Do older and younger patients derive similar survival benefits from novel oncology drugs? A systematic review and meta-analysis

Do older and younger patients derive similar survival benefits from novel oncology drugs? A... Abstract Background older patients are commonly believed to derive less benefit from cancer drugs, even if they fulfil clinical trial eligibility [Talarico et al. (2004, J Clin Oncol, 22(22):4626–31)]. We aim to examine if novel oncology drugs provide differential age-based treatment outcomes for patients on clinical trials. Methods a systematic review of randomised control trials (RCTs) cited for clinical efficacy evidence in novel oncology drug approvals by the Food and Drug Administration, European Medicines Agency and Health Canada between 2006 and 2017 was conducted. Studies reporting age-based subgroup analyses for overall or progression-free survival (OS/PFS) were included. Hazard ratios (HRs) and confidence intervals (CIs) for age-based subgroups were extracted. Meta-analyses with random effects were conducted, examining patient subgroups <65 and ≥65 years separately and pooled HRs of studies primary endpoints (OS or PFS) compared to examine if differences existed between age-based subgroups. Sensitivity analyses were conducted for cancer type, primary endpoint and systemic treatment. Results one-hundred-two RCTs, including 65,122 patients, met the inclusion criteria. One study reported age-based toxicity and none reported age-based quality of life (QOL) results. Pooled HRs [95% CIs] for patients <65 and ≥65 years were 0.61 [0.57–0.65] and 0.65 [0.61–0.70], respectively, with no difference between them (P = 0.14). Sensitivity analyses revealed similar results. Conclusion our results suggest that older and young patients, who fulfil clinical trial eligibility, may derive similar relative survival benefits from novel oncology drugs. There is, however, a need to report age-based toxicity and QOL results to support patient discussions regarding the balance of treatment benefit and harm, to encourage informed decision-making. geriatric, oncology, meta-analysis, survival, toxicity, older people, systematic review Introduction Despite the immense disease burden among older individuals, this population is generally an underserved group. While ~60% of cancers and 70% of cancer-related mortalities occur in patients 65 years or older [2], older patients’ unique considerations often pose as a barrier to treatment and may limit the potential benefit they derive. Multiple theoretical reasons why older patients may derive less benefit or more toxicity from specific treatments are presented in the literature. For example, the physiology of ageing may be associated with changes in cancer biology [3] and drug metabolism [4]. Reduced liver mass and blood flow may result in decreased first-pass metabolism and bioavailability in older patients [4]. Additional physiological changes, such as decreased intestinal absorption, hepatic metabolism and renal excretion [5], may alter the pharmacodynamics of drugs through changes in distribution and excretion [6]. Ultimately, this may increase the prevalence of high-grade toxicities and decrease the tolerance of low-grade toxicities in older patients [5], due to increased drug exposure [6]. Treatment-related toxicities, which greatly impact patients’ daily activities, more commonly result in dose reductions in older adults, lessening treatment efficacy due to reduced dose intensity [7]. Prescribing physicians often opt to modify patients’ doses early in an attempt to prevent future toxicity [8]. Such decisions may be based on demographics, co-morbidity and the nature of the prescribed regimen, amongst others [8]. In a recent secondary data analysis, Gajra et al. [8] determined that age alone was independently associated with a primary dose reduction for patients receiving chemotherapy with both curative and palliative intents. This population, however, is an extraordinarily heterogeneous group. While frailty is more common with age, the progression towards frailty is considerably variable across older individuals. When considering functional status alone, trials typically describe their patient population in terms of the Eastern Cooperative Oncology Group Scale of Performance Status (PS). A recent systematic review and meta-analysis conducted to determine if patients of lesser PS-derived differential treatment benefits from novel oncology drugs in comparison with their ‘excellent’ counterparts, determined there was indeed no difference between the groups [9]. PS, however, is a measure of only one aspect of frailty. In addition to the concerns about frailty, PS, potential toxicity and dose reduction, older patients are more likely to have co-morbidities and die from other causes. The increased likelihood of death from another cause may attenuate or even negate the ability of cancer-specific treatments to improve progression-free survival (PFS) or overall survival (OS) in this group. While some studies suggested there may be differential age-based survival benefits [10, 11], others presented opposing results [12–19]. Most literature, however, is focused on older drugs with substantial side effects commonly affecting older patients. To date, there has been limited literature systematically examining the effects of age on the benefits of novel oncology drugs in contemporary medicine. Molecular-targeted agents, for example, are believed to be associated with decreased toxicities when compared with older, less specific targeted agents and chemotherapy regimens [20]. The decreased toxicity profile of novel oncology treatments may suggest potentially improved tolerability and outcomes in older patients. Therefore, there is a need to systematically review the literature to determine if age is associated with the relative treatment-related efficacy and toxicity of novel oncology drugs. Such study can allow for the development of optimised medical care by better understanding the effects of older patients’ co-morbidities and drug tolerance on the net clinical benefit of treatment. Additionally, it may help clinicians determine if age should be a considerable factor when making treatment decisions for generally well and fit older patients. Thus, we aim to conduct a systematic review and meta-analysis to examine if novel oncology drugs provide differential treatment outcomes for older and young patients enroled in clinical trials. Methods Selection of studies Clinical trials between January 2006 and June 2017 from the Food and Drug Administration’s (FDA) Haematology/Oncology Approvals and Safety Notifications page [21], European Medicines Agency’s (EMA) Public Assessment Reports [22] and Health Canada’s (HC) Summary of Basis Decision documents [23] were reviewed. Primary publications cited for clinical efficacy evidence in novel oncology drug approvals, along with their corresponding appendices, were collected. As defined by our study, a drug was considered ‘novel’ if leading to regulatory approval by ≥1 of the FDA, EMA or HC within our defined collection period. Updated publications and companion studies were located using Web of Science and ClinicalTrials.gov/NCT numbers, to investigate if there was subsequent age-based toxicity or quality of life (QOL) data available. Single-arm studies were excluded, and randomised phase II or III clinical trials reporting OS and/or PFS as their primary or co-primary endpoint(s) were identified for inclusion. Studies reporting time-to-progression (TTP) as their primary endpoint were considered together with those investigating PFS. Studies that did not report age subgroup analyses in the primary publication or Supplementary Appendix, and those that did not administer chemotherapy or targeted agents as their experimental regimen were excluded. Data extraction and statistical analyses Age-based reported OS and PFS hazard ratios (HRs) and confidence intervals (CIs) were extracted by two independent reviewers. If age-based HRs and CIs were not reported numerically, values were extracted from forest plots and independently digitised by two reviewers using DigitizeIt Software (version 2.2, Germany). Any discrepancies or disagreements were resolved by consulting a third independent reviewer. Eligible randomised control trials (RCTs) were further assessed for the reporting of age-based toxicity and QOL analyses; if available, data was extracted. Updated publications were subsequently reviewed to determine the frequency of reporting age-based toxicity and QOL data. Meta-analyses based on an inverse variance random-effects model were performed and forest plots constructed using Review Manager 5.3 Software (Copenhagen, Denmark). The primary analysis was based on studies’ primary endpoints (OS/PFS), comparing patients <65 and ≥65 years of age. As each RCT established their own age cut-off to define ‘young’ versus ‘older’, only RCTs that reported age-based data using 65 years as their age cut-off was included in the ‘primary’ analysis. Our decision to select 65 years as the age cut-off, was based on the World Health Organization’s (WHO’s) definition of ‘elderly’ [24], and as a reflection of the included studies. Moreover, if a study reported OS and PFS as co-primary endpoints, the HR allotted a higher alpha value was included for analysis. Pooled HRs with 95% CIs were calculated for each subgroup and tested for subgroup differences between patients <65 and ≥65. Sensitivity analyses including all RCTs (i.e. those reporting age-based data using 65 years as their age cut-off, together with those that used various other age cut-offs) were subsequently conducted to examine the congruency of results with the primary analysis. For these analyses, patients were dichotomised into older versus young subgroups based on each RCT’s own age cut-off. That is, for each trial the youngest age group was taken to be ‘young’ and the oldest as ‘older’. If three or more groups were established, the oldest group was selected as ‘older’ and the youngest group as ‘young’. Sensitivity analyses categorising the study population by the type of primary endpoint (OS or PFS) and systemic treatment (chemotherapy or targeted agents), were also conducted. Assessment of the risk of bias was completed at the level of individual studies. Studies were evaluated for their use of; (1) selection bias (generation of a randomised sequence and concealment of group allocations), (2) performance bias (participants and study personals knowledge of group assignment), (3) detection bias (study personnel whom assess outcomes having knowledge of group assignment), (4) attrition bias (incomplete data and its management), (5) reporting bias (lack of data reporting for pre-specified endpoints) and (6) any other bias. Such biases were judged as low, unclear or high risk, and compiled using Review Manager 5.3 Software, and a risk of bias graph composed. Results Characteristics of included studies One-hundred-two RCTs, involving 65,122 patients, spanning Asia, Central, South, and North Americas, Europe, Middle East, Africa and Oceania (including Australia and New Zealand), reported age-based survival data and were thus included in our meta-analysis (study details may be found in Supplementary Appendix Table A1, available in Age and Ageing online). All identified RCTs were published in English, and common reasons for exclusion are described in Figure 1. Figure 1. View largeDownload slide PRISMA flow diagram; FDA, Food and Drug Administration; EMA, European Medicines Agency; HC, Health Canada; OS, overall survival; PFS, progression-free survival. Figure 1. View largeDownload slide PRISMA flow diagram; FDA, Food and Drug Administration; EMA, European Medicines Agency; HC, Health Canada; OS, overall survival; PFS, progression-free survival. Of the 102 included studies, 87 studies administered targeted agents and 15 studies administered chemotherapy as their experimental regimen. Experimental regimens included monoclonal antibodies (N = 24 studies), vascular endothelial growth factors (VEGF) inhibitors (N = 13 studies), tyrosine kinase inhibitors (N = 11 studies), multi-kinase inhibitors (N = 6 studies) and various others. Ninety-five (93.1%) were phase III trials, while six (5.9%) were phase II trials (one was a phase II/III trial). The majority of studies (N = 72, 70.6%), presented age-based subgroup analyses in accordance with WHO’s classification of older (65 chronological years or above). Three studies (2.9%) reported patient survival results as <60 versus ≥60 years, and three other studies (2.9%) as <75 versus ≥75 years. Five (4.9%) studies presented age-based results as three groups, divided as <65, ≥65–<75 and ≥75 years, and four (3.9%) studies divided as <60 years, 60–69 and 70–74 years. Fifteen (14.7%) studies divided their patients by various other age cut-offs (Table 1). When considering all included studies, the median age of patients randomised to the experimental arm spanned from 33 to 76 years, similar to that of the control (32–77 years). Table 1. Summary of study characteristics Studies 102 Patients 65,122 Characteristic Number (n) Percentage (%) Experimental drug type  Chemotherapy 15 14.7  Targeted agent 87 85.3 Type of cancer  Lung cancer 20 19.6  Gastric cancer 5 4.9  Colorectal cancer 9 8.8  Melanoma 7 6.9  Renal cell carcinoma 7 6.9  Multiple myeloma 9 8.8  Chronic lymphocytic leukaemia 7 6.9  Pancreatic neuroendocrine tumours 2 2.0  Thyroid cancer 4 3.9  Gastroesophageal adenocarcinoma 1 1.0  Prostate cancer 5 4.9  Other 26 25.5 Primary endpoint  OS 38 37.3  PFS 56 54.9  TTP 2 2.0  OS and PFS (co-primary) 6 5.9 Age subgroups  <60 and ≥60 3 2.9  <60, 60–69 and 70–74 4 3.9  <65 and ≥65 72 70.6  <65, ≥65-<75 and ≥75 5 4.9  <75 and ≥75 3 2.9  Other 15 14.7 Phase  II 6 5.9  II/III 1 1.0  III 95 93.1 Studies 102 Patients 65,122 Characteristic Number (n) Percentage (%) Experimental drug type  Chemotherapy 15 14.7  Targeted agent 87 85.3 Type of cancer  Lung cancer 20 19.6  Gastric cancer 5 4.9  Colorectal cancer 9 8.8  Melanoma 7 6.9  Renal cell carcinoma 7 6.9  Multiple myeloma 9 8.8  Chronic lymphocytic leukaemia 7 6.9  Pancreatic neuroendocrine tumours 2 2.0  Thyroid cancer 4 3.9  Gastroesophageal adenocarcinoma 1 1.0  Prostate cancer 5 4.9  Other 26 25.5 Primary endpoint  OS 38 37.3  PFS 56 54.9  TTP 2 2.0  OS and PFS (co-primary) 6 5.9 Age subgroups  <60 and ≥60 3 2.9  <60, 60–69 and 70–74 4 3.9  <65 and ≥65 72 70.6  <65, ≥65-<75 and ≥75 5 4.9  <75 and ≥75 3 2.9  Other 15 14.7 Phase  II 6 5.9  II/III 1 1.0  III 95 93.1 OS, overall survival; PFS, progression-free survival; TTP, time-to-progression. Table 1. Summary of study characteristics Studies 102 Patients 65,122 Characteristic Number (n) Percentage (%) Experimental drug type  Chemotherapy 15 14.7  Targeted agent 87 85.3 Type of cancer  Lung cancer 20 19.6  Gastric cancer 5 4.9  Colorectal cancer 9 8.8  Melanoma 7 6.9  Renal cell carcinoma 7 6.9  Multiple myeloma 9 8.8  Chronic lymphocytic leukaemia 7 6.9  Pancreatic neuroendocrine tumours 2 2.0  Thyroid cancer 4 3.9  Gastroesophageal adenocarcinoma 1 1.0  Prostate cancer 5 4.9  Other 26 25.5 Primary endpoint  OS 38 37.3  PFS 56 54.9  TTP 2 2.0  OS and PFS (co-primary) 6 5.9 Age subgroups  <60 and ≥60 3 2.9  <60, 60–69 and 70–74 4 3.9  <65 and ≥65 72 70.6  <65, ≥65-<75 and ≥75 5 4.9  <75 and ≥75 3 2.9  Other 15 14.7 Phase  II 6 5.9  II/III 1 1.0  III 95 93.1 Studies 102 Patients 65,122 Characteristic Number (n) Percentage (%) Experimental drug type  Chemotherapy 15 14.7  Targeted agent 87 85.3 Type of cancer  Lung cancer 20 19.6  Gastric cancer 5 4.9  Colorectal cancer 9 8.8  Melanoma 7 6.9  Renal cell carcinoma 7 6.9  Multiple myeloma 9 8.8  Chronic lymphocytic leukaemia 7 6.9  Pancreatic neuroendocrine tumours 2 2.0  Thyroid cancer 4 3.9  Gastroesophageal adenocarcinoma 1 1.0  Prostate cancer 5 4.9  Other 26 25.5 Primary endpoint  OS 38 37.3  PFS 56 54.9  TTP 2 2.0  OS and PFS (co-primary) 6 5.9 Age subgroups  <60 and ≥60 3 2.9  <60, 60–69 and 70–74 4 3.9  <65 and ≥65 72 70.6  <65, ≥65-<75 and ≥75 5 4.9  <75 and ≥75 3 2.9  Other 15 14.7 Phase  II 6 5.9  II/III 1 1.0  III 95 93.1 OS, overall survival; PFS, progression-free survival; TTP, time-to-progression. As demonstrated in Supplementary Appendix Figure A1 (available in Age and Ageing online), the risk of specific biases within all included studies were generally low. Toxicity and QOL subgroup reporting frequency Following the review of primary and updated publications, only one primary publication investigating chlorambucil plus ofatumumab for previously untreated chronic lymphocytic leukaemia [25], was found to present age-based toxicity results. This study reported that the incidence of all adverse events were comparable between patients considered older and young for those randomised to the experimental and control regimens [25], suggesting that older and young patients who fit this trial’s eligibility may experience similar relative toxicities. No studies were found to present age-based QOL results. Primary analysis Pooled HRs [95% CI] of studies primary endpoints (OS or PFS) for patients <65 and ≥65 were 0.61 [0.57–0.65] and 0.65 [0.61–0.70], respectively (Figure 2), with no survival difference between the two groups (P = 0.14). The forest plot for the primary analysis can be found in the Supplementary Appendix (Figure A2, available in Age and Ageing online). Figure 2. View largeDownload slide Subgroup analysis hazard ratios and test for subgroup differences based on a random effects model: <65 versus ≥65 subgroup analysis; all drugs analyses contains pooled HRs [95% CI] of studies primary endpoints (OS or PFS); OS, overall survival; PFS, progression-free survival; RCC, renal cell carcinoma; CLL, chronic lymphocytic leukaemia. Figure 2. View largeDownload slide Subgroup analysis hazard ratios and test for subgroup differences based on a random effects model: <65 versus ≥65 subgroup analysis; all drugs analyses contains pooled HRs [95% CI] of studies primary endpoints (OS or PFS); OS, overall survival; PFS, progression-free survival; RCC, renal cell carcinoma; CLL, chronic lymphocytic leukaemia. Sensitivity analyses For the endpoint of OS, pooled HRs [95% CI] for patients <65 and ≥65 were 0.77 [0.72–0.81] and 0.80 [0.75–0.86], respectively. For the endpoint of PFS, pooled HRs [95% CI] were 0.51 [0.47–0.56] and 0.54 [0.48–0.61], respectively. Tests for differences between patients <65 and ≥65 were not significant for OS or PFS (P = 0.37 for OS, P = 0.36 for PFS). A sensitivity analysis was conducted for all eligible RCTs with patients dichotomised as older or young based on each trial’s defined age cut-off. It also showed no difference in relative survival benefits, as presented in Supplementary Appendix Figure A3 (available in Age and Ageing online) (P = 0.19). Similarly, using trial-based age cut-offs, older and young patient subgroups presented no significant differences for analyses focused on OS or PFS endpoints (P = 0.59 and P = 0.72, respectively) (Supplementary Appendix Figures A4 and A5, available in Age and Ageing online). Further analyses based on the type of systemic treatment (targeted agent or chemotherapy) and specific cancer types also proved no subgroup differences. Therefore, all results from our secondary sensitivity analyses were congruent with the primary meta-analysis (Figure 2). Forest plots for all sensitivity analyses can be found in Supplementary Appendix Figures A3–A13, available in Age and Ageing online. Given the paucity of age-based toxicity and QOL data, these planned meta-analyses could not be performed. Discussion Our results suggest that the relative efficacy of novel oncology drugs is not associated with age for patients enroled in clinical trials leading to regulatory approval. Our primary analysis presents no differential relative survival benefit from novel oncology drugs for patient subgroups divided as <65 and ≥65 years. While it may appear there is a trend towards significance for this analysis (<65 HR of 0.61 and ≥65 HR of 0.65, P = 0.14), the numerical difference of 0.04 between HRs is not likely to be of clinical significance. Sensitivity meta-analyses further extend these findings when considering OS, PFS, specific cancer types and various systemic treatments, independently. A pooled analysis study investigating the treatment efficacy of adjuvant chemotherapy for resected colon cancer across various age-based subgroups presents comparable results to our analysis [26]. This study suggests that older and young patients derive a similar relative treatment benefit from adjuvant chemotherapy; as OS and freedom from tumour occurrence did not have significant interactions with age (P = 0.61 and P = 0.33, respectively) [26]. The findings of this study are supported by an observational cohort study investigating bevacizumab and chemotherapy for metastatic colorectal cancer [27]. Comparing OS and PFS between the studies’ older (≥70 years) and young (<70) subgroups, Tahover et al. [27] determined that all patients derived a similar relative survival benefit (P = 0.093 and P = 0.096), respectively. These findings are supported by various RCTs examining age-based survival benefits for specific cancer sites [12–19]. Our analysis further supports these results, while also serving to generalise these conclusions from specific cancer sites and drugs, across multiple indications and survival endpoints. Nearly 71% (N = 72) of studies included in our analysis defined ‘old’ and ‘young’ in accordance with the WHO, however, such classification is highly dependent on the cancer type investigated. For example, prostate cancer commonly occurs in older patients, thus there may not be an adequate number of patients below 65 years of age enroled in clinical trials for this indication. Whereas studies investigating acute lymphocytic leukaemia may be challenged with the opposite; that being a potentially limited number of patients above 65 years of age. As our study examines pooled data from various cancer types, there is heterogeneity amongst our older population when considering only those ≥65 years. Therefore, the results of our sensitivity analyses categorising patients based on each RCT’s own age cut-off, further support the conclusion of our primary analysis. As all patients on clinical trials considered ‘old’, by either definition, derived similar relative survival benefits to their younger counterparts. Moreover, while our study investigated RCTs’ age-based survival data, toxicity and QOL must also be considered when determining the net benefit of treatment. A more comprehensive review of the risks and benefits of treatment, beyond survival, may provide insight into the influence of novel oncology drugs on activities of daily living and cognition for older patients [5]. When investigating treatment toxicity and tolerability for older and young patients, Stein et al. [28] found that those ≥70 chronological years treated with 5-fluorouracil-based chemotherapy for advanced colorectal cancer, experienced a significant increase in the incidence of any severe (≥grade 3) toxicities (P < 0.001); such as leukopenia, diarrhoea and vomiting. While it was inconclusive if these results were directly correlated with the treatment regimen or a decrease in physiological reserve with age [28], it is critical to understand the relationship between advanced age and toxicity in order to accurately determine the net clinical benefit of treatment. Although novel oncology drugs are perceived to have less toxicity when compared to older drugs, potentially implying better tolerability, the physiological changes associated with ageing may pose a challenge to older patients. Grade 2 diarrhoea, for example, may severely impact the activities of daily living for an older patient by increasing the risk of dehydration and other various side effects [5]. QOL however, is a measure that is intrinsically different from toxicity, efficacy or symptoms. Involving multiple dimensions, including physical, social/family, emotional and functional well-being [29], as highlighted in the FACT-G QOL questionnaire, these measures ask patients to self-report their feelings in response to prompts that range in topic from ‘I have a lack of energy’, to ‘my family has accepted my illness’ and ‘I am enjoying the things I usually do for fun’ [29]. While older patients have complex considerations, often sourced from increased frailty with age, in the absence of data or literature, one cannot presume that all treatment effects, as measured in a QOL assessment, are associated with frailty. Toxicity and QOL sensitivity analyses would be beneficial to help establish the net clinical benefit of treatment for older patients. However, limited age-based toxicity results and the absence of age-based QOL results reported in the included RCTs unfortunately prevented meta-analyses of these endpoints from being conducted. This serves as a large gap in the current field of literature, as it is imperative to understand the balance of treatment benefits and harms, especially those that may greatly impact QOL for older patients. Our results are likely generalisable for older patients who are fit and well enough to fulfil eligibility criteria of clinical trials. However, in a systematic review investigating the inclusion of older patients in RCTs, 45.6% of the investigated studies contained inclusion criteria that commonly excluded older patients with complex considerations [30]; the presence of a physical or functional disability, cognitive impairment directly related to age and an unmanaged co-morbidity [30]. This may manifest as selection bias against the typically co-morbid older cancer patient, as RCTs are recruiting and offering novel oncology drugs only to those whom are fit and well enough to meet stringent inclusion criteria. Unfortunately, this subset of older patients may be excluded from our selected study population due to the nature of clinical trials; therefore, caution should be exercised when considering generalising our findings to this population. An additional limitation is that we did not contact individual authors for missing data or request individual patient-level data to conduct individual patient-level meta-analyses. Based on a recent review [31], the possibility of successfully obtaining complete records and individual patient-level data is low, as our studies included all approved oncology drugs involving many registration trials. In case, if a small proportion of investigators or pharmaceutical trial sponsors were to be willing to share their data, such group will likely be related to some degree of response bias, as authors’ willingness to respond and provide data will likely be related to whether their data reflects favourably in relation to our research questions. Also, real-world studies are needed to examine whether older patients who do not fulfil clinical trials’ criteria derive the same effectiveness of novel oncology drugs as their younger counterparts; providing necessary evidence to support discussions surrounding treatment options for individualised and complex cases. Conclusions The results from our systematic review and meta-analysis suggest that age does not appear to be associated with the relative efficacy of novel oncology drugs for patients who were enroled in clinical trials. Therefore, physicians may reasonably propose novel oncology drugs to older patients who fulfil clinical trial criteria in instances where no direct age-based evidence suggests otherwise. The impact of age on toxicity could not be discerned by our study. Thus, we recommend the reporting of age-based toxicity and QOL analyses in future RCTs, to aid clinicians in better understanding the impact of age on the overall net clinical benefit of novel oncology treatments. Key points Systematic review and meta-analysis of 102 RCTs, exploring treatment derived survival benefit for patients ≥65 and <65 years. Older patients derive similar survival benefits from novel oncology drugs when compared to their younger counterparts. Fit older patients should be presented with all reasonable treatment options, where no high-level evidence suggests otherwise. Need to report age-based toxicity analyses in future RCTs to accurately determine overall net clinical benefit of treatment. Funding The Canadian Centre for Applied Research in Cancer Control (ARCC) is funded by the Canadian Cancer Society Research Institute. Conflicts of interest None. References 1 Talarico L , Chen G , Pazdur R . 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For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Age and Ageing Oxford University Press

Do older and younger patients derive similar survival benefits from novel oncology drugs? A systematic review and meta-analysis

Age and Ageing , Volume 47 (5) – Sep 1, 2018

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Abstract

Abstract Background older patients are commonly believed to derive less benefit from cancer drugs, even if they fulfil clinical trial eligibility [Talarico et al. (2004, J Clin Oncol, 22(22):4626–31)]. We aim to examine if novel oncology drugs provide differential age-based treatment outcomes for patients on clinical trials. Methods a systematic review of randomised control trials (RCTs) cited for clinical efficacy evidence in novel oncology drug approvals by the Food and Drug Administration, European Medicines Agency and Health Canada between 2006 and 2017 was conducted. Studies reporting age-based subgroup analyses for overall or progression-free survival (OS/PFS) were included. Hazard ratios (HRs) and confidence intervals (CIs) for age-based subgroups were extracted. Meta-analyses with random effects were conducted, examining patient subgroups <65 and ≥65 years separately and pooled HRs of studies primary endpoints (OS or PFS) compared to examine if differences existed between age-based subgroups. Sensitivity analyses were conducted for cancer type, primary endpoint and systemic treatment. Results one-hundred-two RCTs, including 65,122 patients, met the inclusion criteria. One study reported age-based toxicity and none reported age-based quality of life (QOL) results. Pooled HRs [95% CIs] for patients <65 and ≥65 years were 0.61 [0.57–0.65] and 0.65 [0.61–0.70], respectively, with no difference between them (P = 0.14). Sensitivity analyses revealed similar results. Conclusion our results suggest that older and young patients, who fulfil clinical trial eligibility, may derive similar relative survival benefits from novel oncology drugs. There is, however, a need to report age-based toxicity and QOL results to support patient discussions regarding the balance of treatment benefit and harm, to encourage informed decision-making. geriatric, oncology, meta-analysis, survival, toxicity, older people, systematic review Introduction Despite the immense disease burden among older individuals, this population is generally an underserved group. While ~60% of cancers and 70% of cancer-related mortalities occur in patients 65 years or older [2], older patients’ unique considerations often pose as a barrier to treatment and may limit the potential benefit they derive. Multiple theoretical reasons why older patients may derive less benefit or more toxicity from specific treatments are presented in the literature. For example, the physiology of ageing may be associated with changes in cancer biology [3] and drug metabolism [4]. Reduced liver mass and blood flow may result in decreased first-pass metabolism and bioavailability in older patients [4]. Additional physiological changes, such as decreased intestinal absorption, hepatic metabolism and renal excretion [5], may alter the pharmacodynamics of drugs through changes in distribution and excretion [6]. Ultimately, this may increase the prevalence of high-grade toxicities and decrease the tolerance of low-grade toxicities in older patients [5], due to increased drug exposure [6]. Treatment-related toxicities, which greatly impact patients’ daily activities, more commonly result in dose reductions in older adults, lessening treatment efficacy due to reduced dose intensity [7]. Prescribing physicians often opt to modify patients’ doses early in an attempt to prevent future toxicity [8]. Such decisions may be based on demographics, co-morbidity and the nature of the prescribed regimen, amongst others [8]. In a recent secondary data analysis, Gajra et al. [8] determined that age alone was independently associated with a primary dose reduction for patients receiving chemotherapy with both curative and palliative intents. This population, however, is an extraordinarily heterogeneous group. While frailty is more common with age, the progression towards frailty is considerably variable across older individuals. When considering functional status alone, trials typically describe their patient population in terms of the Eastern Cooperative Oncology Group Scale of Performance Status (PS). A recent systematic review and meta-analysis conducted to determine if patients of lesser PS-derived differential treatment benefits from novel oncology drugs in comparison with their ‘excellent’ counterparts, determined there was indeed no difference between the groups [9]. PS, however, is a measure of only one aspect of frailty. In addition to the concerns about frailty, PS, potential toxicity and dose reduction, older patients are more likely to have co-morbidities and die from other causes. The increased likelihood of death from another cause may attenuate or even negate the ability of cancer-specific treatments to improve progression-free survival (PFS) or overall survival (OS) in this group. While some studies suggested there may be differential age-based survival benefits [10, 11], others presented opposing results [12–19]. Most literature, however, is focused on older drugs with substantial side effects commonly affecting older patients. To date, there has been limited literature systematically examining the effects of age on the benefits of novel oncology drugs in contemporary medicine. Molecular-targeted agents, for example, are believed to be associated with decreased toxicities when compared with older, less specific targeted agents and chemotherapy regimens [20]. The decreased toxicity profile of novel oncology treatments may suggest potentially improved tolerability and outcomes in older patients. Therefore, there is a need to systematically review the literature to determine if age is associated with the relative treatment-related efficacy and toxicity of novel oncology drugs. Such study can allow for the development of optimised medical care by better understanding the effects of older patients’ co-morbidities and drug tolerance on the net clinical benefit of treatment. Additionally, it may help clinicians determine if age should be a considerable factor when making treatment decisions for generally well and fit older patients. Thus, we aim to conduct a systematic review and meta-analysis to examine if novel oncology drugs provide differential treatment outcomes for older and young patients enroled in clinical trials. Methods Selection of studies Clinical trials between January 2006 and June 2017 from the Food and Drug Administration’s (FDA) Haematology/Oncology Approvals and Safety Notifications page [21], European Medicines Agency’s (EMA) Public Assessment Reports [22] and Health Canada’s (HC) Summary of Basis Decision documents [23] were reviewed. Primary publications cited for clinical efficacy evidence in novel oncology drug approvals, along with their corresponding appendices, were collected. As defined by our study, a drug was considered ‘novel’ if leading to regulatory approval by ≥1 of the FDA, EMA or HC within our defined collection period. Updated publications and companion studies were located using Web of Science and ClinicalTrials.gov/NCT numbers, to investigate if there was subsequent age-based toxicity or quality of life (QOL) data available. Single-arm studies were excluded, and randomised phase II or III clinical trials reporting OS and/or PFS as their primary or co-primary endpoint(s) were identified for inclusion. Studies reporting time-to-progression (TTP) as their primary endpoint were considered together with those investigating PFS. Studies that did not report age subgroup analyses in the primary publication or Supplementary Appendix, and those that did not administer chemotherapy or targeted agents as their experimental regimen were excluded. Data extraction and statistical analyses Age-based reported OS and PFS hazard ratios (HRs) and confidence intervals (CIs) were extracted by two independent reviewers. If age-based HRs and CIs were not reported numerically, values were extracted from forest plots and independently digitised by two reviewers using DigitizeIt Software (version 2.2, Germany). Any discrepancies or disagreements were resolved by consulting a third independent reviewer. Eligible randomised control trials (RCTs) were further assessed for the reporting of age-based toxicity and QOL analyses; if available, data was extracted. Updated publications were subsequently reviewed to determine the frequency of reporting age-based toxicity and QOL data. Meta-analyses based on an inverse variance random-effects model were performed and forest plots constructed using Review Manager 5.3 Software (Copenhagen, Denmark). The primary analysis was based on studies’ primary endpoints (OS/PFS), comparing patients <65 and ≥65 years of age. As each RCT established their own age cut-off to define ‘young’ versus ‘older’, only RCTs that reported age-based data using 65 years as their age cut-off was included in the ‘primary’ analysis. Our decision to select 65 years as the age cut-off, was based on the World Health Organization’s (WHO’s) definition of ‘elderly’ [24], and as a reflection of the included studies. Moreover, if a study reported OS and PFS as co-primary endpoints, the HR allotted a higher alpha value was included for analysis. Pooled HRs with 95% CIs were calculated for each subgroup and tested for subgroup differences between patients <65 and ≥65. Sensitivity analyses including all RCTs (i.e. those reporting age-based data using 65 years as their age cut-off, together with those that used various other age cut-offs) were subsequently conducted to examine the congruency of results with the primary analysis. For these analyses, patients were dichotomised into older versus young subgroups based on each RCT’s own age cut-off. That is, for each trial the youngest age group was taken to be ‘young’ and the oldest as ‘older’. If three or more groups were established, the oldest group was selected as ‘older’ and the youngest group as ‘young’. Sensitivity analyses categorising the study population by the type of primary endpoint (OS or PFS) and systemic treatment (chemotherapy or targeted agents), were also conducted. Assessment of the risk of bias was completed at the level of individual studies. Studies were evaluated for their use of; (1) selection bias (generation of a randomised sequence and concealment of group allocations), (2) performance bias (participants and study personals knowledge of group assignment), (3) detection bias (study personnel whom assess outcomes having knowledge of group assignment), (4) attrition bias (incomplete data and its management), (5) reporting bias (lack of data reporting for pre-specified endpoints) and (6) any other bias. Such biases were judged as low, unclear or high risk, and compiled using Review Manager 5.3 Software, and a risk of bias graph composed. Results Characteristics of included studies One-hundred-two RCTs, involving 65,122 patients, spanning Asia, Central, South, and North Americas, Europe, Middle East, Africa and Oceania (including Australia and New Zealand), reported age-based survival data and were thus included in our meta-analysis (study details may be found in Supplementary Appendix Table A1, available in Age and Ageing online). All identified RCTs were published in English, and common reasons for exclusion are described in Figure 1. Figure 1. View largeDownload slide PRISMA flow diagram; FDA, Food and Drug Administration; EMA, European Medicines Agency; HC, Health Canada; OS, overall survival; PFS, progression-free survival. Figure 1. View largeDownload slide PRISMA flow diagram; FDA, Food and Drug Administration; EMA, European Medicines Agency; HC, Health Canada; OS, overall survival; PFS, progression-free survival. Of the 102 included studies, 87 studies administered targeted agents and 15 studies administered chemotherapy as their experimental regimen. Experimental regimens included monoclonal antibodies (N = 24 studies), vascular endothelial growth factors (VEGF) inhibitors (N = 13 studies), tyrosine kinase inhibitors (N = 11 studies), multi-kinase inhibitors (N = 6 studies) and various others. Ninety-five (93.1%) were phase III trials, while six (5.9%) were phase II trials (one was a phase II/III trial). The majority of studies (N = 72, 70.6%), presented age-based subgroup analyses in accordance with WHO’s classification of older (65 chronological years or above). Three studies (2.9%) reported patient survival results as <60 versus ≥60 years, and three other studies (2.9%) as <75 versus ≥75 years. Five (4.9%) studies presented age-based results as three groups, divided as <65, ≥65–<75 and ≥75 years, and four (3.9%) studies divided as <60 years, 60–69 and 70–74 years. Fifteen (14.7%) studies divided their patients by various other age cut-offs (Table 1). When considering all included studies, the median age of patients randomised to the experimental arm spanned from 33 to 76 years, similar to that of the control (32–77 years). Table 1. Summary of study characteristics Studies 102 Patients 65,122 Characteristic Number (n) Percentage (%) Experimental drug type  Chemotherapy 15 14.7  Targeted agent 87 85.3 Type of cancer  Lung cancer 20 19.6  Gastric cancer 5 4.9  Colorectal cancer 9 8.8  Melanoma 7 6.9  Renal cell carcinoma 7 6.9  Multiple myeloma 9 8.8  Chronic lymphocytic leukaemia 7 6.9  Pancreatic neuroendocrine tumours 2 2.0  Thyroid cancer 4 3.9  Gastroesophageal adenocarcinoma 1 1.0  Prostate cancer 5 4.9  Other 26 25.5 Primary endpoint  OS 38 37.3  PFS 56 54.9  TTP 2 2.0  OS and PFS (co-primary) 6 5.9 Age subgroups  <60 and ≥60 3 2.9  <60, 60–69 and 70–74 4 3.9  <65 and ≥65 72 70.6  <65, ≥65-<75 and ≥75 5 4.9  <75 and ≥75 3 2.9  Other 15 14.7 Phase  II 6 5.9  II/III 1 1.0  III 95 93.1 Studies 102 Patients 65,122 Characteristic Number (n) Percentage (%) Experimental drug type  Chemotherapy 15 14.7  Targeted agent 87 85.3 Type of cancer  Lung cancer 20 19.6  Gastric cancer 5 4.9  Colorectal cancer 9 8.8  Melanoma 7 6.9  Renal cell carcinoma 7 6.9  Multiple myeloma 9 8.8  Chronic lymphocytic leukaemia 7 6.9  Pancreatic neuroendocrine tumours 2 2.0  Thyroid cancer 4 3.9  Gastroesophageal adenocarcinoma 1 1.0  Prostate cancer 5 4.9  Other 26 25.5 Primary endpoint  OS 38 37.3  PFS 56 54.9  TTP 2 2.0  OS and PFS (co-primary) 6 5.9 Age subgroups  <60 and ≥60 3 2.9  <60, 60–69 and 70–74 4 3.9  <65 and ≥65 72 70.6  <65, ≥65-<75 and ≥75 5 4.9  <75 and ≥75 3 2.9  Other 15 14.7 Phase  II 6 5.9  II/III 1 1.0  III 95 93.1 OS, overall survival; PFS, progression-free survival; TTP, time-to-progression. Table 1. Summary of study characteristics Studies 102 Patients 65,122 Characteristic Number (n) Percentage (%) Experimental drug type  Chemotherapy 15 14.7  Targeted agent 87 85.3 Type of cancer  Lung cancer 20 19.6  Gastric cancer 5 4.9  Colorectal cancer 9 8.8  Melanoma 7 6.9  Renal cell carcinoma 7 6.9  Multiple myeloma 9 8.8  Chronic lymphocytic leukaemia 7 6.9  Pancreatic neuroendocrine tumours 2 2.0  Thyroid cancer 4 3.9  Gastroesophageal adenocarcinoma 1 1.0  Prostate cancer 5 4.9  Other 26 25.5 Primary endpoint  OS 38 37.3  PFS 56 54.9  TTP 2 2.0  OS and PFS (co-primary) 6 5.9 Age subgroups  <60 and ≥60 3 2.9  <60, 60–69 and 70–74 4 3.9  <65 and ≥65 72 70.6  <65, ≥65-<75 and ≥75 5 4.9  <75 and ≥75 3 2.9  Other 15 14.7 Phase  II 6 5.9  II/III 1 1.0  III 95 93.1 Studies 102 Patients 65,122 Characteristic Number (n) Percentage (%) Experimental drug type  Chemotherapy 15 14.7  Targeted agent 87 85.3 Type of cancer  Lung cancer 20 19.6  Gastric cancer 5 4.9  Colorectal cancer 9 8.8  Melanoma 7 6.9  Renal cell carcinoma 7 6.9  Multiple myeloma 9 8.8  Chronic lymphocytic leukaemia 7 6.9  Pancreatic neuroendocrine tumours 2 2.0  Thyroid cancer 4 3.9  Gastroesophageal adenocarcinoma 1 1.0  Prostate cancer 5 4.9  Other 26 25.5 Primary endpoint  OS 38 37.3  PFS 56 54.9  TTP 2 2.0  OS and PFS (co-primary) 6 5.9 Age subgroups  <60 and ≥60 3 2.9  <60, 60–69 and 70–74 4 3.9  <65 and ≥65 72 70.6  <65, ≥65-<75 and ≥75 5 4.9  <75 and ≥75 3 2.9  Other 15 14.7 Phase  II 6 5.9  II/III 1 1.0  III 95 93.1 OS, overall survival; PFS, progression-free survival; TTP, time-to-progression. As demonstrated in Supplementary Appendix Figure A1 (available in Age and Ageing online), the risk of specific biases within all included studies were generally low. Toxicity and QOL subgroup reporting frequency Following the review of primary and updated publications, only one primary publication investigating chlorambucil plus ofatumumab for previously untreated chronic lymphocytic leukaemia [25], was found to present age-based toxicity results. This study reported that the incidence of all adverse events were comparable between patients considered older and young for those randomised to the experimental and control regimens [25], suggesting that older and young patients who fit this trial’s eligibility may experience similar relative toxicities. No studies were found to present age-based QOL results. Primary analysis Pooled HRs [95% CI] of studies primary endpoints (OS or PFS) for patients <65 and ≥65 were 0.61 [0.57–0.65] and 0.65 [0.61–0.70], respectively (Figure 2), with no survival difference between the two groups (P = 0.14). The forest plot for the primary analysis can be found in the Supplementary Appendix (Figure A2, available in Age and Ageing online). Figure 2. View largeDownload slide Subgroup analysis hazard ratios and test for subgroup differences based on a random effects model: <65 versus ≥65 subgroup analysis; all drugs analyses contains pooled HRs [95% CI] of studies primary endpoints (OS or PFS); OS, overall survival; PFS, progression-free survival; RCC, renal cell carcinoma; CLL, chronic lymphocytic leukaemia. Figure 2. View largeDownload slide Subgroup analysis hazard ratios and test for subgroup differences based on a random effects model: <65 versus ≥65 subgroup analysis; all drugs analyses contains pooled HRs [95% CI] of studies primary endpoints (OS or PFS); OS, overall survival; PFS, progression-free survival; RCC, renal cell carcinoma; CLL, chronic lymphocytic leukaemia. Sensitivity analyses For the endpoint of OS, pooled HRs [95% CI] for patients <65 and ≥65 were 0.77 [0.72–0.81] and 0.80 [0.75–0.86], respectively. For the endpoint of PFS, pooled HRs [95% CI] were 0.51 [0.47–0.56] and 0.54 [0.48–0.61], respectively. Tests for differences between patients <65 and ≥65 were not significant for OS or PFS (P = 0.37 for OS, P = 0.36 for PFS). A sensitivity analysis was conducted for all eligible RCTs with patients dichotomised as older or young based on each trial’s defined age cut-off. It also showed no difference in relative survival benefits, as presented in Supplementary Appendix Figure A3 (available in Age and Ageing online) (P = 0.19). Similarly, using trial-based age cut-offs, older and young patient subgroups presented no significant differences for analyses focused on OS or PFS endpoints (P = 0.59 and P = 0.72, respectively) (Supplementary Appendix Figures A4 and A5, available in Age and Ageing online). Further analyses based on the type of systemic treatment (targeted agent or chemotherapy) and specific cancer types also proved no subgroup differences. Therefore, all results from our secondary sensitivity analyses were congruent with the primary meta-analysis (Figure 2). Forest plots for all sensitivity analyses can be found in Supplementary Appendix Figures A3–A13, available in Age and Ageing online. Given the paucity of age-based toxicity and QOL data, these planned meta-analyses could not be performed. Discussion Our results suggest that the relative efficacy of novel oncology drugs is not associated with age for patients enroled in clinical trials leading to regulatory approval. Our primary analysis presents no differential relative survival benefit from novel oncology drugs for patient subgroups divided as <65 and ≥65 years. While it may appear there is a trend towards significance for this analysis (<65 HR of 0.61 and ≥65 HR of 0.65, P = 0.14), the numerical difference of 0.04 between HRs is not likely to be of clinical significance. Sensitivity meta-analyses further extend these findings when considering OS, PFS, specific cancer types and various systemic treatments, independently. A pooled analysis study investigating the treatment efficacy of adjuvant chemotherapy for resected colon cancer across various age-based subgroups presents comparable results to our analysis [26]. This study suggests that older and young patients derive a similar relative treatment benefit from adjuvant chemotherapy; as OS and freedom from tumour occurrence did not have significant interactions with age (P = 0.61 and P = 0.33, respectively) [26]. The findings of this study are supported by an observational cohort study investigating bevacizumab and chemotherapy for metastatic colorectal cancer [27]. Comparing OS and PFS between the studies’ older (≥70 years) and young (<70) subgroups, Tahover et al. [27] determined that all patients derived a similar relative survival benefit (P = 0.093 and P = 0.096), respectively. These findings are supported by various RCTs examining age-based survival benefits for specific cancer sites [12–19]. Our analysis further supports these results, while also serving to generalise these conclusions from specific cancer sites and drugs, across multiple indications and survival endpoints. Nearly 71% (N = 72) of studies included in our analysis defined ‘old’ and ‘young’ in accordance with the WHO, however, such classification is highly dependent on the cancer type investigated. For example, prostate cancer commonly occurs in older patients, thus there may not be an adequate number of patients below 65 years of age enroled in clinical trials for this indication. Whereas studies investigating acute lymphocytic leukaemia may be challenged with the opposite; that being a potentially limited number of patients above 65 years of age. As our study examines pooled data from various cancer types, there is heterogeneity amongst our older population when considering only those ≥65 years. Therefore, the results of our sensitivity analyses categorising patients based on each RCT’s own age cut-off, further support the conclusion of our primary analysis. As all patients on clinical trials considered ‘old’, by either definition, derived similar relative survival benefits to their younger counterparts. Moreover, while our study investigated RCTs’ age-based survival data, toxicity and QOL must also be considered when determining the net benefit of treatment. A more comprehensive review of the risks and benefits of treatment, beyond survival, may provide insight into the influence of novel oncology drugs on activities of daily living and cognition for older patients [5]. When investigating treatment toxicity and tolerability for older and young patients, Stein et al. [28] found that those ≥70 chronological years treated with 5-fluorouracil-based chemotherapy for advanced colorectal cancer, experienced a significant increase in the incidence of any severe (≥grade 3) toxicities (P < 0.001); such as leukopenia, diarrhoea and vomiting. While it was inconclusive if these results were directly correlated with the treatment regimen or a decrease in physiological reserve with age [28], it is critical to understand the relationship between advanced age and toxicity in order to accurately determine the net clinical benefit of treatment. Although novel oncology drugs are perceived to have less toxicity when compared to older drugs, potentially implying better tolerability, the physiological changes associated with ageing may pose a challenge to older patients. Grade 2 diarrhoea, for example, may severely impact the activities of daily living for an older patient by increasing the risk of dehydration and other various side effects [5]. QOL however, is a measure that is intrinsically different from toxicity, efficacy or symptoms. Involving multiple dimensions, including physical, social/family, emotional and functional well-being [29], as highlighted in the FACT-G QOL questionnaire, these measures ask patients to self-report their feelings in response to prompts that range in topic from ‘I have a lack of energy’, to ‘my family has accepted my illness’ and ‘I am enjoying the things I usually do for fun’ [29]. While older patients have complex considerations, often sourced from increased frailty with age, in the absence of data or literature, one cannot presume that all treatment effects, as measured in a QOL assessment, are associated with frailty. Toxicity and QOL sensitivity analyses would be beneficial to help establish the net clinical benefit of treatment for older patients. However, limited age-based toxicity results and the absence of age-based QOL results reported in the included RCTs unfortunately prevented meta-analyses of these endpoints from being conducted. This serves as a large gap in the current field of literature, as it is imperative to understand the balance of treatment benefits and harms, especially those that may greatly impact QOL for older patients. Our results are likely generalisable for older patients who are fit and well enough to fulfil eligibility criteria of clinical trials. However, in a systematic review investigating the inclusion of older patients in RCTs, 45.6% of the investigated studies contained inclusion criteria that commonly excluded older patients with complex considerations [30]; the presence of a physical or functional disability, cognitive impairment directly related to age and an unmanaged co-morbidity [30]. This may manifest as selection bias against the typically co-morbid older cancer patient, as RCTs are recruiting and offering novel oncology drugs only to those whom are fit and well enough to meet stringent inclusion criteria. Unfortunately, this subset of older patients may be excluded from our selected study population due to the nature of clinical trials; therefore, caution should be exercised when considering generalising our findings to this population. An additional limitation is that we did not contact individual authors for missing data or request individual patient-level data to conduct individual patient-level meta-analyses. Based on a recent review [31], the possibility of successfully obtaining complete records and individual patient-level data is low, as our studies included all approved oncology drugs involving many registration trials. In case, if a small proportion of investigators or pharmaceutical trial sponsors were to be willing to share their data, such group will likely be related to some degree of response bias, as authors’ willingness to respond and provide data will likely be related to whether their data reflects favourably in relation to our research questions. Also, real-world studies are needed to examine whether older patients who do not fulfil clinical trials’ criteria derive the same effectiveness of novel oncology drugs as their younger counterparts; providing necessary evidence to support discussions surrounding treatment options for individualised and complex cases. Conclusions The results from our systematic review and meta-analysis suggest that age does not appear to be associated with the relative efficacy of novel oncology drugs for patients who were enroled in clinical trials. Therefore, physicians may reasonably propose novel oncology drugs to older patients who fulfil clinical trial criteria in instances where no direct age-based evidence suggests otherwise. The impact of age on toxicity could not be discerned by our study. Thus, we recommend the reporting of age-based toxicity and QOL analyses in future RCTs, to aid clinicians in better understanding the impact of age on the overall net clinical benefit of novel oncology treatments. Key points Systematic review and meta-analysis of 102 RCTs, exploring treatment derived survival benefit for patients ≥65 and <65 years. Older patients derive similar survival benefits from novel oncology drugs when compared to their younger counterparts. Fit older patients should be presented with all reasonable treatment options, where no high-level evidence suggests otherwise. Need to report age-based toxicity analyses in future RCTs to accurately determine overall net clinical benefit of treatment. Funding The Canadian Centre for Applied Research in Cancer Control (ARCC) is funded by the Canadian Cancer Society Research Institute. Conflicts of interest None. References 1 Talarico L , Chen G , Pazdur R . 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For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Journal

Age and AgeingOxford University Press

Published: Sep 1, 2018

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