Assessment of the effect on statistical power of regression model misspecification by using techniques of mathematical statistics and simulation study

Objectives. This paper seeks to assess the effect on statistical power of regression model misspecification in a variety of situations. ^ Methods and results. The effect of misspecification in regression can be approximated by evaluating the correlation between the correct specification and the misspecification of the outcome variable (Harris 2010).In this paper, three misspecified models (linear, categorical and fractional polynomial) were considered. In the first section, the mathematical method of calculating the correlation between correct and misspecified models with simple mathematical forms was derived and demonstrated. In the second section, data from the National Health and Nutrition Examination Survey (NHANES 2007-2008) were used to examine such correlations. Our study shows that comparing to linear or categorical models, the fractional polynomial models, with the higher correlations, provided a better approximation of the true relationship, which was illustrated by LOESS regression. In the third section, we present the results of simulation studies that demonstrate overall misspecification in regression can produce marked decreases in power with small sample sizes. However, the categorical model had greatest power, ranging from 0.877 to 0.936 depending on sample size and outcome variable used. The power of fractional polynomial model was close to that of linear model, which ranged from 0.69 to 0.83, and appeared to be affected by the increased degrees of freedom of this model.^ Conclusion. Correlations between alternative model specifications can be used to provide a good approximation of the effect on statistical power of misspecification when the sample size is large. When model specifications have known simple mathematical forms, such correlations can be calculated mathematically. Actual public health data from NHANES 2007-2008 were used as examples to demonstrate the situations with unknown or complex correct model specification. Simulation of power for misspecified models confirmed the results based on correlation methods but also illustrated the effect of model degrees of freedom on power.^

Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth.

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.

Protein quaternary structure determination using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and chemical crosslinking

A means of analyzing protein quaternary structure using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI MS) and chemical crosslinking was evaluated. Proteins of known oligomeric structure, as well as monomeric proteins, were analyzed to evaluate the method. The quaternary structure of proteins of unknown or uncertain structure was investigated using this technique. The stoichiometry of recombinant E. coli carbamoyl phosphate synthetase and recombinant human farnesyl protein transferase were determined to be heterodimers using glutaraldehyde crosslinking, agreeing with the stoichiometry found for the wild type proteins. The stoichiometry of the gamma subunit of E. coli DNA polymerase III holoenzyme was determined in solution without the presence of other subunits to be a homotetramer using glutaraldehyde crosslinking and MALDI MS analysis. Chi and psi subunits of E. coli DNA polymerase III subunits appeared to form a heterodimer when crosslinked with heterobifunctional photoreactive crosslinkers.^ Comparison of relative % peak areas obtained from MALDI MS analysis of crosslinked proteins and densitometric scanning of silver stained sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) gels showed excellent qualitative agreement for the two techniques, but the quantitative analyses differed, sometimes significantly. This difference in quantitation could be due to SDS-PAGE conditions (differential staining, loss of sample) or to MALDI MS conditions (differences in ionization and/or detection). Investigation of pre-purified crosslinked monomers and dimers recombined in a specific ratio revealed the presence of mass discrimination in the MALDI MS process. The calculation of mass discrimination for two different MALDI time-of-flight instruments showed the loss of a factor of approximately 2.6 in relative peak area as the m/z value doubles over the m/z range from 30,000 to 145,000 daltons.^ Indirect symmetry was determined for tetramers using glutaraldehyde crosslinking with MALDI MS analysis. Mathematical modelling and simple graphing allowed the determination of the symmetry for several tetramers known to possess isologous D2 symmetry. These methods also distinguished tetramers that did not fit D2 symmetry such as apo-avidin. The gamma tetramer of E. coli DNA polymerase III appears to have isologous D2 symmetry. ^

GENERAL WELL-BEING AND USE OF SOCIAL SUPPORT

Research interest on well-being and social support has focused largely on social factors as related to attaining and maintaining well-being, self-perceptions of well-being and to a lesser extent the relationship of current level of self-perceived well-being to use of formal or informal sources of social support. This study analyzed responses to the General Well-Being Schedule of 6,913 subjects (25-74 years) interviewed during the National Health and Nutrition Examination Survey (1971-1975). The purpose of this analysis was to relate the level of GWBS scores to the use of social support, both informal (family and friends) and formal (community professionals).^ Study questions addressed were whether well-being level was related to selection of a specific social support resource and/or rate of use of resources and whether gender differences were apparent in level of well-being and social support use. Because age, sex, race, socioeconomic status (income and education) and marital status may confound the relation between level of GWB and type of social support chosen, the association between these variables with GWB and use of social support were considered. For analysis, test scores were grouped into four categories and for detailed analysis, two categories: low (0-70) and high (71-110). Cross tabulations and percentages were computed and the chi-square test of significance was used.^ Although 16 to 25 percent of the sample population reported low well-being, less than 10 percent used formal resources to discuss emotional, mental or behavior problems. Medical resources, mostly physicians, were the most used formal social supports. Informal social support was important for all well-being levels where 65-77% of each category reported using this resource.^ While well-being level does not appear to serve as a screener/selector of type of formal social support used, it is related to rates of use. Females reported slightly lower well-being than males, and except in the lowest well-being group, had higher rates of social support use. Findings support the conclusion that perceived well-being is related to use of social support such that the lower the well-being, the greater tendency to use formal and/or informal social support. ^