A simulation study comparing the impact of experimental error on the performance of experimental designs and artificial neural networks used for process screening

Many variables and their interactions can affect a biotechnological process. Testing a large number of variables and all their possible interactions is a cumbersome task and its cost can be prohibitive. Several screening strategies, with a relatively low number of experiments, can be used to find which variables have the largest impact on the process and estimate the magnitude of their effect. One approach for process screening is the use of experimental designs, among which fractional factorial and Plackett–Burman designs are frequent choices. Other screening strategies involve the use of artificial neural networks (ANNs). The advantage of ANNs is that they have fewer assumptions than experimental designs, but they render black-box models (i.e., little information can be extracted about the process mechanics). In this paper, we simulate a biotechnological process (fed-batch growth of bakers yeast) to analyze and compare the effect of random experimental errors of different magnitudes and statistical distributions on experimental designs and ANNs. Except for the situation in which the error has a normal distribution and the standard deviation is constant, it was not possible to determine a clear-cut rule for favoring one screening strategy over the other. Instead, we found that the data can be better analyzed using both strategies simultaneously.     

Method in macroecology

Macroecology is concerned with understanding the abundance and distribution of species at large spatial and temporal scales. Understanding pattern and process in macroecology thus presents a considerable methodological challenge, as the scales of interest are simply too large for the traditional ecological approach of experimental manipulation to be possible or ethical. Here, I summarise the methods that have been most commonly employed to explore macroecological questions, discuss important methodological issues that need to be considered when interpreting macroecological data, and suggest likely future developments in macroecological methodology.

Zusammenfassung

Die Makroökologie befasst sich mit dem Verständnis der Abundanz und der Verbreitung der Arten in großen räumlichen und zeitlichen Maßstäben. Das Verständnis von Mustern und Prozessen in der Makroökologie stellt deshalb eine beträchtliche methodische Herausforderung dar, weil die Skalen von Interesse für die traditionelle ökologische Vorgehensweise der experimentellen Manipulation einfach zu groß sind, als dass sie möglich oder ethisch vertretbar wäre. Hier fasse ich die Methoden zusammen, die am häufigsten zum Einsatz kommen, um makroökologische Fragen zu erforschen, diskutiere wichtige methodische Punkte, die beachtet werden müssen, wenn makroökologische Daten interpretiert werden, und deute auf wahrscheinliche zukünftige Entwicklungen in der makroökologischen Methodik hin.     

Conditional-cumulant-of-exposure method in logistic missing covariate regression

We consider estimation in logistic regression where some covariate variables may be missing at random. Satten and Kupper (1993, Journal of the American Statistical Association 88, 200-208) proposed estimating odds ratio parameters using methods based on the probability of exposure. By approximating a partial likelihood, we extend their idea and propose a method that estimates the cumulant-generating function of the missing covariate given observed covariates and surrogates in the controls. Our proposed method first estimates some lower order cumulants of the conditional distribution of the unobserved data and then solves a resulting estimating equation for the logistic regression parameter. A simple version of the proposed method is to replace a missing covariate by the summation of its conditional mean and conditional variance given observed data in the controls. We note that one important property of the proposed method is that, when the validation is only on controls, a class of inverse selection probability weighted semiparametric estimators cannot be applied because selection probabilities on cases are zeroes. The proposed estimator performs well unless the relative risk parameters are large, even though it is technically inconsistent. Small-sample simulations are conducted. We illustrate the method by an example of real data analysis.     

Weighted normality-based estimator in correcting correlation coefficient estimation between incomplete nutrient measurements

Consider the problem of estimating the correlation between two nutrient measurements, such as the percent energy from fat obtained from a food frequency questionnaire (FFQ) and that from repeated food records or 24-hour recalls. Under a classical additive model for repeated food records, it is known that there is an attenuation effect on the correlation estimation if the sample average of repeated food records for each subject is used to estimate the underlying long-term average. This paper considers the case in which the selection probability of a subject for participation in the calibration study, in which repeated food records are measured, depends on the corresponding FFQ value, and the repeated longitudinal measurement errors have an autoregressive structure. This paper investigates a normality-based estimator and compares it with a simple method of moments. Both methods are consistent if the first two moments of nutrient measurements exist. Furthermore, joint estimating equations are applied to estimate the correlation coefficient and related nuisance parameters simultaneously. This approach provides a simple sandwich formula for the covariance estimation of the estimator. Finite sample performance is examined via a simulation study, and the proposed weighted normality-based estimator performs well under various distributional assumptions. The methods are applied to real data from a dietary assessment study.     

Inference about misclassification probabilities from repeated binary responses

Repeated binary responses provide efficient information for two purposes: (1) estimating two misclassification (false-positive and false-negative error) probabilities and (2) testing the hypothesis that either is zero in a reliability study. We focus on the assessment of reliability of a diagnostic test when there is no gold standard. This paper uses a latent class model and illustrates some of its properties. In addition, application to data containing variation among individuals is considered. We apply this model to the serological data on the MNSs blood group of atomic bomb survivors and their children. The results provide valuable information for examining measurement reliability.     

Reduction in the prevalence and intensity of hookworm infections after praziquantel treatment for schistosomiasis infection

Praziquantel exhibits activity against all major human schistosome parasites and has become the cornerstone for treatment and morbidity control of schistosomiasis. Praziquantel is also active against a wide range of trematodes, human and veterinary cestodes and displays cysticidal effects. To the best of our knowledge anthelminthic properties have never been documented. Here, we report a study among 96 schoolchildren from an area highly endemic for Schistosoma mansoni and hookworm infection, and place particular emphasis on the effect of praziquantel on the prevalence and intensity of hookworm infections. Stool specimens were screened over several consecutive days prior and 4 weeks after systematic administration of praziquantel. We found a significant reduction in the prevalence of hookworm infection from 75.0 to 40.6% (odds ratio (OR)=0.21; 95% confidence interval (CI): 0.11-0.40). Infection intensities, expressed by geometric mean egg counts of all children, were also reduced significantly from 10.7 to 2.0 eggs per gram stool (paired t-test=7.78, P<0.001). If these findings are confirmed in other epidemiological settings - following a similarly sensitive diagnostic approach - they might become of considerable relevance. In areas where both schistosome and hookworm coexist, and praziquantel is being recommended for schistosomiasis control, large-scale application of this drug might also reduce the burden of hookworms.     

Evaluation of uncertainty in alignment tensors obtained from dipolar couplings

Residual dipolar couplings and their corresponding alignment tensors are useful for structural analysis of macromolecules. The error in an alignment tensor, derived from residual dipolar couplings on the basis of a known structure, is determined not only by the accuracy of the measured couplings but also by the uncertainty in the structure (structural noise). This dependence is evaluated quantitatively on the basis of simulated structures using Monte-Carlo type analyses. When large numbers of dipolar couplings are available, structural noise is found to result in a systematic underestimate of the magnitude of the alignment tensor. Particularly in cases where only few dipolar couplings are available, structural noise can cause significant errors in best-fitted alignment tensor values, making determination of the relative orientation of small fragments and evaluation of local backbone mobility from dipolar couplings difficult. An example for the protein ubiquitin demonstrates the inherent limitations in characterizing motions on the basis of local alignment tensor magnitudes.     

Ranked set sampling: cost and optimal set size

McIntyre (1952, Australian Journal of Agricultural Research 3, 385-390) introduced ranked set sampling (RSS) as a method for improving estimation of a population mean in settings where sampling and ranking of units from the population are inexpensive when compared with actual measurement of the units. Two of the major factors in the usefulness of RSS are the set size and the relative costs of the various operations of sampling, ranking, and measurement. In this article, we consider ranking error models and cost models that enable us to assess the effect of different cost structures on the optimal set size for RSS. For reasonable cost structures, we find that the optimal RSS set sizes are generally larger than had been anticipated previously. These results will provide a useful tool for determining whether RSS is likely to lead to an improvement over simple random sampling in a given setting and, if so, what RSS set size is best to use in this case.     

Comparing the effects of continuous and discrete covariate mismeasurement,with emphasis on the dichotomization of mismeasured predictors

It is well known that imprecision in the measurement of predictor variables typically leads to bias in estimated regression coefficients. We compare the bias induced by measurement error in a continuous predictor with that induced by misclassification of a binary predictor in the contexts of linear and logistic regression. To make the comparison fair, we consider misclassification probabilities for a binary predictor that correspond to dichotomizing an imprecise continuous predictor in lieu of its precise counterpart. On this basis, nondifferential binary misclassification is seen to yield more bias than nondifferential continuous measurement error. However, it is known that differential misclassification results if a binary predictor is actually formed by dichotomizing a continuous predictor subject to nondifferential measurement error. When the postulated model linking the response and precise continuous predictor is correct, this differential misclassification is found to yield less bias than continuous measurement error, in contrast with nondifferential misclassification, i.e., dichotomization reduces the bias due to mismeasurement. This finding, however, is sensitive to the form of the underlying relationship between the response and the continuous predictor. In particular, we give a scenario where dichotomization involves a trade-off between model fit and misclassification bias. We also examine how the bias depends on the choice of threshold in the dichotomization process and on the correlation between the imprecise predictor and a second precise predictor.