Challenges of estimating treatment effects after a positive interim analysis.

BACKGROUND: This research investigates why a beneficial treatment effect reported at the first interim analysis (IA) may diminish at a subsequent analysis (SA). We examined three challenges in interpreting treatment effects from randomized clinical trials (RCTs) after the first positive IA: overestimation bias; non-proportional hazards; and heterogeneity in recruitment. We investigate how a penalized estimation method can address overestimation bias, and discuss additional factors to consider when interpreting positive IA results. METHODS: We identified oncology RCTs reporting positive results at the initial IA and a SA for event-free (EFS) and overall survival (OS). We modeled: (1) the hazard ratio at IA (HRIA) versus its timing as measured by the information fraction (IF; i.e., events at IA versus total events sought); and (2), the ratio of HRIA to HRSA (rHR) versus the IF. This was repeated for HRIA adjusted for overestimation bias. Examples of the other two challenges were sought. RESULTS: Amongst 71 RCTs, HRIA were positively associated with the IF (slope: EFS 0.83, 95 % CI 0.44-1.22; OS 0.25, 95 % CI 0.10-0.41). HRIA tended to exaggerate HRSA, and more so the lower the IF (slope rHR versus IF: EFS 0.10, 95 % CI - 0.22 to 0.42; OS 0.26, 95 % CI 0.07-0.46). Adjusted HRIA did not exaggerate HRSA (slope rHR versus IF: EFS - 0.14, 95 % CI - 0.67 to 0.39; OS 0.02, 95 % CI - 0.26 to 0.30). Examples of two other challenges are shown. CONCLUSION: Overestimation bias, non-proportional hazards, and heterogeneity in recruitment and other important treatments should be considered when communicating estimates of treatment effects from positive IAs.

Validity and Efficiency of Progression-Free Survival-2 as a Surrogate End Point for Overall Survival in Advanced Cancer Randomized Trials.

PURPOSE: Progression-free survival (PFS)-2, defined as the time from randomization to progression on second-line therapy, is potentially a more reliable surrogate than PFS for overall survival (OS), but will require longer follow-up and a larger sample size. We sought to compare the validity and efficiency, defined as proportional increase in follow-up time and sample size, of PFS-2 to PFS. METHODS: We performed an electronic search to identify randomized trials of advanced solid tumors reporting PFS, PFS-2, and OS as prespecified end points. Only studies that had protocols that defined measurement of PFS-2 and follow-up for patients after first disease progression were included. We compared correlations in the relative treatment effect for OS with PFS and PFS-2. We reconstructed individual patient data from survival curves to estimate time to statistical significance (TSS) of the relative treatment effect. We further computed the sample size (person-year [PY] follow-up) required to reach statistical significance. RESULTS: Across the 42 analysis units and 21,255 patients, the correlation of the relative treatment effect between OS and PFS-2, r, was 0.70 (95% CI, 0.41 to 0.80) and r = 0.46 (95% CI, 0.26 to 0.74) for OS and PFS. The median differences in TSS between OS with PFS, OS with PFS-2, and PFS with PFS-2 were 16.59 (95% CI, 4.48 to not reached [NR]), 10.0 (95% CI, 2.2 to NR), and 4.31 (95% CI, 2.92 to 13.13) months, respectively. The median difference in PYs required to reach statistical significance for PFS-2 over PFS was 156 (95% CI, 82 to 500) PYs, equivalent to an estimated median 12.7% increase in PYs. CONCLUSION: PFS-2 offers improved correlation with OS than PFS with a modest increase in follow-up time and sample size. PFS-2 should be considered as a primary end point in future trials of advanced cancers.

Estimation of the treatment effect following a clinical trial that stopped early for benefit.

When a clinical trial stops early for benefit, the maximum likelihood estimate (MLE) of the treatment effect may be subject to overestimation bias. Several authors have proposed adjusting for this bias using the conditional MLE, which is obtained by conditioning on early stopping. However, this approach has a fundamental problem in that the adjusted estimate may not be in the direction of benefit, even though the study has stopped early due to benefit. In this paper, we address this problem by embedding both the MLE and the conditional MLE within a broader class of penalised likelihood estimates, and choosing a member of the class that is a favourable compromise between the two. This penalised MLE, and its associated confidence interval, always lie in the direction of benefit when the study stops early for benefit. We study its properties using both simulations and analyses of the ENZAMET trial in metastatic prostate cancer. Conditional on stopping early for benefit, the method is found to have good unbiasedness and coverage properties, along with very favourable efficiency at earlier interim analyses. We recommend the penalised MLE as a supplementary analysis to a conventional primary analysis when a clinical trial stops early for benefit.

Analysis of adaptive platform trials using a network approach.

BACKGROUND: Adaptive platform trials allow randomized controlled comparisons of multiple treatments using a common infrastructure and the flexibility to adapt key design features during the study. Nonetheless, they have been criticized due to the potential for time trends in the underlying risk level of the population. Such time trends lead to confounding between design features and risk level, which may introduce bias favoring one or more treatments. This is particularly true when experimental treatments are not all randomized during the same time period as the control, leading to the potential for bias from non-concurrent controls. METHODS: Two analysis methods addressing this bias are stratification and adjustment. Stratification uses only comparisons between treatment cohorts randomized during identical time periods and does not use non-concurrent randomizations. Adjustment uses a modeled analysis including time period adjustment, allowing all data to be used, even from periods without concurrent randomization. We show that these competing approaches may be embedded in a common framework using network meta-analysis principles. We interpret the stages between adaptations in a platform trial as separate fixed design trials. This allows platform trials to be viewed as networks of direct randomized comparisons and indirect non-randomized comparisons. Network meta-analysis methodology can be re-purposed to aggregate the total information from a platform trial and to transparently decompose this total information into direct randomized evidence and indirect non-randomized evidence. This allows sensitivity to indirect information to be assessed and the two analysis methods to be clearly compared. RESULTS: Simulations of platform trials were analyzed using a network approach implemented in the netmeta package in R. The results demonstrated bias of unadjusted methods in the presence of time trends in risk level. Adjustment and stratification were both unbiased when direct evidence and indirect evidence were consistent. Network tests of inconsistency may be used to diagnose inconsistency when it exists. In an illustrative network analysis of one of the treatment comparisons from the STAMPEDE platform trial in metastatic prostate cancer, indirect comparisons using non-concurrent controls were inconsistent with the information from direct randomized comparisons. This supports the primary analysis approach of STAMPEDE, which used only direct randomized comparisons. CONCLUSION: Network meta-analysis provides a natural methodology for analyzing the network of direct and indirect treatment comparisons from a platform trial. Such analyses provide transparent separation of direct and indirect evidence, allowing assessment of the impact of non-concurrent controls. We recommend time-stratified analysis of concurrently controlled comparisons for primary analyses, with time-adjusted analyses incorporating non-concurrent controls reserved for secondary analyses. However, regardless of which methodology is used, a network analysis provides a useful supplement to the primary analysis.

PD-L1 expression as a prognostic marker in patients treated with chemotherapy for metastatic non-small-cell lung cancer.

Background: In metastatic non-small-cell lung cancer (mNSCLC), PD-L1 expression is associated with benefit from immune checkpoint inhibitor (ICI) therapy. However, the significance of PD-L1 expression in chemotherapy-treated patients is uncertain. Methods: Using the chemotherapy control arm of first-line randomized trials, a meta-analysis of the association between efficacy outcomes and PD-L1 status was performed, stratified by assay due to inter-assay differences. Results: Across 12 trials and 4378 patients, overall survival (OS) was superior in high PD-L1 versus negative tumors and low versus negative according to 22C3/28-8 assays. When classified by SP142 assay, no significant difference in response or survival was seen between PD-L1 groups. Conclusion: In mNSCLC, high PD-L1-expressing tumors are associated with longer OS and improved objective rate when treated with chemotherapy. Inter-assay variability impacts outcome results.

Biomarkers are naturally occurring cancer traits that can predict certain events. PD-L1 expression is a biomarker used in advanced lung cancer to predict benefit from immunotherapy. However, the association between PD-L1expression and chemotherapy is unclear. The authors analyzed data from 14 large clinical trials and found that PD-L1 expression could also be used to define a type of lung cancer that responds better to chemotherapy.

Circulating Cystatin C Is an Independent Risk Marker for Cardiovascular Outcomes, Development of Renal Impairment, and Long-Term Mortality in Patients With Stable Coronary Heart Disease: The LIPID Study.

Background Elevated plasma cystatin C levels reflect reduced renal function and increased cardiovascular risk. Less is known about whether the increased risk persists long-term or is independent of renal function and other important biomarkers. Methods and Results Cystatin C and other biomarkers were measured at baseline (in 7863 patients) and 1 year later (in 6106 patients) in participants in the LIPID (Long-Term Intervention with Pravastatin in Ischemic Disease) study, who had a previous acute coronary syndrome. Outcomes were ascertained during the study (median follow-up, 6 years) and long-term (median follow-up, 16 years). Glomerular filtration rate (GFR) was estimated using Chronic Kidney Disease Epidemiology Collaboration equations (first GFR-creatinine, then GFR-creatinine-cystatin C). Over 6 years, in fully adjusted multivariable time-to-event models, with respect to the primary end point of coronary heart disease mortality or nonfatal myocardial infarction, for comparison of Quartile 4 versus 1 of baseline cystatin C, the hazard ratio was 1.37 (95% CI, 1.07-1.74; P=0.01), and for major cardiovascular events was 1.47 (95% CI, 1.19-1.82; P<0.001). Over 16 years, the association of baseline cystatin C with coronary heart disease, cardiovascular, and all-cause mortality persisted (each P<0.001) and remained significant after adjustment for estimated GFR-creatinine-cystatin C. Cystatin C also predicted the development of chronic kidney disease for 6 years (odds ratio, 6.61; 95% CI, 4.28-10.20) independently of estimated GFR-creatinine and other risk factors. However, this association was no longer significant after adjustment for estimated GFR-creatinine-cystatin C. Conclusions Cystatin C independently predicted major cardiovascular events, development of chronic kidney disease, and cardiovascular and all-cause mortality. Prediction of long-term mortality was independent of improved estimation of GFR. Registration URL: https://anzctr.org.au; Unique identifier: ACTRN12616000535471.

The validity of progression-free survival 2 as a surrogate trial end point for overall survival.

BACKGROUND: Overall survival (OS) is the gold-standard end point for oncology trials. However, the availability of multiple therapeutic options after progression and crossover to receive investigational agents confound and delay OS data maturation. Progression-free survival 2 (PFS-2), defined as the time from randomization to progression on first subsequent therapy, has been proposed as a surrogate for OS. Using a meta-analytic approach, the authors aimed to assess the association between OS and PFS-2 and compare this with progression-free survival 1 (PFS-1) and the objective response rate (ORR). METHODS: An electronic literature search was performed to identify randomized trials of systemic therapies in advanced solid tumors that reported PFS-2 as a prespecified end point. Correlations between OS and PFS-2, OS and PFS-1, and OS and ORR as hazard ratios (HRs) or odds ratios (ORs) were assessed via linear regression weighted by trial size. RESULTS: Thirty-eight trials were included, and they comprised 19,031 patients across 8 tumor types. PFS-2 displayed a moderate correlation with OS (r = 0.67; 95% confidence interval [CI], 0.08-0.69). Conversely, correlations of ORR (r = 0.12; 95% CI, 0.00-0.13) and PFS-1 (r = 0.21; 95% CI, 0.00-0.33) were poor. The findings for PFS-2 were consistent for subgroup analyses by treatment type (immunotherapy vs nonimmunotherapy: r = 0.67 vs 0.67), survival post progression (<12 vs ≥12 months: r = 0.86 vs 0.79), and percentage not receiving subsequent treatment (<50% vs ≥50%: r = 0.70 vs 0.63). CONCLUSIONS: Across diverse tumors and therapies, the treatment effect on PFS-2 correlated moderately with the treatment effect on OS. PFS-2 performed consistently better than PFS-1 and ORR, regardless of postprogression treatment and postprogression survival. PFS-2 should be included as a key trial end point in future randomized trials of solid tumors.

Pre- and on-treatment lactate dehydrogenase as a prognostic and predictive biomarker in advanced non-small cell lung cancer.

BACKGROUND: The survival outcomes of patients with advanced non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs) are variable. This study investigated whether pre- and on-treatment lactate dehydrogenase (LDH) could better prognosticate and select patients for ICI therapy. METHODS: Using data from the POPLAR and OAK trials of atezolizumab versus docetaxel in previously treated advanced NSCLC, the authors assessed the prognostic and predictive value of pretreatment LDH (less than or equal to vs greater than the upper limit of normal). They further examined changes in on-treatment LDH by performing landmark analyses and estimated overall survival (OS) distributions according to the LDH level stratified by the response category (complete response [CR]/partial response [PR] vs stable disease [SD]). They repeated pretreatment analyses in subgroups defined by the programmed death ligand 1 (PD-L1) status. RESULTS: This study included 1327 patients with available pretreatment LDH. Elevated pretreatment LDH was associated with an adverse prognosis regardless of treatment (hazard ratio [HR] for atezolizumab OS, 1.49; P = .0001; HR for docetaxel OS, 1.30; P = .004; P for treatment by LDH interaction = .28). Findings for elevated pretreatment LDH were similar for patients with positive PD-L1 expression treated with atezolizumab. Persistently elevated on-treatment LDH was associated with a 1.3- to 2.8-fold increased risk of death at weeks 6, 12, 18, and 24 regardless of treatment. Elevated LDH at 6 weeks was associated with significantly shorter OS regardless of radiological response (HR for CR/PR, 2.10; P = .04; HR for SD, 1.50; P < .01), with similar findings observed at 12 weeks. CONCLUSIONS: In previously treated advanced NSCLC, elevated pretreatment LDH is an independent adverse prognostic marker. There is no evidence that pretreatment LDH predicts ICI benefit. Persistently elevated on-treatment LDH is associated with worse OS despite radiologic response.

Incorporating circulating tumor DNA detection to radiographic assessment for treatment response in advanced EGFR-mutant lung cancer.

PURPOSE: Response Evaluation Criteria in Solid Tumors (RECIST) has limitations but remains the conventional approach for tumor assessments. We explored whether circulating tumor DNA (ctDNA) can be incorporated into RECIST to provide a more robust measure of tumor response in advanced EGFR-mutant NSCLC. PATIENTS AND METHODS: In FASTACT-2, patients with advanced NSCLC received platinum/gemcitabine intercalated with erlotinib or placebo. EGFR mutation (tumor and plasma ctDNA) was detected using cobas v2. Patients selected for this hypothesis-generating analysis had EGFR mutations (on either tumor or plasma) at baseline and evaluable week 8 plasma EGFR. Week 8 ctDNA and radiologic response status were correlated with survival using landmark cox regression analyses. RESULTS: Of the original 451 patients, 86 (19.1%) were eligible for this analysis. 73% (n = 63) had detectable ctDNA at baseline. At week 8, 40% (n = 34) had radiologic partial response (PR), 60% (n = 52) had stable disease (SD); 80% (n = 69) had a ctDNA response (undetectable ctDNA). In patients who had initial PR and undetectable ctDNA, 93% (28/30) had ongoing PR subsequently at week 16. The median duration of response was 14.9 months. In patients with SD and undetectable ctDNA at week 8, 28% had radiological PR at week 16. Amongst those with PR at week 8, survival outcomes for those with undetectable vs detectable ctDNA were not statistically significant (PFS HR 0.49, 95%CI 0.16-1.48, p = 0.21; OS HR 0.39, 95%CI 0.13-1.19, p = 0.10). Amongst those with SD at week 8, there was significantly longer survival for those with undetectable vs detectable ctDNA (PFS HR 0.27, 95% CI 0.13-0.57, p < 0.0001; OS HR 0.40, 95% CI 0.20-0.80, p = 0.009). CONCLUSION: In patients with SD, undetectable ctDNA at week 8 correlated with survival improvement. Both radiologic and ctDNA responses are prognostic of PFS. Incorporation of ctDNA with RECIST may improve tumor response assessment in EGFR-mutant NSCLC.

Penalized likelihood estimation of the proportional hazards model for survival data with interval censoring.

This paper considers the problem of semi-parametric proportional hazards model fitting where observed survival times contain event times and also interval, left and right censoring times. Although this is not a new topic, many existing methods suffer from poor computational performance. In this paper, we adopt a more versatile penalized likelihood method to estimate the baseline hazard and the regression coefficients simultaneously. The baseline hazard is approximated using basis functions such as M-splines. A penalty is introduced to regularize the baseline hazard estimate and also to ease dependence of the estimates on the knots of the basis functions. We propose a Newton-MI (multiplicative iterative) algorithm to fit this model. We also present novel asymptotic properties of our estimates, allowing for the possibility that some parameters of the approximate baseline hazard may lie on the parameter space boundary. Comparisons of our method against other similar approaches are made through an intensive simulation study. Results demonstrate that our method is very stable and encounters virtually no numerical issues. A real data application involving melanoma recurrence is presented and an R package 'survivalMPL' implementing the method is available on R CRAN.

Tumor Response End Points as Surrogates for Overall Survival in Immune Checkpoint Inhibitor Trials: A Systematic Review and Meta-Analysis.

PURPOSE: Many immune checkpoint inhibitors (ICIs) have been approved on the basis of tumor response end points in nonrandomized trials, including objective response rate (ORR) and duration and depth of response. We aimed to assess the validity of these end points as surrogate end points for overall survival (OS) in patients with advanced solid tumors treated with ICIs at trial and treatment arm levels. METHODS: ICI trials in advanced solid cancers published between January 1, 2000, and March 31, 2020, were included. Correlations between ORR, durable response (DR) of ≥ 6 months, complete response (CR), and OS were assessed for treatment comparisons (trial-level) and for patients receiving ICI (arm-level), using weighted linear regression. RESULTS: Sixty-three trials were eligible, including 58 randomized controlled trials and 20 nonrandomized controlled trials (78 ICI arms and 30,815 patients). The majority were phase III (63%), and OS was the most common primary end point (40%). In relative treatment comparisons, correlations between ORR risk ratio and OS hazard ratio (HR), 6-month DR ratio and OS HR, and CR ratio and OS HR were r = 0.58, r = 0.62, and r = 0.42, respectively. Exploratory studies in melanoma, non-small-cell lung cancer, and other tumors showed similar results, although 6-month DR ratio was strongly correlated with OS HR (r = 0.89). Within ICI arms only, correlations between ORR and 12-month OS, 6-month DR and 12-month OS, and CR and 12-month OS were r = 0.76, r = 0.84, and r = 0.50, respectively, in all eligible trials. CONCLUSION: Relative measures of tumor response (ORR, 6-month DR, and CR) are poor surrogate end points for OS in ICI studies. However, ORR and 6-month DR are prognostic of 12-month OS in ICI studies supporting their use for screening activity of novel agents in early-phase nonrandomized trials.

Using recurrent time-to-event models with multinomial outcomes to generate toxicity profiles.

Most clinical studies, which investigate the impact of therapy simultaneously, record the frequency of adverse events in order to monitor safety of the intervention. Study reports typically summarise adverse event data by tabulating the frequencies of the worst grade experienced but provide no details of the temporal profiles of specific types of adverse events. Such 'toxicity profiles' are potentially important tools in disease management and in the assessment of newer therapies including targeted treatments and immunotherapy where different types of toxicity may be more common at various times during long-term drug exposure. Toxicity profiles of commonly experienced adverse events occurring due to exposure to long-term treatment could assist in evaluating the costs of the health care benefits of therapy. We show how to generate toxicity profiles using an adaptation of the ordinal time-to-event model comprising of a two-step process, involving estimation of the multinomial response probabilities using multinomial logistic regression and combining these with recurrent time to event hazard estimates to produce cumulative event probabilities for each of the multinomial adverse event response categories. Such a model permits the simultaneous assessment of the risk of events over time and provides cumulative risk probabilities for each type of adverse event response. The method can be applied more generally by using different models to estimate outcome/response probabilities. The method is illustrated by developing toxicity profiles for three distinct types of adverse events associated with two treatment regimens for patients with advanced breast cancer.

Recurrent time-to-event models with ordinal outcomes.

A model to accommodate time-to-event ordinal outcomes was proposed by Berridge and Whitehead. Very few studies have adopted this approach, despite its appeal in incorporating several ordered categories of event outcome. More recently, there has been increased interest in utilizing recurrent events to analyze practical endpoints in the study of disease history and to help quantify the changing pattern of disease over time. For example, in studies of heart failure, the analysis of a single fatal event no longer provides sufficient clinical information to manage the disease. Similarly, the grade/frequency/severity of adverse events may be more important than simply prolonged survival in studies of toxic therapies in oncology. We propose an extension of the ordinal time-to-event model to allow for multiple/recurrent events in the case of marginal models (where all subjects are at risk for each recurrence, irrespective of whether they have experienced previous recurrences) and conditional models (subjects are at risk of a recurrence only if they have experienced a previous recurrence). These models rely on marginal and conditional estimates of the instantaneous baseline hazard and provide estimates of the probabilities of an event of each severity for each recurrence over time. We outline how confidence intervals for these probabilities can be constructed and illustrate how to fit these models and provide examples of the methods, together with an interpretation of the results.

Validation of Progression-Free Survival Rate at 6 Months and Objective Response for Estimating Overall Survival in Immune Checkpoint Inhibitor Trials: A Systematic Review and Meta-analysis.

Importance: Progression-free survival (PFS) rate at 6 months has been proposed as a potential surrogate for overall survival (OS) rate at 12 months for immune checkpoint inhibitor (ICI) trials but requires further assessment for validation. Objective: To validate 6-month PFS and objective response rate (ORR) as estimators of 12-month OS in the ICI arms of randomized clinical trials (RCTs). Data Sources: Electronic databases (Medline, EMBASE, and the Cochrane Central Register of Controlled Trials) were searched for ICI RCTs published between January 2000 and June 2019. Study Selection: Eligible studies were phase 2 and phase 3 ICI RCTs in advanced solid cancers that reported ORR, PFS, and OS. A total of 99 articles (from 60 studies) of 2502 articles were selected by consensus. Data Extraction and Synthesis: Data were screened and extracted independently. Estimation models for 12-month OS and to assess correlation coefficient between end points were developed using linear regression. Data were extracted in July 2019, and analyses were conducted in September 2019. This study is reported following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Main Outcomes and Measures: Validation of previously reported 6-month PFS and ORR estimation models for 12-month OS using contemporary RCTs. Calibration of 6-month PFS and ORR model-estimated vs observed 12-month OS in ICI arms were assessed by correlation coefficient (r) and weighted Brier scores. Secondary analyses were performed for subgroups (ie, ICI-only, ICI-combination, line of therapy, programmed cell death 1 ligand 1 selected, and unselected). Results: Data from 60 RCTs with 74 experimental ICI arms were used. The development data set included 25 arms from studies published January 2000 to January 2017. The estimation model for 12-month OS using 6-month PFS was: (1.06 × PFS6) + 0.16 + (0.04 × melanoma) - (0.03 × NSCLC) + (0 × other tumors), in which PFS6 indicates 6-month PFS and NSCLC indicates non-small cell lung cancer. The estimation model for 12-month OS using ORR was (0.15 × ORR) + 0.52 + (0 × melanoma) - (0.02 × NSCLC) - (0.01 × other tumors). A total of 49 arms from studies published after January 2017 to June 2019 formed the validation data set. When the models were applied on the validation data set, calibration between the 6-month PFS model estimated vs observed 12-month OS was good (r = 0.89; Brier score, 0.008), but poor for the ORR model (r = 0.47; Brier score, 0.03). Findings were similar across all subgroups. Conclusions and Relevance: The findings of this study suggest that the estimation model using 6-month PFS could reliably estimate 12-month OS in ICI trials. This study could assist in better selection and prioritization of ICI agents for testing in RCTs based on phase 2 single-arm RCT results.

Benchmarking single-arm studies against historical controls from non-small cell lung cancer trials - an empirical analysis of bias.

Background: Recent trials of novel agents in 'rare' molecular subtypes of non-small cell lung cancer (NSCLC) have used single-arm trial designs and benchmarked outcomes against historical controls. We assessed the consistency of historical control outcomes using docetaxel data from published NSCLC randomized controlled trials (RCTs).Material and methods: Advanced NSCLC RCTs including a docetaxel monotherapy arm were included. Heterogeneity in tumor objective response rates (ORRs), progression-free survival (PFS) and overall survival (OS), and correlations between outcomes and year of trial commencement were assessed.Results: Among 63 trials (N = 10,633) conducted between 2000 and 2017, ORR ranged from 0% to 26% (I2 = 76.1%, pheterogeneity < .0001). Mean of the median PFS was 3.0 months (range: 1.4-6.4), 3-month PFS ranged from 25% to 85% (I2 = 86.0%, pheterogeneity < .0001). Mean of the median OS was 9.1 months (range: 4.7-22.9), 9-month OS ranged from 23% to 79% (I2 = 83.0%, pheterogeneity < .0001). Each later year of trial commencement was associated with 0.3% (p = .046), 0.5% (p = .11) and 0.9% (p = .001) improvement in ORR, 3-month PFS and 9-month OS rates, respectively.Conclusions: There was significant heterogeneity and an improving trend in docetaxel outcomes across trials conducted over 20 years. Benchmarking biomarker-targeted agents against historical controls may not be a valid approach to replace RCTs. Innovative study designs involving a concurrent control arm should be considered.

Efficacy of immune checkpoint inhibitors in older adults with advanced stage cancers: A meta-analysis.

OBJECTIVES: There is uncertainty whether older patients derive a similar benefit from immune checkpoint inhibitors (ICI) as younger patients. We performed a meta-analysis of ICI trials in advanced cancers to better estimate treatment benefit in the older population. MATERIALS AND METHODS: We performed an electronic search for randomized trials of ICI, either as monotherapy or in combination with other agents. Hazard ratios (HR) for subgroups defined by different age cut-offs were extracted. Pooled overall survival (OS) treatment estimates were calculated using the inverse variance weighted method. RESULTS: In nineteen trials comparing ICI monotherapy versus non-ICI treatment, there was no significant treatment-age interaction (age ≥ 65 years: N = 6064, HR 0.73; age < 65 years: N = 7250, HR 0.79; P-interaction = 0.27). Findings were similar at older age cut-offs of 70 years (age ≥ 70 years: N = 433, HR = 0.93; age < 70 years: N = 169, HR = 0.95; P-interaction = 0.91) and 75 years (age ≥ 75 years: N = 139, HR = 0.75; age < 75 years: N = 1133, HR = 0.61; P-interaction = 0.72) respectively, and for trials of ICI combination therapy. CONCLUSION: ICI therapy improves OS in both younger and older patients with advanced cancers, and the magnitude of improvement does not depend on age. Patient selection for ICI therapy should be done based on performance status and adequate organ function independently of age.

Evaluation of Phase II Trial Design in Advanced Pancreatic Cancer.

OBJECTIVES: We evaluated how well phase II trials in locally advanced and metastatic pancreatic cancer (LAMPC) meet current recommendations for trial design. METHODS: We conducted a systematic review of phase II first-line treatment trial for LAMPC. We assessed baseline characteristics, type of comparison, and primary end point to examine adherence to the National Cancer Institute recommendations for trial design. RESULTS: We identified 148 studies (180 treatment arms, 7505 participants). Forty-seven (32%) studies adhered to none of the 5 evaluated National Cancer Institute recommendations, 62 (42%) followed 1, 31 (21%) followed 2, and 8 (5%) followed 3 recommendations. Studies varied with respect to the proportion of patients with good performance status (range, 0%-80%) and locally advanced disease (range, 14%-100%). Eighty-two (55%) studies concluded that investigational agents should progress to phase III testing; of these, 24 (16%) had documented phase III trials. Three (8%) phase III trials demonstrated clinically meaningful improvements for investigational agents. One of 38 phase II trials that investigated biological investigational agents was enriched for a biomarker. CONCLUSIONS: Phase II trials do not conform well to current recommendations for trial design in LAMPC.

Predictive value of PD-L1 and other clinical factors for chemoimmunotherapy in advanced non-small-cell lung cancer.

We investigate if PD-L1 expression and other clinical characteristics predict chemoimmunotherapy (CIT) benefits versus chemotherapy in advanced non-small-cell lung cancer. We performed a meta-analysis of randomized controlled trials of CIT versus chemotherapy identified through electronic searches. In seven randomized controlled trials (n = 4170), CIT prolonged progression-free survival over chemotherapy (hazard ratio [HR]: 0.62; 95% CI: 0.58-0.67; p < 0.00001). The treatment benefits differed between PD-L1-high (HR: 0.41; 95% CI: 0.34-0.49) and PD-L1 low (HR: 0.63; 95% CI: 0.55-0.72; interaction-p = 0.00002) and PD-L1-high and PD-L1-negative (HR: 0.72; 95% CI: 0.65-0.80; interaction-p < 0.00001). Similar benefits were observed regardless of gender, EGFR/ALK status and histological subtype. PD-L1 status is predictive of CIT benefit and may assist patient selection and design of future trials.

Progression-free survival as a surrogate endpoint for overall survival in modern ovarian cancer trials: a meta-analysis.

BACKGROUND: Progression-free survival (PFS) has been adopted as the primary endpoint in many randomized controlled trials, and can be determined much earlier than overall survival (OS). We investigated whether PFS is a good surrogate endpoint for OS in trials of first-line treatment for epithelial ovarian cancer (EOC), and whether this relationship has changed with the introduction of new treatment types. METHODS: In a meta-analysis, we identified summary data [hazard ratio (HR) and median time] from published randomized controlled trials. Linear regression was used to assess the association between treatment effects on PFS and OS overall, and for subgroups defined by treatment type, postprogression survival (PPS) and established prognostic factors. RESULTS: Correlation between HRs for PFS and OS, in 26 trials with 30 treatment comparisons comprising 24,870 patients, was modest (r2 = 0.52, weighted by trial sample size). The correlation diminished with recency: preplatinum/paclitaxel era, r2= 0.66; platinum/paclitaxel, r2= 0.44; triplet combinations, r2= 0.22; biologicals, r2= 0.30. The median PPS increased over time for the experimental (Ptrend = 0.03) and control arms (Ptrend = 0.003). The difference in median PPS between treatment arms strongly correlated with the difference in median OS (r2 = 0.83). In trials where the control therapy had median PPS of less than 18 months, correlation between PFS and OS was stronger (r2 = 0.64) than where the median PPS was longer (r2 = 0.48). CONCLUSIONS: In EOC, correlation in the relative treatment effect between PFS and OS in first-line platinum-based chemotherapy randomized controlled trials is moderate and has weakened with increasing availability of effective salvage therapies.