Assessing of case-cohort design: a case study for breast cancer patients in Xinjiang, China

Objective

To assess the effectiveness and clinical value of case-cohort design and determine prognostic factors of breast cancer patients in Xinjiang on the basis of case-cohort design.

Methods

The survival data with different sample characteristics were simulated by using Cox proportional risk models. To evaluate the effectiveness for the case-cohort, entire cohort, and simple random sampling design by comparing the mean, coefficient of variation, etc., of covariate parameters. Furthermore, the prognostic factors of breast cancer patients in Xinjiang were determined based on case-cohort sampling designs. The models were comprehensively evaluated by likelihood ratio test, the area under the receiver operating characteristic curve (AUC), and Akaike Information Criterion (AIC).

Results

In a simulations study, the case-cohort design shows better stability and improves the estimation efficiency when the censored rate is high. In the breast cancer data, molecular subtypes, T-stage, N-stage, M-stage, types of surgery, and postoperative chemotherapy were identified as the prognostic factors of patients in Xinjiang. These models based on the different sampling designs both passed the likelihood ratio test (p<0.05). Moreover, the model constructed under the case-cohort design had better fitting effect (AIC=3,999.96) and better discrimination (AUC=0.807).

Conclusion

Simulations study confirmed the effectiveness of case-cohort design and further determined the prognostic factors of breast cancer patients in Xinjiang based on this design, which presented the practicality of case-cohort design in actual data.

Variance Estimation for Logistic Regression in Case-cohort Studies

BACKGROUND

The logistic regression analysis proposed by Schouten et al (Stat Med. 1993;12:1733-1745) has been a standard method in current statistical analysis of case-cohort studies, and it enables effective estimation of risk ratios from selected subsamples, with adjustment of potential confounding factors. Schouten et al (1993) also proposed the standard error estimate of the risk ratio estimator can be calculated using the robust variance estimator, and this method has been widely adopted.

METHODS AND RESULTS

The robust variance estimator does not account for the duplications of case and subcohort samples and generally has certain bias (ie, inaccurate confidence intervals and P-values are possibly obtained). To address the invalid statistical inference problem, we provide an alternative bootstrap-based valid variance estimator. Through simulation studies, the bootstrap method consistently provided more precise confidence intervals compared with those provided using the robust variance method, while retaining adequate coverage probabilities.

CONCLUSION

The robust variance estimator has certain bias, and inadequate conclusions might be deduced from the resultant statistical analyses. The proposed bootstrap variance estimator can provide more accurate and precise interval estimates. The bootstrap method would be an alternative effective approach in practice to provide accurate evidence.

Sure Joint Screening for High Dimensional Cox's Proportional Hazards Model Under the Case-Cohort Design

This study develops a sure joint feature screening method for the case-cohort design with ultrahigh-dimensional covariates. Our method is based on a sparsity-restricted Cox proportional hazards model. An iterative reweighted hard thresholding algorithm is proposed to approximate the sparsity-restricted, pseudo-partial likelihood estimator for joint screening. We rigorously show that our method possesses the sure screening property, with the probability of retaining all relevant covariates tending to 1 as the sample size goes to infinity. Our simulation results demonstrate that the proposed procedure has substantially improved screening performance over some existing feature screening methods for the case-cohort design, especially when some covariates are jointly correlated, but marginally uncorrelated, with the event time outcome. A real data illustration is provided using breast cancer data with high-dimensional genomic covariates. We have implemented the proposed method using MATLAB and made it available to readers through GitHub.

Machine Learning-Based Urine Peptidome Analysis to Predict and Understand Mechanisms of Progression to Kidney Failure

Introduction

The identification of patients with chronic kidney disease (CKD) at risk of progressing to kidney failure (KF) is important for clinical decision-making. In this study we assesed whether urinary peptidome (UP) analysis may help classify patients with CKD and improve KF risk prediction.

Methods

The UP was analyzed using capillary electrophoresis coupled to mass spectrometry in a case-cohort sample of 1000 patients with CKD stage G3 to G5 from the French CKD-Renal Epidemiology and Information Network (REIN) cohort. We used unsupervised and supervised machine learning to classify patients into homogenous UP clusters and to predict 3-year KF risk with UP, respectively. The predictive performance of UP was compared with the KF risk equation (KFRE), and evaluated in an external cohort of 326 patients.

Results

More than 1000 peptides classified patients into 3 clusters with different CKD severities and etiologies at baseline. Peptides with the highest discriminative power for clustering were fragments of proteins involved in inflammation and fibrosis, highlighting those derived from α-1-antitrypsin, a major acute phase protein with anti-inflammatory and antiapoptotic properties, as the most significant. We then identified a set of 90 urinary peptides that predicted KF with a c-index of 0.83 (95% confidence interval [CI]: 0.81-0.85) in the case-cohort and 0.89 (0.83-0.94) in the external cohort, which were close to that estimated with the KFRE (0.85 [0.83-0.87]). Combination of UP with KFRE variables did not further improve prediction.

Conclusion

This study shows the potential of UP analysis to uncover new pathophysiological CKD progression pathways and to predict KF risk with a performance equal to that of the KFRE.

Pulmonary surfactant protein B carried by HDL predicts incident CVD in patients with type 1 diabetes

Atherosclerotic CVD is the major cause of death in patients with type 1 diabetes mellitus (T1DM). Alterations in the HDL proteome have been shown to associate with prevalent CVD in T1DM. We therefore sought to determine which proteins carried by HDL might predict incident CVD in patients with T1DM. Using targeted MS/MS, we quantified 50 proteins in HDL from 181 T1DM subjects enrolled in the prospective Coronary Artery Calcification in Type 1 Diabetes study. We used Cox proportional regression analysis and a case-cohort design to test associations of HDL proteins with incident CVD (myocardial infarction, coronary artery bypass grafting, angioplasty, or death from coronary heart disease). We found that only one HDL protein-SFTPB (pulmonary surfactant protein B)-predicted incident CVD in all the models tested. In a fully adjusted model that controlled for lipids and other risk factors, the hazard ratio was 2.17 per SD increase of SFTPB (95% confidence interval, 1.12-4.21, P = 0.022). In addition, plasma fractionation demonstrated that SFTPB is nearly entirely bound to HDL. Although previous studies have shown that high plasma levels of SFTPB associate with prevalent atherosclerosis only in smokers, we found that SFTPB predicted incident CVD in T1DM independently of smoking status and a wide range of confounding factors, including HDL-C, LDL-C, and triglyceride levels. Because SFTPB is almost entirely bound to plasma HDL, our observations support the proposal that SFTPB carried by HDL is a marker-and perhaps mediator-of CVD risk in patients with T1DM.

Semiparametric regression analysis of case-cohort studies with multiple interval-censored disease outcomes

Interval-censored failure time data commonly arise in epidemiological and biomedical studies where the occurrence of an event or a disease is determined via periodic examinations. Subject to interval-censoring, available information on the failure time can be quite limited. Cost-effective sampling designs are desirable to enhance the study power, especially when the disease rate is low and the covariates are expensive to obtain. In this work, we formulate the case-cohort design with multiple interval-censored disease outcomes and also generalize it to nonrare diseases where only a portion of diseased subjects are sampled. We develop a marginal sieve weighted likelihood approach, which assumes that the failure times marginally follow the proportional hazards model. We consider two types of weights to account for the sampling bias, and adopt a sieve method with Bernstein polynomials to handle the unknown baseline functions. We employ a weighted bootstrap procedure to obtain a variance estimate that is robust to the dependence structure between failure times. The proposed method is examined via simulation studies and illustrated with a dataset on incident diabetes and hypertension from the Atherosclerosis Risk in Communities study.

A class of goodness-of-fit test for the additive hazards model with case-cohort data

The case-cohort design is commonly used in epidemiological studies due to its cost-effectiveness. The additive hazards model is widely used in survival analysis when the hazards difference is constant. In this article, we propose a class of goodness-of-fit test statistics for the assumption of the additive hazards model with case-cohort data through a class of asymptotically mean-zero multiparameter stochastic processes. We also establish the asymptotic theory of the proposed test statistics and a resampling scheme is adopted to approximate its asymptotic distribution. The performance of the proposed test statistics is evaluated through simulation studies and a real dataset is analyzed to illustrate the proposed method.

Designing a Study of Correlates of Risk for Ebola Vaccination

The recombinant vesicular stomatitis virus (rVSV) Ebola vaccine was shown to be very efficacious in a novel ring vaccination trial in Guinea. However, no correlates of vaccine protection have been established for Ebola vaccines. Several Ebola vaccine candidates are available, but conducting randomized trials of additional candidates in outbreaks is difficult. Establishing correlates of vaccine protection is essential. Here we explore power and sample-size calculations to evaluate potential correlates of risk during an Ebola vaccination campaign in an outbreak. The method requires that a blood draw be made at a predetermined time after vaccination. The statistical analysis estimates the relative risk of the Ebola endpoint occurring from after the blood draw through to the end of follow-up, contrasting vaccine recipients with different values of the immune response marker. The analysis can be done assuming a trichotomous or continuous marker. Under certain assumptions, at an overall vaccine efficacy of 75%, 50 Ebola endpoints in the vaccinees provided good power. At an overall vaccine efficacy of 90%, 20 Ebola endpoints gave good power. Power was highest when more vaccinees were in the high- and low-responder groups versus the middle group and when vaccine efficacy differed the most between the high- and low-responder groups.

Joint models for longitudinal and time-to-event data in a case-cohort design

Studies with longitudinal measurements are common in clinical research. Particular interest lies in studies where the repeated measurements are used to predict a time‐to‐event outcome, such as mortality, in a dynamic manner. If event rates in a study are low, however, and most information is to be expected from the patients experiencing the study endpoint, it may be more cost efficient to only use a subset of the data. One way of achieving this is by applying a case‐cohort design, which selects all cases and only a random samples of the noncases. In the standard way of analyzing data in a case‐cohort design, the noncases who were not selected are completely excluded from analysis; however, the overrepresentation of the cases will lead to bias. We propose to include survival information of all patients from the cohort in the analysis. We approach the fact that we do not have longitudinal information for a subset of the patients as a missing data problem and argue that the missingness mechanism is missing at random. Hence, results obtained from an appropriate model, such as a joint model, should remain valid. Simulations indicate that our method performs similar to fitting the model on a full cohort, both in terms of parameters estimates and predictions of survival probabilities. Estimating the model on the classical version of the case‐cohort design shows clear bias and worse performance of the predictions. The procedure is further illustrated in data from a biomarker study on acute coronary syndrome patients, BIOMArCS.

A case-cohort approach for multi-state models in hospital epidemiology

Analysing the determinants and consequences of hospital-acquired infections involves the evaluation of large cohorts. Infected patients in the cohort are often rare for specific pathogens, because most of the patients admitted to the hospital are discharged or die without such an infection. Death and discharge are competing events to acquiring an infection, because these individuals are no longer at risk of getting a hospital-acquired infection. Therefore, the data is best analysed with an extended survival model - the extended illness-death model. A common problem in cohort studies is the costly collection of covariate values. In order to provide efficient use of data from infected as well as uninfected patients, we propose a tailored case-cohort approach for the extended illness-death model. The basic idea of the case-cohort design is to only use a random sample of the full cohort, referred to as subcohort, and all cases, namely the infected patients. Thus, covariate values are only obtained for a small part of the full cohort. The method is based on existing and established methods and is used to perform regression analysis in adapted Cox proportional hazards models. We propose estimation of all cause-specific cumulative hazards and transition probabilities in an extended illness-death model based on case-cohort sampling. As an example, we apply the methodology to infection with a specific pathogen using a large cohort from Spanish hospital data. The obtained results of the case-cohort design are compared with the results in the full cohort to investigate the performance of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

Power/sample size calculations for assessing correlates of risk in clinical efficacy trials

In a randomized controlled clinical trial that assesses treatment efficacy, a common objective is to assess the association of a measured biomarker response endpoint with the primary study endpoint in the active treatment group, using a case-cohort, case-control, or two-phase sampling design. Methods for power and sample size calculations for such biomarker association analyses typically do not account for the level of treatment efficacy, precluding interpretation of the biomarker association results in terms of biomarker effect modification of treatment efficacy, with detriment that the power calculations may tacitly and inadvertently assume that the treatment harms some study participants. We develop power and sample size methods accounting for this issue, and the methods also account for inter-individual variability of the biomarker that is not biologically relevant (e.g., due to technical measurement error). We focus on a binary study endpoint and on a biomarker subject to measurement error that is normally distributed or categorical with two or three levels. We illustrate the methods with preventive HIV vaccine efficacy trials and include an R package implementing the methods. Copyright © 2016 John Wiley & Sons, Ltd.

Sample size/power calculation for stratified case-cohort design

The case-cohort (CC) study design usually has been used for risk factor assessment in epidemiologic studies or disease prevention trials for rare diseases. The sample size/power calculation for a stratified CC (SCC) design has not been addressed before. This article derives such result based on a stratified test statistic. Simulation studies show that the proposed test for the SCC design utilizing small sub-cohort sampling fractions is valid and efficient for situations where the disease rate is low. Furthermore, optimization of sampling in the SCC design is discussed and compared with proportional and balanced sampling techniques. An epidemiological study is provided to illustrate the sample size calculation under the SCC design.

Survival analysis with incomplete genetic data

Genetic data are now collected frequently in clinical studies and epidemiological cohort studies. For a large study, it may be prohibitively expensive to genotype all study subjects, especially with the next-generation sequencing technology. Two-phase sampling, such as case-cohort and nested case-control sampling, is cost-effective in such settings but entails considerable analysis challenges, especially if efficient estimators are desired. Another type of missing data arises when the investigators are interested in the haplotypes or the genetic markers that are not on the genotyping platform used for the current study. Valid and efficient analysis of such missing data is also interesting and challenging. This article provides an overview of these issues and outlines some directions for future research.