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Gamma models for estimating the odds ratio for a skewed biomarker measured in pools and subject to errors.

ABSTRACT: Measuring a biomarker in pooled samples from multiple cases or controls can lead to cost-effective estimation of a covariate-adjusted odds ratio, particularly for expensive assays. But pooled measurements may be affected by assay-related measurement error (ME) and/or pooling-related processing error (PE), which can induce bias if ignored. Building on recently developed methods for a normal biomarker subject to additive errors, we present two related estimators for a right-skewed biomarker subject to multiplicative errors: one based on logistic regression and the other based on a Gamma discriminant function model. Applied to a reproductive health dataset with a right-skewed cytokine measured in pools of size 1 and 2, both methods suggest no association with spontaneous abortion. The fitted models indicate little ME but fairly severe PE, the latter of which is much too large to ignore. Simulations mimicking these data with a non-unity odds ratio confirm validity of the estimators and illustrate how PE can detract from pooling-related gains in statistical efficiency. These methods address a key issue associated with the homogeneous pools study design and should facilitate valid odds ratio estimation at a lower cost in a wide range of scenarios.

SUBMITTER: Van Domelen DR

PROVIDER: S-EPMC8035988 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Gamma models for estimating the odds ratio for a skewed biomarker measured in pools and subject to errors.

Van Domelen Dane R DR Mitchell Emily M EM Perkins Neil J NJ Schisterman Enrique F EF Manatunga Amita K AK Huang Yijian Y Lyles Robert H RH

Biostatistics (Oxford, England) 20210401 2

Measuring a biomarker in pooled samples from multiple cases or controls can lead to cost-effective estimation of a covariate-adjusted odds ratio, particularly for expensive assays. But pooled measurements may be affected by assay-related measurement error (ME) and/or pooling-related processing error (PE), which can induce bias if ignored. Building on recently developed methods for a normal biomarker subject to additive errors, we present two related estimators for a right-skewed biomarker subjec ...[more]

PMID: 31373355

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Project description:Evaluation of sleep quality in critically ill patients is difficult using conventional scoring criteria. The aim of this study was to examine sleep in critically ill patients with and without light sedation using the odds ratio product, a validated continuous metric of sleep depth (0 = deep sleep; 2.5 = full wakefulness) that does not rely on the features needed for conventional staging.DesignRetrospective study.SettingsA 16-bed medical-surgical ICU.PatientsTwenty-three mechanically ventilated patients who had previously undergone two nocturnal sleep studies, one without and one with sedation (propofol, n = 12; dexmedetomidine, n = 11).InterventionsNone.Measurements and main resultsSleep architecture was evaluated with odds ratio product analysis by the distribution of 30-second epochs with different odds ratio product values. Electroencephalogram spectral patterns and frequency of wake intrusions (3-s odds ratio product > 1.75) were measured at different odds ratio product levels. Thirty-seven normal sleepers were used as controls. Compared with normal sleepers, unsedated critically ill patients spent little time in stable sleep (percent odds ratio product < 1.0: 31% vs 63%; p < 0.001), whereas most of the time were either in stage wake (odds ratio product > 1.75) or in a transitional state (odds ratio product 1.0-1.75), characterized by frequent wake intrusions. Propofol and dexmedetomidine had comparable effects on sleep. Sedation resulted in significant shift in odds ratio product distribution toward normal; percent odds ratio product less than 1.0 increased by 54% (p = 0.006), and percent odds ratio product greater than 1.75 decreased by 48% (p = 0.013). In six patients (26%), sedation failed to improve sleep.ConclusionsIn stable critically ill unsedated patients, sleep quality is poor with frequent wake intrusions and little stable sleep. Light sedation with propofol or dexmedetomidine resulted in a shift in sleep architecture toward normal in most, but not all, patients.

Project description:BackgroundThe species pool concept was formulated over the past several decades and has since played an important role in explaining multi-scale ecological patterns. Previous statistical methods were developed to identify species pools based on broad-scale species range maps or community similarity computed from data collected from many areas. No statistical method is available for estimating species pools for a single local community (sampling area size may be very small as ≤ 1 km2). In this study, based on limited local abundance information, we developed a simple method to estimate the area size and richness of a species pool for a local ecological community. The method involves two steps. In the first step, parameters from a truncated negative trinomial model characterizing the distributional aggregation of all species (i.e., non-random species distribution) in the local community were estimated. In the second step, we assume that the unseen species in the local community are most likely the rare species, only found in the remaining part of the species pool, and vice versa, if the remaining portion of the pool was surveyed and was contrasted with the sampled area. Therefore, we can estimate the area size of the pool, as long as an abundance threshold for defining rare species is given. Since the size of the pool is dependent on the rarity threshold, to unanimously determine the pool size, we developed an optimal method to delineate the rarity threshold based on the balance of the changing rates of species absence probabilities in the sampled and unsampled areas of the pool.ResultsFor a 50 ha (0.5 km2) forest plot in the Barro Colorado Island of central Panama, our model predicted that the local, if not regional, species pool for the 0.5 km2 forest plot was nearly the entire island. Accordingly, tree species richness in this pool was estimated as around 360. When the sampling size was smaller, the upper bound of the 95% confidence interval could reach 418, which was very close to the flora record of tree richness for the island. A numerical test further demonstrated the power and reliability of the proposed method, as the true values of area size and species richness for the hypothetical species pool have been well covered by the 95% confidence intervals of the true values.ConclusionsOur method fills the knowledge gap on estimating species pools for a single local ecological assemblage with little information. The method is statistically robust and independent of sampling size, as proved by both empirical and numerical tests.

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Gamma models for estimating the odds ratio for a skewed biomarker measured in pools and subject to errors.

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Gamma models for estimating the odds ratio for a skewed biomarker measured in pools and subject to errors.

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