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The marginal survival function of the time between successive events is estimated using a new class of nonparametric estimators. The proposed approach considers a one-sample problem when the recurrence times from various episodes have the same marginal distribution. The appropriateness of the nonparametric estimators is validated by statistical theory and simulations while their performance is demonstrated in simulation and analysis of schizophrenia data., 1. INTRODUCTION Recurrent event data are frequently encountered in studies with longitudinal designs. To analyze recurrent event data, the focus can be placed on the time between events, the transition [...]

Background: Neuropsychiatric symptoms (NPS) among cognitively normal older adults are increasingly recognized as risk factors for cognitive decline and impairment. However, the underlying mechanisms remain unclear.Objective: To examine whether biomarkers of Alzheimer's disease (amyloid burden) and cerebrovascular disease (white matter hyperintensity (WMH) volume) modify the association between NPS and cognitive decline among cognitively unimpaired older adults.Methods: Analyses included 193 cognitively unimpaired participants (M age = 70 years) from the BIOCARD study, including 148 with PET amyloid and WMH biomarker data. NPS were measured with Neuropsychiatric Inventory and Geriatric Depression Scale scores. Linear mixed effects models were used to examine the association between baseline NPS and longitudinal cognitive trajectories (M follow-up = 3.05 years), using separate models for global, episodic memory, and executive function cognitive composite scores. In a subset of individuals with biomarker data, we evaluated whether WMH or cortical amyloid burden modified the relationship between NPS and cognitive change (as indicated by the NPS×biomarker×time interactions).Results: Higher baseline NPS were associated with lower executive function scores, but not a faster rate of decline in executive function. NPS symptoms were unrelated to the global or episodic memory composite scores, and there was little evidence of a relationship between NPS symptoms and cognitive change over time. The associations between NPS and cognitive decline did not differ by amyloid or WMH burden, and NPS were unrelated to amyloid and WMH burden.Conclusion: These results suggest that the effect of neuropsychiatric symptoms on executive dysfunction may occur through mechanisms outside of amyloid and cerebrovascular disease. [ABSTRACT FROM AUTHOR]

In biomedical practices, multiple biomarkers are often combined using a prespecified classification rule with tree structure for diagnostic decisions. The classification structure and cutoff point at each node of a tree are usually chosen on an ad hoc basis, depending on decision makers' experience. There is a lack of analytical approaches that lead to optimal prediction performance, and that guide the choice of optimal cutoff points in a pre‐specified classification tree. In this paper, we propose to search for and estimate the optimal decision rule through an approach of rank correlation maximization. The proposed method is flexible, theoretically sound, and computationally feasible when many biomarkers are available for classification or prediction. Using the proposed approach, for a prespecified tree‐structured classification rule, we can guide the choice of optimal cutoff points at tree nodes and estimate optimal prediction performance from multiple biomarkers combined. [ABSTRACT FROM AUTHOR]

Objectives. Past research on low birthweight has focused on individual-level risk factors. We sought rolevel social factors by using census tract--level data on social stratification, community empowerment, and environmental stressors. Methods. Census tract--level information on social risk was linked to birth certificate records from Baltimore, Md, for the period 1985 through 1989. Individual-level factors included maternal education. health insurance (Medicaid), and trimester of prenatal care initiation. Methods of multilevel modeling using two-stage regression analyses were employed. Results. Macrolevel factors had both direct associations with low birthweight. All individual risk factors showed interaction with macrolevel variables; that is, individual-level risk factors for low birthweight behaved differently depending upon the characteristics of the neighborhood of residence. For example, women living in high-risk neighborhoods benefited less from prenatal care than did women living in lower-risk neighborhoods. Conclusions. Multilevel modeling is an important tool that allows simultaneous study of macro- and individual-level risk factors. Multilevel analyses should play a larger role in the formulation of public health policies. (Am J Public Health. 1997;87: 1113-1118)

Introduction: We examined longitudinal cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarker changes among cognitively normal individuals with 10.7 years follow‐up, on average. Methods: Analyses included 278 participants (M age = 57.5 years); 94 have progressed from normal cognition to mild cognitive impairment (MCI). Amyloid beta (Aβ)42/Aβ40, phosphorylated tau181 (p‐tau181), and total tau (t‐tau) were measured using automated electrochemiluminescence assays. Results: Apolipoprotein E (APOE) ε4 carriers had lower baseline Aβ42/Aβ40, but longitudinal Aβ42/Aβ40 decreases did not differ by APOE ε4 after accounting for Aβ42/Aβ40 positivity. Lower baseline Aβ42/Aβ40 was associated with greater increases in tau (more strongly in males), and APOE ε4 genotype was associated with greater tau increases after reaching Aβ42/Aβ40 positivity. Participants who progressed to MCI had more abnormal biomarker levels and greater tau increases prior to MCI symptom onset. Biomarkers were more abnormal among older adults, but unrelated to sex or education. Discussion: Our results confirm accelerated biomarker changes during preclinical AD and highlight the important role of amyloid levels in tau accelerations. [ABSTRACT FROM AUTHOR]

Objectives. Many contextual analyses that bridge the micro-level--macro-level gap in identifying risk factors for adverse outcomes have not used methods appropriate for multilevel data. The purpose of this paper is to illustrate the application of appropriate multi-level analytic methods and discuss their implications for public health. Methods. A previously published individual-level model of physical violence perpetrated by male partners during the childbearing year was reanalyzed to include variables describing the neighborhoods where the women resided. Logistic regression with estimation methods of the generalized estimating equation was used for the contextual analysis. To assess the advantages of the generalized estimating equation over conventional logistic regression, both were used for the two-level model. Results. The regression coefficients from the contextual model differed from the betas obtained in the individual-level model. Not only were neighborhood-level variables related to the risk of partner-perpetrated violence, but the presence of these macro-level variables in the models modified the relationships of the individual-level variables to the risk of violence. Conclusions. Two-level models that include individual- and community-level factors may be beneficial for purposes of explanation in public health research. (Am J Public Health. 1995; 85:1092-1097), Community factors may affect study outcomes as well as individual factors. This has implications for planning appropriate interventions. To illustrate the effects of community factors, a study of violence against pregnant women by their male partners was reanalyzed. Previously only individual factors such as the woman's support system were considered. Now, information about income, employment level, and crime level in the woman's neighborhood were factored in. This resulted in substantial changes in many of the previously calculated risk predictions. Most prominently, adding neighborhood factors increased the risk associated with being white from nil to a nine-fold risk. This suggests that high-risk neighborhoods could be targeted for programs or that changing social factors such as reducing unemployment could reduce the risk of battering.

Abstract Background In epidemiological studies, subjects are often followed for a period during which study outcomes are measured at selected time points, such as by diagnostic testing performed on biological samples collected at each visit. Although test results may indicate the presence or absence of a disease or condition, they cannot provide information on when exactly it occurred. Such study designs generate arbitrarily censored time-to-event data, which can include left, interval and right censoring. Adding to this complexity, the data may be clustered such that observations within the same cluster are not independent, such as time to recovery of an infectious disease of family or community members. This data structure is observed when evaluating circumcision's effect on clearance of penile high risk human papillomavirus (HR-HPV) infections using data collected from the male circumcision(MC) trial conducted in Rakai, Uganda, where the multiple infections within individual and HPV testings performed at trial follow-up visits gave rise to the clustered data with arbitrary censoring. Methods We describe the use of parametric proportional hazards frailty models and accelerated failure time frailty models to examine the relationship between explanatory variables and the survival outcomes that are subject to arbitrary censoring, while accounting for the correlation within clusters. Standard software such as SAS can be used for parameter estimation. Results Circumcision's effect on HPV infection was a secondary end point in the Rakai MC trial, and HPV genotyping was conducted for penile samples of a subset of trial participants collected at enrollment, 6, 12 and 24-month follow up visits. At enrollment, 36.7% intervention arm men (immediate circumcision) and 36.6% control arm men (delayed circumcision at 2 years) were infected with HR-HPV, with the number of infections per man being 1-5. The proposed models were used to examine whether MC facilitated clearance of the prevalent infections. Results show that clearance of multiple infections within each man is highly correlated, and clearance was 60% faster if a man was circumcised. Conclusions Parametric frailty models provide viable ways to study the relationship between exposure variables and clustered survival outcome that is subject to arbitrary censoring, as is often observed in HPV epidemiology studies.

Abstract Background Incidence and lifetime risk of diabetes are important public health measures. Traditionally, nonparametric estimates are obtained from survey data by means of a Nelson-Aalen estimator which requires data information on both incident events and risk sets from the entire cohort. Such data information is rarely available in real studies. Methods We compare two different approaches for obtaining nonparametric estimates of age-specific incidence and lifetime risk with emphasis on required assumptions. The first and novel approach only considers incident cases occurring within a fixed time window–we have termed this cohort-of-cases data–which is linked explicitly to the birth process in the past. The second approach is the usual Nelson-Aalen estimate which requires knowledge on observed time at risk for the entire cohort and their incident events. Both approaches are used on data on anti-diabetic medications obtained from Odense Pharmacoepidemiological Database, which covers a population of approximately 470,000 over the period 1993–2003. For both methods we investigate if and how incidence rates can be projected. Results Both the new and standard method yield similar sigmoidal shaped estimates of the cumulative distribution function of age-specific incidence. The Nelson-Aalen estimator gives somewhat higher estimates of lifetime risk (15.65% (15.14%; 16.16%) for females, and 17.91% (17.38%; 18.44%) for males) than the estimate based on cohort-of-cases data (13.77% (13.74%; 13.81%) for females, 15.61% (15.58%; 15.65%) for males). Accordingly the projected incidence rates are higher based on the Nelson-Aalen estimate–also too high when compared to observed rates. In contrast, the cohort-of-cases approach gives projections that fit observed rates better. Conclusion The developed methodology for analysis of cohort-of-cases data has potential to become a cost-effective alternative to a traditional survey based study of incidence. To allow more general use of the methodology, more research is needed on how to relax stationarity assumptions.

This paper introduces two sets of measures as exploratory tools to study physical activity patterns: active‐to‐sedentary/sedentary‐to‐active rate function (ASRF/SARF) and active/sedentary rate function (ARF/SRF). These two sets of measures are complementary to each other and can be effectively used together to understand physical activity patterns. The specific features are illustrated by an analysis of wearable device data from National Health and Nutrition Examination Survey (NHANES). A two‐level semiparametric regression model for ARF and the associated activity magnitude is developed under a unified framework using the marked point process formulation. The inactive and active states measured by accelerometers are treated as a 0‐1 point process, and the activity magnitude measured at each active state is defined as a marked variable. The commonly encountered missing data problem due to device nonwear is referred to as "window censoring," which is handled by a proper estimation approach that adopts techniques from recurrent event data. Large sample properties of the estimator and comparison between two regression models as measurement frequency increases are studied. Simulation and NHANES data analysis results are presented. The statistical inference and analysis results suggest that ASRF/SARF and ARF/SRF provide useful analytical tools to practitioners for future research on wearable device data. [ABSTRACT FROM AUTHOR]