Visualizing hypothesis tests in survival analysis under anticipated delayed effects.

What can be considered an appropriate statistical method for the primary analysis of a randomized clinical trial (RCT) with a time-to-event endpoint when we anticipate non-proportional hazards owing to a delayed effect? This question has been the subject of much recent debate. The standard approach is a log-rank test and/or a Cox proportional hazards model. Alternative methods have been explored in the statistical literature, such as weighted log-rank tests and tests based on the Restricted Mean Survival Time (RMST). While weighted log-rank tests can achieve high power compared to the standard log-rank test, some choices of weights may lead to type-I error inflation under particular conditions. In addition, they are not linked to a mathematically unambiguous summary measure. Test statistics based on the RMST, on the other hand, allow one to investigate the average difference between two survival curves up to a pre-specified time point τ $$ \tau $$ -a mathematically unambiguous summary measure. However, by emphasizing differences prior to τ $$ \tau $$ , such test statistics may not fully capture the benefit of a new treatment in terms of long-term survival. In this article, we introduce a graphical approach for direct comparison of weighted log-rank tests and tests based on the RMST. This new perspective allows a more informed choice of the analysis method, going beyond power and type I error comparison.

Non-parametric frailty Cox models for hierarchical time-to-event data.

We propose a novel model for hierarchical time-to-event data, for example, healthcare data in which patients are grouped by their healthcare provider. The most common model for this kind of data is the Cox proportional hazard model, with frailties that are common to patients in the same group and given a parametric distribution. We relax the parametric frailty assumption in this class of models by using a non-parametric discrete distribution. This improves the flexibility of the model by allowing very general frailty distributions and enables the data to be clustered into groups of healthcare providers with a similar frailty. A tailored Expectation-Maximization algorithm is proposed for estimating the model parameters, methods of model selection are compared, and the code is assessed in simulation studies. This model is particularly useful for administrative data in which there are a limited number of covariates available to explain the heterogeneity associated with the risk of the event. We apply the model to a clinical administrative database recording times to hospital readmission, and related covariates, for patients previously admitted once to hospital for heart failure, and we explore latent clustering structures among healthcare providers.

Evaluating the effect of healthcare providers on the clinical path of heart failure patients through a semi-Markov, multi-state model.

BACKGROUND: Investigating similarities and differences among healthcare providers, on the basis of patient healthcare experience, is of interest for policy making. Availability of high quality, routine health databases allows a more detailed analysis of performance across multiple outcomes, but requires appropriate statistical methodology. METHODS: Motivated by analysis of a clinical administrative database of 42,871 Heart Failure patients, we develop a semi-Markov, illness-death, multi-state model of repeated admissions to hospital, subsequent discharge and death. Transition times between these health states each have a flexible baseline hazard, with proportional hazards for patient characteristics (case-mix adjustment) and a discrete distribution for frailty terms representing clusters of providers. Models were estimated using an Expectation-Maximization algorithm and the number of clusters was based on the Bayesian Information Criterion. RESULTS: We are able to identify clusters of providers for each transition, via the inclusion of a nonparametric discrete frailty. Specifically, we detect 5 latent populations (clusters of providers) for the discharge transition, 3 for the in-hospital to death transition and 4 for the readmission transition. Out of hospital death rates are similar across all providers in this dataset. Adjusting for case-mix, we could detect those providers that show extreme behaviour patterns across different transitions (readmission, discharge and death). CONCLUSIONS: The proposed statistical method incorporates both multiple time-to-event outcomes and identification of clusters of providers with extreme behaviour simultaneously. In this way, the whole patient pathway can be considered, which should help healthcare managers to make a more comprehensive assessment of performance.

Laparoscopic Retroperitoneal Lymph Node Dissection for Clinical Stage I Nonseminomatous Germ Cell Tumors of the Testis: Safety and Efficacy Analyses at a High Volume Center.

PURPOSE: The prognosis of stage I nonseminomatous germ cell tumor of the testis is favorable. Early and late side effects of treatment may affect quality of life and survival. We determined the tolerability, safety and efficacy of laparoscopic retroperitoneal lymph node dissection in patients with stage I nonseminomatous germ cell tumor of the testis at a high volume center. MATERIALS AND METHODS: Unilateral laparoscopic retroperitoneal lymph node dissection was prospectively recorded in 225 patients from 2000 to 2014. Since 2007, patients have been treated at a multidisciplinary clinic and were proposed surgery as an alternative to surveillance or adjuvant chemotherapy. The indication for adjuvant chemotherapy changed during the study period. Descriptive statistics and regression analyses were used to evaluate the domains of safety and oncologic outcomes. RESULTS: A total of 221 patients were evaluable. Median operative time was 200 minutes. Conversion to open surgery was done in 20 cases (9%). A median of 14 nodes (IQR 11-20) was retrieved. Grade greater than 2 complications in 8 cases (3.6%) increased as the number of retrieved nodes increased. Antegrade ejaculation was maintained in 98.6% of patients. Nodal metastases were found in 29 patients (13%), of whom 7 underwent adjuvant chemotherapy. There were 14 recurrences (6.3%), including 8 of 192 (4.2%) associated with no nodal metastases and 6 of 22 (27.3%) associated with nodal metastases in patients not undergoing adjuvant chemotherapy. At regression analyses lymph node ratio was the only significant factor predictive of recurrence and of the administration of any chemotherapy (each p <0.001). Operative time, the number of retrieved nodes and conversions improved with time. CONCLUSIONS: In the context of a high volume center laparoscopic retroperitoneal lymph node dissection was safe and its oncologic efficacy was comparable to that of open surgery. Select patients with stage I nonseminomatous germ cell tumor could be offered laparoscopic retroperitoneal lymph node dissection as an alternative to other options.

Score-Driven Modeling of Spatio-Temporal Data.

A simultaneous autoregressive score-driven model with autoregressive disturbances is developed for spatio-temporal data that may exhibit heavy tails. The model specification rests on a signal plus noise decomposition of a spatially filtered process,where the signal can be approximated by a nonlinear function of the past variables and a set of explanatory variables, while the noise follows a multivariate Student-t distribution. The key feature of the model is that the dynamics of the space-time varying signal are driven by the score of the conditional likelihood function.When the distribution is heavy-tailed, the score provides a robust update of the space-time varying location. Consistency and asymptotic normality ofmaximum likelihood estimators are derived along with the stochastic properties of the model. The motivating application of the proposed model comes from brain scans recorded through functional magnetic resonance imaging when subjects are at rest and not expected to react to any controlled stimulus. We identify spontaneous activations in brain regions as extreme values of a possibly heavy-tailed distribution, by accounting for spatial and temporal dependence.

Adherence to Disease-Modifying Therapy in Patients Hospitalized for HF: Findings from a Community-Based Study.

BACKGROUND: Much data about prescription adherence in patients with heart failure (HF) are available, but few exist about the evaluation of true patient adherence. Further, methods for analyzing this issue are poorly known. OBJECTIVES: Our objective was to evaluate the impact of patient adherence to disease-modifying drugs after HF hospitalization in a community-based cohort. METHODS AND RESULTS: Patients hospitalized with first diagnostic HF code and at least one post-discharge purchase of evidence-based drugs for HF between 2009 and 2015 were included (12,938 patients). A new method for measuring adherence to polypharmacy (patient adherence indicator [PAI]) was introduced, based on proportion of days covered (PDC) and medication possession ratio (MPR). The investigated drugs were ß-blockers (BBs), angiotensin-converting enzyme inhibitors (ACEIs), angiotensin-receptor blockers (ARBs), and anti-aldosterone agents (AAs). Regional administrative databases were analyzed. RESULTS: The mean age of the cohort was 80 years; 53% was female; the median Charlson Comorbidity Index score was 2, and the overall death rate was 60%. PAI based on PDC estimated a nonadherence rate of 47%. Median daily dosages were well below target dosages for all drugs considered. A good PAI significantly lowered the mortality risk, irrespective of the computational method used: PDC (PAI adjusted hazard ratio [HR] 0.93; 95% confidence interval [CI] 0.88-0.97; p = 0.001) or MPR (PAI adjusted HR 0.93; 95% CI 0.89-0.98; p = 0.004). CONCLUSIONS: In a real-world setting, medication adherence of patients with HF remains unsatisfactory, especially when in a polypharmacy setting. Irrespective of PDC and MPR, good patient adherence to polypharmacy was associated with a lower death rate.

A Deep Learning Approach Validates Genetic Risk Factors for Late Toxicity After Prostate Cancer Radiotherapy in a REQUITE Multi-National Cohort.

Background: REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) is an international prospective cohort study. The purpose of this project was to analyse a cohort of patients recruited into REQUITE using a deep learning algorithm to identify patient-specific features associated with the development of toxicity, and test the approach by attempting to validate previously published genetic risk factors. Methods: The study involved REQUITE prostate cancer patients treated with external beam radiotherapy who had complete 2-year follow-up. We used five separate late toxicity endpoints: ≥grade 1 late rectal bleeding, ≥grade 2 urinary frequency, ≥grade 1 haematuria, ≥ grade 2 nocturia, ≥ grade 1 decreased urinary stream. Forty-three single nucleotide polymorphisms (SNPs) already reported in the literature to be associated with the toxicity endpoints were included in the analysis. No SNP had been studied before in the REQUITE cohort. Deep Sparse AutoEncoders (DSAE) were trained to recognize features (SNPs) identifying patients with no toxicity and tested on a different independent mixed population including patients without and with toxicity. Results: One thousand, four hundred and one patients were included, and toxicity rates were: rectal bleeding 11.7%, urinary frequency 4%, haematuria 5.5%, nocturia 7.8%, decreased urinary stream 17.1%. Twenty-four of the 43 SNPs that were associated with the toxicity endpoints were validated as identifying patients with toxicity. Twenty of the 24 SNPs were associated with the same toxicity endpoint as reported in the literature: 9 SNPs for urinary symptoms and 11 SNPs for overall toxicity. The other 4 SNPs were associated with a different endpoint. Conclusion: Deep learning algorithms can validate SNPs associated with toxicity after radiotherapy for prostate cancer. The method should be studied further to identify polygenic SNP risk signatures for radiotherapy toxicity. The signatures could then be included in integrated normal tissue complication probability models and tested for their ability to personalize radiotherapy treatment planning.

Multi-state modelling of heart failure care path: A population-based investigation from Italy.

BACKGROUND: How different risk profiles of heart failure (HF) patients can influence multiple readmissions and outpatient management is largely unknown. We propose the application of two multi-state models in real world setting to jointly evaluate the impact of different risk factors on multiple hospital admissions, Integrated Home Care (IHC) activations, Intermediate Care Unit (ICU) admissions and death. METHODS AND FINDINGS: The first model (model 1) concerns only hospitalizations as possible events and aims at detecting the determinants of repeated hospitalizations. The second model (model 2) considers both hospitalizations and ICU/IHC events and aims at evaluating which profiles are associated with transitions in intermediate care with respect to repeated hospitalizations or death. Both are characterized by transition specific covariates, adjusting for risk factors. We identified 4,904 patients (4,129 de novo and 775 worsening heart failure, WHF) hospitalized for HF from 2009 to 2014. 2,714 (55%) patients died. Advanced age and higher morbidity load increased the rate of dying and of being rehospitalized (model 1), decreased the rate of being discharged from hospital (models 1 and 2) and increased the rate of inactivation of IHC (model 2). WHF was an important risk factor associated with hospital readmission. CONCLUSION: Multi-state models enable a better identification of two patterns of HF patients. Once adjusted for age and comorbidity load, the WHF condition identifies patients who are more likely to be readmitted to hospital, but does not represent an increasing risk factor for activating ICU/IHC. This highlights different ways to manage specific patients' patterns of care. These results provide useful healthcare support to patients' management in real world context. Our study suggests that the epidemiology of the considered clinical characteristics is more nuanced than traditionally presented through a single event.