- Email: [email protected]
A simple technique to estimate best- and worst-case survival in patients with metastatic colorectal cancer treated with chemotherapy
original articles 11. Der CJ, Finkel T, Cooper GM. Biological and biochemical properties of human rasH genes mutated at codon 61. Cell 1986; 44: 167–176. 12. San Lucas FA, Wang G, Scheet P, Peng B. Integrated annotation and analysis of genetic variants from next-generation sequencing studies with variant tools. Bioinformatics 2012; 28: 421–422. 13. Modest D, Welkersthal LV, Stintzing S et al. FOLFIRI plus cetuximab bersus FOLFIRI plus bevacizumab as first-line treatment of KRAS-wildtype metastatic colorectal cancer: German AIO study KRK-0306 (FIRE-3). Ann Oncol 2013; 24: iv11–iv24.
Annals of Oncology 14. Imamura Y, Morikawa T, Liao X et al. Specific mutations in KRAS codons 12 and 13, and patient prognosis in 1075 BRAF wild-type colorectal cancers. Clin Cancer Res 2012; 18: 4753–4763. 15. Kim MJ, Lee HS, Kim JH et al. Different metastatic pattern according to the KRAS mutational status and site-specific discordance of KRAS status in patients with colorectal cancer. BMC Cancer 2012; 12: 347. 16. Tejpar S, Celik I, Schlichting M et al. Association of KRAS G13D tumor mutations with outcome in patients with metastatic colorectal cancer treated with first-line chemotherapy with or without cetuximab. J Clin Oncol 2012; 30: 3570–3577.
Annals of Oncology 25: 2014–2019, 2014 doi:10.1093/annonc/mdu372 Published online 5 August 2014
M. Williams1*, R. A. Singer2 & A. Lerner3 1 Department of Clinical Oncology, Charing Cross Hospital, Imperial College Healthcare NHS Trust, London; 2Department of Clinical Oncology, Kent and Canterbury Hospital, Canterbury; 3Department of Clinical Oncology, The Royal Surrey County Hospital, Guildford, UK
Received 19 May 2014; revised 16 July 2014; accepted 27 July 2014
Background: Patients with incurable cancer usually want specific information about prognosis, and clinicians’ estimates are often inaccurate. Studies in breast and lung cancer have suggested that simple multiples of the median overall survival (OS) can accurately estimate the time at which 90%, 75%, 25% and 10% of patients are alive. Patients and methods: We identified 46 phase III randomised clinical trials of chemotherapy in metastatic colorectal cancer, representing data from 29 011 patients. We extracted data on demographics, treatment and survival from 96 patient cohorts and assessed agreement with the estimated survival time points, calculated as 0.25, 0.5, 2 and 3 times the median OS. Results: Median OS was 16.8 months in the trials. There were 342 assessable time points. For 301 of these, the estimated survival time was within 0.75–1.33 of the actual survival time (88% agreement). The worst agreement (76%) was at the earliest (90%) level of survival. Conclusions: Simple multiples of the median OS give reasonable estimates of the times at which different survival levels are reached in patients with metastatic colorectal cancer. Taken with previous studies, these findings are likely to be valid across a large range of patients. We would encourage clinicians to think of prognosis as a trajectory, and to consider quoting survival ranges instead of point estimates, in discussions with patients. Key words: survival, prognosis, metastatic colorectal cancer, review
introduction Estimating survival in patients with incurable cancer is difficult, and predictions made by medical staff are often inaccurate [1, 2]. Such predictions are important for patients, and may influence their decisions on treatment; they are also important in planning services [3]. *Correspondence to: Dr Matt Williams, Department of Radiotherapy, Imperial College Healthcare NHS Trust, Charing Cross Hospital, Fulham Palace Rd, Northwood, London W6 8RF, UK. Tel: +44-203-311-8427; Fax: +44-203-311-1603; E-mail: mhw@doctors. net.uk
When asked for prognosis, many clinicians quote the median overall survival (OS). However, the median represents the time by which half the patients will have died and the relevance of this figure is not clear to patients [4, 5]. Ideally, patients should have access to better prognostic information without the need for complex formulae or electronic aids. There has been substantial work on estimating survival in the last few weeks of life, often in the hospice environment (see [6] for an overview). However, there is much less work on estimating survival in those patients who cannot be cured but may have life expectancies measured in months and years. Such patients have high
© The Author 2014. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: [email protected].
Downloaded from http://annonc.oxfordjournals.org/ at Laurentian University on December 7, 2014
A simple technique to estimate best- and worst-case survival in patients with metastatic colorectal cancer treated with chemotherapy
original articles
Annals of Oncology
reporting. For each trial, we obtained data from the full manuscript; where trials had more than one publication, we used the one that contained the most complete report of OS. For each trial, we recorded the year of publication, number of patients enrolled in each arm, patient demographics, proportion of patients with rectal involvement, number of treatment arms, treatment regimens used, whether a targeted agent was used, and the median OS for each treatment arm. We included data on all treatment arms with 100 or more patients, where survival had reached the 25% level or less. Each survival curve was converted to JPEG format, magnified to 400% size, and overlaid with a pixelated grid. Two authors independently measured the actual time points at which 90%, 75%, 25% and 10% levels of survival were reached, in each arm of each study (Figure 1) . We extracted the median OS from each arm of each study, and then calculated simple multiples of the median OS (0.25, 0.5, 2 and 3) to estimate the time to the 90%, 75%, 25% and 10% levels of survival. We compared the actual time taken to reach each survival level with the time estimated from the relevant multiple of the median OS. In keeping with previous work [9, 12, 14], we considered the agreement between the two to be acceptable if the estimate
References (n = 156)
methods We identified randomised clinical trials of first-line palliative chemotherapy in patients with metastatic colorectal cancer published between 2000 and 2011 and indexed in Medline (supplementary Appendix S1, available at Annals of Oncology online). We supplemented this list with additional references in reviews and through discussion with subject-matter experts. Trials were screened by two authors and were eligible for inclusion if they were phase III randomised trials, compared at least two different chemotherapy regimens, were given as first-line treatment of metastatic colorectal cancer, included at least 100 patients per treatment arm, included censored curves of OS, were published in English and where, in at least one treatment arm, at least 75% of patients had died at the time of
Included trials (n = 46)
Insufficient information (n = 5; 3%)
Number of OS curves (n = 101)
Not randomised chemotherapy trials in mCRC (n = 52; 33%)
Curve without data for 25% centile (n = 5)
Duplicate references for the same study (n = 22; 14%)
OS curves for analysis (n = 96)
100 Not first line (n = 18;12%)
Proportion alive
80
60
<100 patients/arm (n = 5; 3%)
40
20
No OS curve (n = 4; 3%)
0 0
6
12
18
36
42
48
54
60
10% of patients alive
25% alive
Median OS
75% alive
90% of patients alive
24 30 Follow-up (months)
Figure 1. Measuring the time to different levels of survival from published survival curves.
Volume 25 | No. 10 | October 2014
No data for 25% percentile survival (n = 4; 3%)
Included trials (n = 46; 29%)
Figure 2. Flow diagram showing study and cohort inclusion.
doi:10.1093/annonc/mdu372 |
Downloaded from http://annonc.oxfordjournals.org/ at Laurentian University on December 7, 2014
health-care utilisation rates [7], and their decisions around medical care are influenced by their perception of prognosis [8]. Kiely et al. has presented a method [9, 10] that provides estimates of survival for groups of patients having palliative chemotherapy. It generates estimates of the times at which most people in the group will still be alive, and times to which smaller proportions of the population might be expected to live. These are termed the ‘worst case’ (90% of patients survive at least this long), ‘best case’ (only 10% of patients survive longer than this), ‘lower typical’ (75% of patients survive at least this long) and ‘upper typical’ (25% level). The method calculates these estimates by using simple multiples of the median OS from clinical trials, and is based upon the fact that survival curves approximate an exponential decay pattern. This method has been evaluated in metastatic breast [9], lung [11, 12] and prostate [13] but not colorectal cancer. In this study, we have systematically reviewed recent phase III randomised trials of chemotherapy in metastatic colorectal cancer, to see whether simple multiples of the median OS time can estimate the time points at which different survival levels (90%, 75%, 25%, 10%) are reached.
original articles
Annals of Oncology
lay within 0.75–1.33 times of the actual time point for that survival level. We assessed whether median OS or rates of agreement varied by trial characteristics. We used Fisher’s exact test to assess statistical significance for rates of agreement, and Kendall’s correlation to assess correlation between trial characteristics and median OS. We used a cut-off of P < 0.01 to account for using multiple tests.
A
results We identified 139 trials, of which 46 met our inclusion criteria (references in supplementary Appendices S2 and S3a–e, available at Annals of Oncology online), yielding a total of 96 Kaplan–Meier curves (Figure 2). Studies had varying length of follow-up and we were able to measure 96 data points for 90%,
B
Median survival 25
Time to 90% survival level 15
Frequency
15 10
10
5
5 0
0 0
20
80
0
2
D
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10
8
8
6 4 2
4 6 Time in months
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10
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40 Time in months
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E
14
16
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35
40
Time to 90% survival level 8
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4
2
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40
50 Time in months
60
Figure 3. (A) Median overall survival, in months, of all patient cohorts. (B) Time in months to the 90% survival level. (C) Time in months to the 75% survival level. (D) Time in months to 25% survival level. (E) Time in months to 10% survival level.
| Williams et al.
Volume 25 | No. 10 | October 2014
Downloaded from http://annonc.oxfordjournals.org/ at Laurentian University on December 7, 2014
Frequency
20
original articles
Annals of Oncology
discussion In this analysis of 46 trials enrolling 29 011 patients receiving first-line chemotherapy for metastatic colorectal cancer, simple multiples (0.25, 0.5, 2 and 3) of the median OS time can be used to estimate survival centiles (90%, 75%, 25% and 10%). Despite the variation in median OS between different trials, the relationship between median OS and different survival levels was consistent. Our findings are in keeping with previous work in breast and lung cancer [9, 11], which have widely different median survival times. We therefore expect that this relationship will remain true, even if the median OS were to increase in future trials. This should allow clinicians to provide, and patients to understand, more nuanced representations of prognosis. There are three main weaknesses with this study. First, at the earliest time point (90% survival), the agreement between the estimated time to the survival level and the actual value drops to 76%, and at the other extreme (10% survival) the agreement is 81%, compared with 88% overall. This pattern is in keeping with previous studies. Secondly, the data to support this
approach come from randomised trials, which may not include a representative population (e.g. European patients with colorectal cancer have a median age of 71 years; the median age in this study was 62 years). Thirdly, the definition of agreement (0.75–1.33) is somewhat arbitrary, although it is in keeping with previous studies, and is more conservative than some other authors [15]. Although we used 0.75–1.33 as a definition of agreement, in fact most data are more tightly grouped than this,
Table 2. Trial characteristics Characteristics
Trials No. Percentage
Year of publication 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 No. of cohorts analysed per trial 1 2 3 4 Agents used in the 96 cohorts Fluorouracil Oxaliplatin Irinotecan Capecitabine Bevacizumab Cetuximab Panitumumab Unspecified fluoropyrimidine (capecitabine or fluorouracil) Vatalinib Mitomycin C Tegafur–uracil Trimetrexate Others (4 agents)
4 1 3 1 3 5 6 6 5 2 4 6
9 2 7 2 7 11 13 13 11 4 9 13
4 35 6 1
9 76 13 2
61 48 31 18 12 5 4 3
64 50 32 19 13 6 5 3
1 1 1 1 1
1 1 1 1 1
Table 1. Patient characteristics
Number of patients per cohort Age (years) % Male Follow-up (months) Rectal involvement
Volume 25 | No. 10 | October 2014
Median
Range
Number of cohorts reported
% of cohort reported
218 62 61% 31.0 29%
100–1017 58–69 38–73 17.2–61.0 14%–54%
96 88 90 64 63
100 92 94 66 66
doi:10.1093/annonc/mdu372 |
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75% and 25% survival, and 54 points for 10% survival. This gave a total of 342 time points at which we could assess the agreement between the actual and estimated times. The included trials enrolled 29 011 patients. The median OS in the included studies was 16.8 months (IQR: 14.3–19.4; Figure 3A). The median time to 90% OS level was 4.6 months (IQR: 3.7–6), to the 75% level was 9.5 months (IQR: 8–11), to the 25% level was 27.7 (IQR: 24–32.2) and to the 10% survival level was 39.3 months (IQR: 34.9–44.4) (Figure 3B–E; supplementary Material, available at Annals of Oncology online) (Table 1) . The median age of included patients was 62 years, the median proportion of male patients was 61%, and the median proportion of patients with rectal cancer was 29%. Thirteen of the cohorts (14%) included at least one biological agent (Table 2). Of the 342 data points, 301 (88%) of the estimates were acceptable (lay within 0.75–1.33 of the actual value). The agreement between the estimated and actual time points was worst at the earliest (90%) survival level, where only 76% of the estimates were acceptable. Of the 68 cohorts where we had data on all four survival points, half agreed at all four points. There was a tendency to underestimate the time to the 90% and 75% survival levels, and overestimate the time to the 25% and 10% survival levels (Figure 3B–E). There was no significant variation in the rate of agreement by trial or trial population subgroup, although median OS increased over time (Kendall’s τ = 0.35; P = 0.001) (Figure 4).
original articles
Annals of Oncology
We can add additional information to make our estimates more accurate. For example, there is a well-documented impact of performance status on OS in metastatic colorectal cancer [18, 19], with a median OS of 8.5 months in patients who are ECOG performance status 2 [19]. In this case, we would expect the calculated estimates to be 3, 4, 17 and 25 months. In principle, additional clinical factors (site of metastases etc.) could also be added to a prognostic model, in order to provide better estimates of the survival. However, the use of additional information to provide better prognostic estimates is orthogonal to this approach, which provides a simple and accessible way to provide more information about multiple points on the survival curve. In both cases, the volume of data on which to base decisions is limited, and emphasises the fact that clinical information and acumen is still required, even if only to provide the initial estimate of the median OS time point [20]. Whether we use more data to provide better estimates of the median survival, or use our approach to calculate multiple points on the survival curve (or both), it is important to note that they are statistical measures that we use to communicate risk to patients. Each patient has a single, initially unknown, prognosis and therefore data from groups of patients can only provide an estimate of risk for individuals. We would encourage clinicians to start considering survival in terms of a trajectory of multiple time points. How this is discussed with patients will vary, but terms such as ‘most’, ‘many’, ‘some’ and ‘few’ may convey the approximate survival levels, and techniques such as this allow clarification with numerical values where useful [21]. Previous work has suggested that clinicians often overestimate prognosis [1, 15] and we hope that this study and others will provide more objective estimates on which to base discussions with patients and their families.
Median overall survival by year of trial publication
Overall survival in months
25
20
15
10
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Year of trial reporting Figure 4. Changes in overall survival by year of trial publication. Horizontal dark line indicates the median OS; the extent of shaded area indicates the range from first to third quartile.
| Williams et al.
Volume 25 | No. 10 | October 2014
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suggesting a reasonable degree of homogeneity within the different trials. The low rate of agreement at the 90% survival time point is in part due to the short timescales at this survival point: our definition of agreement allows for ∼5 weeks difference (±) between the actual and predicted values, which is a narrow time window and thus more susceptible to random variation. In addition, many trials have an inclusion criterion of expected survival of more than 8 or 12 weeks. Since the median time to the 90% survival time is only 4 months, we might expect the agreement to be reduced by trial clinicians adhering to this stipulation, as it excludes patients with short survival times, and hence explain why this approach underestimates the time to the 90% survival level. This approach provides a robust way of estimating the chance of survival to different time points. It is better calibrated than clinical opinion, and is simpler than many end-of-life scoring systems. We hope that the greater information provided by this approach will allow for better decision making by patients and doctors. Initial studies suggest that this approach is acceptable and preferred to use of median OS alone [16]. Given the data presented here, we can expect that, for patients with metastatic colorectal cancer having palliative chemotherapy as part of a clinical trial, most patients will live for at least 5 months (the 90% survival level); many will live for between 10 and 28 months (between the 75% and 25% survival levels); few will live longer than 40 months (10% survival level). Metastatic colorectal cancer may be different from other tumours, as hepatic resection in patients with oligo-metastatic disease may alter their prognosis. This has been documented at a population level [17], but such patients are relatively rare and may be excluded from clinical trials.
Annals of Oncology
In conclusion, we have demonstrated that simple multiples of the median OS accurately estimate the times at which different survival levels are reached for patients with metastatic colorectal cancer. This is in keeping with other studies in patients with lung, prostate and breast cancer treated with palliative chemotherapy. We hope that the use of a simple formula will allow clinicians to approximate survival at different time points, and to use this information in discussion with their patients to give realistic descriptions on likely survival. Although there are some areas of clinical practice where this approach is unlikely to hold, it seems likely that it will apply across a variety of tumour sites where cure is unlikely.
acknowledgement
disclosure The authors have declared no conflicts of interest.
references 1. Glare P, Virik K, Jones M et al. A systematic review of physicians’ survival predictions in terminally ill cancer patients. BMJ 2003; 327(7408): 195–198. 2. Mackillop WJ, Quirt CF. Measuring the accuracy of prognostic judgments in oncology. J Clin Epidemiol 1997; 50(1): 21–29. 3. Maher J, McConnell H. New pathways of care for cancer survivors: adding the numbers. Br J Cancer 2011; 105(Suppl S1): S5–10. 4. Hagerty RG, Butow PN, Ellis PA et al. Cancer patient preferences for communication of prognosis in the metastatic setting. Ann Oncol 2004; 22(9): 1721–1730. 5. Butow PN, Dowsett S, Hagerty R, Tattersall MHN. Communicating prognosis to patients with metastatic disease: what do they really want to know? Support Care Cancer 2002; 10(2): 161–168. 6. Stone P, Lund S. Predicting prognosis in patients with advanced cancer. Ann Oncol 2007; 18(6): 971–976. 7. Maddams J, Utley M, Møller H. Levels of acute health service use among cancer survivors in the United Kingdom. Eur J Cancer 2011; 47(14): 2211–2220.
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8. Weeks JC, Cook EF, O’Day SJ et al. Relationship between cancer patients’ predictions of prognosis and their treatment preferences. JAMA 1998; 279(21): 1709–1714. 9. Kiely BE, Soon YY, Tattersall MHN, Stockler MR. How long have i got? Estimating typical, best-case, and worst-case scenarios for patients starting first-line chemotherapy for metastatic breast cancer: a systematic review of recent randomized trials. J Clin Oncol 2011; 29(4): 456–463. 10. Stockler M, Tattersall M, Boyer M et al. Disarming the guarded prognosis: predicting survival in newly referred patients with incurable cancer. Br J Cancer 2006; 94: 208–212. 11. Kiely BE, Alam M, Blinman P et al. Lung cancer estimating typical, best-case and worst-case life expectancy scenarios for patients starting chemotherapy for advanced non-small-cell lung cancer: a systematic review of contemporary randomized trials. Lung Cancer 2012; 77(3): 537–544. 12. Lerner A, Williams M. Simple multiples of median overall survival (OS) may not estimate survival in patients with non-small cell lung cancer (NSCLC) treated with first-line EGFR-targeted agents. J Thorac Oncol 2012; 7(6 Supp. 1): S75–S76. 13. West TA, Kiely BE, Stockler M. Estimating scenarios for survival time in men starting systemic therapies for castration-resistant prostate cancer: A systematic review of randomised trials. Eur J Cancer 2014; 50(11): 1916–1924. 14. Christakis NA, Lamont EB. Terminally ill patients: prospective cohort study. Br Med J 2000; 320(7233): 469–473. 15. Christakis NA, Lamont EB. Extent and determinants of error in doctors’ prognoses in terminally ill patients: prospective cohort study. BMJ 2000; 320(7233): 469–473. 16. Kiely B, McCaughan G, Christodoulou S et al. Using scenarios to explain life expectancy in advanced cancer: attitudes of people with a cancer experience. Support Care Cancer 2013; 21: 369–376. 17. Chang GJ, Hu C-Y, Eng C et al. Practical application of a calculator for conditional survival in colon cancer. J Clin Oncol 2009; 27(35): 5938–5943. 18. Asmis TR, Powell E, Karapetis CS et al. Comorbidity, age and overall survival in cetuximab-treated patients with advanced colorectal cancer (ACRC)—results from NCIC CTG CO.17: a phase III trial of cetuximab versus best supportive care. Ann Oncol 2011; 22(1): 118–126. 19. Sargent DJ, Köhne CH, Sanoff HK et al. Pooled safety and efficacy analysis examining the effect of performance status on outcomes in nine first-line treatment trials using individual data from patients with metastatic colorectal cancer. J Clin Oncol 2009; 27: 1948–1955. 20. Kiely BE, Martin AJ, Tattersall MHN et al. The median informs the message: accuracy of individualized scenarios for survival time based on oncologists’ estimates. J Clin Oncol 2013; 31(28): 3565–3571. 21. Shaw NJ, Dear PR. How do parents of babies interpret qualitative expressions of probability? Arch Dis Child 1990; 65(5): 520–523.
doi:10.1093/annonc/mdu372 |
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An earlier version of the work was presented at the NCRI conference, UK, 2012.
original articles
Annals of Oncology 14. Imamura Y, Morikawa T, Liao X et al. Specific mutations in KRAS codons 12 and 13, and patient prognosis in 1075 BRAF wild-type colorectal cancers. Clin Cancer Res 2012; 18: 4753–4763. 15. Kim MJ, Lee HS, Kim JH et al. Different metastatic pattern according to the KRAS mutational status and site-specific discordance of KRAS status in patients with colorectal cancer. BMC Cancer 2012; 12: 347. 16. Tejpar S, Celik I, Schlichting M et al. Association of KRAS G13D tumor mutations with outcome in patients with metastatic colorectal cancer treated with first-line chemotherapy with or without cetuximab. J Clin Oncol 2012; 30: 3570–3577.
Annals of Oncology 25: 2014–2019, 2014 doi:10.1093/annonc/mdu372 Published online 5 August 2014
M. Williams1*, R. A. Singer2 & A. Lerner3 1 Department of Clinical Oncology, Charing Cross Hospital, Imperial College Healthcare NHS Trust, London; 2Department of Clinical Oncology, Kent and Canterbury Hospital, Canterbury; 3Department of Clinical Oncology, The Royal Surrey County Hospital, Guildford, UK
Received 19 May 2014; revised 16 July 2014; accepted 27 July 2014
Background: Patients with incurable cancer usually want specific information about prognosis, and clinicians’ estimates are often inaccurate. Studies in breast and lung cancer have suggested that simple multiples of the median overall survival (OS) can accurately estimate the time at which 90%, 75%, 25% and 10% of patients are alive. Patients and methods: We identified 46 phase III randomised clinical trials of chemotherapy in metastatic colorectal cancer, representing data from 29 011 patients. We extracted data on demographics, treatment and survival from 96 patient cohorts and assessed agreement with the estimated survival time points, calculated as 0.25, 0.5, 2 and 3 times the median OS. Results: Median OS was 16.8 months in the trials. There were 342 assessable time points. For 301 of these, the estimated survival time was within 0.75–1.33 of the actual survival time (88% agreement). The worst agreement (76%) was at the earliest (90%) level of survival. Conclusions: Simple multiples of the median OS give reasonable estimates of the times at which different survival levels are reached in patients with metastatic colorectal cancer. Taken with previous studies, these findings are likely to be valid across a large range of patients. We would encourage clinicians to think of prognosis as a trajectory, and to consider quoting survival ranges instead of point estimates, in discussions with patients. Key words: survival, prognosis, metastatic colorectal cancer, review
introduction Estimating survival in patients with incurable cancer is difficult, and predictions made by medical staff are often inaccurate [1, 2]. Such predictions are important for patients, and may influence their decisions on treatment; they are also important in planning services [3]. *Correspondence to: Dr Matt Williams, Department of Radiotherapy, Imperial College Healthcare NHS Trust, Charing Cross Hospital, Fulham Palace Rd, Northwood, London W6 8RF, UK. Tel: +44-203-311-8427; Fax: +44-203-311-1603; E-mail: mhw@doctors. net.uk
When asked for prognosis, many clinicians quote the median overall survival (OS). However, the median represents the time by which half the patients will have died and the relevance of this figure is not clear to patients [4, 5]. Ideally, patients should have access to better prognostic information without the need for complex formulae or electronic aids. There has been substantial work on estimating survival in the last few weeks of life, often in the hospice environment (see [6] for an overview). However, there is much less work on estimating survival in those patients who cannot be cured but may have life expectancies measured in months and years. Such patients have high
© The Author 2014. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: [email protected].
Downloaded from http://annonc.oxfordjournals.org/ at Laurentian University on December 7, 2014
A simple technique to estimate best- and worst-case survival in patients with metastatic colorectal cancer treated with chemotherapy
original articles
Annals of Oncology
reporting. For each trial, we obtained data from the full manuscript; where trials had more than one publication, we used the one that contained the most complete report of OS. For each trial, we recorded the year of publication, number of patients enrolled in each arm, patient demographics, proportion of patients with rectal involvement, number of treatment arms, treatment regimens used, whether a targeted agent was used, and the median OS for each treatment arm. We included data on all treatment arms with 100 or more patients, where survival had reached the 25% level or less. Each survival curve was converted to JPEG format, magnified to 400% size, and overlaid with a pixelated grid. Two authors independently measured the actual time points at which 90%, 75%, 25% and 10% levels of survival were reached, in each arm of each study (Figure 1) . We extracted the median OS from each arm of each study, and then calculated simple multiples of the median OS (0.25, 0.5, 2 and 3) to estimate the time to the 90%, 75%, 25% and 10% levels of survival. We compared the actual time taken to reach each survival level with the time estimated from the relevant multiple of the median OS. In keeping with previous work [9, 12, 14], we considered the agreement between the two to be acceptable if the estimate
References (n = 156)
methods We identified randomised clinical trials of first-line palliative chemotherapy in patients with metastatic colorectal cancer published between 2000 and 2011 and indexed in Medline (supplementary Appendix S1, available at Annals of Oncology online). We supplemented this list with additional references in reviews and through discussion with subject-matter experts. Trials were screened by two authors and were eligible for inclusion if they were phase III randomised trials, compared at least two different chemotherapy regimens, were given as first-line treatment of metastatic colorectal cancer, included at least 100 patients per treatment arm, included censored curves of OS, were published in English and where, in at least one treatment arm, at least 75% of patients had died at the time of
Included trials (n = 46)
Insufficient information (n = 5; 3%)
Number of OS curves (n = 101)
Not randomised chemotherapy trials in mCRC (n = 52; 33%)
Curve without data for 25% centile (n = 5)
Duplicate references for the same study (n = 22; 14%)
OS curves for analysis (n = 96)
100 Not first line (n = 18;12%)
Proportion alive
80
60
<100 patients/arm (n = 5; 3%)
40
20
No OS curve (n = 4; 3%)
0 0
6
12
18
36
42
48
54
60
10% of patients alive
25% alive
Median OS
75% alive
90% of patients alive
24 30 Follow-up (months)
Figure 1. Measuring the time to different levels of survival from published survival curves.
Volume 25 | No. 10 | October 2014
No data for 25% percentile survival (n = 4; 3%)
Included trials (n = 46; 29%)
Figure 2. Flow diagram showing study and cohort inclusion.
doi:10.1093/annonc/mdu372 |
Downloaded from http://annonc.oxfordjournals.org/ at Laurentian University on December 7, 2014
health-care utilisation rates [7], and their decisions around medical care are influenced by their perception of prognosis [8]. Kiely et al. has presented a method [9, 10] that provides estimates of survival for groups of patients having palliative chemotherapy. It generates estimates of the times at which most people in the group will still be alive, and times to which smaller proportions of the population might be expected to live. These are termed the ‘worst case’ (90% of patients survive at least this long), ‘best case’ (only 10% of patients survive longer than this), ‘lower typical’ (75% of patients survive at least this long) and ‘upper typical’ (25% level). The method calculates these estimates by using simple multiples of the median OS from clinical trials, and is based upon the fact that survival curves approximate an exponential decay pattern. This method has been evaluated in metastatic breast [9], lung [11, 12] and prostate [13] but not colorectal cancer. In this study, we have systematically reviewed recent phase III randomised trials of chemotherapy in metastatic colorectal cancer, to see whether simple multiples of the median OS time can estimate the time points at which different survival levels (90%, 75%, 25%, 10%) are reached.
original articles
Annals of Oncology
lay within 0.75–1.33 times of the actual time point for that survival level. We assessed whether median OS or rates of agreement varied by trial characteristics. We used Fisher’s exact test to assess statistical significance for rates of agreement, and Kendall’s correlation to assess correlation between trial characteristics and median OS. We used a cut-off of P < 0.01 to account for using multiple tests.
A
results We identified 139 trials, of which 46 met our inclusion criteria (references in supplementary Appendices S2 and S3a–e, available at Annals of Oncology online), yielding a total of 96 Kaplan–Meier curves (Figure 2). Studies had varying length of follow-up and we were able to measure 96 data points for 90%,
B
Median survival 25
Time to 90% survival level 15
Frequency
15 10
10
5
5 0
0 0
20
80
0
2
D
Time to 75% survival level
10
8
8
6 4 2
4 6 Time in months
8
10
Time to 25% survival level
10
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Frequency
C
60
40 Time in months
6 4 2
0
0 4
6
8
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E
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16
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25 30 Time in months
35
40
Time to 90% survival level 8
Frequency
6
4
2
0 30
40
50 Time in months
60
Figure 3. (A) Median overall survival, in months, of all patient cohorts. (B) Time in months to the 90% survival level. (C) Time in months to the 75% survival level. (D) Time in months to 25% survival level. (E) Time in months to 10% survival level.
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Frequency
20
original articles
Annals of Oncology
discussion In this analysis of 46 trials enrolling 29 011 patients receiving first-line chemotherapy for metastatic colorectal cancer, simple multiples (0.25, 0.5, 2 and 3) of the median OS time can be used to estimate survival centiles (90%, 75%, 25% and 10%). Despite the variation in median OS between different trials, the relationship between median OS and different survival levels was consistent. Our findings are in keeping with previous work in breast and lung cancer [9, 11], which have widely different median survival times. We therefore expect that this relationship will remain true, even if the median OS were to increase in future trials. This should allow clinicians to provide, and patients to understand, more nuanced representations of prognosis. There are three main weaknesses with this study. First, at the earliest time point (90% survival), the agreement between the estimated time to the survival level and the actual value drops to 76%, and at the other extreme (10% survival) the agreement is 81%, compared with 88% overall. This pattern is in keeping with previous studies. Secondly, the data to support this
approach come from randomised trials, which may not include a representative population (e.g. European patients with colorectal cancer have a median age of 71 years; the median age in this study was 62 years). Thirdly, the definition of agreement (0.75–1.33) is somewhat arbitrary, although it is in keeping with previous studies, and is more conservative than some other authors [15]. Although we used 0.75–1.33 as a definition of agreement, in fact most data are more tightly grouped than this,
Table 2. Trial characteristics Characteristics
Trials No. Percentage
Year of publication 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 No. of cohorts analysed per trial 1 2 3 4 Agents used in the 96 cohorts Fluorouracil Oxaliplatin Irinotecan Capecitabine Bevacizumab Cetuximab Panitumumab Unspecified fluoropyrimidine (capecitabine or fluorouracil) Vatalinib Mitomycin C Tegafur–uracil Trimetrexate Others (4 agents)
4 1 3 1 3 5 6 6 5 2 4 6
9 2 7 2 7 11 13 13 11 4 9 13
4 35 6 1
9 76 13 2
61 48 31 18 12 5 4 3
64 50 32 19 13 6 5 3
1 1 1 1 1
1 1 1 1 1
Table 1. Patient characteristics
Number of patients per cohort Age (years) % Male Follow-up (months) Rectal involvement
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Median
Range
Number of cohorts reported
% of cohort reported
218 62 61% 31.0 29%
100–1017 58–69 38–73 17.2–61.0 14%–54%
96 88 90 64 63
100 92 94 66 66
doi:10.1093/annonc/mdu372 |
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75% and 25% survival, and 54 points for 10% survival. This gave a total of 342 time points at which we could assess the agreement between the actual and estimated times. The included trials enrolled 29 011 patients. The median OS in the included studies was 16.8 months (IQR: 14.3–19.4; Figure 3A). The median time to 90% OS level was 4.6 months (IQR: 3.7–6), to the 75% level was 9.5 months (IQR: 8–11), to the 25% level was 27.7 (IQR: 24–32.2) and to the 10% survival level was 39.3 months (IQR: 34.9–44.4) (Figure 3B–E; supplementary Material, available at Annals of Oncology online) (Table 1) . The median age of included patients was 62 years, the median proportion of male patients was 61%, and the median proportion of patients with rectal cancer was 29%. Thirteen of the cohorts (14%) included at least one biological agent (Table 2). Of the 342 data points, 301 (88%) of the estimates were acceptable (lay within 0.75–1.33 of the actual value). The agreement between the estimated and actual time points was worst at the earliest (90%) survival level, where only 76% of the estimates were acceptable. Of the 68 cohorts where we had data on all four survival points, half agreed at all four points. There was a tendency to underestimate the time to the 90% and 75% survival levels, and overestimate the time to the 25% and 10% survival levels (Figure 3B–E). There was no significant variation in the rate of agreement by trial or trial population subgroup, although median OS increased over time (Kendall’s τ = 0.35; P = 0.001) (Figure 4).
original articles
Annals of Oncology
We can add additional information to make our estimates more accurate. For example, there is a well-documented impact of performance status on OS in metastatic colorectal cancer [18, 19], with a median OS of 8.5 months in patients who are ECOG performance status 2 [19]. In this case, we would expect the calculated estimates to be 3, 4, 17 and 25 months. In principle, additional clinical factors (site of metastases etc.) could also be added to a prognostic model, in order to provide better estimates of the survival. However, the use of additional information to provide better prognostic estimates is orthogonal to this approach, which provides a simple and accessible way to provide more information about multiple points on the survival curve. In both cases, the volume of data on which to base decisions is limited, and emphasises the fact that clinical information and acumen is still required, even if only to provide the initial estimate of the median OS time point [20]. Whether we use more data to provide better estimates of the median survival, or use our approach to calculate multiple points on the survival curve (or both), it is important to note that they are statistical measures that we use to communicate risk to patients. Each patient has a single, initially unknown, prognosis and therefore data from groups of patients can only provide an estimate of risk for individuals. We would encourage clinicians to start considering survival in terms of a trajectory of multiple time points. How this is discussed with patients will vary, but terms such as ‘most’, ‘many’, ‘some’ and ‘few’ may convey the approximate survival levels, and techniques such as this allow clarification with numerical values where useful [21]. Previous work has suggested that clinicians often overestimate prognosis [1, 15] and we hope that this study and others will provide more objective estimates on which to base discussions with patients and their families.
Median overall survival by year of trial publication
Overall survival in months
25
20
15
10
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Year of trial reporting Figure 4. Changes in overall survival by year of trial publication. Horizontal dark line indicates the median OS; the extent of shaded area indicates the range from first to third quartile.
| Williams et al.
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suggesting a reasonable degree of homogeneity within the different trials. The low rate of agreement at the 90% survival time point is in part due to the short timescales at this survival point: our definition of agreement allows for ∼5 weeks difference (±) between the actual and predicted values, which is a narrow time window and thus more susceptible to random variation. In addition, many trials have an inclusion criterion of expected survival of more than 8 or 12 weeks. Since the median time to the 90% survival time is only 4 months, we might expect the agreement to be reduced by trial clinicians adhering to this stipulation, as it excludes patients with short survival times, and hence explain why this approach underestimates the time to the 90% survival level. This approach provides a robust way of estimating the chance of survival to different time points. It is better calibrated than clinical opinion, and is simpler than many end-of-life scoring systems. We hope that the greater information provided by this approach will allow for better decision making by patients and doctors. Initial studies suggest that this approach is acceptable and preferred to use of median OS alone [16]. Given the data presented here, we can expect that, for patients with metastatic colorectal cancer having palliative chemotherapy as part of a clinical trial, most patients will live for at least 5 months (the 90% survival level); many will live for between 10 and 28 months (between the 75% and 25% survival levels); few will live longer than 40 months (10% survival level). Metastatic colorectal cancer may be different from other tumours, as hepatic resection in patients with oligo-metastatic disease may alter their prognosis. This has been documented at a population level [17], but such patients are relatively rare and may be excluded from clinical trials.
Annals of Oncology
In conclusion, we have demonstrated that simple multiples of the median OS accurately estimate the times at which different survival levels are reached for patients with metastatic colorectal cancer. This is in keeping with other studies in patients with lung, prostate and breast cancer treated with palliative chemotherapy. We hope that the use of a simple formula will allow clinicians to approximate survival at different time points, and to use this information in discussion with their patients to give realistic descriptions on likely survival. Although there are some areas of clinical practice where this approach is unlikely to hold, it seems likely that it will apply across a variety of tumour sites where cure is unlikely.
acknowledgement
disclosure The authors have declared no conflicts of interest.
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doi:10.1093/annonc/mdu372 |
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An earlier version of the work was presented at the NCRI conference, UK, 2012.
original articles