Hazard ratios in cancer clinical trials--a primer

Elucidating Cancer Subtypes by Using the Relationship between DNA Methylation and Gene Expression

Advancements in the field of next generation sequencing (NGS) have generated vast amounts of data for the same set of subjects. The challenge that arises is how to combine and reconcile results from different omics studies, such as epigenome and transcriptome, to improve the classification of disease subtypes. In this study, we introduce sCClust (sparse canonical correlation analysis with clustering), a technique to combine high-dimensional omics data using sparse canonical correlation analysis (sCCA), such that the correlation between datasets is maximized. This stage is followed by clustering the integrated data in a lower-dimensional space. We apply sCClust to gene expression and DNA methylation data for three cancer genomics datasets from the Cancer Genome Atlas (TCGA) to distinguish between underlying subtypes. We evaluate the identified subtypes using Kaplan-Meier plots and hazard ratio analysis on the three types of cancer-GBM (glioblastoma multiform), lung cancer and colon cancer. Comparison with subtypes identified by both single- and multi-omics studies implies improved clinical association. We also perform pathway over-representation analysis in order to identify up-regulated and down-regulated genes as tentative drug targets. The main goal of the paper is twofold: the integration of epigenomic and transcriptomic datasets followed by elucidating subtypes in the latent space. The significance of this study lies in the enhanced categorization of cancer data, which is crucial to precision medicine.

Communicating and understanding statistical measures when quantifying the between-group difference in competing risks

Competing risks issues are common in clinical trials and epidemiological studies for patients in follow-up who may experience a variety of possible outcomes. Under such competing risks, two hazard-based statistical methods, cause-specific hazard (CSH) and subdistribution hazard (SDH), are frequently used to assess treatment effects among groups. However, the outcomes of the CSH-based and SDH-based methods have a close connection with the proportional hazards (CSH or SDH) assumption and may have an non-intuitive interpretation. Recently, restricted mean time lost (RMTL) has been used as an alternative summary measure for analysing competing risks, due to its clinical interpretability and robustness to the proportional hazards assumption. Considering the above approaches, we summarize the differences between hazard-based and RMTL-based methods from the aspects of practical interpretation, proportional hazards model assumption and the selection of restricted time points, and propose corresponding suggestions for the analysis of between-group differences under competing risks. Moreover, an R package 'cRMTL' and corresponding step-by-step guidance are available to help users for applying these approaches.

A comparison of different population-level summary measures for randomised trials with time-to-event outcomes, with a focus on non-inferiority trials

BACKGROUND

The population-level summary measure is a key component of the estimand for clinical trials with time-to-event outcomes. This is particularly the case for non-inferiority trials, because different summary measures imply different null hypotheses. Most trials are designed using the hazard ratio as summary measure, but recent studies suggested that the difference in restricted mean survival time might be more powerful, at least in certain situations. In a recent letter, we conjectured that differences between summary measures can be explained using the concept of the non-inferiority frontier and that for a fair simulation comparison of summary measures, the same analysis methods, making the same assumptions, should be used to estimate different summary measures. The aim of this article is to make such a comparison between three commonly used summary measures: hazard ratio, difference in restricted mean survival time and difference in survival at a fixed time point. In addition, we aim to investigate the impact of using an analysis method that assumes proportional hazards on the operating characteristics of a trial designed with any of the three summary measures.

METHODS

We conduct a simulation study in the proportional hazards setting. We estimate difference in restricted mean survival time and difference in survival non-parametrically, without assuming proportional hazards. We also estimate all three measures parametrically, using flexible survival regression, under the proportional hazards assumption.

RESULTS

Comparing the hazard ratio assuming proportional hazards with the other summary measures not assuming proportional hazards, relative performance varies substantially depending on the specific scenario. Fixing the summary measure, assuming proportional hazards always leads to substantial power gains compared to using non-parametric methods. Fixing the modelling approach to flexible parametric regression assuming proportional hazards, difference in restricted mean survival time is most often the most powerful summary measure among those considered.

CONCLUSION

When the hazards are likely to be approximately proportional, reflecting this in the analysis can lead to large gains in power for difference in restricted mean survival time and difference in survival. The choice of summary measure for a non-inferiority trial with time-to-event outcomes should be made on clinical grounds; when any of the three summary measures discussed here is equally justifiable, difference in restricted mean survival time is most often associated with the most powerful test, on the condition that it is estimated under proportional hazards.

Stemness genes and miR-1247-3p expression associate with clinicopathological parameters and prognosis in lung adenocarcinoma

Lung cancer makes up one-fourth of all cancer-related mortality with the highest mortality rate among all cancers. Despite recent scientific advancements in cancer therapeutics, the 5-year survival rate of lung adenocarcinoma (LUAD) cancer patients remains below 15 percent. It has been suggested that the high mortality rate of LUAD is linked to the acquisition of progenitor-like cells with stem-like characteristics that assist the whole tumor in regulating immune cell infiltration. To examine this hypothesis further, this study mined several databases to explore the presence of stemness-related genes and miRNAs in LUAD cancers. We examine their association with immune and accessory cell infiltration rates and patient survival. We found 3 stem cell-related genes, ORC1L, KIF20A, and DLGAP5, present in LUAD that also correlate with changes in immune infiltration rates and reduced patient survival rates. Additionally, the modulation in myeloid-derived suppressor cell (MDSC) infiltration and miRNA hsa-mir-1247-3p mediated targeting of tumor suppressor SLC24A4 and oncogenes RAB3B and HJURP appears to primarily regulate LUAD patient survival. Given these findings, hsa-mir-1247-3p and/or its associated gene targets may offer a promising avenue to enhance patient survivability.

A Dynamic Prediction Model Supporting Individual Life Expectancy Prediction Based on Longitudinal Time-Dependent Covariates

In the field of clinical chronic diseases, common prediction results (such as survival rate) and effect size hazard ratio (HR) are relative indicators, resulting in more abstract information. However, clinicians and patients are more interested in simple and intuitive concepts of (survival) time, such as how long a patient may live or how much longer a patient in a treatment group will live. In addition, due to the long follow-up time, resulting in generation of longitudinal time-dependent covariate information, patients are interested in how long they will survive at each follow-up visit. In this study, based on a time scale indicator-restricted mean survival time (RMST)-we proposed a dynamic RMST prediction model by considering longitudinal time-dependent covariates and utilizing joint model techniques. The model can describe the change trajectory of longitudinal time-dependent covariates and predict the average survival times of patients at different time points (such as follow-up visits). Simulation studies through Monte Carlo cross-validation showed that the dynamic RMST prediction model was superior to the static RMST model. In addition, the dynamic RMST prediction model was applied to a primary biliary cirrhosis (PBC) population to dynamically predict the average survival times of the patients, and the average C-index of the internal validation of the model reached 0.81, which was better than that of the static RMST regression. Therefore, the proposed dynamic RMST prediction model has better performance in prediction and can provide a scientific basis for clinicians and patients to make clinical decisions.

Euclidean distance-based Raman spectroscopy (EDRS) for the prognosis analysis of gastric cancer: A solution to tumor heterogeneity

The prognosis analysis of gastric cancer is critical for selection of treatments and development of advanced therapeutic methods. A prognosis approach that is accurate, fast, convenient, and of low cost for gastric cancers is in high demand. Raman spectroscopy is a label-free and non-destructive technique to provide molecular fingerprints of biological samples, holding promises for cancer prognosis. However, the major challenge of gastric cancer prognosis lies in the widely existing tumor heterogeneity, which leads to unexpected spectral variations within one type of samples. In this work, we have developed the Euclidean distance (ED)-based Raman spectroscopy (EDRS) method for the prognosis analysis of gastric cancer to eliminate the influence of tumor heterogeneity. Raman spectra were first collected on the slices of paraffin-preserved tumor tissues from gastric cancer patients. A standard spectrum to represent the 'worst prognostic tumor cells' was then established. The similarity between each spectrum of tissues and the standard spectrum was assessed by ED, to provide a direct assessment on the prognosis status. We have successfully classified the patients into poor and favorable prognosis groups, either based on the averaged regional ED values (sensitivity of 75 %, specificity of 96.8 %), or based on the minimal ED values at the patient level (sensitivity of 90 %, specificity of 100 %). EDRS was also investigated for survival analysis (AUC = 0.955), much better than the commonly applied post-neoadjuvant therapy (ypTNM) category (AUC = 0.718). Our work highlights EDRS as a rapid, accurate, low-cost and robust tool for heterogeneous cancer-related prognosis assessment and survival prediction, providing new insights for spectroscopic tumor analysis.

Investigations of methods for multiple time-to-event endpoints: A chronic myeloid leukemia data analysis

BACKGROUND

In randomized controlled trials, multiple time-to-event endpoints are commonly used to determine treatment effects. However, choosing an appropriate method to address multiple endpoints, according to different purposes of clinical practice, is a challenge for researchers.

METHODS

We applied single endpoint, composite endpoint and win ratio analysis to chronic myeloid leukemia (CML) data to illustrate the distinctions with different multiple endpoints, including relapse, recovery and death after transplantation.

RESULTS

Regarding relapse and death, the hazard ratio in single endpoint analysis (HR_s ) were 1.281 (95% CI: 1.061-1.546) and hazard ratio in composite endpoint analysis (HR_c ) were 1.286 (95% CI: 1.112-1.486) and 1/WR (win ratio) was 1.292 (95% CI: 1.115-1.497) indicated a similar negative effect for non-prophylaxis patients. However, when considering recovery and death, the corresponding HR_s  = 1.280 (95% CI: 1.056-1.552) may not be enough to describe the effect on death with nonproportional hazards (p < 0.05), and for the composite endpoint analysis, the HR_c  = 0.828 (95% CI: 0.740-0.926) cannot quantify and interpret the clinical effect on the composite endpoint with the combination of recovery and death, while the 1/WR = 1.351 (95% CI: 1.207-1.513) showed an unfavourable effect for non-prophylaxis patients CONCLUSIONS: When dealing with multiple endpoints, single endpoints, researchers may choose single endpoints, composite endpoints and WR analysis due to different clinical applications and purposes. However, both single and composite endpoint analyses are hazard-based measures, and thus, the proportional hazards assumption should be considered. Moreover, composite endpoint analysis should be applied for endpoints with similar clinical meanings but not opposing implications. Win ratio analysis can be considered for different clinical importance of multiple endpoints, but the meaning of 'winner' needs to be specified for desired or undesired endpoints.

Performance of Restricted Mean Survival Time Based Methods and Traditional Survival Methods: An Application in an Oncological Data

Objective

To compare restricted mean survival time- (RMST-) based methods with traditional survival methods when multiple covariates are of interest.

Methods

4405 osteosarcomas were captured from Surveillance, Epidemiology, and End Results Program Database. RMST-based methods included group comparison using Kaplan-Meier (KM) method, pseudovalue (PV) regression, and inverse probability of censoring probability (IPCW) regressions with group-specific and individual weights. Log-rank test, Wilcoxon test, Cox regression, and its extension with time-dependent variables were selected as traditional methods. Proportional hazard (PH) assumption and homogeneity of censoring mechanism assumption were assessed. We estimated hazard ratio (HR) and difference in RMST and explored their relationships.

Results

When covariate violated PH assumption, time-varying HR was inconvenient to report as a single value but PH assumption-free RMST allowed to report a single value of difference in RMST. In univariable analyses, using the difference in RMST calculated by KM method as reference, PV regressions (slope = 1.02 and R ² = 0.98) and IPCW regressions with group-specific weights (slope = 0.98 and R ² = 0.99) gave more consistent estimation than IPCW with individual weights (slope = 0.31 and R ² = 0.06), moreover, PV regressions presented more robust statistical power than IPCW regressions with group-specific weights. In multivariable analyses, IPCW regression with group-specific weights was limited when multiple covariates violated homogeneity of censoring mechanism assumption. For covariates met PH assumption, well-fitted logarithmic relationships between HR and difference in RMST estimated by PV regression were observed in both univariable and multivariable analyses (R ² = 0.97 and R ² = 0.94, respectively), which supported the robustness of PV regression and possible conversion between the two effect measures.

Conclusions

Difference in RMST is more interpretable than time-varying HR. The performance supports KM method and PV regression to be the preferred ones in RMST-based methods. IPCW regression can be an alternative sensitivity analysis. We encourage adoption of both traditional methods and RMST-based methods to present effects of covariates comprehensively.

A novel analytical framework for risk stratification of real-world data using machine learning: A small cell lung cancer study

In recent studies, small cell lung cancer (SCLC) treatment guidelines based on Veterans' Administration Lung Study Group limited/extensive disease staging and resulted in broad and inseparable prognostic subgroups. Evidence suggests that the eight versions of tumor, node, and metastasis (TNM) staging can play an important role to address this issue. The aim of the present study was to improve the detection of prognostic subgroups from a real-word data (RWD) cohort of patients and analyze their patterns using a development pipeline with thoracic oncologists and machine learning methods. The method detected subgroups of patients informing unsupervised learning (partition around medoids) including the impact of covariates on prognosis (Cox regression and random survival forest). An analysis was carried out using patients with SCLC (n = 636) with stage IIIA-IVB according to TNM classification. The analysis yielded k = 7 compacted and well-separated clusters of patients. Performance status (Eastern Cooperative Oncology Group-Performance Status), lactate dehydrogenase, spreading of metastasis, cancer stage, and CRP were the baselines that characterized the subgroups. The selected clustering method outperformed standard clustering techniques, which were not capable of detecting meaningful subgroups. From the analysis of cluster treatment decisions, we showed the potential of future RWD applications to understand disease, develop individualized therapies, and improve healthcare decision making.

Visualizing Time-Varying Effect in Survival Analysis: 5 Complementary Plots to Kaplan-Meier Curve

Background

Kaplan-Meier (KM) curve has been widely used in the field of oxidative medicine and cellular longevity. However, time-varying effect might be presented in KM curve and cannot be intuitively observed. Complementary plots might promote clear insights in time-varying effect from KM curve.

Methods

Three KM curves were identified from published randomized control trials: (a) curves diverged immediately; (b) intersected curves with statistical significance; and (c) intersected curves without statistical significance. We reconstructed individual patient data, and plotted 5 complementary plots (difference in survival probability and risk difference, difference in restricted mean survival time, landmark analyses, and hazard ratio over time), along with KM curve.

Results

Entanglement and intersection of two KM curves would make the 5 complementary plots to fluctuate over time intuitively. Absolute effects were presented in the 3 plots of difference in survival probability, risk, and restricted mean survival time. Changed P values from landmark analyses were used to inspect conditional treatment effect; the turning points could be identified for further landmark analysis. When proportional hazard assumption was not met, estimated hazard ratio from traditional Cox regression was not appropriate, and time-varying hazard ratios could be presented instead of an average and single value.

Conclusions

The 5 complementary plots with KM curve give a broad and straightforward picture of potential time-varying effect. They will provide clear insight in treatment effect and assist clinicians to make decision comprehensively.

Decreased Expression of PACSIN1 in Brain Glioma Samples Predicts Poor Prognosis

Gliomas are the most severe brain tumours with a poor prognosis. Although surgery, postoperative radiotherapy and chemotherapy can improve the survival rate of glioma patients, the prognosis of most glioma patients is still poor. In recent years, the influence of gene-targeted therapy on gliomas has been gradually discovered, and intervening the occurrence and development of brain gliomas from the perspective of the gene will significantly improve treatment prognosis. Protein Kinase C and Casein Kinase Substrate in Neurons 1 (PACSIN1) is a member of the conserved peripheral membrane protein family in eukaryotes. Improper expression of PACSIN1 can lead to neurological diseases such as Huntington’s disease and schizophrenia. However, its relationship with tumours or even gliomas has not been explored. The study aims to explore PACSIN1 as a prognostic factor that can predict overall survival (OS) for gliomas. We collected the data from CGGA, TCGA, GEO databases and the pathological glioma tissue specimens from 15 clinical glioma patients surgically resected. The differential expression of PACSIN1 in various clinical indicators, the genes related to PACSIN1 expression, the prognostic value of PACSIN1 and the functional annotations and pathway analysis of differently expressed genes (DEGs) were analysed. The results revealed that PACSIN1 had low expression levels in grade IV, IDH1 wild-type and 1p/19q non-codel group gliomas, and PACSIN1 was considered a mesenchymal molecular subtype marker. PACSIN1 expression is positively correlated with OS in all gliomas and it was found that PACSIN1 influenced the occurrence and development of gliomas through synaptic transmission. The PACSIN1 expression is negatively correlated with the malignant degree of gliomas and positively associated with the OS, indicating that PACSIN1 would play an essential role in the occurrence and development of gliomas and might be a potential new biomarker and targeted therapy site for gliomas.

MCM4 Is a Novel Biomarker Associated With Genomic Instability, BRCAness Phenotype, and Therapeutic Potentials in Soft-Tissue Sarcoma

Soft-tissue sarcoma (STS) is represented by a heterogeneous group of rare malignancies with various molecular oncogenesis. Therapies targeting DNA repair pathways in STS have achieved minimal progress, potentially due to the lack of molecular biomarker(s) beyond the histology subtype. In this report, we comprehensively analyzed the expression profiles of 100 liposarcomas (LPSs), the most common STS subtype, in comparison with 21 adipose tissues from multiple GEO datasets to identify the potential prognostic and therapeutic biomarker for LPS. Furthermore, we investigated TCGA database, our archived tumor samples, and patient-derived tumor cell cultures (PTCCs) as a validation. We identified a total of 69 common differentially expressed genes (DEGs) among public datasets, with mini-chromosome maintenance protein 4 (MCM4) identified as a novel biomarker correlated with patients’ clinical staging and survival outcome. MCM4-high expression LPS was characterized by MCM4 copy number increase, genomic instability, and BRCAness phenotype compared with the MCM4-low expression counterpart. In contrast, the mutational and the immune landscape were minimally different between the two groups. Interestingly, the association of MCM4-high expression with genomic instability and BRCAness were not only validated in LPS samples from our institution (n = 66) but also could be expanded to the pan-sarcoma cohort from TCGA database (n = 263). Surprisingly, based on four sarcoma cell lines and eight PTCCs (three LPS and five other sarcoma), we demonstrated that MCM4 overexpression tumors were therapeutically sensitive to PARP inhibitor (PARPi) and platinum chemotherapy, independent of the histology subtypes. Our study, for the first time, suggested that MCM4 might be a novel prognostic biomarker, associated with dysregulated DNA repair pathways and potential therapeutic vulnerability in STS.

The Decline of the Experimental Paradigm During the COVID-19 Pandemic: A Template for the Future

The current Coronavirus Disease 2019 (COVID-19) pandemic has exerted an unprecedented impact across the globe. As a consequence of this overwhelming catastrophe, long-established prevailing medical and scientific paradigms have been disrupted. The response of the scientific community, medical journals, media, and some politicians has been far from ideal. The present manuscript discusses the failure of the scientific enterprise in its initiatives to address the COVID-19 outbreak as a consequence of the disarray attributable to haste and urgency. To enhance conveying our message, this manuscript is organized into 3 interrelated sections: 1) the accelerated pace of publications coupled with a dysfunctional review process; 2) failure of the clinical trial enterprise; 3) propagation of misleading information by the media. In response we propose a template comprising a focus on randomized controlled clinical trials, and an insistence on responsible journal publication, and enumeration of policies to deal with social media-propagated news. We conclude with a reconsideration of the appropriate role of academic medicine and journals.

Treatment effect of radiofrequency ablation versus liver transplantation and surgical resection for hepatocellular carcinoma within Milan criteria: a population-based study

OBJECTIVES

Restricted mean survival time (RMST) has been increasingly used to assess the treatment effect. We aimed to evaluate a treatment effect of radiofrequency ablation (RFA) versus liver transplantation (LT) and surgical resection (SR) for hepatocellular carcinoma (HCC) within Milan criteria by using an adjusted RMST.

METHODS

A total of 7,218 HCC patients (RFA, 3,327; LT, 2,332; SR 1,523) within Milan criteria were eligible for this retrospectively study. The RMST using inverse probability of treatment weighting (IPTW) adjustment were applied to estimate the treatment effect between RFA and LT, RFA, and SR groups.

RESULTS

The 3-, 5-, and 10-year IPTW-adjusted difference in RMST of OS for LT over RFA were + 4.5, + 12.4, and + 36.3 months, respectively. For SR versus RFA group, the survival benefit was + 2.3, + 6.1, and + 15.8 months at 3, 5, and 10 years, respectively. But the incremental survival benefit of SR over RFA was only half than that of LT over RFA. In the subgroup of solitary tumor ≤ 2 cm, the adjusted RMST of RFA versus SR was comparable with no statistical differences. Beyond that, in comparison with RFA, a notably greater efficacy of LT and SR was consistently across all subgroups with solitary HCC > 2.0 cm, AFP positive or negative, and fibrosis score 0-4 or 5-6.

CONCLUSIONS

RMST provides a measure of absolute survival benefit at a specific time point. Using IPTW-adjusted RMST, we showed that the incremental survival benefit of SR over RFA was about half than that of LT over RFA.

KEY POINTS

• The restricted mean survival time offers an intuitive, clinically meaningful interpretation to quantify the treatment effect than the hazard ratio. • Liver transplantation and surgical resection provided better overall survival compared to radiofrequency ablation for HCC patients within Milan criteria, but RFA and SR provide equivalent long-term overall survival for solitary HCC ≤ 2 cm. • The incremental survival benefit of surgical resection over radiofrequency ablation was only half than that of liver transplantation over radiofrequency ablation.

Germination Data Analysis by Time-to-Event Approaches

Germination data are analyzed by several methods, which can be mainly classified as germination indexes and traditional regression techniques to fit non-linear parametric functions to the temporal sequence of cumulative germination. However, due to the nature of germination data, often different from other biological data, the abovementioned methods may present some limits, especially when ungerminated seeds are present at the end of an experiment. A class of methods that could allow addressing these issues is represented by the so-called “time-to-event analysis”, better known in other scientific fields as “survival analysis” or “reliability analysis”. There is relatively little literature about the application of these methods to germination data, and some reviews dealt only with parts of the possible approaches such as either non-parametric and semi-parametric or parametric ones. The present study aims to give a contribution to the knowledge about the reliability of these methods by assessing all the main approaches to the same germination data provided by sugar beet (Beta vulgaris L.) seeds cohorts. The results obtained confirmed that although the different approaches present advantages and disadvantages, they could generally represent a valuable tool to analyze germination data providing parameters whose usefulness depends on the purpose of the research.

Interpretation of time-to-event outcomes in randomized trials: an online randomized experiment

Background

Multiple features in the presentation of randomized controlled trial (RCT) results are known to influence comprehension and interpretation. We aimed to compare interpretation of cancer RCTs with time-to-event outcomes when the reported treatment effect measure is the hazard ratio (HR), difference in restricted mean survival times (RMSTD), or both (HR+RMSTD). We also assessed the prevalence of misinterpretation of the HR.

Methods

We carried out a randomized experiment. We selected 15 cancer RCTs with statistically significant treatment effects for the primary outcome. We masked each abstract and created three versions reporting either the HR, RMSTD, or HR+RMSTD. We randomized corresponding authors of RCTs and medical residents and fellows to one of 15 abstracts and one of 3 versions. We asked how beneficial the experimental treatment was (0–10 Likert scale). All participants answered a multiple-choice question about interpretation of the HR. Participants were unaware of the study purpose.

Results

We randomly allocated 160 participants to evaluate an abstract reporting the HR, 154 to the RMSTD, and 155 to both HR+RMSTD. The mean Likert score was statistically significantly lower in the RMSTD group when compared with the HR group (mean difference −0.8, 95% confidence interval, −1.3 to −0.4, P < 0.01) and when compared with the HR+RMSTD group (difference −0.6, −1.1 to −0.1, P = 0.05). In all, 47.2% (42.7%−51.8%) of participants misinterpreted the HR, with 40% equating it with a reduction in absolute risk.

Conclusion

Misinterpretation of the HR is common. Participants judged experimental treatments to be less beneficial when presented with RMSTD when compared with HR. We recommend that authors present RMST-based measures alongside the HR in reports of RCT results.

Potential Bias Associated with Modeling the Effectiveness of Healthcare Interventions in Reducing Mortality Using an Overall Hazard Ratio

BACKGROUND

Clinical trials often report intervention efficacy in terms of the reduction in all-cause mortality between the treatment and control arms (i.e., an overall hazard ratio [oHR]) instead of the reduction in disease-specific mortality (i.e., a disease-specific hazard ratio [dsHR]). Using oHR to reduce all-cause mortality beyond the time horizon of the trial may introduce bias if the relative proportion of other-cause mortality increases with age. We sought to quantify this oHR extrapolation bias and propose a new approach to overcome this bias.

METHODS

We simulated a hypothetical cohort of patients with a generic disease that increased background mortality by a constant additive disease-specific rate. We quantified the bias in terms of the percentage change in life expectancy gains with the intervention under an oHR compared with a dsHR approach as a function of the cohort start age, the disease-specific mortality rate, dsHR, and the duration of the intervention's effect. We then quantified the bias in a cost-effectiveness analysis (CEA) of implantable cardioverter-defibrillators based on efficacy estimates from a clinical trial.

RESULTS

For a cohort of 50-year-old patients with a disease-specific mortality of 0.05, a dsHR of 0.5, a calculated oHR of 0.55, and a lifetime duration of effect, the bias was 28%. We varied these key parameters over wide ranges and the resulting bias ranged between 3 and 140%. In the CEA, the use of oHR as the intervention's effectiveness overestimated quality-adjusted life expectancy by 9% and costs by 3%, biasing the incremental cost-effectiveness ratio by - 6%.

CONCLUSIONS

The use of an oHR approach to model the intervention's effectiveness beyond the time horizon of the trial overestimates its benefits. In CEAs, this bias could decrease the cost of a QALY, overestimating interventions' cost effectiveness.

Adjusted restricted mean survival times in observational studies

In observational studies with censored data, exposure-outcome associations are commonly measured with adjusted hazard ratios from multivariable Cox proportional hazards models. The difference in restricted mean survival times (RMSTs) up to a pre-specified time point is an alternative measure that offers a clinically meaningful interpretation. Several regression-based methods exist to estimate an adjusted difference in RMSTs, but they digress from the model-free method of taking the area under the survival function. We derive the adjusted RMST by integrating an adjusted Kaplan-Meier estimator with inverse probability weighting (IPW). The adjusted difference in RMSTs is the area between the two IPW-adjusted survival functions. In a Monte Carlo-type simulation study, we demonstrate that the proposed estimator performs as well as two regression-based approaches: the ANCOVA-type method of Tian et al and the pseudo-observation method of Andersen et al. We illustrate the methods by reexamining the association between total cholesterol and the 10-year risk of coronary heart disease in the Framingham Heart Study.

Survival Analysis and Interpretation of Time-to-Event Data: The Tortoise and the Hare

Survival analysis, or more generally, time-to-event analysis, refers to a set of methods for analyzing the length of time until the occurrence of a well-defined end point of interest. A unique feature of survival data is that typically not all patients experience the event (eg, death) by the end of the observation period, so the actual survival times for some patients are unknown. This phenomenon, referred to as censoring, must be accounted for in the analysis to allow for valid inferences. Moreover, survival times are usually skewed, limiting the usefulness of analysis methods that assume a normal data distribution. As part of the ongoing series in Anesthesia & Analgesia, this tutorial reviews statistical methods for the appropriate analysis of time-to-event data, including nonparametric and semiparametric methods—specifically the Kaplan-Meier estimator, log-rank test, and Cox proportional hazards model. These methods are by far the most commonly used techniques for such data in medical literature. Illustrative examples from studies published in Anesthesia & Analgesia demonstrate how these techniques are used in practice. Full parametric models and models to deal with special circumstances, such as recurrent events models, competing risks models, and frailty models, are briefly discussed.

Changes in plasma lipids predict pravastatin efficacy in secondary prevention

BACKGROUNDStatins have pleiotropic effects on lipid metabolism. The relationship between these effects and future cardiovascular events is unknown. We characterized the changes in lipids upon pravastatin treatment and defined the relationship with risk reduction for future cardiovascular events.METHODSPlasma lipids (n = 342) were measured in baseline and 1-year follow-up samples from a Long-Term Intervention with Pravastatin in Ischaemic Disease (LIPID) study subcohort (n = 4991). The associations of changes in lipids with treatment and cardiovascular outcomes were investigated using linear and Cox regression. The effect of treatment on future cardiovascular outcomes was examined by the relative risk reduction (RRR).RESULTSPravastatin treatment was associated with changes in 206 lipids. Species containing arachidonic acid were positively associated while phosphatidylinositol species were negatively associated with pravastatin treatment. The RRR from pravastatin treatment for cardiovascular events decreased from 23.5% to 16.6% after adjustment for clinical risk factors and change in LDL-cholesterol (LDL-C) and to 3.0% after further adjustment for the change in the lipid ratio PI(36:2)/PC(38:4). Change in PI(36:2)/PC(38:4) mediated 58% of the treatment effect. Stratification of patients into quartiles of change in PI(36:2)/PC(38:4) indicated no benefit of pravastatin in the fourth quartile.CONCLUSIONThe change in PI(36:2)/PC(38:4) predicted benefit from pravastatin, independent of change in LDL-C, demonstrating its potential as a biomarker for monitoring the clinical benefit of statin treatment in secondary prevention.TRIAL REGISTRATIONAustralian New Zealand Clinical Trials Registry identifier ACTRN12616000535471.FUNDINGBristol-Myers Squibb; NHMRC grants 211086, 358395, and 1029754; NHMRC program grant 1149987; NHMRC fellowship 108026; and the Operational Infrastructure Support Program of the Victorian government of Australia.

Cancer drug development: The missing links

IMPACT STATEMENT

The success rate for cancer drugs which enter into phase 1 clinical trials is utterly less. Why the vast majority of drugs fail is not understood but suggests that pre-clinical studies are not adequate for human diseases. In 1975, as per the Tufts Center for the Study of Drug Development, pharmaceutical industries expended 100 million dollars for research and development of the average FDA approved drug. By 2005, this figure had more than quadrupled, to $1.3 billion. In order to recover their high and risky investment cost, pharmaceutical companies charge more for their products. However, there exists no correlation between drug development cost and actual sale of the drug. This high drug development cost could be due to the reason that all patients might not respond to the drug. Hence, a given drug has to be tested in large number of patients to show drug benefits and obtain significant results.

Assessment of proportional hazard assumption in aggregate data: a systematic review on statistical methodology in clinical trials using time-to-event endpoint

Background

The evaluation of the proportional hazards (PH) assumption in survival analysis is an important issue when Hazard Ratio (HR) is chosen as summary measure. The aim is to assess the appropriateness of statistical methods based on the PH assumption in oncological trials.

Methods

We selected 58 randomised controlled trials comparing at least two pharmacological treatments with a time-to-event as primary endpoint in advanced non-small-cell lung cancer. Data from Kaplan–Meier curves were used to calculate the relative hazard at each time point and the Restricted Mean Survival Time (RMST). The PH assumption was assessed with a fixed-effect meta-regression.

Results

In 19% of the trials, there was evidence of non-PH. Comparison of treatments with different mechanisms of action was associated (P = 0.006) with violation of the PH assumption. In all the superiority trials where non-PH was detected, the conclusions using the RMST corresponded to that based on the Cox model, although the magnitude of the effect given by the HR was systematically greater than the one from the RMST ratio.

Conclusion

As drugs with new mechanisms of action are being increasingly employed, particular attention should be paid on the statistical methods used to compare different types of agents.

Choosing wisely in oncology: necessity and obstacles

In the last decades, the survival of many patients with cancer improved thanks to modern diagnostic methods and progresses in therapy. Still for several tumours, especially when diagnosed at an advanced stage, the benefits of treatment in terms of increased survival or quality of life are at best modest when not marginal, and should be weighed against the potential discomfort caused by medical procedures. As in other specialties, in oncology as well the dialogue between doctor and patient should be encouraged about the potential overuse of diagnostic procedures or treatments. Several oncological societies produced recommendations similar to those proposed by other medical disciplines adhering to the Choosing Wisely (CW) campaign. In this review, we describe what was reported in the medical literature concerning adequacy of screening, diagnostic, treatment and follow-up procedures and the potential impact on them of the CW. We only marginally touch on the more complex topic of treatment appropriateness, for which several evaluation methods have been developed (including the European Society for Medical Oncology-magnitude of clinical benefit scale). Finally, we review the possible obstacles for the development of CW in the oncological setting and focus on the strategies which could allow CW to evolve in the cancer field, so as to enhance the therapeutic relationship between medical professionals and patients and promote more appropriate management.

Interpretation of clinical endpoints in trials of acute myeloid leukemia

Treatment regimens for acute myeloid leukemia (AML) have remained largely unchanged until recently. Molecular advances have opened the door to targeted therapies, many of which are in late-phase clinical trials. As new therapeutic opportunities arise, it is appropriate to review key aspects of clinical trial design, statistical interpretation of outcomes, and methods of data reporting. Complete remission and overall survival (OS) are common primary endpoints in early-phase AML clinical trials. OS and event-free survival are frequent primary endpoints in phase 3 trials. Clinical trials are designed to address the primary endpoint using prespecified α and power levels. Interpretation of additional endpoints (eg, secondary endpoints and subgroup analyses) must be viewed in light of a trial's statistical design. Furthermore, variations in reporting of endpoints must be considered in order to understand trial outcomes. Time-to-event endpoints are typically reported using Kaplan-Meier curves, which are visually informative. Statistical data derived from these curves can be complex, and a variety of factors may impact interpretation. The purpose of this review is to discuss the nuances of common AML trial endpoints and their data presentation to better inform evaluation and understanding of clinical trial data.

Effectiveness of mifamurtide in addition to standard chemotherapy for high-grade osteosarcoma: a systematic review

BACKGROUND

Osteosarcoma mostly occurs during the period of rapid bone growth in children and adolescents as high-grade osteosarcomas. Current treatment recommended for high-grade non-metastatic and metastatic and/or relapsed osteosarcoma involves neoadjuvant multiagent conventional chemotherapy, followed by surgical resection of macroscopically detected tumor and postoperative adjuvant chemotherapy. However, residual micrometastatic deposits that develop following surgery have shown resistance to postoperative/adjuvant chemotherapy. Therefore, there is a critical need for more effective and innovative therapeutic approaches such as immune stimulatory agents. The most extensively studied immune stimulatory agent in the treatment of osteosarcoma is mifamurtide. The aim of this systematic review was to identify and synthesize the evidence on the effectiveness of mifamurtide in addition to standard chemotherapy on survival outcomes.

OBJECTIVES

To present the best available evidence on the treatment of high-grade non-metastatic and metastatic osteosarcoma with mifamurtide in addition to standard chemotherapy.

INCLUSION CRITERIA TYPES OF PARTICIPANTS

All populations of patients regardless of age, gender or ethnicity with high-grade, resectable, non-metastatic and metastatic osteosarcoma based on histological diagnosis.

TYPES OF INTERVENTIONS AND COMPARATORS

This review focused on intravenous infusion of either of the pharmaceutical formulations of mifamurtide (MTP-PE or L-MTP-PE) in addition to standard chemotherapy, and the comparator was chemotherapy alone.

TYPES OF STUDIES

This review considered any experimental study design including randomized controlled trials, non-randomized trials and quasi-experimental studies.

OUTCOMES

The primary outcomes of interest were event-free survival, overall survival and recurrence of osteosarcoma. Secondary outcomes that were considered included health-related quality of life and any mifamurtide-related adverse events.

SEARCH STRATEGY

A search for published and unpublished literature in English was undertaken (seven published literature databases, four unpublished literature databases, and three government agency and organizational websites were searched). Studies published between 1990 to June 2016 were considered. A three-step strategy was developed using MeSH terminology and keywords to ensure that all relevant studies were included related to this review.

METHODOLOGICAL QUALITY

The methodological quality of included studies was assessed by two reviewers, who appraised each study independently, using a standardized Joanna Briggs Institute (JBI) critical appraisal tool.

DATA EXTRACTION

Data was extracted from the studies that were identified as meeting the criteria for methodological quality using the standard JBI data extraction tool.

DATA SYNTHESIS

Due to the heterogeneity of populations and interventions in available studies, meta-analysis was not possible and results are presented in narrative form.

RESULTS

Three papers outlining two studies involving 802 patients evaluated the effectiveness of mifamurtide in addition of chemotherapy. Results indicated no significant difference in event-free survival between the addition of mifamurtide to standard chemotherapy regimen and chemotherapy alone, both in non-metastatic and metastatic osteosarcoma patients. There was a significant difference in progression-free survival favoring the addition of mifamurtide in pulmonary metastatic and/or relapsed osteosarcoma. There was no significant difference in overall survival between the addition of mifamurtide and chemotherapy alone in metastatic osteosarcoma; however there was a significant difference favoring the addition of mifamurtide in non-metastatic osteosarcoma patients. The addition of mifamurtide resulted in a significant difference in survival after relapse in pulmonary metastatic and/or relapsed osteosarcoma patients. Both studies reported on mifamurtide-related adverse events - the first was reported as toxicity which included haematological, hepatic, renal, gastrointestinal disorders, cardiac, rhythm and nervous system disorders, ear disorders and others (infection, fever; and performance status) in metastatic osteosarcoma patients. Results were similar across all combined treatment regimens. Although no statistical analysis was undertaken, the figures suggest there were no significant differences between the treatment regimens. In the other study, mifamurtide-related adverse events were reported as clinical toxic effects of mifamurtide in relapsed osteosarcoma, which included chills, fever and headache for the initial dose of mifamurtide, while for the subsequent doses of mifamurtide all patients reported toxicity as delayed fatigue.

CONCLUSIONS

The available evidence on the effectiveness of mifamurtide in addition to a standard chemotherapy regimen for the treatment of high-grade osteosarcoma is limited and therefore no definitive conclusions can be made.

Comparison of Treatment Effects Measured by the Hazard Ratio and by the Ratio of Restricted Mean Survival Times in Oncology Randomized Controlled Trials

PURPOSE

We aimed to compare empirically the treatment effects measured by the hazard ratio (HR) and by the difference (and ratio) of restricted mean survival times (RMST) in oncology randomized trials.

METHODS

We selected oncology randomized controlled trials from five leading journals during the last 6 months of 2014. We reconstructed individual patient data for one time-to-event outcome from each trial, preferably the primary outcome. We reanalyzed each trial and compared the treatment effect estimated by the HR with that by the difference (and ratio) of RMST. We estimated an average ratio of the HR to the ratio of RMST; an average ratio less than one indicates more optimistic assessments with HRs.

RESULTS

We analyzed 54 randomized controlled trials totaling 33,212 patients. The selected outcome was overall survival in 21 (39%) trials. There was evidence of nonproportionality of hazards in 13 (24%) trials. The HR and RMST-based measures were in agreement regarding the statistical significance of the effect, except in one case. The median HR was 0.84 (Q1 to Q3 range, 0.67 to 0.97) and the median difference in RMST was 1.12 months (range, 0.22 to 2.75 months). The average ratio of the HR to the ratio of RMST was 1.11 (95% CI, 1.07 to 1.15), with substantial between-trial variability (I(2) = 86%). Results were consistent by outcome type (overall survival v other outcomes) and whether the proportional hazard assumption held or not.

CONCLUSION

On average, the HR provided significantly larger treatment effect estimates than the ratio of RMST. The HR may seem large when the absolute effect is small. RMST-based measures should be routinely reported in randomized trials with time-to-event outcomes.

Early Benefit Assessments in Oncology in Germany: How Can a Clinically Relevant Endpoint Not Be Relevant to Patients?

After 4 years of early benefit assessment (EBA) in Germany, it is becoming evident that the Federal Joint Committee (FJC) frequently considers well-established clinical endpoints as not being relevant to patients. Focusing on assessments of oncology medicines, we analysed the FJC's view on primary endpoints and compared it with the approach used by regulatory authorities. Mortality data were accepted by both stakeholders. Whereas regulatory authorities accepted primary morbidity endpoints such as progression-free survival and response rates, the FJC mostly excluded these from its assessments. Health-related quality of life (HRQoL) data have been poorly reflected in the approval process; for EBAs, those data have rarely impacted on benefit ratings. We argue that agreement between regulatory authorities and the FJC is required regarding primary study endpoints that are relevant to patients, and that clarification of acceptable endpoints by the FJC, especially in the morbidity domain, has to be provided. Moreover, in order to fully acknowledge the benefit of a new medicinal product, mortality, morbidity and HRQoL should be weighted differentially, according to the condition.