Robust estimating functions and bias correction for longitudinal data analysis

Robust methods are useful in making reliable statistical inferences when there are small deviations from the model assumptions. The widely used method of the generalized estimating equations can be "robustified" by replacing the standardized residuals with the M-residuals. If the Pearson residuals are assumed to be unbiased from zero, parameter estimators from the robust approach are asymptotically biased when error distributions are not symmetric. We propose a distribution-free method for correcting this bias. Our extensive numerical studies show that the proposed method can reduce the bias substantially. Examples are given for illustration.     

On the effect of misclassification on bias of perfectly measured covariates in regression

This note clarifies under what conditions a naive analysis using a misclassified predictor will induce bias for the regression coefficients of other perfectly measured predictors in the model. An apparent discrepancy between some previous results and a result for measurement error of a continuous variable in linear regression is resolved. We show that similar to the linear setting, misclassification (even when not related to the other predictors) induces bias in the coefficients of the perfectly measured predictors, unless the misclassified variable and the perfectly measured predictors are independent. Conditional and asymptotic biases are discussed in the case of linear regression, and explored numerically for an example relating birth weight to the weight and smoking status of the mother.     

Validity of hand-to-foot measurement of bioimpedance: standing compared with lying position

Objective: To assess the reliability of the standing measurement of hand‐to‐foot bioimpedance compared with measurements made in the lying position. Research Methods and Procedures: In 205 volunteers 6 to 89 years of age, 111 males and 94 females from six ethnic groups, effects of posture, time, and age on hand‐to‐foot resistance were studied over a range of body size. The effect of time in a position on resistance was also recorded in a small subset (n = 10), and repeat measurements over 3 days at the same time of the day were recorded in another subset (n = 12). Results: Lying impedance was consistently higher than standing, with the relationship (resistance lying/resistance standing) for the children (5 to 14 years) being 1.031, progressing to a ratio of 1.016 in those >60 years. The time spent static in either position did change resistance measurements—a decrease of up to 9 Ω (mean 5 Ω, 1.0%) over 10 minutes of standing and an increase of up to 7 Ω (mean 3 Ω, 0.7%) with lying. Discussion: In the field, measurements of hand‐to‐foot bioimpedance can be made in the standing position, and, with appropriate adjustment, previously validated recumbent equations can be used. Given that errors in the measurement of height and weight also affect the reliability of the derivation of body fat from bioelectrical conductance, the errors that may arise from a more practical standing measurement rather than lying are minimal.     

Case-control and case-only designs with genotype and family history data: estimating relative risk, residual familial aggregation, and cumulative risk

In case-control studies of inherited diseases, participating subjects (probands) are often interviewed to collect detailed data about disease history and age-at-onset information in their family members. Genotype data are typically collected from the probands, but not from their relatives. In this article, we introduce an approach that combines case-control analysis of data on the probands with kin-cohort analysis of disease history data on relatives. Assuming a marginally specified multivariate survival model for joint risk of disease among family members, we describe methods for estimating relative risk, cumulative risk, and residual familial aggregation. We also describe a variation of the methodology that can be used for kin-cohort analysis of the family history data from a sample of genotyped cases only. We perform simulation studies to assess performance of the proposed methodologies with correct and mis-specified models for familial aggregation. We illustrate the proposed methodologies by estimating the risk of breast cancer from BRCA1/2 mutations using data from the Washington Ashkenazi Study.     

Recursive construction of perfect DNA molecules from imperfect oligonucleotides

Making faultless complex objects from potentially faulty building blocks is a fundamental challenge in computer engineering, nanotechnology and synthetic biology. Here, we show for the first time how recursion can be used to address this challenge and demonstrate a recursive procedure that constructs error‐free DNA molecules and their libraries from error‐prone oligonucleotides. Divide and Conquer (D&C), the quintessential recursive problem‐solving technique, is applied in silico to divide the target DNA sequence into overlapping oligonucleotides short enough to be synthesized directly, albeit with errors; error‐prone oligonucleotides are recursively combined in vitro, forming error‐prone DNA molecules; error‐free fragments of these molecules are then identified, extracted and used as new, typically longer and more accurate, inputs to another iteration of the recursive construction procedure; the entire process repeats until an error‐free target molecule is formed. Our recursive construction procedure surpasses existing methods for de novo DNA synthesis in speed, precision, amenability to automation, ease of combining synthetic and natural DNA fragments, and ability to construct designer DNA libraries. It thus provides a novel and robust foundation for the design and construction of synthetic biological molecules and organisms.     

Chemical gene synthesis: strategies, softwares, error corrections, and applications

Chemical synthesis of DNA sequences provides a powerful tool for modifying genes and for studying gene structure, expression and function. Modified genes and consequently protein/enzymes can bridge genomics and proteomics research or facilitate commercial applications of gene and protein technologies. In this review, we will summarize various strategies, designing softwares and error correction methods for chemical gene synthesis, particularly for the synthesis and assembly of long DNA molecules based on polymerase cycling assembly. Also, we will briefly discuss some of the major applications of chemical synthesis of DNA sequences in basic research and applied areas.     

On the consequences of overstratification

It is common, in particular in observational studies in epidemiology,to impose stratification to adjust for possible effects of ageand other variables on the binary outcome of interest. Overstratificationmay lower the precision of the estimated effects of interest.Understratification risks bias. These issues are studied analytically.Asymptotic results show that loss of efficiency depends on thetrue effect and on a measure of the average imbalance acrossstrata between exposed and unexposed individuals. Bias dependson the correlation between stratum-specific size imbalancesand event rates in the unexposed. Approximate results are alsogiven. An example is used.     

Split-test Bonferroni correction for QEEG statistical maps

With statistical testing, corrections for multiple comparisons, such as Bonferroni adjustments, have given rise to controversies in the scientific community, because of their negative impact on statistical power. This impact is especially problematic for high-multidimensional data, such as multi-electrode brain recordings. With brain imaging data, a reliable method is needed to assess statistical significance of the data without losing statistical power. Conjunction analysis allows the combination of significance and consistency of an effect. Through a balanced combination of information from retest experiments (multiple trials split testing), we present an intuitively appealing, novel approach for brain imaging conjunction. The method is then tested and validated on synthetic data followed by a real-world test on QEEG data from patients with Alzheimer’s disease. This latter application requires both reliable type-I error and type-II error rates, because of the poor signal-to-noise ratio inherent in EEG signals.     

Effect of trap design, trap size and pheromone dose on male capture of two pine bast scales species (Hemiptera: Matsucoccidae): implications for monitoring and mass-trapping

Abstract 1 The effects of pheromone dose, trap size and trap design on the capture of male Matsucoccus feytaudi were studied in maritime pine forests of Portugal, France and Italy, and Matsucoccus josephi in Aleppo pine stands of Israel. 2 Males of both species respond to racemic mixtures of the respective sex pheromone at a dosage as low as 25 µg. However, for low population densities of M. feytaudi, a 50 µg dosage was needed to guarantee male catches significantly different from the control trap. 3 Male capture increased with increasing dosage up to a threshold level for both species. Overdose repellence was not observed even with baits containing 1600 and 2200 µg of the pheromone of M. josephi and M. feytaudi, respectively. 4 For M. feytaudi, a higher dose–response was observed at medium population densities, whereas lesser captures were registered at low and high population densities, suggesting female competition in the latter case. 5 Catches of M. feytaudi males were not affected by trap design, whereas M. josephi males were caught in significantly greater numbers in delta traps. Large traps caught significantly more males of both species. 6 The relative higher male catches in the marginal zone of the sticky traps is probably related to males landing behaviour in the vicinity of the pheromone source. 7 The shape and size of the trap did not affect the bias of the estimates of male catches. However, the plate traps provided higher precision. Both bias and precision improved with increasing dose.