Naive hypothesis testing for case series analysis with time-varying exposure onset measurement error: inference for infection-cardiovascular risk in patients on dialysis.

The case series method is useful in studying the relationship between time-varying exposures, such as infections, and acute events observed during the observation periods of individuals. It provides estimates of the relative incidences of events in risk periods (e.g., 30-day period after infections) relative to the baseline periods. When the times of exposure onsets are not known precisely, application of the case series model ignoring exposure onset measurement error leads to biased estimates. Bias-correction is necessary in order to understand the true directions and effect sizes associated with exposure risk periods, although uncorrected estimators have smaller variance. Thus, inference via hypothesis testing based on uncorrected test statistics, if valid, is potentially more powerful. Furthermore, the tests can be implemented in standard software and do not require additional auxiliary data. In this work, we examine the validity and power of naive hypothesis testing, based on applying the case series analysis to the imprecise data without correcting for the error. Based on simulation studies and theoretical calculations, we determine the validity and relative power of common hypothesis tests of interest in case series analysis. In particular, we illustrate that the tests for the global null hypothesis, the overall null hypotheses associated with all risk periods or all age effects are valid. However, tests of individual risk period parameters are not generally valid. Practical guidelines are provided and illustrated with data from patients on dialysis.

Design considerations for case series models with exposure onset measurement error.

The case series model allows for estimation of the relative incidence of events, such as cardiovascular events, within a pre-specified time window after an exposure, such as an infection. The method requires only cases (individuals with events) and controls for all fixed/time-invariant confounders. The measurement error case series model extends the original case series model to handle imperfect data, where the timing of an infection (exposure) is not known precisely. In this work, we propose a method for power/sample size determination for the measurement error case series model. Extensive simulation studies are used to assess the accuracy of the proposed sample size formulas. We also examine the magnitude of the relative loss of power due to exposure onset measurement error, compared with the ideal situation where the time of exposure is measured precisely. To facilitate the design of case series studies, we provide publicly available web-based tools for determining power/sample size for both the measurement error case series model as well as the standard case series model.

Modeling time-varying effects with generalized and unsynchronized longitudinal data.

We propose novel estimation approaches for generalized varying coefficient models that are tailored for unsynchronized, irregular and infrequent longitudinal designs/data. Unsynchronized longitudinal data refer to the time-dependent response and covariate measurements for each individual measured at distinct time points. Data from the Comprehensive Dialysis Study motivate the proposed methods. We model the potential age-varying association between infection-related hospitalization status and the inflammatory marker, C-reactive protein, within the first 2 years from initiation of dialysis. We cannot directly apply traditional longitudinal modeling to unsynchronized data, and no method exists to estimate time-varying or age-varying effects for generalized outcomes (e.g., binary or count data) to date. In addition, through the analysis of the Comprehensive Dialysis Study data and simulation studies, we show that preprocessing steps, such as binning, needed to synchronize data to apply traditional modeling can lead to significant loss of information in this context. In contrast, the proposed approaches discard no observation; they exploit the fact that although there is little information in a single subject trajectory because of irregularity and infrequency, the moments of the underlying processes can be accurately and efficiently recovered by pooling information from all subjects using functional data analysis. We derive subject-specific mean response trajectory predictions and study finite sample properties of the estimators.

Measurement Error Case Series Models with Application to Infection-Cardiovascular Risk in OlderPatients on Dialysis.

Infection and cardiovascular disease are leading causes of hospitalization and death in older patients on dialysis. Our recent work found an increase in the relative incidence of cardiovascular outcomes during the ~ 30 days after infection-related hospitalizations using the case series model, which adjusts for measured and unmeasured baseline confounders. However, a major challenge in modeling/assessing the infection-cardiovascular risk hypothesis is that the exact time of infection, or more generally "exposure," onsets cannot be ascertained based on hospitalization data. Only imprecise markers of the timing of infection onsets are available. Although there is a large literature on measurement error in the predictors in regression modeling, to date there is no work on measurement error on the timing of a time-varying exposure to our knowledge. Thus, we propose a new method, the measurement error case series (MECS) models, to account for measurement error in time-varying exposure onsets. We characterized the general nature of bias resulting from estimation that ignores measurement error and proposed a bias-corrected estimation for the MECS models. We examined in detail the accuracy of the proposed method to estimate the relative incidence. Hospitalization data from United States Renal Data System, which captures nearly all (> 99%) patients with end-stage renal disease in the U.S. over time, is used to illustrate the proposed method. The results suggest that the estimate of the cardiovascular incidence following the 30 days after infections, a period where acute effects of infection on vascular endothelium may be most pronounced, is substantially attenuated in the presence of infection onset measurement error.

Infant siblings and the investigation of autism risk factors.

Infant sibling studies have been at the vanguard of autism spectrum disorders (ASD) research over the past decade, providing important new knowledge about the earliest emerging signs of ASD and expanding our understanding of the developmental course of this complex disorder. Studies focused on siblings of children with ASD also have unrealized potential for contributing to ASD etiologic research. Moving targeted time of enrollment back from infancy toward conception creates tremendous opportunities for optimally studying risk factors and risk biomarkers during the pre-, peri- and neonatal periods. By doing so, a traditional sibling study, which already incorporates close developmental follow-up of at-risk infants through the third year of life, is essentially reconfigured as an enriched-risk pregnancy cohort study. This review considers the enriched-risk pregnancy cohort approach of studying infant siblings in the context of current thinking on ASD etiologic mechanisms. It then discusses the key features of this approach and provides a description of the design and implementation strategy of one major ASD enriched-risk pregnancy cohort study: the Early Autism Risk Longitudinal Investigation (EARLI).

Risk of cardiovascular events after infection-related hospitalizations in older patients on dialysis.

BACKGROUND AND OBJECTIVES: Infection and cardiovascular disease are leading causes of hospitalization and death in patients on dialysis. The objective of this study was to determine whether an infection-related hospitalization increased the short-term risk of a cardiovascular event in older patients on dialysis. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: With use of the United States Renal Data System, patients aged 65 to 100 years who started dialysis between January 1, 2000, and December 31, 2002, were examined. All hospitalizations were examined from study entry until time of transplant, death, or December 31, 2004. All discharge diagnoses were examined to determine if an infection occurred during hospitalization. Only principal discharge diagnoses were examined to ascertain cardiovascular events of interest. We used the self-controlled case-series method to estimate the relative incidence of a cardiovascular event within 90 days after an infection-related hospitalization as compared with other times not within 90 days of such a hospitalization. RESULTS: A total of 16,874 patients had at least one cardiovascular event and were included in the self-controlled case-series analysis. The risk of a cardiovascular event was increased by 25% in the first 30 days after an infection and was overall increased 18% in the 90 days after an infection-related hospitalization relative to control periods. CONCLUSIONS: The first 90 days, and in particular the first 30 days, after an infection-related hospitalization is a high-risk period for cardiovascular events and may be an important timeframe for cardiovascular risk reduction, monitoring, and intervention in older patients on dialysis.

Racial disparities on the use of invasive and noninvasive staging in patients with non-small cell lung cancer.

INTRODUCTION: Racial disparities have been reported in non-small cell lung cancer (NSCLC) staging and therapeutic outcomes. We investigated whether such disparities exist in the era of modern noninvasive staging modalities, including positron emission tomography scan use. METHODS: NSCLC patients from the California Cancer Registry diagnosed between January 1, 1994, and December 31, 2004, were included. The likelihood of obtaining invasive (thoracoscopy, bronchoscopy, and mediastinoscopy) and noninvasive staging procedures (computed tomography, magnetic resonance imaging, and positron emission tomography scans), along with surgical resection, were analyzed using logistic regression adjusted for known confounders. RESULTS: Of 13,762 NSCLC patients, 12,395 with adequate staging information were included. 10,217 patients (82%) were classified as white, 2178 patients (18%) were non-white, and 738 were black patients (6%). No association was seen between race and the use of either noninvasive (odds ratio [OR] = 1.02; p = 0.76) or invasive staging procedures (OR = 0.96; p = 0.44). However, compared with white patients, black patients had a lower likelihood of undergoing surgery, regardless of noninvasive (OR = 0.6; p <0.001) or invasive staging use (OR = 0.63; p = 0.02). There was no survival difference for those who underwent surgery between white and non-white patients, regardless of noninvasive (hazard ratio = 0.95; p = 0.45) or invasive staging (hazard ratio = 1.03; p = 0.79). CONCLUSIONS: In contrast to prior published work, we found no difference in rates of both invasive and noninvasive staging between white and non-white patients. However, non-white patients-particularly blacks-were less likely to receive surgery. The reason for the apparent difference in surgical rates could not be explained by the variables we evaluated. Thus, other factors such as personal preference or access to care require further investigation.