
 1.
The World Health Organization. Global status report on noncommunicable diseases. Geneva; 2010, p. 9–31. http://www.who.int/nmh/publications/ncd_report_chapter1.pdf. Accessed 10 Jan 2019.
 2.
Muka T, Imo D, Jaspers L, Colpani V, Chaker L, van der Lee SJ, Mendis S, Chowdhury R, Bramer WM, Falla A, Pazoki R, Franco OH. The global impact of noncommunicable diseases on healthcare spending and national income: a systematic review. Eur J Epidemiol. 2015;30(4):251–77.
 3.
Drummond MF, Sculpher MJ, Torrance GW, O’Brien BJ, Stoddart GL. Methods for the economic evaluation of health care programmes. Oxford: Oxford University Press; 2005, p. 400. http://www.amazon.com/MethodsEconomicEvaluationHealthProgrammes/dp/0198529457. Accessed 10 Jan 2019.
 4.
Kind P, Lafata JE, Matuszewski K, Raisch D. The use of QALYs in clinical and patient decisionmaking: issues and prospects. Value Health. 2009;12(Suppl 1):27–30.
 5.
Titman AC, Sharples LD. Model diagnostics for multistate models. Stat Methods Med Res. 2010;19(6):621–51.
 6.
Woods B, Sideris E, Palmer S, Latimer N, Soares M. Partitioned survival analysis for decision modelling in health care: a critical review. Technical report June, NICE decision support unit, Sheffield; 2017. http://scharr.dept.shef.ac.uk/nicedsu/wpcontent/uploads/sites/7/2017/06/PartitionedSurvivalAnalysisfinalreport.pdf. Accessed 10 Jan 2019.
 7.
Williams C, Lewsey JD, Mackay DF, Briggs AH. Estimation of survival probabilities for use in costeffectiveness analyses: a comparison of a multistate modeling survival analysis approach with partitioned survival and markov decisionanalytic modeling. Med Decis Making. 2017;37(4):427–39.
 8.
Hawkins N, Grieve R. Extrapolation of survival data in costeffectiveness analyses: the need for causal clarity. Med Decis Making. 2017;37(4):337–9.
 9.
Bagust A, Beale S. Survival analysis and extrapolation modeling of timetoevent clinical trial data for economic evaluation: an alternative approach. Med Decis Making. 2014;34(3):343–51.
 10.
Latimer NR. Survival analysis for economic evaluations alongside clinical trialsextrapolation with patientlevel data: inconsistencies, limitations, and a practical guide. Med Decis Making. 2013;33(6):743–54.
 11.
Grieve R, Hawkins N, Pennington M. Extrapolation of survival data in costeffectiveness analyses: improving the current state of play. Med Decis Making. 2013;33(6):740–2.
 12.
Connock M, Hyde C, Moore D. Cautions regarding the fitting and interpretation of survival curves: examples from NICE single technology appraisals of drugs for cancer. Pharmacoeconomics. 2011;29(10):827–37.
 13.
Tappenden P, Chilcott J, Ward S, Eggington S, Hind D, Hummel S. Methodological issues in the economic analysis of cancer treatments. Eur J Cancer. 2006;42(17):2867–75.
 14.
Andersen PK, Abildstrom SZ, Rosthøj S. Competing risks as a multistate model. Stat Methods Med Res. 2002;11(2):203–15.
 15.
Hougaard P. Multistate models: a review. Lifetime Data Anal. 1999;5(3):239–64.
 16.
Wan X, Peng L, Li Y. A review and comparison of methods for recreating individual patient data from published KaplanMeier survival curves for economic evaluations: a simulation study. PLoS ONE. 2015;10(3):0121353.
 17.
Datta S. Estimating the mean life time using right censored data. Stat Methodol. 2005;2(1):65–9.
 18.
Lee CH, Ning J, Shen Y. Analysis of restricted mean survival time for lengthbiased data. Biometrics. 2018;74(2):575–83.
 19.
Royston P, Parmar MK. Restricted mean survival time: an alternative to the hazard ratio for the design and analysis of randomized trials with a timetoevent outcome. BMC Med Res Methodol. 2013;13(1):152.
 20.
Zhao L, Claggett B, Tian L, Uno H, Pfeffer MA, Solomon SD, Trippa L, Wei LJ. On the restricted mean survival time curve in survival analysis. Biometrics. 2016;72(1):215–21.
 21.
Brent RP. Algorithms for minimization without derivatives. New Jersey: PretinceHall; 1973. p. 195.
 22.
Halpern EF. Behind the numbers: inverse probability weighting. Radiology. 2014;271(3):625–8.
 23.
Welton NJ, Ades AE. Estimation of markov chain transition probabilities and rates from fully and partially observed data: uncertainty propagation, evidence synthesis, and model calibration. Med Decis Making. 2005;25(6):633–45.
 24.
Patchell RA, Tibbs PA, Regine WF, Payne R, Saris S, Kryscio RJ, Mohiuddin M, Young B. Direct decompressive surgical resection in the treatment of spinal cord compression caused by metastatic cancer: a randomised trial. Lancet. 2005;366(9486):643–8.
 25.
Stan Development Team. The Stan Core Library, Version 2.17.0; 2017. http://mcstan.org/.
 26.
R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2018. http://www.rproject.org/ http://www.rproject.org.
 27.
Allignol A, Schumacher M, Beyersmann J. Empirical transition matrix of multistate models: the etm package. J Stat Softw. 2011;38(4):1–5.
 28.
Kalbfleisch JD, Pretince RL. The statistical analysis of failure time data. 2nd ed. Hoboken: Wiley; 2002. p. 439.
Acknowledgements
None.
Funding
Not applicable.
Author information
Affiliations
Contributions
MAP and DC conceived and designed this work and interpreted the data. RAP conducted the MESCC RCT. All authors contributed to drafting and revising this work. All authors read and approved the final manuscript.
Corresponding author
Correspondence to Markian A. Pahuta.
Ethics declarations
Competing interests
The authors declare that they have no competing interests.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Additional files
12962_2019_182_MOESM1_ESM.pdf
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
About this article
Cite this article
Pahuta, M.A., Werier, J., Wai, E.K. et al. A technique for approximating transition rates from published survival analyses. Cost Eff Resour Alloc 17, 12 (2019) doi:10.1186/s1296201901827
Received:
Accepted:
Published:
Keywords
 Cancer
 Multistate model
 Survival analysis
 Quality adjusted life year