Case-cohort Sampling Research Articles

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

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. 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). 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). 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.

Multiplexed high-throughput immune cell imaging in patients with high-risk triple negative early breast cancer: Analysis from the International Breast Cancer Study Group (IBCSG) Trial 22-00

BackgroundTriple-negative breast cancer (TNBC) is the most aggressive breast cancer (BC) subtype, with dismal prognosis and limited option in advanced settings, yet stromal tumor infiltrating lymphocytes (sTILs) in this subtype has a predictive role. Patients and methods: The International Breast Cancer Study Group (IBCSG) Trial 22–00 is a randomized phase III clinical trial testing the efficacy of low-dose metronomic oral Cyclophosphamide-Methotrexate (CM) maintenance following standard adjuvant chemotherapy treatment for early-stage hormone receptor-negative breast cancer patients. A case-cohort sampling was used. We characterized immune cells infiltrates in patients with TNBC by 6 plex immunofluorescence (IF) staining for CD4, FOXP3, CD3, cytokeratine and CD8 ResultsWe confirmed that high immune CD3+ T cells as well as stromal and intra-epithelial Tregs (CD4+Foxp3+ T cells) infiltrates were associated with a better Distant Recurrence-Free Interval (DRFI), especially in LN+ patient, regardless of the treatment. More importantly, we showed that the spatial distribution of immune cells at baseline is crucial, as CM maintenance was detrimental for T cells excluded LN+ TNBC patients. Conclusionsimmune spatial classification on immune cells infiltrates seems crucial and could help patients’ selection in clinical trial and greatly improve responses to specific therapies.

Immune correlates analysis of a phase 3 trial of the AZD1222 (ChAdOx1 nCoV-19) vaccine

In the phase 3 trial of the AZD1222 (ChAdOx1 nCoV-19) vaccine conducted in the U.S., Chile, and Peru, anti-spike binding IgG concentration (spike IgG) and pseudovirus 50% neutralizing antibody titer (nAb ID50) measured four weeks after two doses were assessed as correlates of risk and protection against PCR-confirmed symptomatic SARS-CoV-2 infection (COVID-19). These analyses of SARS-CoV-2 negative participants were based on case-cohort sampling of vaccine recipients (33 COVID-19 cases by 4 months post dose two, 463 non-cases). The adjusted hazard ratio of COVID-19 was 0.32 (95% CI: 0.14, 0.76) per 10-fold increase in spike IgG concentration and 0.28 (0.10, 0.77) per 10-fold increase in nAb ID50 titer. At nAb ID50 below the limit of detection (< 2.612 IU50/ml), 10, 100, and 270 IU50/ml, vaccine efficacy was −5.8% (−651%, 75.6%), 64.9% (56.4%, 86.9%), 90.0% (55.8%, 97.6%) and 94.2% (69.4%, 99.1%). These findings provide further evidence towards defining an immune marker correlate of protection to help guide regulatory/approval decisions for COVID-19 vaccines.

The Case for Case-Cohort: An Applied Epidemiologist's Guide to Reframing Case-Cohort Studies to Improve Usability and Flexibility.

When research questions require the use of precious samples, expensive assays or equipment, or labor-intensive data collection or analysis, nested case-control or case-cohort sampling of observational cohort study participants can often reduce costs. These study designs have similar statistical precision for addressing a singular research question, but case-cohort studies have broader efficiency and superior flexibility. Despite this, case-cohort designs are comparatively underutilized in the epidemiologic literature. Recent advances in statistical methods and software have made analyses of case-cohort data easier to implement, and advances from casual inference, such as inverse probability of sampling weights, have allowed the case-cohort design to be used with a variety of target parameters and populations. To provide an accessible link to this technical literature, we give a conceptual overview of case-cohort study analysis with inverse probability of sampling weights. We show how this general analytic approach can be leveraged to more efficiently study subgroups of interest or disease subtypes or to examine associations independent of case status. A brief discussion of how this framework could be extended to incorporate other related methodologic applications further demonstrates the broad cost-effectiveness and adaptability of case-cohort methods for a variety of modern epidemiologic applications in resource-limited settings.

Do genetic risk scores for childhood adiposity operate independent of BMI of their mothers?

ObjectivesGenetic predisposition and maternal body mass index (BMI) are risk factors for childhood adiposity, defined by either BMI or overweight. We aimed to investigate whether childhood-specific genetic risk scores (GRSs) for adiposity-related traits are associated with childhood adiposity independent of maternal BMI, or whether the associations are modified by maternal BMI.MethodsWe constructed a weighted 26-SNP child BMI-GRS and a weighted 17-SNP child obesity-GRS in overall 1674 genotyped children within the Danish National Birth Cohort. We applied a case-cohort (N = 1261) and exposure-based cohort (N = 912) sampling design. Using logistic regression models we estimated associations of the GRSs and child overweight at age 7 years and examined if the GRSs influence child adiposity independent of maternal BMI (per standard deviation units).ResultsIn the case-cohort design analysis, maternal BMI and the child GRSs were associated with increased odds for childhood overweight [OR for maternal BMI: 2.01 (95% CI: 1.86; 2.17), OR for child BMI-GRS: 1.56 (95% CI: 1.47; 1.66), and OR for child obesity-GRS 1.46 (95% CI: 1.37; 1.54)]. Adjustment for maternal BMI did not change the results, and there were no significant interactions between the GRSs and maternal BMI. However, in the exposure-based cohort design analysis, significant interactions between the child GRSs and maternal BMI on child overweight were observed, suggesting 0.85–0.87-fold attenuation on ORs of child overweight at higher values of maternal BMI and child GRS.ConclusionGRSs for childhood adiposity are strongly associated with childhood adiposity even when adjusted for maternal BMI, suggesting that the child-specific GRSs and maternal BMI contribute to childhood overweight independent of each other. However, high maternal BMI may attenuate the effects of child GRSs in children.

Proportional Subdistribution Hazards Model for Competing Risks in Case-Cohort Studies

Competing risks refer to the situation where there are multiple causes of failure and the occurrence of one type of event prohibits the occurrence of the other types of event or alters the chance to observe them. In large cohort studies with long-term follow-up, there are often competing risks. When the failure events are rare, or the information on certain risk factors is difficult or costly to measure for the full cohort, a case-cohort study design can be a desirable approach. In this paper, we consider a semiparametric proportional subdistribution hazards model in the presence of competing risks in case-cohort studies. The subdistribution hazards function, unlike the cause-specific hazards function, gives the advantage of outlining the marginal probability of a particular type of event. We propose estimating equations based on inverse probability weighting techniques for the estimation of the model parameters. In the estimation methods, we considered a weighted availability indicator to properly account for the case-cohort sampling scheme. We also proposed a Breslow-type estimator for the cumulative baseline subdistribution hazard function. The resulting estimators are shown, using empirical processes and martingale properties, to be consistent and asymptotically normally distributed. The performance of the proposed methods in finite samples are examined through simulation studies by considering different levels of censoring and event of interest percentages. The simulation results from the different scenarios suggest that the parameter estimates are reasonably close to the true values of the respective parameters in the model. Finally, the proposed estimation methods are applied to a case-cohort sample from the Sister Study, in which we illustrated the proposed methods by studying the association between selected CpGs and invasive breast cancer in the presence of ductal carcinoma in situ as competing risk.

A Hybrid Approach for the Stratified Mark-Specific Proportional Hazards Model with Missing Covariates and Missing Marks, with Application to Vaccine Efficacy Trials.

Deployment of the recently licensed CYD-TDV dengue vaccine requires understanding of how the risk of dengue disease in vaccine recipients depends jointly on a host biomarker measured after vaccination (neutralization titer - NAb) and on a "mark" feature of the dengue disease failure event (the amino acid sequence distance of the dengue virus to the dengue sequence represented in the vaccine). The CYD14 phase 3 trial of CYD-TDV measured NAb via case-cohort sampling and the mark in dengue disease failure events, with about a third missing marks. We addressed the question of interest by developing inferential procedures for the stratified mark-specific proportional hazards model with missing covariates and missing marks. Two hybrid approaches are investigated that leverage both augmented inverse probability weighting and nearest neighborhood hot deck multiple imputation. The two approaches differ in how the imputed marks are pooled in estimation. Our investigation shows that NNHD imputation can lead to biased estimation without properly selected neighborhood. Simulations show that the developed hybrid methods perform well with unbiased NNHD imputations from proper neighborhood selection. The new methods applied to CYD14 show that NAb is strongly inversely associated with risk of dengue disease in vaccine recipients, more strongly against dengue viruses with shorter distances.

Cumulative risk regression in case-cohort studies using pseudo-observations.

Case-cohort studies are useful when information on certain risk factors is difficult or costly to ascertain. Particularly, a case-cohort study may be well suited in situations where several case series are of interest, e.g. in studies with competing risks, because the same sub-cohort may serve as a comparison group for all case series. Previous analyses of this kind of sampled cohort data most often involved estimation of rate ratios based on a Cox regression model. However, with competing risks this method will not provide parameters that directly describe the association between covariates and cumulative risks. In this paper, we study regression analysis of cause-specific cumulative risks in case-cohort studies using pseudo-observations. We focus mainly on the situation with competing risks. However, as a by-product, we also develop a method by which absolute mortality risks may be analyzed directly from case-cohort survival data. We adjust for the case-cohort sampling by inverse sampling probabilities applied to a generalized estimation equation. The large-sample properties of the proposed estimator are developed and small-sample properties are evaluated in a simulation study. We apply the methodology to study the effect of a specific diet component and a specific gene on the absolute risk of atrial fibrillation.

Generalized case-cohort analysis for constrained estimation in the Cox’s model

ABSTRACTThe generalized case-cohort sampling is a best choice to reduce study cost and time, which contains many sampling steps, each step takes a random sample of a certain size without replacement from a certain subset of the cohort, the number of steps, the choice of sample size and the sampling subset must not use the information about the observed covariates. The constrained estimation in the Cox’s model for the right-censored survival data has been studied by Ding, Tian, and Kam, who proposed a novel minorization-maximization (MM) algorithm for the constrained estimation. In this paper, we derive an estimating equation by a sample reuse and local average weight method based on generalized case-cohort data and generalize the MM algorithm to get the constrained estimator in the Cox’s model with parameter subject to box or linear inequality constraint. Asymptotic properties are presented. The small sample operating characteristics of the proposed method are examined via simulation studies and are illustrated on a dataset from Wilms tumor studies.

Multiplicative rates model for recurrent events in case-cohort studies.

In large prospective cohort studies, accumulation of covariate information and follow-up data make up the majority of the cost involved in the study. This might lead to the study being infeasible when there are some expensive variables and/or the event is rare. Prentice (Biometrika 73(1):1-11, 1986) proposed the case-cohort study for time to event data to tackle this problem. There has been extensive research on the analysis of univariate and clustered failure time data, where the clusters are formed among different individuals under case-cohort sampling scheme. However, recurrent event data are quite common in biomedical and public health research. In this paper, we propose case-cohort sampling schemes for recurrent events. We consider a multiplicative rates model for the recurrent events and propose a weighted estimating equations approach for parameter estimation. We show that the estimators are consistent and asymptotically normally distributed. The proposed estimator performed well in finite samples in our simulation studies. For illustration purposes, we examined the association between prior occurrence of measles on acute lower respiratory tract infections (ALRI) among young children in Brazil.

Nonparametric Maximum Likelihood Estimators of Time-Dependent Accuracy Measures for Survival Outcome Under Two-Stage Sampling Designs

ABSTRACTLarge prospective cohort studies of rare chronic diseases require thoughtful planning of study designs, especially for biomarker studies when measurements are based on stored tissue or blood specimens. Two-phase designs, including nested case–control and case-cohort sampling designs, provide cost-effective strategies for conducting biomarker evaluation studies.Existing literature for biomarker assessment under two-phase designs largely focuses on simple inverse probability weighting (IPW) estimators. Drawing on recent theoretical development on the maximum likelihood estimators for relative risk parameters in two-phase studies, we propose nonparametric maximum likelihood-based estimators to evaluate the accuracy and predictiveness of a risk prediction biomarker under both types of two-phase designs. In addition, hybrid estimators that combine IPW estimators and maximum likelihood estimation procedure are proposed to improve efficiency and alleviate computational burden. We derive large sample properties of proposed estimators and evaluate their finite sample performance using numerical studies. We illustrate new procedures using a two-phase biomarker study aiming to evaluate the accuracy of a novel biomarker, des-γ-carboxy prothrombin, for early detection of hepatocellular carcinoma. Supplementary materials for this article are available online.

Abstract P1-06-08: Independent validation of EarlyR gene signature in E2197: A randomized clinical trial comparing doxorubicin plus docetaxel to doxorubicin plus cyclophosphamide as adjuvant chemotherapy in breast cancer

Abstract Background: EarlyR is a prognostic gene signature score in ER+ breast cancer (BC) computed from the expression values of ESPL1, SPAG5, MKI67, PLK1 and PGR using a nonlinear mathematical formula. EarlyR has been validated in multiple cohorts profiled on Affymetrix and Illumina microarrays and by RNA-seq. This study sought to assess the prognostic features of EarlyR in a cohort of E2197. Patients and Methods: Illumina DASL assay was used to measure gene expression in FFPE tissue of primary BC from a case-cohort sampling subset of women in E2197 treated with doxorubicin plus docetaxel (AT) or doxorubicin plus cyclophosphamide (AC). ER+ patients received hormone therapy at physician's discretion. After 79.5 months median follow-up, disease-free survival was 85% in both treatment arms. Among patients centrally reviewed with sufficient RNA material for the DASL assay, 319 with ER+ status and assessed for EarlyR are included in the analytic cohort. EarlyR scores and pre-specified risk strata (≤25=low, 26-75=intermediate, &amp;gt;75=high) were computed, while blinded to clinical data. The analysis endpoint was disease-free survival (DFS), defined as the time from randomization to date of invasive BC recurrence or death from any cause within 8 years. Weighted Cox proportional hazards models were used to associate EarlyR score or risk strata with DFS. Variances of the estimated coefficients were adjusted to account for the case-cohort design. Results: The distribution of the EarlyR risk groups was 59% low, 11% intermediate and 30% high risk in this ER+ cohort. The continuous EarlyR score was significantly prognostic of DFS up to 8 years after randomization (p = 0.02). Patients with low EarlyR score (≤ 25) had significantly lower risk of BC recurrence within 8 years (p = 0.031, univariate HR=0.562, 95%CI: 0.334-0.948) compared to those with high EarlyR score (&amp;gt; 75). Analysis within the AC arm showed that patients with low EarlyR score had significantly lower risk of 8-year BC recurrence (p = 0.023, univariate HR=0.392, 95%CI: 0.175-0.878) compared to those with high EarlyR score. Within the AT arm there was no significant difference in 8-year DFS prognosis between any of the EarlyR risk groups. Conclusions: This study confirmed the prognostic significance of EarlyR using FFPE tissue in a cohort of patients treated with AC chemotherapy from E2197. Patients with high EarlyR score who were treated with AC had significantly higher risk of recurrence than low EarlyR score patients treated with AC. On the other hand, prognosis of high EarlyR score AT-treated patients was not significantly lower than the prognosis of low EarlyR score AT-treated patients. Further study in a larger cohort is needed to assess the relative benefits of AC versus AT within the EarlyR high risk group and the EarlyR low risk group. Citation Format: Badve S, Wang V, Willis S, Leyland-Jones B, Gokmen-Polar Y, Shulman L, Martino S, Sparano J, Davidson N, Goldstein L, Buechler S. Independent validation of EarlyR gene signature in E2197: A randomized clinical trial comparing doxorubicin plus docetaxel to doxorubicin plus cyclophosphamide as adjuvant chemotherapy in breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P1-06-08.

Abstract P4-12-01: Independent validation of EarlyR gene signature in BIG 1-98: A randomized, double-blind, phase III trial comparing letrozole and tamoxifen as adjuvant endocrine therapy for postmenopausal women with hormone receptor-positive, early breast cancer

Abstract Background: EarlyR is a prognostic gene signature score in ER+ breast cancer (BC) computed from the expression values of ESPL1, SPAG5, MKI67, PLK1 and PGR using a novel algorithm. EarlyR has been validated in multiple cohorts profiled on Affymetrix and Illumina microarrays. This study sought to verify prognostic features of EarlyR in a cohort of BIG 1-98. Patients and Methods: Illumina DASL assay was used to measure gene expression in FFPE tissue of primary BC from a case-cohort sampling subset of postmenopausal women in BIG 1-98 treated with adjuvant endocrine therapy (letrozole or tamoxifen). Chemotherapy treatment was at the discretion of individual physicians and patients. Among the 1218 patients centrally reviewed with sufficient RNA material for the DASL assay, 1174 with ER+ status and assessed for EarlyR are included in the analytic cohort. EarlyR scores and pre-specified risk strata (≤25=low, 26-75=intermediate, &amp;gt;75=high) were computed, while blinded to clinical data. The analysis endpoints included distant recurrence free interval (DRFI) defined as time from randomization to BC recurrence at a distant site within 8 years and BC free-interval (BCFI) defined as time from randomization to first invasive BC recurrence at a local, regional or distant site or invasive contralateral BC within 8 years. Weighted proportional hazards models (univariate and multivariate, stratified by treatment assignment) were used to adjust for Kaplan-Meier, hazard ratio estimates and Wald test statistics to obtain unbiased analyses and to give consistent estimates. Results: The distribution of the EarlyR risk groups was 67% low, 19% intermediate and 14% high risk in this ER+ cohort. EarlyR was prognostic for 8-year DRFI (P-trend=0.008). Patients with high EarlyR risk score (&amp;gt;75) had significantly increased risk of distant recurrence within 8 years (univariate HR=1.73, 95%CI: 1.14-2.64) compared to low EarlyR risk group (≤25). The estimated 8-year DRFI (95%CI) is 84%(80%-88%) for high risk vs. 91%( 89%-92%) for low risk, corresponding to an absolute DRFI risk reduction of 7% (low vs high). EarlyR is also prognostic of 8-year BCFI in ER+ (P-trend=0.002) with the estimated 8-year BCFI (95%CI) 79%(75%-84%) for high risk vs. 88%( 86%-89%) for low risk. Consistent results were observed in ER+, HER2- (P-trend=0.01 for DRFI, P-trend=0.004 for BCFI), in ER+, LN- (P-trend=0.05 for DRFI, P-trend=0.03 for BCFI) and ER+, LN+ (P-trend=0.08 for DRFI, P-trend=0.03 for BCFI) subsets. Conclusions: This study confirmed the prognostic significance of EarlyR using FFPE tissue from the BIG 1-98 trial. In analyses of all ER+ patients and subsets LN-, LN+ and HER2-, EarlyR classifies 65%-70% of patients as low risk, 11-16% as high risk, and &amp;lt; 20% as intermediate risk. In these subsets, the size of the low risk group is larger and the size of the intermediate risk group is smaller than those reported for commercially available signatures. EarlyR identifies a set of high-risk patients with relatively poor prognosis who may be considered for additional treatment. The clinical utility of EarlyR requires further study. Citation Format: Buechler S, Gray KP, Gökmen-Polar Y, Willis S, Thürlimann B, Kammler R, Leyland-Jones B, Badve SS, Regan MM. Independent validation of EarlyR gene signature in BIG 1-98: A randomized, double-blind, phase III trial comparing letrozole and tamoxifen as adjuvant endocrine therapy for postmenopausal women with hormone receptor-positive, early breast cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P4-12-01.

Optimal generalized case–cohort analysis with accelerated failure time model

Case–cohort design has been widely advocated in large cohort studies when the disease rate is low. When the event is not rare, it is desirable to consider a generalized case–cohort design where the covariates are observed only for a subcohort randomly selected from the underlying cohort and a subset of additional failures outside the subcohort. In this article, we propose the smoothed weighted Gehan estimating equation for regression parameters in the accelerated failure time model under generalized case–cohort design. Asymptotic properties of the proposed estimators are developed. To demonstrate the effectiveness of the generalized case–cohort sampling, we compare it with simple random sampling in terms of asymptotic relative efficiency. Furthermore, we derive the optimal allocation of the subsamples for the proposed design. The performance of the finite sample properties are evaluated via simulation studies. A real data set is analyzed to illustrate the estimating procedure.

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.

Association Between Results of a Gene Expression Signature Assay and Recurrence-Free Interval in Patients With Stage II Colon Cancer in Cancer and Leukemia Group B 9581 (Alliance).

Conventional staging methods are inadequate to identify patients with stage II colon cancer (CC) who are at high risk of recurrence after surgery with curative intent. ColDx is a gene expression, microarray-based assay shown to be independently prognostic for recurrence-free interval (RFI) and overall survival in CC. The objective of this study was to further validate ColDx using formalin-fixed, paraffin-embedded specimens collected as part of the Alliance phase III trial, C9581. C9581 evaluated edrecolomab versus observation in patients with stage II CC and reported no survival benefit. Under an initial case-cohort sampling design, a randomly selected subcohort (RS) comprised 514 patients from 901 eligible patients with available tissue. Forty-nine additional patients with recurrence events were included in the analysis. Final analysis comprised 393 patients: 360 RS (58 events) and 33 non-RS events. Risk status was determined for each patient by ColDx. The Self-Prentice method was used to test the association between the resulting ColDx risk score and RFI adjusting for standard prognostic variables. Fifty-five percent of patients (216 of 393) were classified as high risk. After adjustment for prognostic variables that included mismatch repair (MMR) deficiency, ColDx high-risk patients exhibited significantly worse RFI (multivariable hazard ratio, 2.13; 95% CI, 1.3 to 3.5; P < .01). Age and MMR status were marginally significant. RFI at 5 years for patients classified as high risk was 82% (95% CI, 79% to 85%), compared with 91% (95% CI, 89% to 93%) for patients classified as low risk. ColDx is associated with RFI in the C9581 subsample in the presence of other prognostic factors, including MMR deficiency. ColDx could be incorporated with the traditional clinical markers of risk to refine patient prognosis.

Optimal generalized case–cohort sampling design under the additive hazard model

ABSTRACTGeneralized case–cohort designs have been proved to be a cost-effective way to enhance effectiveness in large epidemiological cohort. In generalized case–cohort design, we first select a subcohort from the underlying cohort by simple random sampling, and then sample a subset of the failures in the remaining subjects. In this article, we propose the inference procedure for the unknown regression parameters in the additive hazards model and develop an optimal sample size allocations to achieve maximum power at a given budget in generalized case–cohort design. The finite sample performance of the proposed method is evaluated through simulation studies. The proposed method is applied to a real data set from the National Wilm's Tumor Study Group.

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.

Marginal structural cox models with case-cohort sampling

A common objective of biomedical cohort studies is assessing the effect of a time-varying treatment or exposure on a survival time. In the presence of time-varying confounders, marginal structural models fit using inverse probability weighting can be employed to obtain a consistent and asymptotically normal estimator of the causal effect of a time-varying treatment. This article considers estimation of parameters in the semiparametric marginal structural Cox model (MSCM) from a case-cohort study. Case-cohort sampling entails assembling covariate histories only for cases and a random subcohort, which can be cost effective, particularly in large cohort studies with low outcome rates. Following Cole et al. (2012), we consider estimating the causal hazard ratio from a MSCM by maximizing a weighted-pseudo-partial-likelihood. The estimator is shown to be consistent and asymptotically normal under certain regularity conditions. Finite sample performance of the proposed estimator is evaluated in a simulation study. In the corresponding supplementary document, computation of the estimator using standard survival analysis software is presented.

Marginal structural cox models with case-cohort sampling

Marginal structural cox models with case-cohort sampling