Increased efficiency of analyses: cumulative logistic regression vs ordinary logistic regression

The common practice of collapsing inherently continuous or ordinal variables into two categories causes information loss that may potentially weaken power to detect effects of explanatory variables and result in Type II errors in statistical inference. The purpose of this investigation was to illustrate, using a substantive example, the potential increase in power gained from an ordinal in-stead of a dichotomous specification for an inherently continuous response. Ordinary (OLR) and cumulative logistic regression (CLR) modeling were used to test the hypothesis that the risk of alveolar bone loss over 2 years is greater for subjects with poorer control of non-insulin-dependent diabetes mellitus (NIDDM) than for those who do not have diabetes or have better controlled NIDDM. There were 359 subjects; 21 of whom had NIDDM. Analysis of main effects using OLR for the dichotomous outcome (no change in radiographic bone loss vs any change) produced parameter estimates for better control and poorer control that were not statistically significant. CLR analysis of main effects using a 4-category ordinal specification for radiographic bone loss also produced a parameter estimate for better control that was not statistically significant, but which estimated poorer control to have a significant effect. Thet of this CLR model was significantly better at P < 0.05 than that for the OLR. While an OLR model testing the interaction between age and control status did not converge after 100 iterations, the CLR interaction model converged without difficulty and estimated a significant effect for interaction between age and poorer control. Results from the CLR analysis, in contrast to the OLR model, would lead one to conclude that the risk for more severe bone loss progression after 2 years is greater in subjects with poorer controlled NIDDM and that subjects with better controlled NIDDM may not have greater risk of bone loss progression than those without diabetes. The use of an ordinal instead of a dichotomous specification for an inherently continuous response provided increased power, more precise parameter estimates, and a significantly better fitting model. In estimating parameter estimates for odds ratios or risks, it is important to consider using ordinal logistic regression where the response is inherently continuous or ordinal.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72576/1/j.1600-0528.1998.tb01916.x.pd

Individual differences point to two separate processes involved in the resolution of binocular rivalry

Although binocular rivalry is different from other perceptually bistable phenomena in requiring interocular conflict, it also shares numerous features with those phenomena. This raises the question of whether, and to what extent, the neural bases of binocular rivalry and other bistable phenomena overlap. Here we examine this question using an individual-differences approach. In a first experiment, observers reported perception during four binocular rivalry tasks that differed in the features and retinal locations of the stimuli used. Perceptual dominance durations were highly correlated when compared between stimuli that differed in location only. Correlations were substantially weaker, however, when comparing stimuli comprised of different features. Thus, individual differences in binocular-rivalry perception partly reflect a feature-specific factor that is not shared among all variants of binocular rivalry. Our second experiment again included several binocular rivalry variants, but also a different form of bistability: moving plaid rivalry. Correlations in dominance durations between binocular rivalry variants that differed in feature content were again modest. Moreover, and surprisingly, correlations between binocular rivalry and moving plaid rivalry were of similar magnitude. This indicates a second, more general, factor underlying individual differences in binocular rivalry perception: one that is shared across binocular rivalry and moving plaid rivalry. We propose that the first, feature-specific factor corresponds to feature-tuned mechanisms involved in the treatment of interocular conflict, whereas the second, general factor corresponds to mechanisms involved in representing surfaces. These latter mechanisms would operate at a binocular level and be central to both binocular rivalry and other forms of bistability

Glycemic Control and Alveolar Bone Loss Progression in Type 2 Diabetes

This study tested the hypothesis that the risk for alveolar bone loss is greater, and bone loss progression more severe, for subjects with poorly controlled (PC) type 2 diabetes mellitus (type 2 DM) compared to those without type 2 DM or with better controlled (BC) type 2 DM. The PC group had glycosylated hemoglobin (HbA1) ≥ 9%; the BC group had HbA1 < 9%. Data from the longitudinal study of the oral health of residents of the Gila River Indian Community were analyzed. Of the 359 subjects, aged 15 to 57 with less than 25% radiographic bone loss at baseline, 338 did not have type 2 DM, 14 were BC, and 7 were PC. Panoramic radiographs were used to assess interproximal bone level. Bone scores (scale 0–4) corresponding to bone loss of 0%, 1% to 24%, 25% to 49%, 50% to 74%, or ≥ 75% were used to identify the worst bone score (WBS) in the dentition. Change in worst bone score at follow‐up, the outcome, was specified on a 4‐category ordinal scale as no change, or a 1‐, 2‐, 3‐, or 4‐category increase over baseline WBS (WBS1). Poorly controlled diabetes, age, calculus, time to follow‐up examination, and WBS1 were statistically significant explanatory variables in ordinal logistic regression models. Poorly controlled type 2 DM was positively associated with greater risk for a change in bone score (compared to subjects without type 2 DM) when the covariates were included in the model. The cumulative odds ratio (COR) at each threshold of the ordered response was 11.4 (95% CI = 2.5, 53.3). When contrasted with subjects with BC type 2 DM, the COR for those in the PC group was 5.3 (95% CI = 0.8, 53.3). The COR for subjects with BC type 2 DM was 2.2 (95% CI = 0.7, 6.5), when contrasted to those without type 2 DM. These results suggest that poorer glycemic control leads to both an increased risk for alveolar bone loss and more severe progression over those without type 2 DM, and that there may be a gradient, with the risk for bone loss progression for those with better controlled type 2 DM intermediate to the other 2 groups. Ann Periodontol 1998;3:30–39.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142262/1/aape0030.pd

Ibudilast reduces alcohol drinking in multiple animal models of alcohol dependence

Neuroinflammatory signaling pathways in the central nervous system are of current interest as potential pharmacotherapy targets for alcohol dependence. In this study, we examined the ability of ibudilast, a non-selective phosphodiesterase inhibitor, to reduce alcohol drinking and relapse in alcohol-preferring P rats, high-alcohol drinking HAD1 rats, and in mice made dependent on alcohol through cycles of alcohol vapor exposure. When administered twice daily, ibudilast reduced alcohol drinking in rats by approximately 50% and reduced drinking by alcohol-dependent mice at doses which had no effect in non-dependent mice. These findings support the viability of ibudilast as a possible treatment for alcohol dependence

Non‐Insulin Dependent Diabetes Mellitus and Alveolar Bone Loss Progression Over 2 Years

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141702/1/jper0076.pd

Lightcurves of Type Ia Supernovae from Near the Time of Explosion

We present a set of 11 type Ia supernova (SN Ia) lightcurves with dense, pre-maximum sampling. These supernovae (SNe), in galaxies behind the Large Magellanic Cloud (LMC), were discovered by the SuperMACHO survey. The SNe span a redshift range of z = 0.11 - 0.35. Our lightcurves contain some of the earliest pre-maximum observations of SNe Ia to date. We also give a functional model that describes the SN Ia lightcurve shape (in our VR-band). Our function uses the "expanding fireball" model of Goldhaber et al. (1998) to describe the rising lightcurve immediately after explosion but constrains it to smoothly join the remainder of the lightcurve. We fit this model to a composite observed VR-band lightcurve of three SNe between redshifts of 0.135 to 0.165. These SNe have not been K-corrected or adjusted to account for reddening. In this redshift range, the observed VR-band most closely matches the rest frame V-band. Using the best fit to our functional description of the lightcurve, we find the time between explosion and observed VR-band maximum to be 17.6+-1.3(stat)+-0.07(sys) rest-frame days for a SN Ia with a VR-band Delta m_{-10} of 0.52mag. For the redshifts sampled, the observed VR-band time-of-maximum brightness should be the same as the rest-frame V-band maximum to within 1.1 rest-frame days.Comment: 35 pages, 18 figures, 15 tables; Higher quality PDF available at http://ctiokw.ctio.noao.edu/~sm/sm/SNrise/index.html; AJ accepte

Isolation and characterization of cidofovir resistant vaccinia viruses

© 2008 Becker et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens