A new comparison of nested case-control and case-cohort designs and methods

Privacy-preserving analysis of time-to-event data under nested case-control sampling

Analyses of distributed data networks of rare diseases are constrained by legitimate privacy and ethical concerns. Analytical centers (e.g. research institutions) are thus confronted with the challenging task of obtaining data from recruiting sites that are often unable or unwilling to share personal records of participants. For time-to-event data, recently popularized disclosure techniques with privacy guarantees (e.g., Differentially Private Generative Adversarial Networks) are generally computationally expensive or inaccessible to applied researchers. To perform the widely used Cox proportional hazards regression, we propose an easy-to-implement privacy-preserving data analysis technique by pooling (i.e. aggregating) individual records of covariates at recruiting sites under the nested case-control sampling framework before sharing the pooled nested case-control subcohort. We show that the pooled hazard ratio estimators, under the pooled nested case-control subsamples from the contributing sites, are maximum likelihood estimators and provide consistent estimates of the individual level full cohort HRs. Furthermore, a sampling technique for generating pseudo-event times for individual subjects that constitute the pooled nested case-control subsamples is proposed. Our method is demonstrated using extensive simulations and analysis of the National Lung Screening Trial data. The utility of our proposed approach is compared to the gold standard (full cohort) and synthetic data generated using classification and regression trees. The proposed pooling technique performs to near-optimal levels comparable to full cohort analysis or synthetic data; the efficiency improves in rare event settings when more controls are matched on during nested case-control subcohort sampling.

Goodness-of-fit two-phase sampling designs for time-to-event outcomes: a simulation study based on New York University Women's Health Study for breast cancer

BACKGROUND

Sub-cohort sampling designs such as a case-cohort study play a key role in studying biomarker-disease associations due to their cost effectiveness. Time-to-event outcome is often the focus in cohort studies, and the research goal is to assess the association between the event risk and risk factors. In this paper, we propose a novel goodness-of-fit two-phase sampling design for time-to-event outcomes when some covariates (e.g., biomarkers) can only be measured on a subgroup of study subjects.

METHODS

Assuming that an external model, which can be the well-established risk models such as the Gail model for breast cancer, Gleason score for prostate cancer, and Framingham risk models for heart diseases, or built from preliminary data, is available to relate the outcome and complete covariates, we propose to oversample subjects with worse goodness-of-fit (GOF) based on an external survival model and time-to-event. With the cases and controls sampled using the GOF two-phase design, the inverse sampling probability weighting method is used to estimate the log hazard ratio of both incomplete and complete covariates. We conducted extensive simulations to evaluate the efficiency gain of our proposed GOF two-phase sampling designs over case-cohort study designs.

RESULTS

Through extensive simulations based on a dataset from the New York University Women's Health Study, we showed that the proposed GOF two-phase sampling designs were unbiased and generally had higher efficiency compared to the standard case-cohort study designs.

CONCLUSION

In cohort studies with rare outcomes, an important design question is how to select informative subjects to reduce sampling costs while maintaining statistical efficiency. Our proposed goodness-of-fit two-phase design provides efficient alternatives to standard case-cohort designs for assessing the association between time-to-event outcome and risk factors. This method is conveniently implemented in standard software.

Common maternal infections during pregnancy and childhood leukaemia in the offspring: findings from six international birth cohorts

BACKGROUND

Previous epidemiological studies have found positive associations between maternal infections and childhood leukaemia; however, evidence from prospective cohort studies is scarce. We aimed to examine the associations using large-scale prospective data.

METHODS

Data were pooled from six population-based birth cohorts in Australia, Denmark, Israel, Norway, the UK and the USA (recruitment 1950s-2000s). Primary outcomes were any childhood leukaemia and acute lymphoblastic leukaemia (ALL); secondary outcomes were acute myeloid leukaemia (AML) and any childhood cancer. Exposures included maternal self-reported infections [influenza-like illness, common cold, any respiratory tract infection, vaginal thrush, vaginal infections and urinary tract infection (including cystitis)] and infection-associated symptoms (fever and diarrhoea) during pregnancy. Covariate-adjusted hazard ratio (HR) and 95% confidence interval (CI) were estimated using multilevel Cox models.

RESULTS

Among 312 879 children with a median follow-up of 13.6 years, 167 leukaemias, including 129 ALL and 33 AML, were identified. Maternal urinary tract infection was associated with increased risk of any leukaemia [HR (95% CI) 1.68 (1.10-2.58)] and subtypes ALL [1.49 (0.87-2.56)] and AML [2.70 ([0.93-7.86)], but not with any cancer [1.13 (0.85-1.51)]. Respiratory tract infection was associated with increased risk of any leukaemia [1.57 (1.06-2.34)], ALL [1.43 (0.94-2.19)], AML [2.37 (1.10-5.12)] and any cancer [1.33 (1.09-1.63)]; influenza-like illness showed a similar pattern but with less precise estimates. There was no evidence of a link between other infections and any outcomes.

CONCLUSIONS

Urinary tract and respiratory tract infections during pregnancy may be associated with childhood leukaemia, but the absolute risk is small given the rarity of the outcome.

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.

Inhaled Corticosteroids, Vitamin K Antagonists and Amlodipine Were Associated with an Increased Risk of Acute Periprosthetic Joint Infection in Patients with Total Hip Arthroplasty: A Retrospective Case–Cohort Study

The perioperative use of certain medication may influence the risk of developing a periprosthetic joint infection (PJI). Inhaled corticosteroids (ICSs) and cardiovascular drugs are widely used against pulmonary and cardiovascular diseases. While oral corticosteroids and anticoagulants have been shown to increase the risk of developing PJI, this is not clear for ICSs. In contrast, some cardiovascular drugs, such as amlodipine, nifedipine and statins, have been documented to show an antimicrobial effect, suggesting a synergistic effect with antibiotics in the treatment of (multi-resistant) microorganisms. We performed a case–cohort study to assess the association between the occurrence of PJI after THA and the use of inhaled corticosteroids, anticoagulants, or previously mentioned cardiovascular agents. In a cohort of 5512 primary THAs, we identified 75 patients with a PJI (1.4%), and randomly selected 302 controls. A weighted Cox proportional hazard regression model was used for the study design and to adjust for potential confounders (age, sex, smoking, and cardiovascular/pulmonary disease). We found ICS use (HR 2.6 [95% CI 1.1–5.9]), vitamin K antagonist use (HR 5.3 [95% CI 2.5–11]), and amlodipine use (HR 3.1 [95% CI 1.4–6.9]) to be associated with an increased risk of developing PJI after THA. The effect remained after correction for the mentioned possible confounders. The underlying diseases for which the medications are prescribed could also play a role in the mentioned association; we believe, however, that the usages of ICSs, vitamin K antagonists and amlodipine appear to be potential modifiable risk factors for PJI, and therefore have to be questioned during preoperative screening and consultation.

Effect of adverse events on non-adherence and study non-completion in malaria chemoprevention during pregnancy trial: A nested case control study

Background

In drug trials, adverse events (AEs) burden can induce treatment non-adherence or discontinuation. The non-adherence and discontinuation induce selection bias, affecting drug safety interpretation. Nested case-control (NCC) study can efficiently quantify the impact of the AEs, although choice of sampling approach is challenging. We investigated whether NCC study with incidence density sampling is more efficient than NCC with path sampling under conditional logistic or weighted Cox models in assessing the effect of AEs on treatment non-adherence and participation in preventive antimalarial drug during pregnancy trial.

Methods

Using data from a trial of medication to prevent malaria in pregnancy that randomized 600 women to receive chloroquine or sulfadoxine-pyrimethamine during pregnancy, we conducted a NCC study assessing the role of prospectively collected AEs, as exposure of interest, on treatment non-adherence and study non-completion. We compared estimates from NCC study with incidence density against those from NCC with path sampling under conditional logistic and weighted Cox models.

Results

Out of 599 women with the outcomes of interest, 474 (79%) experienced at least one AE before delivery. For conditional logistic model, the hazard ratio for the effect of AE occurrence on treatment non-adherence was 0.70 (95% CI: 0.42, 1.17; p = 0.175) under incidence density sampling and 0.68 (95% CI: 0.41, 1.13; p = 0.137) for path sampling. For study non-completion, the hazard ratio was 1.02 (95% CI: 0.56, 1.83; p = 0.955) under incidence density sampling and 0.85 (95% CI: 0.45, 1.60; p = 0.619) under path sampling. We obtained similar hazard ratios and standard errors under incidence density sampling and path sampling whether weighted Cox or conditional logistic models were used.

Conclusion

NCC with incidence density sampling and NCC with path sampling are practically similar in efficiency whether conditional logistic or weighted Cox analytical methods although path sampling uses more unique controls to achieve the similar estimates.

Trial registration

ClinicalTrials.gov: NCT01443130.

PFAS and cancer, a scoping review of the epidemiologic evidence

BACKGROUND

The number of studies addressing per- and polyfluoroalkyl substances (PFAS) and cancer is increasing. Many communities have had water contaminated by PFAS, and cancer is one of the important community concerns related to PFAS exposure.

OBJECTIVES

We critically reviewed the evidence relating to PFAS and cancer from an epidemiologic standpoint to highlight directions for future research that would be the most likely to meaningfully increase knowledge.

METHODS

We conducted a search in PubMed for studies of cancer and PFAS (through 9/20/2020). We identified epidemiologic studies that provided a quantitative estimate for some measure of the association between PFAS and cancer. Here, we review that literature, including several aspects of epidemiologic study design that impact the usefulness of study results.

RESULTS

We identified 16 cohort (or case-cohort) studies, 10 case-control studies (4 nested within cohorts and 6 non-nested), 1 cross sectional study and 1 ecologic study. The cancer sites with the most evidence of an association with PFAS are testicular and kidney cancer. There are also some suggestions in a few studies of an association with prostate cancer, but the data are inconsistent.

DISCUSSION

Each study's design has strengths and limitations. Weaknesses in study design and methods can, in some cases, lead to questionable associations, but in other cases can make it more difficult to detect true associations, if they are present. Overall, the evidence for an association between cancer and PFAS remains sparse. A variety of studies with different strengths and weaknesses can be helpful to clarify associations between PFAS and cancer. Long term follow-up of large-sized cohorts with large exposure contrasts are most likely to be informative.

Semiparametric isotonic regression analysis for risk assessment under nested case-control and case-cohort designs

Two-phase sampling designs, including nested case-control and case-cohort designs, are frequently utilized in large cohort studies involving expensive biomarkers. To analyze data from two-phase designs with a binary outcome, parametric models such as logistic regression are often adopted. However, when the model assumptions are not valid, parametric models may lead to biased estimation and risk evaluation. In this paper, we propose a robust semiparametric regression model for binary outcomes and an easy-to-implement computational procedure that combines the pool-adjacent violators algorithm with inverse probability weighting. The asymptotic properties are established, including consistency and the convergence rate. Simulation studies show that the proposed method performs well and is more robust than logistic regression methods. We demonstrate the application of the proposed method to real data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial.

Catalonia Suicide Risk Code Epidemiology (CSRC-Epi) study: protocol for a population-representative nested case-control study of suicide attempts in Catalonia, Spain

INTRODUCTION

Suicide attempts represent an important public health burden. Centralised electronic health record (EHR) systems have high potential to provide suicide attempt surveillance, to inform public health action aimed at reducing risk for suicide attempt in the population, and to provide data-driven clinical decision support for suicide risk assessment across healthcare settings. To exploit this potential, we designed the Catalonia Suicide Risk Code Epidemiology (CSRC-Epi) study. Using centralised EHR data from the entire public healthcare system of Catalonia, Spain, the CSRC-Epi study aims to estimate reliable suicide attempt incidence rates, identify suicide attempt risk factors and develop validated suicide attempt risk prediction tools.

METHODS AND ANALYSIS

The CSRC-Epi study is registry-based study, specifically, a two-stage exposure-enriched nested case-control study of suicide attempts during the period 2014-2019 in Catalonia, Spain. The primary study outcome consists of first and repeat attempts during the observation period. Cases will come from a case register linked to a suicide attempt surveillance programme, which offers in-depth psychiatric evaluations to all Catalan residents who present to clinical care with any suspected risk for suicide. Predictor variables will come from centralised EHR systems representing all relevant healthcare settings. The study's sampling frame will be constructed using population-representative administrative lists of Catalan residents. Inverse probability weights will restore representativeness of the original population. Analysis will include the calculation of age-standardised and sex-standardised suicide attempt incidence rates. Logistic regression will identify suicide attempt risk factors on the individual level (ie, relative risk) and the population level (ie, population attributable risk proportions). Machine learning techniques will be used to develop suicide attempt risk prediction tools.

ETHICS AND DISSEMINATION

This protocol is approved by the Parc de Salut Mar Clinical Research Ethics Committee (2017/7431/I). Dissemination will include peer-reviewed scientific publications, scientific reports for hospital and government authorities, and updated clinical guidelines.

TRIAL REGISTRATION NUMBER

NCT04235127.

Pre-pregnancy Maternal Weight and Gestational Weight Gain Increase the Risk for Childhood Asthma and Wheeze: An Updated Meta-Analysis

Background: Mounting evidence suggests that childhood asthma is closely associated with maternal weight before pregnancy and gestational weight gain (GWG), yet the results are not often reproducible. Objectives: We conducted a comprehensive meta-analysis, aiming to evaluate the association of pre-pregnancy maternal obesity or overweight and high GWG with the risk for childhood asthma and wheeze. Methods: Literature search, quality assessment, and data extraction were completed independently and in duplicate. Effect-size estimates are expressed as odds ratio (OR) with 95% confidence interval (CI). Results: Twenty-two observational studies involving 145,574 mother-child pairs were meta-analyzed. In overall analyses, maternal obesity or overweight in pre-pregnancy significantly increased the risk of both childhood asthma and wheeze (adjusted OR: 1.41 and 1.13, 95% CI: 1.26-1.59 and 1.07-1.20, both p < 0.001). Per 1 kg/m² increment in maternal body mass index was associated with a significantly increased risk of childhood asthma and wheeze (adjusted OR: 1.03, 95% CI: 1.02-1.03, p < 0.001). Compared with normal GWG, very high GWG (adjusted OR: 1.24, 95% CI: 1.04-1.47, p: 0.018), moderate high GWG (adjusted OR: 1.12, 95% CI: 1.04-1.21, p: 0.004), and very low GWG (adjusted OR: 1.26, 95% CI: 1.08-1.47, p: 0.004) increased the risk of childhood asthma and wheeze. There was a low probability of publication bias. Conclusions: Our findings indicate that both pre-pregnancy maternal obesity or overweight and very to moderate high or low GWG render their offspring susceptible to a significantly increased risk of having childhood asthma and wheeze.

Estimation of Relative and Absolute Risks in a Competing-Risks Setting Using a Nested Case-Control Study Design: Example From the ProMort Study

In this paper, we describe the Prognostic Factors for Mortality in Prostate Cancer (ProMort) study and use it to demonstrate how the weighted likelihood method can be used in nested case-control studies to estimate both relative and absolute risks in the competing-risks setting. ProMort is a case-control study nested within the National Prostate Cancer Register (NPCR) of Sweden, comprising 1,710 men diagnosed with low- or intermediate-risk prostate cancer between 1998 and 2011 who died from prostate cancer (cases) and 1,710 matched controls. Cause-specific hazard ratios and cumulative incidence functions (CIFs) for prostate cancer death were estimated in ProMort using weighted flexible parametric models and compared with the corresponding estimates from the NPCR cohort. We further drew 1,500 random nested case-control subsamples of the NPCR cohort and quantified the bias in the hazard ratio and CIF estimates. Finally, we compared the ProMort estimates with those obtained by augmenting competing-risks cases and by augmenting both competing-risks cases and controls. The hazard ratios for prostate cancer death estimated in ProMort were comparable to those in the NPCR. The hazard ratios for dying from other causes were biased, which introduced bias in the CIFs estimated in the competing-risks setting. When augmenting both competing-risks cases and controls, the bias was reduced.

Association Between HLA Genotype and Cutaneous Adverse Reactions to Antiepileptic Drugs Among Epilepsy Patients in Northwest China

This study aimed to investigate the association between HLA genotypes and antiepileptic drug-induced cutaneous adverse reactions (AEDs-cADRs) among patients with epilepsy in Ningxia Hui Autonomous Region of Northwest China. Fifteen patients with AEDs-cADRs and 30 matched AEDs tolerant controls from anested case-control study were tested the HLA-A, HLA-B, and HLA-DRB1 genotype using the polymerase chain reaction sequence-based typing (PCR-SBT). Significant difference was not observed between AEDs-cADRs and AEDs tolerant groups in terms of HLA-A, HLA-B, and HLA-DRB1 genotype frequencies. Future studies using larger cohorts are needed to verify this observation.

Apolipoproteins E and CIII interact to regulate HDL metabolism and coronary heart disease risk

BACKGROUND

Subspecies of HDL contain apolipoprotein E (apoE) and/or apoCIII. Both proteins have properties that could affect HDL metabolism. The relation between HDL metabolism and risk of coronary heart disease (CHD) is not well understood.

METHODS

Eighteen participants were given a bolus infusion of [D3]L-leucine to label endogenous proteins on HDL. HDL was separated into subspecies containing apoE and/or apoCIII and then into 4 sizes. Metabolic rates for apoA-I in HDL subspecies and sizes were determined by interactive modeling. The concentrations of apoE in HDL that contain or lack apoCIII were measured in a prospective study in Denmark including 1,949 incident CHD cases during 9 years.

RESULTS

HDL containing apoE but not apoCIII is disproportionately secreted into the circulation, actively expands while circulating, and is quickly cleared. These are key metabolic steps in reverse cholesterol transport, which may protect against atherosclerosis. ApoCIII on HDL strongly attenuates these metabolic actions of HDL apoE. In the epidemiological study, the relation between HDL apoE concentration and CHD significantly differed depending on whether apoCIII was present. HDL apoE was associated significantly with lower risk of CHD only in the HDL subspecies lacking apoCIII.

CONCLUSIONS

ApoE and apoCIII on HDL interact to affect metabolism and CHD. ApoE promotes metabolic steps in reverse cholesterol transport and is associated with lower risk of CHD. ApoCIII, when coexisting with apoE on HDL, abolishes these benefits. Therefore, differences in metabolism of HDL subspecies pertaining to reverse cholesterol transport are reflected in differences in association with CHD.

TRIAL REGISTRATION

Clinicaltrials.gov NCT01399632.

FUNDING

This work was supported by NIH grant R01HL095964 to FMS and by a grant to the Harvard Clinical and Translational Science Center (8UL1TR0001750) from the National Center for Advancing Translational Science.

Prospective study of serum B vitamins levels and oesophageal and gastric cancers in China

B vitamins play an essential role in DNA synthesis and methylation, and may protect against oesophageal and gastric cancers. In this case-cohort study, subjects were enrolled from the General Population Nutrition Intervention Trial in Linxian, China. Subjects included 498 oesophageal squamous cell carcinomas (OSCCs), 255 gastric cardia adenocarcinomas (GCAs), and an age- and sex-matched sub-cohort of 947 individuals. Baseline serum riboflavin, pyridoxal phosphate (PLP), folate, vitamin B12, and flavin mononucleotide (FMN) were measured for all subjects. We estimated the associations with Cox proportional hazard models, with adjustment for potential confounders. Compared to those in the lowest quartile of serum riboflavin, those in the highest had a 44% lower risk of OSCC (HR: 0.56, 95% CI: 0.41 to 0.75). Serum vitamin B12 as a continuous variable was observed to be significantly inversely associated with OSCC (HR: 0.95, 95% CI: 0.89 to 1.01, P for score test = 0.041). Higher serum FMN levels were significantly associated with increased risk of OSCC (HR: 1.08, 95% CI: 1.01 to 1.16) and GCA (HR: 1.09, 95% CI: 1.00 to 1.20). Our study prompted that B vitamins have the potential role as chemopreventive agents for upper gastrointestinal cancers.

Feasibility of reusing time-matched controls in an overlapping cohort

The methods developed for secondary analysis of nested case-control data have been illustrated only in simplified settings in a common cohort and have not found their way into biostatistical practice. This paper demonstrates the feasibility of reusing prior nested case-control data in a realistic setting where a new outcome is available in an overlapping cohort where no new controls were gathered and where all data have been anonymised. Using basic information about the background cohort and sampling criteria, the new cases and prior data are "aligned" to identify the common underlying study base. With this study base, a Kaplan-Meier table of the prior outcome extracts the risk sets required to calculate the weights to assign to the controls to remove the sampling bias. A weighted Cox regression, implemented in standard statistical software, provides unbiased hazard ratios. Using the method to compare cases of contralateral breast cancer to available controls from a prior study of metastases, we identified a multifocal tumor as a risk factor that has not been reported previously. We examine the sensitivity of the method to an imperfect weighting scheme and discuss its merits and pitfalls to provide guidance for its use in medical research studies.

Using the whole cohort in the analysis of countermatched samples

We present a technique for using calibrated weights to incorporate whole-cohort information in the analysis of a countermatched sample. Following Samuelsen's approach for matched case-control sampling, we derive expressions for the marginal sampling probabilities, so that the data can be treated as an unequally-sampled case-cohort design. Pseudolikelihood estimating equations are used to find the estimates. The sampling weights can be calibrated, allowing all whole-cohort variables to be used in estimation; in contrast, the partial likelihood analysis makes use only of a single discrete surrogate for exposure. Using a survey-sampling approach rather than a martingale approach simplifies the theory; in particular, the sampling weights need not be a predictable process. Our simulation results show that pseudolikelihood estimation gives lower efficiency than partial likelihood estimation, but that the gain from calibration of weights can more than compensate for this loss. If there is a good surrogate for exposure, countermatched sampling still outperforms case-cohort and two-phase case-control sampling even when calibrated weights are used. Findings are illustrated with data from the National Wilms' Tumour Study and the Welsh nickel refinery workers study.

A method making fewer assumptions gave the most reliable estimates of exposure-outcome associations in stratified case-cohort studies

Objective

A case–cohort study is an efficient epidemiological study design for estimating exposure–outcome associations. When sampling of the subcohort is stratified, several methods of analysis are possible, but it is unclear how they compare. Our objective was to compare five analysis methods using Cox regression for this type of data, ranging from a crude model that ignores the stratification to a flexible one that allows nonproportional hazards and varying covariate effects across the strata.

Study Design and Setting

We applied the five methods to estimate the association between physical activity and incident type 2 diabetes using data from a stratified case–cohort study and also used artificial data sets to exemplify circumstances in which they can give different results.

Results

In the diabetes study, all methods except the method that ignores the stratification gave similar results for the hazard ratio associated with physical activity. In the artificial data sets, the more flexible methods were shown to be necessary when certain assumptions of the simpler models failed. The most flexible method gave reliable results for all the artificial data sets.

Conclusion

The most flexible method is computationally straightforward, and appropriate whether or not key assumptions made by the simpler models are valid.

Analysis of secondary outcomes in nested case-control study designs

One of the main perceived advantages of using a case-cohort design compared with a nested case-control design in an epidemiologic study is the ability to evaluate with the same subcohort outcomes other than the primary outcome of interest. In this paper, we show that valid inferences about secondary outcomes can also be achieved in nested case-control studies by using the inclusion probability weighting method in combination with an approximate jackknife standard error that can be computed using existing software. Simulation studies demonstrate that when the sample size is sufficient, this approach yields valid type 1 error and coverage rates for the analysis of secondary outcomes in nested case-control designs. Interestingly, the statistical power of the nested case-control design was comparable with that of the case-cohort design when the primary and secondary outcomes were positively correlated. The proposed method is illustrated with the data from a cohort in Cardiovascular Health Study to study the association of C-reactive protein levels and the incidence of congestive heart failure.