Multiple self-controlled case series for large-scale longitudinal observational databases.

Characterization of relationships between time-varying drug exposures and adverse events (AEs) related to health outcomes represents the primary objective in postmarketing drug safety surveillance. Such surveillance increasingly utilizes large-scale longitudinal observational databases (LODs), containing time-stamped patient-level medical information including periods of drug exposure and dates of diagnoses for millions of patients. Statistical methods for LODs must confront computational challenges related to the scale of the data, and must also address confounding and other biases that can undermine efforts to estimate effect sizes. Methods that compare on-drug with off-drug periods within patient offer specific advantages over between patient analysis on both counts. To accomplish these aims, we extend the self-controlled case series (SCCS) for LODs. SCCS implicitly controls for fixed multiplicative baseline covariates since each individual acts as their own control. In addition, only exposed cases are required for the analysis, which is computationally advantageous. The standard SCCS approach is usually used to assess single drugs and therefore estimates marginal associations between individual drugs and particular AEs. Such analyses ignore confounding drugs and interactions and have the potential to give misleading results. In order to avoid these difficulties, we propose a regularized multiple SCCS approach that incorporates potentially thousands or more of time-varying confounders such as other drugs. The approach successfully handles the high dimensionality and can provide a sparse solution via an L1 regularizer. We present details of the model and the associated optimization procedure, as well as results of empirical investigations.

Empirical performance of a new user cohort method: lessons for developing a risk identification and analysis system.

BACKGROUND: Observational healthcare data offer the potential to enable identification of risks of medical products, but appropriate methodology has not yet been defined. The new user cohort method, which compares the post-exposure rate among the target drug to a referent comparator group, is the prevailing approach for many pharmacoepidemiology evaluations and has been proposed as a promising approach for risk identification but its performance in this context has not been fully assessed. OBJECTIVES: To evaluate the performance of the new user cohort method as a tool for risk identification in observational healthcare data. RESEARCH DESIGN: The method was applied to 399 drug-outcome scenarios (165 positive controls and 234 negative controls across 4 health outcomes of interest) in 5 real observational databases (4 administrative claims and 1 electronic health record) and in 6 simulated datasets with no effect and injected relative risks of 1.25, 1.5, 2, 4, and 10, respectively. MEASURES: Method performance was evaluated through Area Under ROC Curve (AUC), bias, and coverage probability. RESULTS: The new user cohort method achieved modest predictive accuracy across the outcomes and databases under study, with the top-performing analysis near AUC >0.70 in most scenarios. The performance of the method was particularly sensitive to the choice of comparator population. For almost all drug-outcome pairs there was a large difference, either positive or negative, between the true effect size and the estimate produced by the method, although this error was near zero on average. Simulation studies showed that in the majority of cases, the true effect estimate was not within the 95 % confidence interval produced by the method. CONCLUSION: The new user cohort method can contribute useful information toward a risk identification system, but should not be considered definitive evidence given the degree of error observed within the effect estimates. Careful consideration of the comparator selection and appropriate calibration of the effect estimates is required in order to properly interpret study findings.

Empirical performance of the self-controlled case series design: lessons for developing a risk identification and analysis system.

BACKGROUND: The self-controlled case series (SCCS) offers potential as an statistical method for risk identification involving medical products from large-scale observational healthcare data. However, analytic design choices remain in encoding the longitudinal health records into the SCCS framework and its risk identification performance across real-world databases is unknown. OBJECTIVES: To evaluate the performance of SCCS and its design choices as a tool for risk identification in observational healthcare data. RESEARCH DESIGN: We examined the risk identification performance of SCCS across five design choices using 399 drug-health outcome pairs in five real observational databases (four administrative claims and one electronic health records). In these databases, the pairs involve 165 positive controls and 234 negative controls. We also consider several synthetic databases with known relative risks between drug-outcome pairs. MEASURES: We evaluate risk identification performance through estimating the area under the receiver-operator characteristics curve (AUC) and bias and coverage probability in the synthetic examples. RESULTS: The SCCS achieves strong predictive performance. Twelve of the twenty health outcome-database scenarios return AUCs >0.75 across all drugs. Including all adverse events instead of just the first per patient and applying a multivariate adjustment for concomitant drug use are the most important design choices. However, the SCCS as applied here returns relative risk point-estimates biased towards the null value of 1 with low coverage probability. CONCLUSIONS: The SCCS recently extended to apply a multivariate adjustment for concomitant drug use offers promise as a statistical tool for risk identification in large-scale observational healthcare databases. Poor estimator calibration dampens enthusiasm, but on-going work should correct this short-coming.

Disproportionality methods for pharmacovigilance in longitudinal observational databases.

Data mining disproportionality methods (PRR, ROR, EBGM, IC, etc.) are commonly used to identify drug safety signals in spontaneous report system (SRS) databases. Newer data sources such as longitudinal observational databases (LOD) provide time-stamped patient-level information and overcome some of the SRS limitations such as an absence of the denominator, total number of patients who consume a drug, and limited temporal information. Application of the disproportionality methods to LODs has not been widely explored. The scale of the LOD data provides an interesting computational challenge. Larger health claims databases contain information on more than 50 million patients and each patient has records for up to 10 years. In this article we systematically explore the application of commonly used disproportionality methods to simulated and real LOD data.

Massive parallelization of serial inference algorithms for a complex generalized linear model.

Following a series of high-profile drug safety disasters in recent years, many countries are redoubling their efforts to ensure the safety of licensed medical products. Large-scale observational databases such as claims databases or electronic health record systems are attracting particular attention in this regard, but present significant methodological and computational concerns. In this paper we show how high-performance statistical computation, including graphics processing units, relatively inexpensive highly parallel computing devices, can enable complex methods in large databases. We focus on optimization and massive parallelization of cyclic coordinate descent approaches to fit a conditioned generalized linear model involving tens of millions of observations and thousands of predictors in a Bayesian context. We find orders-of-magnitude improvement in overall run-time. Coordinate descent approaches are ubiquitous in high-dimensional statistics and the algorithms we propose open up exciting new methodological possibilities with the potential to significantly improve drug safety.

Statistical methods for comparative phenomics using high-throughput phenotype microarrays.

We propose statistical methods for comparing phenomics data generated by the Biolog Phenotype Microarray (PM) platform for high-throughput phenotyping. Instead of the routinely used visual inspection of data with no sound inferential basis, we develop two approaches. The first approach is based on quantifying the distance between mean or median curves from two treatments and then applying a permutation test; we also consider a permutation test applied to areas under mean curves. The second approach employs functional principal component analysis. Properties of the proposed methods are investigated on both simulated data and data sets from the PM platform.

Particle filtering for dispersion curve tracking in ocean acoustics.

A particle filtering method is developed for dispersion curve extraction from spectrograms of broadband acoustic signals propagating in underwater media. The goal is to obtain accurate representation of modal dispersion which can be employed for source localization and geoacoustic inversion. Results are presented from the application of the method to synthetic data, demonstrating the potential of the approach for accurate estimation of waveguide dispersion characteristics. The method outperforms simple time-frequency analysis providing estimates that are very close to numerically calculated dispersion curves. The method also provides uncertainty information on modal arrival time estimates, typically unavailable when traditional methods are used.

Rapid-phase modulation of terahertz radiation for high-speed terahertz imaging and spectroscopy.

Rapid voltage-controlled phase modulation of cw terahertz (THz) radiation is demonstrated. By transmitting an infrared beam through a lithium niobate phase modulator the phase of the THz radiation, which is generated by the photomixing of two infrared beams, can be directly modulated through a 2pi phase shift. The 100 kHz modulation rate that is demonstrated with this technique is approximately 3 orders of magnitude faster than what can be achieved by mechanical scanning.