Screening mammographic performance by race and age in the National Mammography Database: 29,479,665 screening mammograms from 13,181,241 women.

PURPOSE: There are insufficient large-scale studies comparing the performance of screening mammography in women of different races. This study aims to compare the screening performance metrics across racial and age groups in the National Mammography Database (NMD). METHODS: All screening mammograms performed between January 1, 2008, and December 31, 2021, in women aged 30-100 years from 746 mammography facilities in 46 U.S. states in the NMD were included. Patients were stratified by 10-year age intervals and 5 racial groups (African American, American Indian, Asian, White, unknown). Incidence of risk factors (breast density, personal history, family history of breast cancer, age), and time since prior exams were compared. Five screening mammography metrics were calculated: recall rate (RR), cancer detection rate (CDR), positive predictive values for recalls (PPV1), biopsy recommended (PPV2) and biopsy performed (PPV3). RESULTS: 29,479,655 screening mammograms performed in 13,181,241 women between January 1, 2008, and December 31, 2021, from the NMD were analyzed. The overall mean performance metrics were RR 10.00% (95% CI 9.99-10.02), CDR 4.18/1000 (4.16-4.21), PPV1 4.18% (4.16-4.20), PPV2 25.84% (25.72-25.97), PPV3 25.78% (25.66-25.91). With advancing age, RR significantly decreases, while CDR, PPV1, PPV2, and PPV3 significantly increase. Incidence of personal/family history of breast cancer, breast density, age, prior mammogram availability, and time since prior mammogram were mostly similar across all races. Compared to White women, African American women had significantly higher RR, but lower CDR, PPV1, PPV2 and PPV3. CONCLUSIONS: Benefits of screening mammography increase with age, including for women age > 70 and across all races. Screening mammography is effective; with lower RR and higher CDR, PPV2, and PPV3 with advancing age. African American women have poorer outcomes from screening mammography (higher RR and lower CDR), compared to White and all women in the NMD. Racial disparity can be partly explained by higher rate of African American women lost to follow up.

Are associations between obesity and prostate cancer outcomes following radical prostatectomy the same in smokers and non-smokers? Results from the SEARCH Cohort.

PURPOSE: Obesity and smoking have been associated with poor prostate cancer (PC) outcomes. We investigated associations between obesity and biochemical recurrence (BCR), metastasis, castrate resistant-PC (CRPC), PC-specific mortality (PCSM), and all-cause mortality (ACM) and examined if smoking modified these associations. METHODS: We analyzed SEARCH Cohort data from men undergoing RP between 1990 and 2020. Cox regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for associations between body mass index (BMI) as a continuous variable and weight status classifications (normal: 18.5 ≤ 25 kg/m2; overweight: 25-29.9 kg/m2; obese: ≥ 30 kg/m2) and PC outcomes. RESULTS: Among 6,241 men, 1,326 (21%) were normal weight, 2,756 (44%) overweight and 2159 (35%) obese; 1,841 (30%) were never-smokers, 2,768 (44%) former and 1,632 (26%) current-smokers. Among all men, obesity was associated with non-significant increased risk of PCSM, adj-HR = 1.71; 0.98-2.98, P = 0.057, while overweight and obesity were inversely associated with ACM, adj-HR = 0.75; 0.66-0.84, P < 0.001 and adj-HR = 0.86; 0.75-0.99, P = 0.033, respectively. Other associations were null. BCR and ACM were stratified for smoking status given evidence for interactions (P = 0.048 and P = 0.054, respectively). Among current-smokers, overweight was associated with an increase in BCR (adj-HR = 1.30; 1.07-1.60, P = 0.011) and a decrease in ACM (adj-HR = 0.70; 0.58-0.84, P < 0.001). Among never-smokers, BMI (continuous) was associated with an increase in ACM (adj-HR = 1.03; 1.00-1.06, P = 0.033). CONCLUSIONS: While our results are consistent with obesity as a risk factor for PCSM, we present evidence of effect modification by smoking for BCR and ACM highlighting the importance of stratifying by smoking status to better understand associations with body weight.

Generalized estimating equations to estimate the ordered stereotype logit model for panel data.

By modeling the effects of predictor variables as a multiplicative function of regression parameters being invariant over categories, and category-specific scalar effects, the ordered stereotype logit model is a flexible regression model for ordinal response variables. In this article, we propose a generalized estimating equations (GEE) approach to estimate the ordered stereotype logit model for panel data based on working covariance matrices, which are not required to be correctly specified. A simulation study compares the performance of GEE estimators based on various working correlation matrices and working covariance matrices using local odds ratios. Estimation of the model is illustrated using a real-world dataset. The results from the simulation study suggest that GEE estimation of this model is feasible in medium-sized and large samples and that estimators based on local odds ratios as realized in this study tend to be less efficient compared with estimators based on a working correlation matrix. For low true correlations, the efficiency gains seem to be rather small and if the working covariance structure is too flexible, the corresponding estimator may even be less efficient compared with the GEE estimator assuming independence. Like for GEE estimators more generally, if the true correlations over time are high, then a working covariance structure which is close to the true structure can lead to considerable efficiency gains compared with assuming independence.

Statistical Generalized Derivative Applied to the Profile Likelihood Estimation in a Mixture of Semiparametric Models.

There is a difficulty in finding an estimate of the standard error (SE) of the profile likelihood estimator in the joint model of longitudinal and survival data. The difficulty is on the differentiation of an implicit function that appear in the profile likelihood estimation. We solve the difficulty by introducing the "statistical generalized derivative". The derivative is used to show the asymptotic normality of the estimator with the SE expressed in terms of the profile likelihood score function.

A goodness-of-fit test for the ordered stereotype model.

This paper presents a new goodness-of-fit test for an ordered stereotype model used for an ordinal response variable. The proposed test is based on the well-known Hosmer-Lemeshow test and its version for the proportional odds regression model. The latter test statistic is calculated from a grouping scheme assuming that the levels of the ordinal response are equally spaced which might be not true. One of the main advantages of the ordered stereotype model is that it allows us to determine a new uneven spacing of the ordinal response categories, dictated by the data. The proposed test takes the use of this new adjusted spacing to partition data. A simulation study shows good performance of the proposed test under a variety of scenarios. Finally, the results of the application in two examples are presented. Copyright © 2016 John Wiley & Sons, Ltd.

Generalized Mantel-Haenszel Estimators for Simultaneous Differential Item Functioning Tests.

The Mantel-Haenszel estimator is one of the most popular techniques for measuring differential item functioning (DIF). A generalization of this estimator is applied to the context of DIF to compare items by taking the covariance of odds ratio estimators between dependent items into account. Unlike the Item Response Theory, the method does not rely on the local item independence assumption which is likely to be violated when one item provides clues about the answer of another item. Furthermore, we use these (co)variance estimators to construct a hypothesis test to assess DIF for multiple items simultaneously. A simulation study is presented to assess the performance of several tests. Finally, the use of these DIF tests is illustrated via application to two real data sets.

Are environmental pollution and biodiversity levels associated to the spread and mortality of COVID-19? A four-month global analysis.

On March 12th, 2020, the WHO declared COVID-19 as a pandemic. The collective impact of environmental and ecosystem factors, as well as biodiversity, on the spread of COVID-19 and its mortality evolution remain empirically unknown, particularly in regions with a wide ecosystem range. The aim of our study is to assess how those factors impact on the COVID-19 spread and mortality by country. This study compiled a global database merging WHO daily case reports with other publicly available measures from January 21st to May 18th, 2020. We applied spatio-temporal models to identify the influence of biodiversity, temperature, and precipitation and fitted generalized linear mixed models to identify the effects of environmental variables. Additionally, we used count time series to characterize the association between COVID-19 spread and air quality factors. All analyses were adjusted by social demographic, country-income level, and government policy intervention confounders, among 160 countries, globally. Our results reveal a statistically meaningful association between COVID-19 infection and several factors of interest at country and city levels such as the national biodiversity index, air quality, and pollutants elements (PM10, PM2.5, and O3). Particularly, there is a significant relationship of loss of biodiversity, high level of air pollutants, and diminished air quality with COVID-19 infection spread and mortality. Our findings provide an empirical foundation for future studies on the relationship between air quality variables, a country's biodiversity, and COVID-19 transmission and mortality. The relationships measured in this study can be valuable when governments plan environmental and health policies, as alternative strategy to respond to new COVID-19 outbreaks and prevent future crises.

Assigning scores for ordered categorical responses.

Deciding on the best statistical method to apply when the response variable is ordinal is essential because the way the categories are ordered in the data is relevant as it could change the results of the analysis. Although the models for continuous variables have similarities to those for ordinal variables, this paper presents the advantages of the use of the ordering information on the outcomes with methods developed for modeling ordinal data such as the ordered stereotype model. The novelty of this article lies in showing the dangers of assigning equally spaced scores to ordered response categories in statistical analysis, which are illustrated with a simulation study and a case study. We propose a new way to use the score parameters, which incorporates the fitted spacing dictated by the data. Additionally, this article uses score parameter estimates in the ordered stereotype model to propose a new measure to calculate continuous medians in the raw data: the adjusted c-median. It benefits the general audience who can easily understand the median as a summary statistic. Supplementary materials for this article are available online.

Model-based goodness-of-fit tests for the ordered stereotype model.

This paper presents two new model-based goodness-of-fit tests for the ordered stereotype model applied to an ordinal response variable. The proposed tests are based on the Lipsitz test, which partitions the subjects into G groups following the popular Hosmer-Lemeshow test for binary data. The tests construct an alternative model where group effects are added into the null model. If the model fits the data well then the null model is correct, and there should be no group effects. One of the main advantages of the ordered stereotype model is that it allows us to determine a new uneven spacing of the ordinal response categories, dictated by the data. The two proposed tests use this new adjusted spacing. One test uses the form of the original ordered stereotype model, and the other uses an ordinary linear model. We demonstrate the performance of both tests under a variety of scenarios. Finally, the results of the application in three examples are presented.

A method for ordinal outcomes: The ordered stereotype model.

OBJECTIVE: The collection and use of ordinal variables are common in many psychological and psychiatric studies. Although the models for continuous variables have similarities to those for ordinal variables, there are advantages when a model developed for modeling ordinal data is used such as avoiding "floor" and "ceiling" effects and avoiding to assign scores, as it happens in continuous models, which can produce results sensitive to the score assigned. This paper introduces and focuses on the application of the ordered stereotype model, which was developed for modeling ordinal outcomes and is not so popular as other models such as linear regression and proportional odds models. This paper aims to compare the performance of the ordered stereotype model with other more commonly used models among researchers and practitioners. METHODS: This article compares the performance of the stereotype model against the proportional odd and linear regression models, with three, four, and five levels of ordinal categories and sample sizes 100, 500, and 1000. This paper also discusses the problem of treating ordinal responses as continuous using a simulation study. The trend odds model is also presented in the application. RESULTS: Three types of models were fitted in one real-life example, including ordered stereotype, proportional odds, and trend odds models. They reached similar conclusions in terms of the significance of covariates. The simulation study evaluated the performance of the ordered stereotype model under four cases. The performance varies depending on the scenarios. CONCLUSIONS: The method presented can be applied to several areas of psychiatry dealing with ordinal outcomes. One of the main advantages of this model is that it breaks with the assumption of levels of the ordinal response are equally spaced, which might be not true.

The analysis of stratified multiple responses.

Surveys often contain qualitative variables for which respondents may select any number of the outcome categories. For instance, for the question "What type of contraception have you used?" with possible responses (oral, condom, lubricated condom, spermicide, and diaphragm), respondents would be instructed to select as many of the outcomes that apply. This situation is known as multiple responses. When the data includes stratification variables, we discuss two approaches: (1) the "GEE" approach which uses logit models directly applying the generalized estimating equations (GEE) method (Liang and Zeger, 1986); and (2) the "GMH" approach which extends the generalized Mantel-Haenszel type estimators (Greenland, 1989) to make inferences across multiple responses. These approaches can also be used for data with dependent observations across strata.

Biclustering Models for Two-Mode Ordinal Data.

The work in this paper introduces finite mixture models that can be used to simultaneously cluster the rows and columns of two-mode ordinal categorical response data, such as those resulting from Likert scale responses. We use the popular proportional odds parameterisation and propose models which provide insights into major patterns in the data. Model-fitting is performed using the EM algorithm, and a fuzzy allocation of rows and columns to corresponding clusters is obtained. The clustering ability of the models is evaluated in a simulation study and demonstrated using two real data sets.

The Initiation of Smooth Pursuit is Delayed in Anisometropic Amblyopia.

PURPOSE: Several behavioral studies have shown that the reaction times of visually guided movements are slower in people with amblyopia, particularly during amblyopic eye viewing. Here, we tested the hypothesis that the initiation of smooth pursuit eye movements, which are responsible for accurately keeping moving objects on the fovea, is delayed in people with anisometropic amblyopia. METHODS: Eleven participants with anisometropic amblyopia and 14 visually normal observers were asked to track a step-ramp target moving at ±15°/s horizontally as quickly and as accurately as possible. The experiment was conducted under three viewing conditions: amblyopic/nondominant eye, binocular, and fellow/dominant eye viewing. Outcome measures were smooth pursuit latency, open-loop gain, steady state gain, and catch-up saccade frequency. RESULTS: Participants with anisometropic amblyopia initiated smooth pursuit significantly slower during amblyopic eye viewing (206 ± 20 ms) than visually normal observers viewing with their nondominant eye (183 ± 17 ms, P = 0.002). However, mean pursuit latency in the anisometropic amblyopia group during binocular and monocular fellow eye viewing was comparable to the visually normal group. Mean open-loop gain, steady state gain, and catch-up saccade frequency were similar between the two groups, but participants with anisometropic amblyopia exhibited more variable steady state gain (P = 0.045). CONCLUSIONS: This study provides evidence of temporally delayed smooth pursuit initiation in anisometropic amblyopia. After initiation, the smooth pursuit velocity profile in anisometropic amblyopia participants is similar to visually normal controls. This finding differs from what has been observed previously in participants with strabismic amblyopia who exhibit reduced smooth pursuit velocity gains with more catch-up saccades.

Calligraphy and meditation for stress reduction: an experimental comparison.

BACKGROUND: Chinese calligraphic handwriting (CCH) has demonstrated a new role in health and therapy. Meanwhile, meditation is an traditional and effective method for coping with stress and staying healthy. This study compared the effectiveness of CCH and meditation as distinctive and parallel stress reduction interventions. METHODS: Thirty graduate students and academic staff members in Taiwan who suffered from stress were selected by the General Health Questionnaire and randomly assigned to one of three treatment groups, ie, a CCH group, a meditation group, or a control group, for 8 consecutive weeks. Changes in physiological parameters were measured before, during, and after treatment. RESULTS: CCH and meditation showed their strength in the respective indices of stress. There was a significant difference in respiratory rate, heart rate, and electromyographic scores between the groups. Comparing pre- and post-effects, a decrease in heart rate and an increase in skin temperature was seen in subjects who practiced CCH. Increased skin temperature and decreased respiratory rate were also seen in subjects who practiced meditation, along with reduced muscle tension and heart rate. CONCLUSION: CCH and meditation have good effects in stress reduction. CCH is a particularly promising new approach to reducing stress.

Effect of calligraphy training on hyperarousal symptoms for childhood survivors of the 2008 China earthquakes.

BACKGROUND: This study investigated the treatment effects of calligraphy therapy on childhood survivors of the 2008 Sichuan earthquakes in the People's Republic of China. METHODS: In experiment 1, 129 children participated in a 30-day calligraphic training, and 81 children were controls. The Children's Revised Impact of Event Scale was adopted to assess behavioral effects. Experiment 2 involved 41 treatment subjects and 39 controls, with the same procedure as in experiment 1 except that salivary cortisol level was also measured as a physiological indicator. RESULTS: After 30 days of calligraphy treatment, the arousal symptoms and salivary cortisol levels in the experimental group decreased from 5.72±0.31 and 13.34±2.88 to 4.98±0.31 and 9.99±2.81, respectively. In the control group, there was not a significant decrease from pretest to post-test. In addition, the arousal scores in posttest (4.98±4.39) were significantly lower than midtest (5.71±4.14) for girls; in contrast, for boys, posttest (4.90±4.24) showed little change compared with midtest (5.04±4.36), but both were significantly lower than pretest (6.42±4.59). CONCLUSIONS: Calligraphy therapy was effective in reducing hyperarousal symptoms among child survivors.

The relationship between income and health using longitudinal data from New Zealand.

BACKGROUND: Evidence for a cross-sectional relationship between income and health is strong but is probably biased by substantial confounding. Longitudinal data with repeated income and health measures on the same individuals can be analysed to control completely for time-invariant confounding, giving a more accurate estimate of the impact of short-term changes in income on health. METHODS: 4 years of annual data (2002--2005) from the New Zealand longitudinal Survey of Family, Income and Employment were used to investigate the relationship between annual household income and self-rated health (SRH) using a fixed-effects ordinal logistic regression model. Possible effect modification of the income--SRH relationship by poverty and baseline health was tested with interactions. RESULTS: An increase in income of $10 000 over the past year increased the odds of reporting better SRH by 1% (OR 1.01, 95% CI 1.00 to 1.02). Poor baseline health significantly modified the association between income and SRH. A $10 000 increase in income increased the odds of better SRH by 10% for those with two or more chronic conditions. Poverty or deprivation did not modify the income--health association. CONCLUSIONS: The overall small, positive, but statistically non-significant, income--health effect size is consistent with similar analyses from other longitudinal studies. Despite the overwhelming consensus that income matters for health over the medium and long-term, evidence free of time-invariant confounding for the short-run association remains elusive. However, measurement error in income and health has probably biased estimates towards the null.

A note on bias due to fitting prospective multivariate generalized linear models to categorical outcomes ignoring retrospective sampling schemes.

Outcome dependent sampling designs are commonly used in economics, market research and epidemiological studies. Case-control sampling design is a classic example of outcome dependent sampling, where exposure information is collected on subjects conditional on their disease status. In many situations, the outcome under consideration may have multiple categories instead of a simple dichotomization. For example, in a case-control study, there may be disease sub-classification among the "cases" based on progression of the disease, or in terms of other histological and morphological characteristics of the disease. In this note, we investigate the issue of fitting prospective multivariate generalized linear models to such multiple-category outcome data, ignoring the retrospective nature of the sampling design. We first provide a set of necessary and sufficient conditions for the link functions that will allow for equivalence of prospective and retrospective inference for the parameters of interest. We show that for categorical outcomes, prospective-retrospective equivalence does not hold beyond the generalized multinomial logit link. We then derive an approximate expression for the bias incurred when link functions outside this class are used. Most popular models for ordinal response fall outside the multiplicative intercept class and one should be cautious while performing a naive prospective analysis of such data as the bias could be substantial. We illustrate the extent of bias through a real data example, based on the ongoing Prostate, Lung, Colorectal and Ovarian (PLCO) cancer screening trial by the National Cancer Institute. The simulations based on the real study illustrate that the bias approximations work well in practice.

Graphical diagnostics to check model misspecification for the proportional odds regression model.

The cumulative logit or the proportional odds regression model is commonly used to study covariate effects on ordinal responses. This paper provides some graphical and numerical methods for checking the adequacy of the proportional odds regression model. The methods focus on evaluating functional misspecification for specific covariate effects, but misspecification of the link function can also be dealt with under the same framework. For the logistic regression model with binary responses, Arbogast and Lin (Statist. Med. 2005; 24:229-247) developed similar graphical and numerical methods for assessing the adequacy of the model using the cumulative sums of residuals. The paper generalizes their methods to ordinal responses and illustrates them using an example from the VA Normative Aging Study. Simulation studies comparing the performance of the different diagnostic methods indicate that some of the graphical methods are more powerful in detecting model misspecification than the Hosmer-Lemeshow-type goodness-of-fit statistics for the class of models studied.

Fitting stratified proportional odds models by amalgamating conditional likelihoods.

Classical methods for fitting a varying intercept logistic regression model to stratified data are based on the conditional likelihood principle to eliminate the stratum-specific nuisance parameters. When the outcome variable has multiple ordered categories, a natural choice for the outcome model is a stratified proportional odds or cumulative logit model. However, classical conditioning techniques do not apply to the general K-category cumulative logit model (K>2) with varying stratum-specific intercepts as there is no reduction due to sufficiency; the nuisance parameters remain in the conditional likelihood. We propose a methodology to fit stratified proportional odds model by amalgamating conditional likelihoods obtained from all possible binary collapsings of the ordinal scale. The method allows for categorical and continuous covariates in a general regression framework. We provide a robust sandwich estimate of the variance of the proposed estimator. For binary exposures, we show equivalence of our approach to the estimators already proposed in the literature. The proposed recipe can be implemented very easily in standard software. We illustrate the methods via three real data examples related to biomedical research. Simulation results comparing the proposed method with a random effects model on the stratification parameters are also furnished.