Investigating hospital heterogeneity with a competing risks frailty model

Comparative Analysis of Women's Breast Cancer Survival Time at Three Selected Government Referral Hospitals in Ethiopia's Amhara Region Using Parametric Shared Frailty Models

Background

One in five people will eventually develop cancer, and one in eleven women will lose their lives to the disease. The main aim of this study is to determinants of survival time of women with breast cancer using appropriate Frailty models.

Methods

A study involving 632 Ethiopian women with breast cancer was conducted between 2018 and 2020, utilizing medical records from Felege-Hiwot Referral Hospital, the University of Gondar, and Dessie Referral Hospital. To compare survival, the Kaplan-Meier plot (s) and Log rank test were employed; to assess mean survival, one-way analysis of variance and the t test were utilized. The factors influencing women's survival times from breast cancer were identified using the parametric shared frailty model and the accelerated failure time model.

Results

The median time to die for breast cancer patients treated at FHRH, UoGCSH, and DRH was 14.91 months, 11.14 months, and 12.32 months, respectively. The parametric model of shared frailty fit those who were statistically significant in univariate analysis. The results showed that survival of women with breast cancer was significantly influenced by age, tumor size, comorbidity, nodal status, stage, histologic grade, and type of primary treatment initiated. When comparing mean survival times between hospitals, the results showed a significant difference; patients who were treated in FHRH live significantly longer than patients treated in UoGCSH and DRH, whereas patients treated in UoGCSH have comparatively lower survival. Women with stage IV and comorbidities have 22.4% and 27.1% shorter expected survival, respectively.

Conclusion

This finding suggests that improving the availability and accessibility of radiation therapy and surgery, eliminating disparities between hospitals, raising awareness of early signs and symptoms of breast cancer and encouraging women to seek clinical help, and highlighting women with comorbidities at diagnosis are important ways to increase survival time.

Developing prediction models to estimate the risk of two survival outcomes both occurring: A comparison of techniques

INTRODUCTION

This study considers the prediction of the time until two survival outcomes have both occurred. We compared a variety of analytical methods motivated by a typical clinical problem of multimorbidity prognosis.

METHODS

We considered five methods: product (multiply marginal risks), dual-outcome (directly model the time until both events occur), multistate models (msm), and a range of copula and frailty models. We assessed calibration and discrimination under a variety of simulated data scenarios, varying outcome prevalence, and the amount of residual correlation. The simulation focused on model misspecification and statistical power. Using data from the Clinical Practice Research Datalink, we compared model performance when predicting the risk of cardiovascular disease and type 2 diabetes both occurring.

RESULTS

Discrimination was similar for all methods. The product method was poorly calibrated in the presence of residual correlation. The msm and dual-outcome models were the most robust to model misspecification but suffered a drop in performance at small sample sizes due to overfitting, which the copula and frailty model were less susceptible to. The copula and frailty model's performance were highly dependent on the underlying data structure. In the clinical example, the product method was poorly calibrated when adjusting for 8 major cardiovascular risk factors.

DISCUSSION

We recommend the dual-outcome method for predicting the risk of two survival outcomes both occurring. It was the most robust to model misspecification, although was also the most prone to overfitting. The clinical example motivates the use of the methods considered in this study.

Facility profiling under competing risks using multivariate prognostic scores: Application to kidneytransplant centers

The performance of health care facilities (e.g. hospitals, transplant centers, etc.) is often evaluated through time-to-event outcomes. In this paper, we consider the case where, for each subject, the failure event is due to one of several mutually exclusive causes (competing risks). Since the distribution of patient characteristics may differ greatly by the center, some form of covariate adjustment is generally necessary in order for center-specific outcomes to be accurately compared (to each other or to an overall average). We propose a weighting method for comparing facility-specific cumulative incidence functions to an overall average. The method directly standardizes each facility's non-parametric cumulative incidence function through a weight function constructed from a multivariate prognostic score. We formally define the center effects and derive large-sample properties of the proposed estimator. We evaluate the finite sample performance of the estimator through simulation. The proposed method is applied to the end-stage renal disease setting to evaluate the center-specific pre-transplant mortality and transplant cumulative incidence functions from the Scientific Registry of Transplant Recipients.

Adherence to European ovarian cancer guidelines and impact on survival: a French multicenter study (FRANCOGYN)

OBJECTIVE

The primary objective of the study was to validate the European Society for Medical Oncology (ESMO)-European Society of Gynecologic Oncology (ESGO) ovarian cancer guideline as a method of assessing quality of care, and to identify patient characteristics predictive of non-adherence to European guideline care. The secondary objectives were to analyze the evolution of practices over the years and to evaluate heterogeneity between centers.

METHODS

This retrospective multicenter cohort study of invasive epithelial ovarian cancer reported to the FRANCOGYN database included data from 12 French centers between January 2000 and February 2017. The main outcome was adherence to ESMO-ESGO guidelines, defined by recommended surgical procedures according to the International Federation of Gynecology and Obstetrics (FIGO) stage and appropriate chemotherapy. Mixed multivariable logistic regression analysis with a random center effect was performed to estimate the probability of adherence to the guidelines. Survival analysis was carried out using the Kaplan-Meier method and a mixed Cox proportional hazards model.

RESULTS

1463 patients were included in the study. Overall, 317 (30%) patients received complete guideline adherent care. Patients received appropriate surgical treatment in 69% of cases, while adequate chemotherapy was administered to 44% of patients. Both patient demographics and disease characteristics were significantly associated with the likelihood of receiving guideline adherent care, such as age, performance status, FIGO stage, and initial burden of disease. In univariate and multivariate survival analysis, adherence to the guidelines was a statistically significant and independent predictor of decreased overall survival. Patients receiving suboptimal care experienced an increased risk of death of more than 100% compared with those treated according to the guidelines (hazard ratio 2.14, 95% confidence interval 1.32 to 3.47, p<0.01). In both models, a significant random center effect was observed, confirming the heterogeneity between centers (p<0.001).

CONCLUSIONS

Adherence to ESMO-ESGO guidelines in ovarian cancer was associated with a higher overall survival and may be a useful method of assessing quality of care.

Probability estimation model for the cancellation of container slot booking in long-haul transports of intercontinental liner shipping services

The intercontinental liner shipping services transport containers between two continents and they are crucial for the profitability of a global liner shipping company. In the daily operations of an intercontinental liner shipping service, however, container slot bookings from customers can be freely cancelled during a booking period, which causes loss of revenue and low utilization of ship capacity. Though a pain-point of the liner shipping industry, the container slot cancellation problem has not yet been well investigated in the literature. To fill this research gap, this study aims to estimate the probability for the cancellation of container slot booking in the long haul transports of the intercontinental liner shipping service by considering the primary influential factors of cancellation behavior. To achieve the objective, a container slot booking data-driven model is developed by means of a time-to-event modeling technique. To incorporate the effect of booking region on the cancellation probability, we introduce the frailty term in the model to capture the regionality of the container shipping market. Our case study with real slot booking data shows that the developed model performs well in forecasting the loaded containers of the slot booking requests. In addition, we shed light on how the internal factors of slot booking and external factors of shipping market influence the probability of cancellation.

Dynamic early identification of hip replacement implants with high revision rates. Study based on the NJR data from UK during 2004-2012

Background

Hip replacement and hip resurfacing are common surgical procedures with an estimated risk of revision of 4% over 10 year period. Approximately 58% of hip replacements will last 25 years. Some implants have higher revision rates and early identification of poorly performing hip replacement implant brands and cup/head brand combinations is vital.

Aims

Development of a dynamic monitoring method for the revision rates of hip implants.

Methods

Data on the outcomes following the hip replacement surgery between 2004 and 2012 was obtained from the National Joint Register (NJR) in the UK. A novel dynamic algorithm based on the CUmulative SUM (CUSUM) methodology with adjustment for casemix and random frailty for an operating unit was developed and implemented to monitor the revision rates over time. The Benjamini-Hochberg FDR method was used to adjust for multiple testing of numerous hip replacement implant brands and cup/ head combinations at each time point.

Results

Three poorly performing cup brands and two cup/ head brand combinations have been detected. Wright Medical UK Ltd Conserve Plus Resurfacing Cup (cup o), DePuy ASR Resurfacing Cup (cup e), and Endo Plus (UK) Limited EP-Fit Plus Polyethylene cup (cup g) showed stable multiple alarms over the period of a year or longer. An addition of a random frailty term did not change the list of underperforming components. The model with added random effect was more conservative, showing less and more delayed alarms.

Conclusions

Our new algorithm is an efficient method for early detection of poorly performing components in hip replacement surgery. It can also be used for similar tasks of dynamic quality monitoring in healthcare.

Investigating hospital heterogeneity with a competing risks frailty model

Survival analysis is used in the medical field to identify the effect of predictive variables on time to a specific event. Generally, not all variation of survival time can be explained by observed covariates. The effect of unobserved variables on the risk of a patient is called frailty. In multicenter studies, the unobserved center effect can induce frailty on its patients, which can lead to selection bias over time when ignored. For this reason, it is common practice in multicenter studies to include a random frailty term modeling center effect. In a more complex event structure, more than one type of event is possible. Independent frailty variables representing center effect can be incorporated in the model for each competing event. However, in the medical context, events representing disease progression are likely related and correlation is missed when assuming frailties to be independent. In this work, an additive gamma frailty model to account for correlation between frailties in a competing risks model is proposed, to model frailties at center level. Correlation indicates a common center effect on both events and measures how closely the risks are related. Estimation of the model using the expectation‐maximization algorithm is illustrated. The model is applied to a data set from a multicenter clinical trial on breast cancer from the European Organisation for Research and Treatment of Cancer (EORTC trial 10854). Hospitals are compared by employing empirical Bayes estimates methodology together with corresponding confidence intervals.