Multivariate survival analysis in big data: A divide-and-combine approach.

Multivariate failure time data are frequently analyzed using the marginal proportional hazards models and the frailty models. When the sample size is extraordinarily large, using either approach could face computational challenges. In this paper, we focus on the marginal model approach and propose a divide-and-combine method to analyze large-scale multivariate failure time data. Our method is motivated by the Myocardial Infarction Data Acquisition System (MIDAS), a New Jersey statewide database that includes 73,725,160 admissions to nonfederal hospitals and emergency rooms (ERs) from 1995 to 2017. We propose to randomly divide the full data into multiple subsets and propose a weighted method to combine these estimators obtained from individual subsets using three weights. Under mild conditions, we show that the combined estimator is asymptotically equivalent to the estimator obtained from the full data as if the data were analyzed all at once. In addition, to screen out risk factors with weak signals, we propose to perform the regularized estimation on the combined estimator using its combined confidence distribution. Theoretical properties, such as consistency, oracle properties, and asymptotic equivalence between the divide-and-combine approach and the full data approach are studied. Performance of the proposed method is investigated using simulation studies. Our method is applied to the MIDAS data to identify risk factors related to multivariate cardiovascular-related health outcomes.

A bias correction method in meta-analysis of randomized clinical trials with no adjustments for zero-inflated outcomes.

Many clinical endpoint measures, such as the number of standard drinks consumed per week or the number of days that patients stayed in the hospital, are count data with excessive zeros. However, the zero-inflated nature of such outcomes is sometimes ignored in analyses of clinical trials. This leads to biased estimates of study-level intervention effect and, consequently, a biased estimate of the overall intervention effect in a meta-analysis. The current study proposes a novel statistical approach, the Zero-inflation Bias Correction (ZIBC) method, that can account for the bias introduced when using the Poisson regression model, despite a high rate of inflated zeros in the outcome distribution of a randomized clinical trial. This correction method only requires summary information from individual studies to correct intervention effect estimates as if they were appropriately estimated using the zero-inflated Poisson regression model, thus it is attractive for meta-analysis when individual participant-level data are not available in some studies. Simulation studies and real data analyses showed that the ZIBC method performed well in correcting zero-inflation bias in most situations.

A CD-based mapping method for combining multiple related parameters from heterogeneous intervention trials.

Effect size can differ as a function of the elapsed time since treatment or as a function of other key covariates, such as sex or age. In evidence synthesis, a better understanding of the precise conditions under which treatment does work or does not work well has been highly valued. With increasingly accessible individual patient or participant data (IPD), more precise and informative inference can be within our reach. However, simultaneously combining multiple related parameters across heterogeneous studies is challenging because each parameter from each study has a specific interpretation within the context of the study and other covariates in the model. This paper proposes a novel mapping method to combine study-specific estimates of multiple related parameters across heterogeneous studies, which ensures valid inference at all inference levels by combining sample-dependent functions known as Confidence Distributions (CD). We describe the "CD-based mapping method" and provide a data application example for a multivariate random-effects meta-analysis model. We estimated up to 13 study-specific regression parameters for each of 14 individual studies using IPD in the first step, and subsequently combined the study-specific vectors of parameters, yielding a full vector of hyperparameters in the second step of meta-analysis. Sensitivity analysis indicated that the CD-based mapping method is robust to model misspecification. This novel approach to multi-parameter synthesis provides a reasonable methodological solution when combining complex evidence using IPD.

Exact inference on the random-effects model for meta-analyses with few studies.

We describe an exact, unconditional, non-randomized procedure for producing confidence intervals for the grand mean in a normal-normal random effects meta-analysis. The procedure targets meta-analyses based on too few primary studies, ≤7 , say, to allow for the conventional asymptotic estimators, e.g., DerSimonian and Laird (1986), or non-parametric resampling-based procedures, e.g., Liu et al. (2017). Meta-analyses with such few studies are common, with one recent sample of 22,453 heath-related meta-analyses finding a median of 3 primary studies per meta-analysis (Davey et al., 2011). Reliable and efficient inference procedures are therefore needed to address this setting. The coverage level of the resulting CI is guaranteed to be above the nominal level, up to Monte Carlo error, provided the meta-analysis contains more than 1 study and the model assumptions are met. After employing several techniques to accelerate computation, the new CI can be easily constructed on a personal computer. Simulations suggest that the proposed CI typically is not overly conservative. We illustrate the approach on several contrasting examples of meta-analyses investigating the effect of calcium intake on bone mineral density.

Trajectories and predictors of response in youth anxiety CBT: Integrative data analysis.

OBJECTIVE: Integrative data analysis was used to combine existing data from nine trials of cognitive-behavioral therapy (CBT) for anxious youth (N = 832) and identify trajectories of symptom change and predictors of trajectories. METHOD: Youth- and parent-reported anxiety symptoms were combined using item-response theory models. Growth mixture modeling assessed for trajectories of treatment response across pre-, mid-, and posttreatment and 1-year follow-up. Pretreatment client demographic and clinical traits and treatment modality (individual- and family-based CBT) were examined as predictors of trajectory classes. RESULTS: Growth mixture modeling supported three trajectory classes based on parent-reported symptoms: steady responders, rapid responders, and delayed improvement. A 4-class model was supported for youth-reported symptoms: steady responders, rapid responders, delayed improvement, and low-symptom responders. Delayed improvement classes were predicted by higher number of diagnoses (parent and youth report). Receiving family CBT predicted membership in the delayed improvement class compared to all other classes and membership in the steady responder class compared with rapid responders (youth report). Rapid responders were predicted by older age (parent report) and higher number of diagnoses (parent report). Low-symptom responders were more likely to be male (youth report). CONCLUSIONS: Integrative data analysis identified distinct patterns of symptom change. Diagnostic complexity, age, gender, and treatment modality differentiated response classes. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Meta-analysis framework for exact inferences with application to the analysis of rare events.

The usefulness of meta-analysis has been recognized in the evaluation of drug safety, as a single trial usually yields few adverse events and offers limited information. For rare events, conventional meta-analysis methods may yield an invalid inference, as they often rely on large sample theories and require empirical corrections for zero events. These problems motivate research in developing exact methods, including Tian et al.'s method of combining confidence intervals (2009, Biostatistics, 10, 275-281) and Liu et al.'s method of combining p-value functions (2014, JASA, 109, 1450-1465). This article shows that these two exact methods can be unified under the framework of combining confidence distributions (CDs). Furthermore, we show that the CD method generalizes Tian et al.'s method in several aspects. Given that the CD framework also subsumes the Mantel-Haenszel and Peto methods, we conclude that the CD method offers a general framework for meta-analysis of rare events. We illustrate the CD framework using two real data sets collected for the safety analysis of diabetes drugs.

Multivariate Meta-Analysis of Heterogeneous Studies Using Only Summary Statistics: Efficiency and Robustness.

Meta-analysis has been widely used to synthesize evidence from multiple studies for common hypotheses or parameters of interest. However, it has not yet been fully developed for incorporating heterogeneous studies, which arise often in applications due to different study designs, populations or outcomes. For heterogeneous studies, the parameter of interest may not be estimable for certain studies, and in such a case, these studies are typically excluded from conventional meta-analysis. The exclusion of part of the studies can lead to a non-negligible loss of information. This paper introduces a metaanalysis for heterogeneous studies by combining the confidence density functions derived from the summary statistics of individual studies, hence referred to as the CD approach. It includes all the studies in the analysis and makes use of all information, direct as well as indirect. Under a general likelihood inference framework, this new approach is shown to have several desirable properties, including: i) it is asymptotically as efficient as the maximum likelihood approach using individual participant data (IPD) from all studies; ii) unlike the IPD analysis, it suffices to use summary statistics to carry out the CD approach. Individual-level data are not required; and iii) it is robust against misspecification of the working covariance structure of the parameter estimates. Besides its own theoretical significance, the last property also substantially broadens the applicability of the CD approach. All the properties of the CD approach are further confirmed by data simulated from a randomized clinical trials setting as well as by real data on aircraft landing performance. Overall, one obtains an unifying approach for combining summary statistics, subsuming many of the existing meta-analysis methods as special cases.

Mediators of exposure therapy for youth obsessive-compulsive disorder: specificity and temporal sequence of client and treatment factors.

OBJECTIVE: Behavioral engagement and cognitive coping have been hypothesized to mediate effectiveness of exposure-based therapies. Identifying which specific child factors mediate successful therapy and which therapist factors facilitate change can help make our evidence-based treatments more efficient and robust. The current study examines the specificity and temporal sequence of relations among hypothesized client and therapist mediators in exposure therapy for pediatric Obsessive Compulsive Disorder (OCD). METHOD: Youth coping (cognitive, behavioral), youth safety behaviors (avoidance, escape, compulsive behaviors), therapist interventions (cognitive, exposure extensiveness), and youth anxiety were rated via observational ratings of therapy sessions of OCD youth (N=43; ages=8 - 17; 62.8% male) who had received Exposure and Response Prevention (ERP). Regression analysis using Generalized Estimation Equations and cross-lagged panel analysis (CLPA) were conducted to model anxiety change within and across sessions, to determine formal mediators of anxiety change, and to establish sequence of effects. RESULTS: Anxiety ratings decreased linearly across exposures within sessions. Youth coping and therapist interventions significantly mediated anxiety change across exposures, and youth-interfering behavior mediated anxiety change at the trend level. In CLPA, youth-interfering behaviors predicted, and were predicted by, changes in anxiety. Youth coping was predicted by prior anxiety change. CONCLUSIONS: The study provides a preliminary examination of specificity and temporal sequence among child and therapist behaviors in predicting youth anxiety. Results suggest that therapists should educate clients in the natural rebound effects of anxiety between sessions and should be aware of the negatively reinforcing properties of avoidance during exposure.

Temporal relationship between vitamin D status and parathyroid hormone in the United States.

BACKGROUND: Interpretation of parathyroid hormone (iPTH) requires knowledge of vitamin D status that is influenced by season. OBJECTIVE: Characterize the temporal relationship between 25-hydroxyvitamin D3 levels [25(OH)D3] and intact iPTH for several seasons, by gender and latitude in the U.S. and relate 25-hydrovitamin D2 [25(OH)D2] levels with PTH levels and total 25(OH)D levels. METHOD: We retrospectively determined population weekly-mean concentrations of unpaired [25(OH)D2 and 25(OH)D3] and iPTH using 3.8 million laboratory results of adults. The 25(OH)D3 and iPTH distributions were normalized and the means fit with a sinusoidal function for both gender and latitudes: North >40, Central 32-40 and South <32 degrees. We analyzed PTH and total 25(OH)D separately in samples with detectable 25(OH)D2 (≥4 ng/mL). FINDINGS: Seasonal variation was observed for all genders and latitudes. 25(OH)D3 peaks occurred in September and troughs in March. iPTH levels showed an inverted pattern of peaks and troughs relative to 25(OH)D3, with a delay of 4 weeks. Vitamin D deficiency and insufficiency was common (33% <20 ng/mL; 60% <30 ng/mL) as was elevated iPTH levels (33%>65 pg/mL). The percentage of patients deficient in 25(OH)D3 seasonally varied from 21% to 48% and the percentage with elevated iPTH reciprocally varied from 28% to 38%. Patients with detectable 25(OH)D2 had higher PTH levels and 57% of the samples with a total 25(OH)D > 50 ng/mL had detectable 25(OH)D2. INTERPRETATION: 25(OH)D3 and iPTH levels vary in a sinusoidal pattern throughout the year, even in vitamin D2 treated patients; 25(OH)D3, being higher in the summer and lower in the winter months, with iPTH showing the reverse pattern. A large percentage of the tested population showed vitamin D deficiency and secondary hyperparathyroidism. These observations held across three latitudinal regions, both genders, multiple-years, and in the presence or absence of detectable 25(OH)D2, and thus are applicable for patient care.

Efficient network meta-analysis: a confidence distribution approach.

Network meta-analysis synthesizes several studies of multiple treatment comparisons to simultaneously provide inference for all treatments in the network. It can often strengthen inference on pairwise comparisons by borrowing evidence from other comparisons in the network. Current network meta-analysis approaches are derived from either conventional pairwise meta-analysis or hierarchical Bayesian methods. This paper introduces a new approach for network meta-analysis by combining confidence distributions (CDs). Instead of combining point estimators from individual studies in the conventional approach, the new approach combines CDs which contain richer information than point estimators and thus achieves greater efficiency in its inference. The proposed CD approach can e ciently integrate all studies in the network and provide inference for all treatments even when individual studies contain only comparisons of subsets of the treatments. Through numerical studies with real and simulated data sets, the proposed approach is shown to outperform or at least equal the traditional pairwise meta-analysis and a commonly used Bayesian hierarchical model. Although the Bayesian approach may yield comparable results with a suitably chosen prior, it is highly sensitive to the choice of priors (especially the prior of the between-trial covariance structure), which is often subjective. The CD approach is a general frequentist approach and is prior-free. Moreover, it can always provide a proper inference for all the treatment effects regardless of the between-trial covariance structure.

Exact meta-analysis approach for discrete data and its application to 2 × 2 tables with rare events.

This paper proposes a general exact meta-analysis approach for synthesizing inferences from multiple studies of discrete data. The approach combines the p-value functions (also known as significance functions) associated with the exact tests from individual studies. It encompasses a broad class of exact meta-analysis methods, as it permits broad choices for the combining elements, such as tests used in individual studies, and any parameter of interest. The approach yields statements that explicitly account for the impact of individual studies on the overall inference, in terms of efficiency/power and the type I error rate. Those statements also give rises to empirical methods for further enhancing the combined inference. Although the proposed approach is for general discrete settings, for convenience, it is illustrated throughout using the setting of meta-analysis of multiple 2 × 2 tables. In the context of rare events data, such as observing few, zero or zero total (i.e., zero events in both arms) outcomes in binomial trials or 2 × 2 tables, most existing meta-analysis methods rely on the large-sample approximations which may yield invalid inference. The commonly used corrections to zero outcomes in rare events data, aiming to improve numerical performance can also incur undesirable consequences. The proposed approach applies readily to any rare event setting, including even the zero total event studies without any artificial correction. While debates continue on whether or how zero total event studies should be incorporated in meta-analysis, the proposed approach has the advantage of automatically including those studies and thus making use of all available data. Through numerical studies in rare events settings, the proposed exact approach is shown to be efficient and, generally, outperform commonly used meta-analysis methods, including Mental-Haenszel and Peto methods.

Nurses' practice environments, error interception practices, and inpatient medication errors.

PURPOSE: Medication errors remain a threat to patient safety. Therefore, the purpose of this study was to determine the relationships among characteristics of the nursing practice environment, nurse staffing levels, nurses' error interception practices, and rates of nonintercepted medication errors in acute care hospitals. DESIGN: This study, using a nonexperimental design, was conducted in a sample of 82 medical-surgical units recruited from 14 U.S. acute care hospitals. Registered nurses (RNs) on the 82 units were surveyed, producing a sample of 686 staff nurses. METHODS: Data collected for the 8-month study period included the number of medication errors per 1,000 patient days and the number of RN hours per patient day. Nurse survey data included the Practice Environment Scale of the Nursing Work Index as a measure of environmental characteristics; a metric of nurses' interception practices was developed for the study. All survey measures were aggregated to the unit level prior to analysis with hierarchical linear modeling. FINDINGS: A supportive practice environment was positively associated with error interception practices among nurses in the sample of medical-surgical units. Importantly, nurses' interception practices were inversely associated with medication error rates. CONCLUSIONS: A supportive practice environment enhances nurses' error interception practices. These interception practices play a role in reducing medication errors. CLINICAL RELEVANCE: When supported by their practice environments, nurses employ practices that can assist in interrupting medication errors before they reach the patients.

A note on dichotomization of continuous response variable in the presence of contamination and model misspecification.

The purpose of this note is to raise awareness of the complexity of the practice involving dichotomization. It is well known that the regular regression models are effective tools for analyzing Gaussian-type response variables, and researchers are often told that it is a 'bad idea' to practice dichotomization if continuous measurements are available. We demonstrate through special cases, however, that there is another side of the story if the response variable is contaminated. Although dichotomization causes loss of information, it can also reduce input of contamination. If the reduction of contamination input outweighs the loss of information, analysis based on dichotomization can sometimes provide better results. We derive formulas of bias and variance for binary regression estimators under a contamination model of unknown additive errors, and compare them with both the least squares and robust M-estimators from the corresponding linear regression analysis using continuous responses. As a case study, we study extensively the case in which the observed response is contaminated by an error with a mean and a variance proportional to the mean and the variance of the uncontaminated true response. Conditions under which dichotomization is preferred are obtained. A simulation study based on a real data setting is provided, which supports the theoretical developments.

A latent model to detect multiple clusters of varying sizes.

This article develops a latent model and likelihood-based inference to detect temporal clustering of events. The model mimics typical processes generating the observed data. We apply model selection techniques to determine the number of clusters, and develop likelihood inference and a Monte Carlo expectation-maximization algorithm to estimate model parameters, detect clusters, and identify cluster locations. Our method differs from the classical scan statistic in that we can simultaneously detect multiple clusters of varying sizes. We illustrate the methodology with two real data applications and evaluate its efficiency through simulation studies. For the typical data-generating process, our methodology is more efficient than a competing procedure that relies on least squares.

Patient complexity in quality comparisons for glycemic control: an observational study.

BACKGROUND: Patient complexity is not incorporated into quality of care comparisons for glycemic control. We developed a method to adjust hemoglobin A1c levels for patient characteristics that reflect complexity, and examined the effect of using adjusted A1c values on quality comparisons. METHODS: This cross-sectional observational study used 1999 national VA (US Department of Veterans Affairs) pharmacy, inpatient and outpatient utilization, and laboratory data on diabetic veterans. We adjusted individual A1c levels for available domains of complexity: age, social support (marital status), comorbid illnesses, and severity of disease (insulin use). We used adjusted A1c values to generate VA medical center level performance measures, and compared medical center ranks using adjusted versus unadjusted A1c levels across several thresholds of A1c (8.0%, 8.5%, 9.0%, and 9.5%). RESULTS: The adjustment model had R2 = 8.3% with stable parameter estimates on thirty random 50% resamples. Adjustment for patient complexity resulted in the greatest rank differences in the best and worst performing deciles, with similar patterns across all tested thresholds. CONCLUSION: Adjustment for complexity resulted in large differences in identified best and worst performers at all tested thresholds. Current performance measures of glycemic control may not be reliably identifying quality problems, and tying reimbursements to such measures may compromise the care of complex patients.

Semiparametric Analysis of Heterogeneous Data Using Varying-Scale Generalized Linear Models.

This article describes a class of heteroscedastic generalized linear regression models in which a subset of the regression parameters are rescaled nonparametrically, and develops efficient semiparametric inferences for the parametric components of the models. Such models provide a means to adapt for heterogeneity in the data due to varying exposures, varying levels of aggregation, and so on. The class of models considered includes generalized partially linear models and nonparametrically scaled link function models as special cases. We present an algorithm to estimate the scale function nonparametrically, and obtain asymptotic distribution theory for regression parameter estimates. In particular, we establish that the asymptotic covariance of the semiparametric estimator for the parametric part of the model achieves the semiparametric lower bound. We also describe bootstrap-based goodness-of-scale test. We illustrate the methodology with simulations, published data, and data from collaborative research on ultrasound safety.

Air-water exchange of polychlorinated biphenyls in the Delaware River.

The air-water exchange of polychlorinated biphenyls (PCBs) often results in net volatilization, which is thought to be the most important loss process for PCBs in many systems. Previous investigations of the air-water exchange of PCBs have been hampered by difficulties in treatment of the uncertainty in the calculation of air/water fugacity ratios. This work presents a new framework for the treatment of uncertainty, where uncertainty in physical constants is handled differently from random measurement uncertainty associated with random samples, and it further investigates the sorption of PCBs to colloids (dissolved organic carbon). Simultaneous measurements of PCBs in the air and water of five water quality management zones of the Delaware River were taken in 2002 in support of the total maximum daily load (TMDL) process. Gas-phase concentrations of IPCBs ranged from 110 to 1350 pg m(-3), while dissolved water concentrations were between 420 and 1650 pg L(-1). Shallow slopes of log Koc vs. log Kow plots indicated a colloidal contribution to the apparent dissolved-phase concentrations, such that a three-phase partitioning model was applied. Fugacity ratios for individual congeners were calculated under the most conservative assumptions, and their values (log-transformed) were examined via a single-sample T-test to determine whether they were significantly less than 1 at the 95% confidence level. This method demonstrated that air-water exchange resulted in net volatilization in all zones over all cruises for all but seven high molecular weight congeners. Calculated net fluxes ranged from +360 to +3000 ng m(-2) d(-1) for sigma PCBs. The colloidal correction decreased the volatilization flux of sigma PCBs by approximately 30%. The decachlorinated congener (PCB 209), exhibited unusually high concentrations in the suspended solids, especially in the southern portions of the river, indicating that there is a distinct source of PCB 209 in the Delaware River.

Diabetes healthcare quality report cards: how accurate are the grades?

OBJECTIVE: To evaluate the accuracy and precision of random sampling in identifying healthcare system outliers in diabetes performance measures. STUDY DESIGN: Cross-sectional analysis of 79 Veterans Health Administration facilities serving 250 317 patients with diabetes mellitus between October 1, 1999, and September 30, 2000. METHODS: Primary outcome measures were poor glycosylated hemoglobin (A1C) control and good low-density lipoprotein cholesterol (LDL-C) and blood pressure (BP) control. Facility performance for each measure was calculated using 150 separate random samples and was compared with results using the bootstrap method as the criterion standard for determining outlier status (defined as a >/=5% difference from the mean, within the 10th or 90th percentile, or >/=2 SDs from the mean). RESULTS: The study population was largely male (97.4%), with 54.0% of subjects being 65 years or older. The facility-level mean performances were 22.8% for poor A1C control, 53.1% for good LDL-C control, and 55.3% for good BP control. Comparing the random sampling method with the bootstrap method, the sensitivity ranged between 0.64 and 0.83 for the 3 outcome measures, positive predictive values ranged between 0.55 and 0.88, and specificity and negative predictive values ranged between 0.88 and 0.99. CONCLUSIONS: The specificity and negative predictive value of the random sampling method in identifying nonoutliers in performance were generally high, while its sensitivity and positive predictive value were moderate. The use of random sampling to determine performance for individual outcome measures may be most appropriate for internal quality improvement rather than for public reporting.

Assessing quality of diabetes care by measuring longitudinal changes in hemoglobin A1c in the Veterans Health Administration.

CONTEXT: A1c levels are widely used to assess quality of diabetes care provided by health care systems. Currently, cross-sectional measures are commonly used for such assessments. OBJECTIVE: To study within-patient longitudinal changes in A1c levels at Veterans Health Administration (VHA) facilities as an alternative to cross-sectional measures of quality of diabetes care. DESIGN: Longitudinal study using institutional data on individual patient A1c level over time (October 1, 1998-September 30, 2000) with time variant and invariant covariates. SETTING: One hundred and twenty-five VHA facilities nationwide, October 1, 1998-September 30, 2000. PATIENTS: Diabetic veteran users with A1c measurement performed using National Glycosylated Hemoglobin Standardization Project certified A1c lab assay methods. EXPOSURES: Characteristics unlikely to reflect quality of care, but known to influence A1c levels, demographics, and baseline illness severity. MAIN OUTCOME MEASURE: Monthly change in A1c for average patient cared for at each facility. RESULTS: The preponderance of facilities showed monthly declines in within-patient A1c over the study period (mean change of -0.0148 A1c units per month, range -0.074 to 0.042). Individual facilities varied in their monthly change, with 105 facilities showing monthly declines (70 significant at .05 level) and 20 showing monthly increases (5 significant at .05 level). Case-mix adjustment resulted in modest changes (mean change of -0.0131 case-mix adjusted A1c units per month, range -0.079 to 0.043). Facilities were ranked from worst to best, with attached 90 percent confidence intervals. Among the bottom 10 ranked facilities, four remained within the bottom decile with 90 percent confidence. CONCLUSIONS: There is substantial variation in facility-level longitudinal changes in A1c levels. We propose that evaluation of change in A1c levels over time can be used as a new measure to reflect quality of care provided to populations of individuals with chronic disease.

Seasonal patterns in monthly hemoglobin A1c values.

The purpose of this study was to investigate seasonal variations in population monthly hemoglobin A(1c) (A1c) values over 2 years (from October 1998 to September 2000) among US diabetic veterans. The study cohort included 285,705 veterans with 856,181 A1c tests. The authors calculated the monthly average A1c values for the overall population and for subpopulations defined by age, sex, race, insulin use, and climate regions. A1c values were higher in winter and lower in summer with a difference of 0.22. The proportion of A1c values greater than 9.0% followed a similar seasonal pattern that varied from 17.3% to 25.3%. Seasonal autoregressive models including trigonometric function terms were fit to the monthly average A1c values. There were significant seasonal effects; the seasonal variation was consistent across different subpopulations. Regions with colder winter temperatures had larger winter-summer contrasts than did those with warmer winter temperatures. The seasonal patterns followed trends similar to those of many physiologic markers, cardiovascular and other diabetes outcomes, and mortality. These findings have implications for health-care service research in quality-of-care assessment, epidemiologic studies investigating population trends and risk factors, and clinical trials or program evaluations of treatments or interventions.