A general Bayesian bootstrap for censored data based on the beta-Stacy process.

We introduce a novel procedure to perform Bayesian non-parametric inference with right-censored data, the beta-Stacy bootstrap. This approximates the posterior law of summaries of the survival distribution (e.g. the mean survival time). More precisely, our procedure approximates the joint posterior law of functionals of the beta-Stacy process, a non-parametric process prior that generalizes the Dirichlet process and that is widely used in survival analysis. The beta-Stacy bootstrap generalizes and unifies other common Bayesian bootstraps for complete or censored data based on non-parametric priors. It is defined by an exact sampling algorithm that does not require tuning of Markov Chain Monte Carlo steps. We illustrate the beta-Stacy bootstrap by analyzing survival data from a real clinical trial.

Optimality of testing procedures for survival data in the nonproportional hazards setting.

Most statistical tests for treatment effects used in randomized clinical trials with survival outcomes are based on the proportional hazards assumption, which often fails in practice. Data from early exploratory studies may provide evidence of nonproportional hazards, which can guide the choice of alternative tests in the design of practice-changing confirmatory trials. We developed a test to detect treatment effects in a late-stage trial, which accounts for the deviations from proportional hazards suggested by early-stage data. Conditional on early-stage data, among all tests that control the frequentist Type I error rate at a fixed α level, our testing procedure maximizes the Bayesian predictive probability that the study will demonstrate the efficacy of the experimental treatment. Hence, the proposed test provides a useful benchmark for other tests commonly used in the presence of nonproportional hazards, for example, weighted log-rank tests. We illustrate this approach in simulations based on data from a published cancer immunotherapy phase III trial.

A probabilistic bias analysis for misclassified categorical exposures, with application to oral anti-hyperglycaemic drugs.

PURPOSE: The effect of drug exposure misclassification generally receives little attention in pharmacoepidemiological research. In this paper, we illustrate a probabilistic bias analysis approach for misclassified categorical exposures and apply it in a database study of oral anti-hyperglycaemic drugs (OADs). METHODS: A cohort study based on the Health Search Database general-practice database was carried out by including 12 640 adult (≥40 years) patients newly treated with OADs during 2003-2010. The proportion of days covered by OADs prescriptions during the first year of follow-up was evaluated for each individual, either by means of the prescribed daily dose or the defined daily dose. The effect of misclassification on hypothetical OAD-outcome association profiles was assessed through the proposed probabilistic bias analysis approach, taking advantage of available exposure validation data. RESULTS: During the first year of follow-up, the average (SD) number of months with OADs available was 7 (4) months and 5 (3) months according to the prescribed daily dose and defined daily dose metrics, respectively. Probabilistic bias analysis results based on validation data suggest that the effect of misclassification is complex, as conventional exposure-outcome association estimates may be of greater or lower magnitude than their misclassification-adjusted values. CONCLUSIONS: Misclassification should be taken into account in database studies on the safety of prescribed medications. To this aim, investigators should take advantage of external exposure validation data in sensitivity analysis approaches such as ours. Copyright © 2016 John Wiley & Sons, Ltd.

Persistence with inhaled corticosteroids reduces the risk of exacerbation among adults with asthma: A real-world investigation.

BACKGROUND AND OBJECTIVE: Real-world evidence suggests that persistence with inhaled corticosteroids (ICS), the mainstay of asthma drug therapy, is generally poor. The effect of persistence with ICS on the risk of asthma exacerbation was addressed in a population-based study. METHODS: The cohort of 2335 beneficiaries of the National Health Service provided by the Italian Region of Lombardy, aged 18-40 years and newly treated with ICS during 2005-2008, was followed from their first ICS dispensation until 2010. Discontinuation of treatment with ICS and starting oral corticosteroid therapy during follow-up were respectively regarded as proxies of poor persistence with asthma medication and asthma exacerbation (outcomes). A proportional hazards model was fitted to identify predictors of ICS discontinuation. Case-crossover and case-case-time-control designs and conditional logistic regressions were used to estimate the association between persistence with ICS and asthma exacerbation. RESULTS: Cumulative incidences of discontinuation were 36%, 57% and 78% at 6 months, 1 year and 5 years, respectively. Predictors of poor persistence were female gender, use of antibiotics during follow-up, absence of use of short-acting beta-agonists prior to and after starting treatment with ICS and starting and maintaining ICS monotherapy during follow-up. The odds ratios of asthma exacerbation (and 95% confidence intervals) associated with ICS exposure during the current period, contrasted with exposure during the reference period, were 0.4 (0.2, 0.9) and 0.3 (0.1, 1.0) from case-crossover and case-case-time-control estimates, respectively. CONCLUSION: Persistence with ICS treatment in adults with asthma reduces the risk of exacerbation in the real-life setting.

Incidence, Predictors, and Clinical Implications of Discontinuing Therapy with Inhaled Long-Acting Bronchodilators among Patients with Chronic Obstructive Pulmonary Disease.

Incidence, predictors and effect of discontinuation of long-acting bronchodilators on the risk of death or hospital admission among adults with Chronic Obstructive Pulmonary Disease (COPD) were assessed in a large population-based prospective study carried out by linking Italian healthcare utilization databases. Specifically, the cohort of 17,490 beneficiaries of the National Health Service in the Italian Region of Lombardy, aged 40 years or older, who started long-acting bronchodilators therapy during 2005-2008 was followed from first dispensation until 2012. During this period, patients who experienced discontinuation of long-acting bronchodilators were identified. Hospitalizations for COPD and deaths for any cause (composite clinical outcome) were also identified during follow-up. A Cox proportional hazards model was fitted to identify predictors of discontinuation. The case-crossover design was used to assess the implications of treatment discontinuation on the clinical outcome risk. Cumulative incidences of discontinuation were, respectively, 67%, 80%, and 92% at 6 months, 1 year, and 5 years since initial treatment. Significant predictors of discontinuation were female gender, younger age, starting treatment with fixed-dose combination of inhaled bronchodilators and corticosteroids, using antibiotics, inhaled long-acting bronchodilators and corticosteroids and not using short-acting bronchodilators, other respiratory drugs and systemic corticosteroids during follow-up. Odds ratios (95% confidence intervals) for the clinical outcome associated with not discontinuing long-acting bronchodilators was 0.64 (0.50 to 0.82). In conclusion, in the real-life setting, discontinuation of inhaled long-acting bronchodilators in adults with COPD is high even after just 6 months, even though persistence to these drugs reduces the risk of severe outcomes.

Sensitivity Analyses of Clinical Trial Designs: Selecting Scenarios and Summarizing Operating Characteristics.

The use of simulation-based sensitivity analyses is fundamental for evaluating and comparing candidate designs of future clinical trials. In this context, sensitivity analyses are especially useful to assess the dependence of important design operating characteristics with respect to various unknown parameters. Typical examples of operating characteristics include the likelihood of detecting treatment effects and the average study duration, which depend on parameters that are unknown until after the onset of the clinical study, such as the distributions of the primary outcomes and patient profiles. Two crucial components of sensitivity analyses are (i) the choice of a set of plausible simulation scenarios and (ii) the list of operating characteristics of interest. We propose a new approach for choosing the set of scenarios to be included in a sensitivity analysis. We maximize a utility criterion that formalizes whether a specific set of sensitivity scenarios is adequate to summarize how the operating characteristics of the trial design vary across plausible values of the unknown parameters. Then, we use optimization techniques to select the best set of simulation scenarios (according to the criteria specified by the investigator) to exemplify the operating characteristics of the trial design. We illustrate our proposal in three trial designs.

New clinical trial design borrowing information across patient subgroups based on fusion-penalized regression models.

In cancer research, basket trials aim to assess the efficacy of a drug using baskets, wherein patients are organized into subgroups according to their tumor type. In this context, using information borrowing strategy may increase the probability of detecting drug efficacy in active baskets, by shrinking together the estimates of the parameters characterizing the drug efficacy in baskets with similar drug activity. Here, we propose to use fusion-penalized logistic regression models to borrow information in the setting of a phase 2 single-arm basket trial with binary outcome. We describe our proposed strategy and assess its performance via a simulation study. We assessed the impact of heterogeneity in drug efficacy, prevalence of each tumor types and implementation of interim analyses on the operating characteristics of our proposed design. We compared our approach with two existing designs, relying on the specification of prior information in a Bayesian framework to borrow information across similar baskets. Notably, our approach performed well when the effect of the drug varied greatly across the baskets. Our approach offers several advantages, including limited implementation efforts and fast computation, which is essential when planning a new trial as such planning requires intensive simulation studies.

Molecular and Clinicopathologic Impact of GNAS Variants Across Solid Tumors.

PURPOSE: The molecular drivers underlying mucinous tumor pathogenicity are poorly understood. GNAS mutations predict metastatic burden and treatment resistance in mucinous appendiceal adenocarcinoma. We investigated the pan-cancer clinicopathologic relevance of GNAS variants. METHODS: We assessed 58,043 patients with Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (IMPACT)-sequenced solid tumors to identify oncogenic variants, including GNAS, associated with mucinous tumor phenotype. We then performed comprehensive molecular analyses to compare GNAS-mutant (mut) and wild-type tumors across cancers. Gene expression patterns associated with GNAS-mut tumors were assessed in a The Cancer Genome Atlas cohort. Associations between GNAS variant status and peritoneal metastasis, first-line systemic therapy response, progression-free survival (PFS), and overall survival (OS) were determined using a propensity-matched subcohort of patients with metastatic disease. RESULTS: Mucinous tumors were enriched for oncogenic GNAS variants. GNAS was mutated in >1% of small bowel, cervical, colorectal, pancreatic, esophagogastric, hepatobiliary, and GI neuroendocrine cancers. Across these cancers, GNAS-mut tumors exhibited a generally conserved C-to-T mutation-high, aneuploidy-low molecular profile with co-occurring prevalent KRAS variants (65% of GNAS-mut tumors) and fewer TP53 alterations. GNAS-mut tumors exhibited recurrently comutated alternative tumor suppressors (RBM10, INPPL1) and upregulation of MAPK and cell surface modulators. GNAS-mut tumors demonstrate an increased prevalence of peritoneal metastases (odds ratio [OR], 1.7 [95% CI, 1.1 to 2.5]; P = .006), worse response to first-line systemic therapy (OR, 2.2 [95% CI, 1.3 to 3.8]; P = .003), and shorter PFS (median, 5.6 v 7.0 months; P = .047). In a multivariable analysis, GNAS mutated status was independently prognostic of worse OS (hazard ratio, 1.25 [95% CI, 1.01 to 1.56]; adjusted P = .04). CONCLUSION: Across the assessed cancers, GNAS-mut tumors exhibit a conserved molecular and clinical phenotype defined by mucinous tumor status, increased peritoneal metastasis, poor response to first-line systemic therapy, and worse survival.

Optimal systemic treatment and real-world clinical application of ctDNA in patients with metastatic HER2-mutant lung cancer.

INTRODUCTION: No definitive answers currently exist regarding optimal first-line therapy for HER2-mutant NSCLC. Access to rapid tissue sequencing is a major barrier to precision drug development in the first-line setting. ctDNA analysis has the potential to overcome these obstacles and guide treatment. METHODS: We retrospectively analyzed patients with metastatic HER2-mutant NSCLC who underwent prospective clinical ctDNA sequencing and received systemic therapy at Memorial Sloan Kettering Cancer Center (MSK) from January 2016 to September 2022. HER2 mutations were identified by next-generation sequencing through MSK-IMPACT, MSK-ACCESS or Resolution ctDx LungTM assay. Primary endpoints were time to the next treatment (TTNT) and overall survival (OS). RESULTS: Sixty-three patients were included in the primary analysis. Chemoimmunotherapy (33/63, 52.4 %) was the predominant first-line treatment with a median TTNT of 5.1 months (95 %CI 4.1 - 6.1) whereas 55.0 % (22/40) of patients who received second-line T-DXd obtained a median TTNT of 9.2 m (95 % CI, 0-22.2). Plasma ctDNA was tested before first-line therapy in 40 patients with a median OS of 28.0 months (95 % CI 21-34), in whom 31 patients (78.0 %) had detectable ctDNA. HER2 mutations were detected on ctDNA with a median turnaround time of 13 days, occasionally co-occurred with EGFR and MET alterations and were tracked longitudinally correlating with treatment response. Patients with detectable baseline ctDNA had significantly shorter OS (hazard ratio (HR), 5.25; 95 % CI, 1.2-23.9; p = 0.019). CONCLUSION: Chemoimmunotherapy remains a major treatment option for metastatic HER2-mutant NSCLC. ctDNA can rapidly detect HER2 and co-mutations, and it has the potential to guide and monitor optimal first-line therapy. As a negative prognostic biomarker, detectable ctDNA at baseline would need to be taken into account for patient selection in future studies.

Population-based analysis of non-steroidal anti-inflammatory drug use among children in four European countries in the SOS project: what size of data platforms and which study designs do we need to assess safety issues?

BACKGROUND: Data on utilization patterns and safety of non-steroidal anti-inflammatory drugs (NSAIDs) in children are scarce. The purpose of this study was to investigate the utilization of NSAIDs among children in four European countries as part of the Safety Of non-Steroidal anti-inflammatory drugs (SOS) project. METHODS: We used longitudinal patient data from seven databases (GePaRD, IPCI, OSSIFF, Pedianet, PHARMO, SISR, and THIN) to calculate prevalence rates of NSAID use among children (0-18 years of age) from Germany, Italy, Netherlands, and United Kingdom. All databases contained a representative population sample and recorded demographics, diagnoses, and drug prescriptions. Prevalence rates of NSAID use were stratified by age, sex, and calendar time. The person-time of NSAID exposure was calculated by using the duration of the prescription supply. We calculated incidence rates for serious adverse events of interest. For these adverse events of interest, sample size calculations were conducted (alpha = 0.05; 1-beta = 0.8) to determine the amount of NSAID exposure time that would be required for safety studies in children. RESULTS: The source population comprised 7.7 million children with a total of 29.6 million person-years of observation. Of those, 1.3 million children were exposed to at least one of 45 NSAIDs during observation time. Overall prevalence rates of NSAID use in children differed across countries, ranging from 4.4 (Italy) to 197 (Germany) per 1000 person-years in 2007. For Germany, United Kingdom, and Italian pediatricians, we observed high rates of NSAID use among children aged one to four years. For all four countries, NSAID use increased with older age categories for children older than 11. In this analysis, only for ibuprofen (the most frequently used NSAID), enough exposure was available to detect a weak association (relative risk of 2) between exposure and asthma exacerbation (the most common serious adverse event of interest). CONCLUSIONS: Patterns of NSAID use in children were heterogeneous across four European countries. The SOS project platform captures data on more than 1.3 million children who were exposed to NSAIDs. Even larger data platforms and the use of advanced versions of case-only study designs may be needed to conclusively assess the safety of these drugs in children.

Clinical development of new drugs for adults and children with cancer, 2010-2020.

BACKGROUND: Many new molecular entities enter clinical development to evaluate potential therapeutic benefits for oncology patients. We characterized adult and pediatric development of the set of new molecular entities that started clinical testing in 2010-2015 worldwide. METHODS: We extracted data from AdisInsight, an extensive database of global pharmaceutical development, and the FDA.gov website. We followed the cohort of new molecular entities initiating first-in-human phase I clinical trials in 2010-2015 to the end of 2020. For each new molecular entity, we determined whether it was granted US Food and Drug Administration (FDA) approval, studied in a trial open to pediatric enrollment, or stalled during development. We characterized the cumulative incidence of these endpoints using statistical methods for censored data. RESULTS: The 572 new molecular entities starting first-in-human studies in 2010-2015 were studied in 6142 trials by the end of 2020. Most new molecular entities were small molecules (n = 316, 55.2%), antibodies (n = 148, 25.9%), or antibody-drug conjugates (n = 44, 7.7%). After a mean follow-up of 8.0 years, 173 new molecular entities did not advance beyond first-in-human trials, and 39 were approved by the FDA. New molecular entities had a 10.4% estimated probability (95% confidence interval = 6.6% to 14.1%) of being approved by the FDA within 10 years of first-in-human trials. After a median of 4.6 years since start of first-in-human trials, 67 (11.7%) new molecular entities were tested in trials open to pediatric patients, and 5 (0.9%) were approved for pediatric indications. CONCLUSIONS: More efficient clinical development strategies are needed to evaluate new cancer therapies, especially for children, and incorporate approaches to ensure knowledge gain from investigational products that stall in development.

Validation of Predictive Analyses for Interim Decisions in Clinical Trials.

PURPOSE: Adaptive clinical trials use algorithms to predict, during the study, patient outcomes and final study results. These predictions trigger interim decisions, such as early discontinuation of the trial, and can change the course of the study. Poor selection of the Prediction Analyses and Interim Decisions (PAID) plan in an adaptive clinical trial can have negative consequences, including the risk of exposing patients to ineffective or toxic treatments. METHODS: We present an approach that leverages data sets from completed trials to evaluate and compare candidate PAIDs using interpretable validation metrics. The goal is to determine whether and how to incorporate predictions into major interim decisions in a clinical trial. Candidate PAIDs can differ in several aspects, such as the prediction models used, timing of interim analyses, and potential use of external data sets. To illustrate our approach, we considered a randomized clinical trial in glioblastoma. The study design includes interim futility analyses on the basis of the predictive probability that the final analysis, at the completion of the study, will provide significant evidence of treatment effects. We examined various PAIDs with different levels of complexity to investigate if the use of biomarkers, external data, or novel algorithms improved interim decisions in the glioblastoma clinical trial. RESULTS: Validation analyses on the basis of completed trials and electronic health records support the selection of algorithms, predictive models, and other aspects of PAIDs for use in adaptive clinical trials. By contrast, PAID evaluations on the basis of arbitrarily defined ad hoc simulation scenarios, which are not tailored to previous clinical data and experience, tend to overvalue complex prediction procedures and produce poor estimates of trial operating characteristics such as power and the number of enrolled patients. CONCLUSION: Validation analyses on the basis of completed trials and real world data support the selection of predictive models, interim analysis rules, and other aspects of PAIDs in future clinical trials.

Analysis of BRCA2 Copy Number Loss and Genomic Instability in Circulating Tumor Cells from Patients with Metastatic Castration-resistant Prostate Cancer.

BACKGROUND: BRCA2 alterations predict for a response to poly-ADP-ribose polymerase inhibition in metastatic castration-resistant prostate cancer (mCRPC). However, detection is hindered by insufficient tumor tissue and low sensitivity of cell-free DNA for detecting copy number loss. OBJECTIVE: To evaluate the BRCA2 loss detection using single-cell, shallow whole-genome sequencing (sWGS) of circulating tumor cells (CTCs) in patients with mCRPC. DESIGN, SETTING, AND PARTICIPANTS: We analyzed CTC samples collected concurrently with tumor biopsies intended for clinical sequencing in patients with progressing mCRPC. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Differences in proportions were evaluated using the chi-square test. Correlations between assays were analyzed in linear regression models. Associations between alterations and genomic instability were assessed on the single-cell level using mixed-effect negative binomial models. RESULTS AND LIMITATIONS: We identified 138 patients with concurrent CTC and biopsy samples. CTC sWGS generated copy number profiles in a similar proportion of patients to biopsy samples (83% vs 78%, p = 0.23), but was more effective than bone biopsies (79% vs 50%; p = 0.009). CTC sWGS detected BRCA2 loss in more patients than tissue at the ≥1 (42% vs 16%; p < 0.001) and ≥2 (27% vs 16%; p = 0.028) CTC thresholds. The overall prevalence of BRCA2 loss was not increased in CTCs using sample-level composite z scores (p = 0.4), but was significantly increased compared with a lower-than-expected prevalence in bone samples (21% vs 3%, p = 0.014). Positive/negative predictive values for CTC BRCA2 loss were 89%/96% using the ≥1 CTC threshold and 67%/92% using the composite z score. CTC BRCA2 loss was associated with higher genomic instability in univariate (1.4-fold large-scale transition difference, 95% confidence interval [CI]: 1.2-1.6; p < 0.001) and multivariable analysis (1.4-fold difference, 95% CI: 1.2-1.6; p < 0.001). CONCLUSIONS: Copy number profiles can reliably be generated using CTC sWGS, which detected a majority of tissue-confirmed BRCA2 loss and "CTC-only" losses. BRCA2 losses were supported by increases in genomic instability. PATIENT SUMMARY: Current testing strategies have limitations in their ability to detect BRCA2 loss, a relatively common alteration in prostate cancer that is used to identify patients who may benefit from targeted therapy. In this paper, we evaluated whether we could detect BRCA2 loss in individual tumor cells isolated from patient blood samples and found this method to be suitable for further analysis.

KMDATA: a curated database of reconstructed individual patient-level data from 153 oncology clinical trials.

We created a database of reconstructed patient-level data from published clinical trials that includes multiple time-to-event outcomes such as overall survival and progression-free survival. Outcomes were extracted from Kaplan-Meier (KM) curves reported in 153 oncology Phase III clinical trial publications identified through a PubMed search of clinical trials in breast, lung, prostate and colorectal cancer, published between 2014 and 2016. For each trial that met our search criteria, we curated study-level information and digitized all reported KM curves with the software Digitizelt. We then used the digitized KM survival curves to estimate (possibly censored) patient-level time-to-event outcomes. Collections of time-to-event datasets from completed trials can be used to support the choice of appropriate trial designs for future clinical studies. Patient-level data allow investigators to tailor clinical trial designs to diseases and classes of treatments. Patient-level data also allow investigators to estimate the operating characteristics (e.g. power and type I error rate) of candidate statistical designs and methods. Database URL: https://10.6084/m9.figshare.14642247.v1.

Meta-Analysis of PD-L1 Expression As a Predictor of Survival After Checkpoint Blockade.

PURPOSE: Programmed cell death receptor ligand 1 (PD-L1) expression is the most studied biomarker to predict the efficacy of immune checkpoint inhibitors (ICIs), but its clinical significance is controversial. We estimated the distribution of PD-L1 expression scores (ie, tumor proportion score or combined proportion score) and the relationship between PD-L1 levels and ICIs' impact on overall survival (OS). METHODS: We reconstructed, pooled, and analyzed individual-level data on 7,617 patients with cancer from 14 randomized clinical trials. The effects of ICIs were quantified using differences in 24-month restricted mean survival times (ΔRMSTs; ie, the increase in life expectancy truncated at 2 years associated with ICI therapy). In a simulation study, we compared standard randomized clinical trial designs with a trial design that leverages meta-analytic results like ours. RESULTS: Approximately 93% of patients had a PD-L1 expression ≤ 5% (66% of patients) or > 50% (27% of patients). OS improves with ICIs regardless of PD-L1 expression level, which predicts the benefits' magnitude. For patients with non-small-cell lung cancer (NSCLC), ΔRMSTs ranged from 1.4 months (95% probability interval [PI], 0.7 to 2.2 months) for PD-L1 expression ≤ 1% to 4.1 months (95% PI, 3.2 to 5.2 months) for PD-L1 expression > 80%. For patients with non-NSCLC tumors, ΔRMSTs ranged from 0.8 months (95% PI, -0.1 to 1.7 months) to 2.3 months (95% PI, 1.3 to 4.4 months), again for PD-L1 expression levels of ≤ 1% and > 80%, respectively. Simulations suggested that designs tailored to meta-analytic results can detect the effects of ICIs in PD-L1 subgroups with higher probability (> 15%) than standard designs. CONCLUSION: The practice of dichotomizing the range of PD-L1 expression scores is inadequate for patient stratification. Meta-analytic estimates of the distribution of PD-L1 scores and subgroup-specific treatment effects can improve the designs of future trials of ICIs.

Pathologic Complete Response after Neoadjuvant Chemotherapy and Impact on Breast Cancer Recurrence and Survival: A Comprehensive Meta-analysis.

PURPOSE: While various studies have highlighted the prognostic significance of pathologic complete response (pCR) after neoadjuvant chemotherapy (NAT), the impact of additional adjuvant therapy after pCR is not known. EXPERIMENTAL DESIGN: PubMed was searched for studies with NAT for breast cancer and individual patient-level data was extracted for analysis using plot digitizer software. HRs, with 95% probability intervals (PI), measuring the association between pCR and overall survival (OS) or event-free survival (EFS), were estimated using Bayesian piece-wise exponential proportional hazards hierarchical models including pCR as predictor. RESULTS: Overall, 52 of 3,209 publications met inclusion criteria, totaling 27,895 patients. Patients with a pCR after NAT had significantly better EFS (HR = 0.31; 95% PI, 0.24-0.39), particularly for triple-negative (HR = 0.18; 95% PI, 0.10-0.31) and HER2+ (HR = 0.32; 95% PI, 0.21-0.47) disease. Similarly, pCR after NAT was also associated with improved survival (HR = 0.22; 95% PI, 0.15-0.30). The association of pCR with improved EFS was similar among patients who received subsequent adjuvant chemotherapy (HR = 0.36; 95% PI, 0.19-0.67) and those without adjuvant chemotherapy (HR = 0.36; 95% PI, 0.27-0.54), with no significant difference between the two groups (P = 0.60). CONCLUSIONS: Achieving pCR following NAT is associated with significantly better EFS and OS, particularly for triple-negative and HER2+ breast cancer. The similar outcomes with or without adjuvant chemotherapy in patients who attain pCR likely reflects tumor biology and systemic clearance of micrometastatic disease, highlighting the potential of escalation/deescalation strategies in the adjuvant setting based on neoadjuvant response.See related commentary by Esserman, p. 2771.

Deviation from the Proportional Hazards Assumption in Randomized Phase 3 Clinical Trials in Oncology: Prevalence, Associated Factors, and Implications.

PURPOSE: Deviations from proportional hazards (DPHs), which may be more prevalent in the era of precision medicine and immunotherapy, can lead to underpowered trials or misleading conclusions. We used a meta-analytic approach to estimate DPHs across cancer trials, investigate associated factors, and evaluate data-analysis approaches for future trials.Experimental Design: We searched PubMed for phase III trials in breast, lung, prostate, and colorectal cancer published in a preselected list of journals between 2014 and 2016 and extracted individual patient-level data (IPLD) from Kaplan-Meier curves. We re-analyzed IPLD to identify DPHs. Potential efficiency gains, when DPHs were present, of alternative statistical methods relative to standard log-rank based analysis were expressed as sample-size requirements for a fixed power level. RESULTS: From 152 trials, we obtained IPLD on 129,401 patients. Among 304 Kaplan-Meier figures, 75 (24.7%) exhibited evidence of DPHs, including eight of 14 (57%) KM pairs from immunotherapy trials. Trial type [immunotherapy, odds ratio (OR), 4.29; 95% confidence interval (CI), 1.11-16.6], metastatic patient population (OR, 3.18; 95% CI, 1.26-8.05), and non-OS endpoints (OR, 3.23; 95% CI, 1.79-5.88) were associated with DPHs. In immunotherapy trials, alternative statistical approaches allowed for more efficient clinical trials with fewer patients (up to 74% reduction) relative to log-rank testing. CONCLUSIONS: DPHs were found in a notable proportion of time-to-event outcomes in published clinical trials in oncology and was more common for immunotherapy trials and non-OS endpoints. Alternative statistical methods, without proportional hazards assumptions, should be considered in the design and analysis of clinical trials when the likelihood of DPHs is high.

Risk of ischemic stroke and the use of individual non-steroidal anti-inflammatory drugs: A multi-country European database study within the SOS Project.

BACKGROUND AND PURPOSE: A multi-country European study using data from six healthcare databases from four countries was performed to evaluate in a large study population (>32 million) the risk of ischemic stroke (IS) associated with individual NSAIDs and to assess the impact of risk factors of IS and co-medication. METHODS: Case-control study nested in a cohort of new NSAID users. For each case, up to 100 sex- and age-matched controls were selected and confounder-adjusted odds ratios for current use of individual NSAIDs compared to past use calculated. RESULTS: 49,170 cases of IS were observed among 4,593,778 new NSAID users. Use of coxibs (odds ratio 1.08, 95%-confidence interval 1.02-1.15) and use of traditional NSAIDs (1.16, 1.12-1.19) were associated with an increased risk of IS. Among 32 individual NSAIDs evaluated, the highest significant risk of IS was observed for ketorolac (1.46, 1.19-1.78), but significantly increased risks (in decreasing order) were also found for diclofenac, indomethacin, rofecoxib, ibuprofen, nimesulide, diclofenac with misoprostol, and piroxicam. IS risk associated with NSAID use was generally higher in persons of younger age, males, and those with a prior history of IS. CONCLUSIONS: Risk of IS differs between individual NSAIDs and appears to be higher in patients with a prior history of IS or transient ischemic attack (TIA), in younger or male patients. Co-medication with aspirin, other antiplatelets or anticoagulants might mitigate this risk. The small to moderate observed risk increase (by 13-46%) associated with NSAIDs use represents a public health concern due to widespread NSAID usage.

Risk of acute myocardial infarction during use of individual NSAIDs: A nested case-control study from the SOS project.

BACKGROUND: Use of selective COX-2 non-steroidal anti-inflammatory drugs (NSAIDs) (coxibs) has been associated with an increased risk of acute myocardial infarction (AMI). However, the risk of AMI has only been studied for very few NSAIDs that are frequently used. OBJECTIVES: To estimate the risk of AMI for individual NSAIDs. METHODS: A nested case-control study was performed from a cohort of new NSAID users ≥18 years (1999-2011) matching cases to a maximum of 100 controls on database, sex, age, and calendar time. Data were retrieved from six healthcare databases. Adjusted odds ratios (ORs) of current use of individual NSAIDs compared to past use were estimated per database. Pooling was done by two-stage pooling using a random effects model (ORmeta) and by one-stage pooling (ORpool). RESULTS: Among 8.5 million new NSAID users, 79,553 AMI cases were identified. The risk was elevated for current use of ketorolac (ORmeta 2.06;95%CI 1.83-2.32, ORpool 1.80; 1.49-2.18) followed, in descending order of point estimate, by indometacin, etoricoxib, rofecoxib, diclofenac, fixed combination of diclofenac with misoprostol, piroxicam, ibuprofen, naproxen, celecoxib, meloxicam, nimesulide and ketoprofen (ORmeta 1.12; 1.03-1.22, ORpool 1.00;0.86-1.16). Higher doses showed higher risk estimates than lower doses. CONCLUSIONS: The relative risk estimates of AMI differed slightly between 28 individual NSAIDs. The relative risk was highest for ketorolac and was correlated with COX-2 potency, but not restricted to coxibs.

The lag-time approach improved drug-outcome association estimates in presence of protopathic bias.

OBJECTIVES: Protopathic bias is a systematic error which occurs when measured exposure status may be affected by the latent onset of the target outcome. In this article, we aimed to discuss the benefits and drawbacks of the lag-time approach to address this type of bias. STUDY DESIGN AND SETTING: The lag-time approach consists in excluding from exposure assessment the period immediately preceding the outcome detection date. With the help of simple causal diagrams, we illustrate the rationale and limitations of such strategy. The lag-time approach was illustrated in a case-crossover study, based on the health care utilization databases of the Italian Lombardy Region, on the real-world effectiveness of some respiratory drugs (exposure) in preventing asthma exacerbations (outcome). RESULTS: A total of 7,300 of patients who were admitted to an emergency department (ED) for asthma during 2010-2012 (cases) were included. Use (vs. nonuse) of short-acting beta-agonists (SABAs, an asthma reliever medication) during the 90 days before the ED admission date was associated with an increased risk of the outcome [odds ratio (OR): 1.95; 95% confidence interval (CI): 1.72, 2.22]. This paradoxical finding may be explained by protopathic bias, as SABA use prior the ED admission may be affected by preceding respiratory distress. Indeed, when a 120-day period preceding the ED admission was ignored from drug exposure assessment (lag time), SABAs were found to be associated with a reduced risk of the outcome (OR: 0.81; 95% CI: 0.84, 0.92), as expected. CONCLUSIONS: The lag-time approach can be a useful strategy to circumvent protopathic bias in observational studies.