Inference for case-control studies with incident and prevalent cases.
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ABSTRACT:
SUBMITTER: Maziarz M
PROVIDER: S-EPMC8620395 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Typically, case-control studies to estimate odds-ratios associating risk factors with disease incidence only include newly diagnosed cases. Recently proposed methods allow incorporating information on prevalent cases, individuals who survived from disease diagnosis to sampling, into cross-sectionally sampled case-control studies under parametric assumptions for the survival time after diagnosis. Here we propose and study methods to additionally use prospectively observed survival times from prevalent and incident cases to adjust logistic models for the time between diagnosis and sampling, the backward time, for prevalent cases. This adjustment yields unbiased odds-ratio estimates from case-control studies that include prevalent cases. We propose a computationally simple two-step generalized method-of-moments estimation procedure. First, we estimate the survival distribution assuming a semiparametric Cox model using an expectation-maximization algorithm that yields fully efficient estimates and accommodates left truncation for prevalent cases and right censoring. Then, we use the estimated survival distribution in an extension of the logistic model to three groups (controls, incident, and prevalent cases), to adjust for the survival bias in prevalent cases. In simulations, under modest amounts of censoring, odds-ratios from the two-step procedure were equally efficient as those estimated from a joint logistic and survival data likelihood under parametric assumptions. This indicates that utilizing the cases' prospective survival data lessens model dependencies and improves precision of association estimates for case-control studies with prevalent cases. We illustrate the methods by estimating associations between single nucleotide polymorphisms and breast cancer risk using controls, and incident and prevalent cases sampled from the US Radiologic Technologists Study cohort.
| S-EPMC8620394 | biostudies-literature
Project description:Because dementia is progressive, incident cases are on average milder than prevalent cases, affecting the performance of cognitive tests and questions on functional limitations (i.e., cognition/functional limitation items) used for dementia assessment. Longitudinal studies assess incident cases, while cross-sectional studies assess prevalent cases, but differences are not typically considered when researchers select items to include in studies. We used longitudinal data from the Religious Orders Study and Memory and Aging Project (ROSMAP) (n = 3,446) collected between 1994 and 2021 to characterize differences in associations between items (cognition: 35 items; functional limitations: 14 items) and incident or prevalent dementia using multinomial regression models with generalized estimating equations, controlling for ROSMAP cohort (Religious Orders Study or Memory and Aging Project), age, sex, race, and education. The association between a given item and incident dementia was significantly weaker than the association between the same item and prevalent dementia for 46 of 49 items. However, there was variability, with larger differences for some items, including naming a pencil (prevalence odds ratio = 0.02 (95% confidence interval: 0.02, 0.03); incidence odds ratio = 0.10 (95% confidence interval: 0.06, 0.17); P for difference < 0.001). Important differences exist in the performance of cognition/functional limitation items for measurement of incident versus prevalent dementia. Differences can inform the choice of items for cross-sectional studies of prevalent cases or longitudinal studies of incident cases, leading to reduced misclassification and increased statistical power.
| S-EPMC10404065 | biostudies-literature
Project description:The nested case-control (NCC) design have been widely adopted as a cost-effective solution in many large cohort studies for risk assessment with expensive markers, such as the emerging biologic and genetic markers. To analyze data from NCC studies, conditional logistic regression (Goldstein and Langholz, 1992; Borgan et al., 1995) and maximum likelihood (Scheike and Juul, 2004; Zeng et al., 2006) based methods have been proposed. However, most of these methods either cannot be easily extended beyond the Cox model (Cox, 1972) or require additional modeling assumptions. More generally applicable approaches based on inverse probability weighting (IPW) have been proposed as useful alternatives (Samuelsen, 1997; Chen, 2001; Samuelsen et al., 2007). However, due to the complex correlation structure induced by repeated finite risk set sampling, interval estimation for such IPW estimators remain challenging especially when the estimation involves non-smooth objective functions or when making simultaneous inferences about functions. Standard resampling procedures such as the bootstrap cannot accommodate the correlation and thus are not directly applicable. In this paper, we propose a resampling procedure that can provide valid estimates for the distribution of a broad class of IPW estimators. Simulation results suggest that the proposed procedures perform well in settings when analytical variance estimator is infeasible to derive or gives less optimal performance. The new procedures are illustrated with data from the Framingham Offspring Study to characterize individual level cardiovascular risks over time based on the Framingham risk score, C-reactive protein (CRP) and a genetic risk score.
| S-EPMC3891801 | biostudies-literature
Project description:BackgroundThe concurrent sampling design was developed for case-control studies of recurrent events. It involves matching for time. Standard conditional logistic-regression (CLR) analysis ignores the dependence among recurrent events. Existing methods for clustered observations for CLR do not fit the complex data structure arising from the concurrent sampling design.MethodsWe propose to break the matches, apply unconditional logistic regression with adjustment for time in quintiles and residual time within each quintile, and use a robust standard error for observations clustered within persons. We conducted extensive simulation to evaluate this approach and compared it with methods based on CLR. We analysed data from a study of childhood pneumonia to illustrate the methods.ResultsThe proposed method and CLR methods gave very similar point estimates of association and showed little bias. The proposed method produced confidence intervals that achieved the target level of coverage probability, whereas the CLR methods did not, except when disease incidence was low.ConclusionsThe proposed method is suitable for the analysis of case-control studies with recurrent events.
| S-EPMC7394959 | biostudies-literature
Project description:The idea that epidemiologic studies should start from first exposure onward has been advocated in the past few years. The study of incident exposures is contrasted with studies of prevalent exposures in which follow-up may commence after first exposure. The former approach is seen as a hallmark of a good study and necessary for causal inference. We argue that studying incident exposures may be necessary in some situations, but it is not always necessary and is not the preferred option in many instances. Conducting a study involves decisions as to which person-time experience should be included. Although studies of prevalent exposures involve left truncation (missingness on the left), studies of incident exposures may involve right censoring (missingness on the right) and therefore may not be able to assess the long-term effects of exposure. These considerations have consequences for studies of dynamic (open) populations that involve a mixture of prevalent and incident exposures. We argue that studies with prevalent exposures will remain a necessity for epidemiology. The purpose of this paper is to restore the balance between the emphasis on first exposure cohorts and the richness of epidemiologic information obtained when studying prevalent exposures.
| S-EPMC4634310 | biostudies-literature
Project description:Registry databases are increasingly being used for comparative effectiveness research in cancer. Such databases reflect the real-world patient population and physician practice, and thus are natural sources for comparing multiple treatment scenarios and the associated long-term clinical outcomes. Registry databases usually include both incident and prevalent cohorts, which provide valuable complementary information for patients with more recent diagnoses in the incident cohort as well as patients with long-term follow-up data in the prevalent cohort. However, utilizing such data to derive valid inference poses two major challenges: the data from a prevalent cohort are not random samples of the target population, and there may be substantial differences in the baseline characteristics of patients between treatment arms, which influences the decisions about treatment selection in both cohorts. In this article, we extend propensity score methodology to observational studies that involve both prevalent and incident cohorts, and assess the effectiveness of radiation therapy in SEER-Medicare patients diagnosed with stage IV breast cancer. Specifically, we utilize the incident cohort to estimate the propensity for receiving radiation therapy, and then combine data from both the incident and prevalent cohorts to estimate the effect of radiation therapy by adjusting for the propensity scores in the model. We evaluate the proposed method with simulations. We demonstrate that the proposed propensity score method simultaneously removes sampling bias and selection bias under several assumptions.
| S-EPMC5646711 | biostudies-literature
Project description:Aims/hypothesisData on type 1 diabetes incidence and prevalence are limited, particularly for adults. This study aims to estimate global numbers of incident and prevalent cases of type 1 diabetes in 2017 for all age groups, by country and areas defined by income and region.MethodsIncidence rates of type 1 diabetes in children (available from 94 countries) from the IDF Atlas were used and extrapolated to countries without data. Age-specific incidence rates in adults (only known across full age range for fewer than ten countries) were obtained by applying scaling ratios for each adult age group relative to the incidence rate in children. Age-specific incidence rates were applied to population estimates to obtain incident case numbers. Duration of diabetes was estimated from available data and adjusted using differences in childhood mortality rate between countries from United Nations demographic data. Prevalent case numbers were derived by modelling the relationship between prevalence, incidence and disease duration. Sensitivity analyses were performed to quantify the impact of alternative assumptions and model inputs.ResultsGlobal numbers of incident and prevalent cases of type 1 diabetes were estimated to be 234,710 and 9,004,610, respectively, in 2017. High-income countries, with 17% of the global population, accounted for 49% of global incident cases and 52% of prevalent cases. Asia, which has the largest proportion of the world's population (60%), had the largest number of incident (32%) and prevalent (31%) cases of type 1 diabetes. Globally, 6%, 35%, 43% and 16% of prevalent cases were in the age groups 0-14, 15-39, 40-64 and 65+ years, respectively. Based on sensitivity analyses, the estimates could deviate by ±15%.Conclusions/interpretationGlobally, type 1 diabetes represents about 2% of the estimated total cases of diabetes, ranging from less than 1% in certain Pacific countries to more than 15% in Northern European populations in 2017. This study provides information for the development of healthcare and policy approaches to manage type 1 diabetes. The estimates need further validation due to limitations and assumptions related to data availability and estimation methods.
| S-EPMC8563635 | biostudies-literature
Project description:BackgroundThe management in primary care (PC) of the patients with Heart Failure (HF) is different from the management hospital, in a special way compared to cardiology departments.ObjectiveTo define the characteristics in both phenotypes of HF in prevalent and incident cases of HF in patients recruited in a large PC sample.MethodsWe proposed a and longitudinal analyses, in patients of the IBERICAN cohort, that recruited 8,066 patients in the Spanish primary care system, with 15,488 patients-years of follow-up. Of them, 252 patients (3.1%) had diagnoses of HF. HF was classified according to the 2014 guidelines in two groups: HF with a reduced eject fraction or HFrEF (LVEF < 50%) and HF with preserved eject fraction or HFpEF (LVEF ≥ 50%). Recommended treatment was defined as the patient receiving drug treatment with Renin-Angiotensin-System (RAS) blockers with beta-blockers and, optionally, spironolactone. The incidence of new cases of HF was calculated in the 7,814 patients without HF in the inclusion visit. Finally, we analysed which variables associated the onset new cases and get the hazard ratio (HR) with the confidence interval at 95% ([95%CI]). Clinical trials register: NCT02261441 (02/05/2017).ResultsThe HFpEF was the most frequent phenotype in prevalent cases (61.1%) and incident cases (73.9%). Patients with HFrEF had a higher prevalence of coronary heart disease (p = 0.008) and PAD (p = 0.028), and no statistically significant differences was observed in the therapeutic groups used between both groups. The incidence of HF was 12.8 cases/1000 inhabitants/year, 35.6% of them was diagnosed in PC. The renin-angiotensin system blockers were more used in PC (60%) and beta-blockers (100%) and spironolactone (60%) in hospital. The female sex showed a protective effect for incident cases (0.51 [0.28-0.92]); and AF (HR [95%CI]: 2.90 [1.51-5.54]), coronary heart disease (HR [95%CI]: 2.18 [1.19-4.00]) and hypertension (HR [95%CI]: 1.91 [1.00-3.64]) increased the risk of developing HF.ConclusionsHF phenotype more frequent and incident in PC was the HFpEF, but only one third of them are diagnosed in PC level. The female sex showed a protective effect and atrial fibrillation, ischaemic heart disease and hypertension increased the risk of develop HF.
| S-EPMC11270967 | biostudies-literature
Project description:Background and aimMetabolic syndrome (MetS) is a well-known noncommunicable disease that plays a significant role in emerging other chronic disorders and following complications. MetS is also involved in the pathophysiology of numerous dermatological diseases. We aim to evaluate the association of MetS with the most prevalent dermatological diseases.MethodsA systematic search was carried out on PubMed, Science Direct, Web of Science, Cochrane, as well as the Google Scholar search engine. Only English case-control studies regarding MetS and any skin disease from the beginning of 2010 up to November 15, 2022, were selected. The study was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA).ResultsA total of 37 studies (13,830 participants) met the inclusion criteria. According to our result, patients with psoriasis, hidradenitis suppurativa (HS), vitiligo, androgenetic alopecia (AGA), and lichen planus (LP) have a higher chance of having MetS compared to the general population. Furthermore, people with seborrheic dermatitis (SED) and rosacea are more prone to insulin resistance, high blood pressure (BP), and higher blood lipids. After pooling data, the meta-analysis revealed a significant association between MetS and skin diseases (pooled odds ratio [OR]: 3.28, 95% confidence interval: 2.62-4.10). Concerning the type of disease, MetS has been correlated with AGA (OR: 11.86), HS (OR: 4.46), LP (OR: 3.79), and SED (OR: 2.45). Psoriasis also showed a significant association but with high heterogeneity (OR: 2.89). Moreover, skin diseases and MetS are strongly associated in Spain (OR: 5.25) and Thailand (OR: 11.86). Regarding the metaregression model, the effect size was reduced with increasing age (OR: 0.965), while the size increased with AGA (OR: 3.064).ConclusionsMetS is closely associated with skin complications. Dermatologists and other multidisciplinary teams should be cautious while treating these patients to prevent severe complications resulting from MetS.
| S-EPMC10519158 | biostudies-literature
Project description:In a typical case-control study, exposure information is collected at a single time point for the cases and controls. However, case-control studies are often embedded in existing cohort studies containing a wealth of longitudinal exposure history about the participants. Recent medical studies have indicated that incorporating past exposure history, or a constructed summary measure of cumulative exposure derived from the past exposure history, when available, may lead to more precise and clinically meaningful estimates of the disease risk. In this article, we propose a flexible Bayesian semiparametric approach to model the longitudinal exposure profiles of the cases and controls and then use measures of cumulative exposure based on a weighted integral of this trajectory in the final disease risk model. The estimation is done via a joint likelihood. In the construction of the cumulative exposure summary, we introduce an influence function, a smooth function of time to characterize the association pattern of the exposure profile on the disease status with different time windows potentially having differential influence/weights. This enables us to analyze how the present disease status of a subject is influenced by his/her past exposure history conditional on the current ones. The joint likelihood formulation allows us to properly account for uncertainties associated with both stages of the estimation process in an integrated manner. Analysis is carried out in a hierarchical Bayesian framework using reversible jump Markov chain Monte Carlo algorithms. The proposed methodology is motivated by, and applied to a case-control study of prostate cancer where longitudinal biomarker information is available for the cases and controls.
| S-EPMC3935236 | biostudies-literature