Profiling medical sites based on adverse events data for multi-center clinical trials

Profiling medical sites is an important activity in both clinical research and practice. Many organizations provide public report cards comparing outcomes across hospitals. An analogous concept applied in multicenter clinical trials, such “report cards” guide sponsors to choose sites while designing a study, help identify areas of improvement for sites, and motivate sites to perform better. Sponsors include comparative performance of sites, a concept to perform risk-based monitoring and central statistical monitoring. In clinical research, report cards are powerful tools for relating site performance to treatment benefits. This study evaluates approaches to estimating the proportion of adverse events at the site-level in a multicenter clinical trial setting and also methods in detecting outlying sites. We address three topics. First we assess the performance of different models for obtaining estimates of adverse events rates utilizing Bayesian beta-binomial and binomial logit-normal models with MCMC estimation and fixed effects maximum likelihood estimation (MLE) methods. We - vary sample sizes, number of medical sites, overall adverse event rates, and intraclass correlation within sites. Second, we compare the performance of these methods in identifying outlier sites, contrasting MLE and Bayesian approaches. A fixed threshold method detects sites as outliers under a Bayesian approach, while in the fixed effects assumption, a 95% interval-based approach is applied. Third, we extend this approach in estimating multiple outcomes at the site level and detecting outlier sites. A standard bivariate normal MLE method is compared to a Bayesian bivariate binomial logit-normal MCMC. These are examined using simulation studies. Results show for single outcomes, Bayesian beta-binomial MCMC method perform well under certain parametric conditions for estimation and detecting outlier sites. For multiple outcomes with higher adverse event rate and larger difference between outliers and non-outliers, for detecting outlier sites, both methods – Bayesian MCMC and MLE work well, irrespective of the correlation between outcomes.2020-02-14T00:00:00

TOLERANCE INTERVALS FOR GENE FLOW RATES FROM TRANSGENIC TO NON-TRANSGENIC WHEAT AND CORN USING A LOGISTIC REGRESSION MODEL WITH RANDOM LOCATION EFFECTS

Crop scientists and government regulators are interested in mediating pollen flow from transgenic crops to other crops and weed species. To this end, a multi-year, multilocation series of experiments was conducted in eastern Colorado by the Department of Soil and Crop Sciences at Colorado State University. These experiments were done to estimate the distance required between plots of transgenic corn and wheat and plots of the respective non-transgenic crop to obtain at most a regulated limit of cross-pollination. The experiments involved planting a rectangle of transgenic crop in the middle of a non-transgenic field and measuring the proportion of cross-pollinated crop at various distances along transects radiating in multiple directions. Gene flow to the non-transgenic crop was evaluated in wheat using herbicide tolerance and in corn using kernel color. An initial Generalized Linear Mixed Model with binomial response and logit link was estimated with independent variables: a square root transformation of distance, an additional covariate, and a random location effect. For corn, the additional covariate was transect orientation; for wheat, it was the relative heading time of the recipient variety. An enhanced model that included additional sources of variation was also examined. The analysis for both of these assumed models addresses two problems: 1) an Upper Tolerance Limit on the binomial probability of cross-pollination, which includes 100c% of the locations with 100d% confidence, at set values of the independent variables; and 2) an Upper Tolerance Limit on the distance at which 100c% of the locations will have binomial probability of cross-pollination less than a specified value, with 100d% confidence, at set values of the other independent variables. The problems are addressed using Frequentist and Bayesian methods

Integrating serological and genetic data to quantify cross-species transmission: brucellosis as a case study

Epidemiological data are often fragmented, partial, and/or ambiguous and unable to yield the desired level of understanding of infectious disease dynamics to adequately inform control measures. Here, we show how the information contained in widely available serology data can be enhanced by integration with less common type-specific data, to improve the understanding of the transmission dynamics of complex multi-species pathogens and host communities. Using brucellosis in Northern Tanzania as a case-study, we developed a latent process model based on serology data obtained from the field, to reconstruct Brucella transmission dynamics. We were able to identify sheep and goats as a more likely source of human and animal infection than cattle; however, the highly cross-reactive nature of Brucella spp. meant that it was not possible to determine which Brucella species (B. abortus or B. melitensis) is responsible for human infection. We extended our model to integrate simulated serology and typing data, and show that although serology alone can identify the host source of human infection under certain restrictive conditions, the integration of even small amounts (5%) of typing data can improve understanding of complex epidemiological dynamics. We show that data integration will often be essential when more than one pathogen is present and when the distinction between exposed and infectious individuals is not clear from serology data. With increasing epidemiological complexity, serology data become less informative. However, we show how this weakness can be mitigated by integrating such data with typing data, thereby enhancing the inference from these data and improving understanding of the underlying dynamics

Tailoring Capture-Recapture Methods to Estimate Registry-Based Case Counts Based on Error-Prone Diagnostic Signals

Surveillance research is of great importance for effective and efficient epidemiological monitoring of case counts and disease prevalence. Taking specific motivation from ongoing efforts to identify recurrent cases based on the Georgia Cancer Registry, we extend recently proposed "anchor stream" sampling design and estimation methodology. Our approach offers a more efficient and defensible alternative to traditional capture-recapture (CRC) methods by leveraging a relatively small random sample of participants whose recurrence status is obtained through a principled application of medical records abstraction. This sample is combined with one or more existing signaling data streams, which may yield data based on arbitrarily non-representative subsets of the full registry population. The key extension developed here accounts for the common problem of false positive or negative diagnostic signals from the existing data stream(s). In particular, we show that the design only requires documentation of positive signals in these non-anchor surveillance streams, and permits valid estimation of the true case count based on an estimable positive predictive value (PPV) parameter. We borrow ideas from the multiple imputation paradigm to provide accompanying standard errors, and develop an adapted Bayesian credible interval approach that yields favorable frequentist coverage properties. We demonstrate the benefits of the proposed methods through simulation studies, and provide a data example targeting estimation of the breast cancer recurrence case count among Metro Atlanta area patients from the Georgia Cancer Registry-based Cancer Recurrence Information and Surveillance Program (CRISP) database

Some findings on zero-inflated and hurdle Poisson models for disease mapping

Zero excess in the study of geographically referenced mortality data sets has been the focus of considerable attention in the literature, with zero-inflation being the most common procedure to handle this lack of fit. Although hurdle models have also been used in disease mapping studies, their use is more rare. We show in this paper that models using particular treatments of zero excesses are often required for achieving appropriate fits in regular mortality studies since, otherwise, geographical units with low expected counts are oversmoothed. However, as also shown, an indiscriminate treatment of zero excess may be unnecessary and has a problematic implementation. In this regard, we find that naive zero-inflation and hurdle models, without an explicit modeling of the probabilities of zeroes do not fix zero excesses problems well enough and are clearly unsatisfactory. Results sharply suggest the need for an explicit modeling of the probabilities that should vary across areal units. Unfortunately, these more flexible modeling strategies can easily lead to improper posterior distributions as we prove in several theoretical results. Those procedures have been repeatedly used in the disease mapping literature and one should bear these issues in mind in order to propose valid models. We finally propose several valid modeling alternatives according to the results mentioned that are suitable for fitting zero excesses. We show that those proposals fix zero excesses problems and correct the mentioned oversmoothing of risks in low populated units depicting geographic patterns more suited to the data