A novel nonparametric time-dependent precision-recall curve estimator for right-censored survival data.

In order to assess prognostic risk for individuals in precision health research, risk prediction models are increasingly used, in which statistical models are used to estimate the risk of future outcomes based on clinical and nonclinical characteristics. The predictive accuracy of a risk score must be assessed before it can be used in routine clinical decision making, where the receiver operator characteristic curves, precision-recall curves, and their corresponding area under the curves are commonly used metrics to evaluate the discriminatory ability of a continuous risk score. Among these the precision-recall curves have been shown to be more informative when dealing with unbalanced biomarker distribution between classes, which is common in rare event, even though except one, all existing methods are proposed for classic uncensored data. This paper is therefore to propose a novel nonparametric estimation approach for the time-dependent precision-recall curve and its associated area under the curve for right-censored data. A simulation is conducted to show the better finite sample property of the proposed estimator over the existing method and a real-world data from primary biliary cirrhosis trial is used to demonstrate the practical applicability of the proposed estimator.

Time-dependent receiver operating characteristic curve estimator for correlated right-censored time-to-event data.

In clinical trials, evaluating the accuracy of risk scores (markers) derived from prognostic models for prediction of survival outcomes is of major concern. The time-dependent receiver operating characteristic curve and the corresponding area under the receiver operating characteristic curve are appealing measures to evaluate the predictive accuracy. Several estimation methods have been proposed in the context of classical right-censored data which assumes the event time of individuals are independent. In many applications, however, this may not hold true if, for example, individuals belong to clusters or experience recurrent events. Estimates may be biased if this correlated nature is not taken into account. This paper is then aimed to fill this knowledge gap to introduce a time-dependent receiver operating characteristic curve and the corresponding area under the receiver operating characteristic curve estimation method for right-censored data that take the correlated nature into account. In the proposed method, the unknown status of censored subjects is imputed using conditional survival functions given the marker and frailty of the subjects. An extensive simulation study is conducted to evaluate and demonstrate the finite sample performance of the proposed method. Finally, the proposed method is illustrated using two real-world examples of lung cancer and kidney disease.

Factors affecting utilization of modern contraceptive methods among women of reproductive age in Ethiopia.

INTRODUCTION: Modern contraceptive use is important for improving health and socioeconomic outcomes, but Ethiopia is among the lowest-using countries. Therefore, this study aimed to determine factors affecting modern contraceptive use among women of reproductive age in Ethiopia. METHODS: This population-based cross-sectional study used data obtained from the 2019 Ethiopia Mini Demographic and Health Survey (EMDHS). A total of 8,885 reproductive-age women were included in the analysis. A weighted generalized estimating equation approach was used to account for the clustering and weighting effects in the assessment of associations between modern contraceptive usage and socioeconomic and demographic variables. RESULTS: Modern contraceptive use among women of reproductive age in Ethiopia is low (28%). Prevalence is highest among women aged 25-34 (40.11%), with higher education (30.97%), who are Orthodox Christians (31.67%), married (40.40%), middle wealth index (31.70%), female-headed households (31.42%), with 1-3 living children (44.85%), who headed by under 31 years old (40.07%), and in the Amhara region (34.45%). In the generalized estimating equation analysis, women aged 35-44 and over 45, Muslims, households heads aged 41-50 and over 50, and in female-headed households were less likely to use modern contraceptives, while women with primary, secondary, and higher education, married, middle and rich wealth index, and with 1-3 and more living children were more likely to use modern contraceptive than their counterparts (reference group) and were statistically significant. CONCLUSION: Modern contraceptive use is notably low among women of reproductive age in Ethiopia. Factors such as age, women's educational level, religion, marital status, number of living children, wealth status, gender and age of household head, and region were identified as significant factors associated with modern contraceptive use. Therefore, to increase modern contraceptive use, governmental and non-governmental organizations should invest in women's education and financial empowerment and raise awareness about the benefits of modern contraceptives, especially among older, unmarried, financially poor, elderly-led households, with few living children, and uneducated women.

Time-dependent ROC curve estimation for interval-censored data.

The receiver-operating characteristic (ROC) curve is the most popular graphical method for evaluating the classification accuracy of a diagnostic marker. In time-to-event studies, the subject's event status is time-dependent, and hence, time-dependent extensions of ROC curve have been proposed. However, in practice, the calculation of this curve is not straightforward due to the presence of censoring that may be of different types. Existing methods focus on the more standard and simple case of right-censoring and neglect the general case of mixed interval-censored data that may involve left-, right-, and interval-censored observations. In this context, we propose and study a new time-dependent ROC curve estimator. We also consider some summary measures (area under the ROC curve and Youden index) traditionally associated with ROC as well as the Youden-based cutoff estimation method. The proposed method uses available data very efficiently. To this end, the unknown status (positive or negative) of censored subjects are estimated from the data via the estimation of the conditional survival function given the marker. For that, we investigate both model-based and nonparametric approaches. We also provide variance estimates and confidence intervals using Bootstrap. A simulation study is conducted to investigate the finite sample behavior of the proposed methods and to compare their performance with a competitor. Globally, we observed better finite sample performances for the proposed estimators. Finally, we illustrate the methods using two data sets one from a hypobaric decompression sickness study and the other from an oral health study. The proposed methods are implemented in the R package cenROC.

Smoothed time-dependent receiver operating characteristic curve for right censored survival data.

The prediction reliability is of primary concern in many clinical studies when the objective is to develop new predictive models or improve existing risk scores. In fact, before using a model in any clinical decision making, it is very important to check its ability to discriminate between subjects who are at risk of, for example, developing certain disease in a near future from those who will not. To that end, the time-dependent receiver operating characteristic (ROC) curve is the most commonly used method in practice. Several approaches have been proposed in the literature to estimate the ROC nonparametrically in the context of survival data. But, except one recent approach, all the existing methods provide a nonsmooth ROC estimator whereas, by definition, the ROC curve is smooth. In this article we propose and study a new nonparametric smooth ROC estimator based on a weighted kernel smoother. More precisely, our approach relies on a well-known kernel method used to estimate cumulative distribution functions of random variables with bounded supports. We derived some asymptotic properties for the proposed estimator. As bandwidth is the main parameter to be set, we present and study different methods to appropriately select one. A simulation study is conducted, under different scenarios, to prove the consistency of the proposed method and to compare its finite sample performance with a competitor. The results show that the proposed method performs better and appear to be quite robust to bandwidth choice. As for inference purposes, our results also reveal the good performances of a proposed nonparametric bootstrap procedure. Furthermore, we illustrate the method using a real data example.