A Systematic Review and Meta-analysis of Surgical Treatment of Ectopic Pregnancy with Salpingectomy versus Salpingostomy.

OBJECTIVE: To evaluate the fertility outcomes of salpingectomy compared with those of salpingostomy among patients treated for tubal ectopic pregnancies, including a separate analysis of women with risk factors along with a review of the surgical technique. DATA SOURCES: Systematic review and meta-analysis from 1990 to the present through PubMed, Embase, CINAHL, and Ovid MEDLINE. The search string included "tubal pregnancy" or "ectopic" as well as "salpingectomy" and various terms describing salpingotomy. METHODS OF STUDY SELECTION: Articles studying women who underwent surgical management of an ectopic pregnancy and the contrasted outcomes of salpingectomy vs salpingostomy were reviewed. The primary outcomes included subsequent intrauterine pregnancy (IUP) and repeat ectopic pregnancy (REP). TABULATION, INTEGRATION, AND RESULTS: Two randomized controlled trials (RCTs), which consisted mostly of patients classified as low risk, and patients from 16 cohort studies were included. In the RCTs, there was no significant difference in the odds of subsequent IUP in patients who underwent a salpingectomy compared with those who were treated with salpingotomy (odds ratio [OR] 0.97; 95% confidence interval [CI], 0.71-1.33). However, a significant and clinically meaningful difference was noted in the cohort studies, with the patients having a lower chance of IUP after salpingectomy (OR 0.45; 95% CI, 0.39-0.52). No significant difference was noted in the OR for a REP in the randomized trials (OR 0.77; 95% CI, 0.41-1.47), but the patients followed in the cohort studies had a cumulatively higher risk of REP after a salpingostomy (OR 0.73; 95% CI, 0.60-0.90). The subgroup analysis examining women within the studies with risk factors for tubal pathology found an even more impressive lowering in the odds of a subsequent IUP in patients classified as at-risk who were treated with salpingectomy (OR 0.30; 95% CI, 0.17-0.54), with a change in the direction of the odds for an REP rate favoring those who were treated with salpingostomy (OR 1.96; 95% CI, 0.88-4.35). CONCLUSION: Salpingectomy has clear advantages over salpingostomy, and RCTs consisting mainly of patients classified as low risk show no difference in outcomes between salpingectomy and salpingostomy. However, in cohort studies inclusive of all patients, the likelihood of a subsequent spontaneous IUP is decreased in patients treated with salpingectomy, and salpingostomies may be especially underused in women with risk factors for tubal disease.

Use of partitioned GMM marginal regression model with time-dependent covariates: analysis of Chinese Longitudinal Healthy Longevity Study.

BACKGROUND: Elderly population's health is a major concern for most industrial nations. National health surveys provide a measure of the state of elderly health. One such survey is the Chinese Longitudinal Healthy Longevity Survey. It collects data on risk factors and outcomes on the elderly. We examine these longitudinal survey data to determine the changes in health and to identify risk factors as they impact health outcomes including the elderly's ability to do a physical check. METHODS: We use a Partitioned GMM logistic regression model to identify risk factors. The model also accounts for the correlation between lagged time-dependent covariates and the outcomes. It addresses present and past measures of time-dependent covariates on simultaneous outcomes. The relation produces additional regression coefficients as byproduct of the Partitioned model, identifying the immediate, delayed effects (lag - 1), further delayed (lag-2), etc. Therefore, the model presents the opportunity for decision makers to monitor the covariate over time. This technique is particularly useful in healthcare and health related research. We use the Chinese Longitudinal Health Longevity Survey data to identify those risk factors and to display the utility of the model. RESULTS: We found that one's ability to make own decisions, frequently consuming vegetables, exercise frequently, one's ability to transfer without assistance, having visual difficulties and being able to pick book from floor while standing had varying effects of significance on one's health and ability to complete physical checks as they get older. CONCLUSIONS: The partitioning of the covariates as immediate effect, delayed effect or further delayed effect are important measures in a declining population.

Split bootstrap hierarchical modeling of antibiotics abuse in China.

In the 1990s, China experienced a high degree of antibiotics abuse, which resulted in increased drug resistance. As a result, the World Health Organization introduced a program for children under the age of 5 years who had an acute respiratory tract infection. We analyze the data pertaining to the treatment provided by doctors in several hospitals in China in order to understand the relationships in the data. The data are nested in a three-level hierarchical structure with small cluster sizes ranging from 2 to 10. While large sample theory provides a mechanism to construct confidence intervals and test hypotheses about regression coefficients, the estimation algorithms often fail to converge when they are applied to small cluster sizes. This paper presents a combination of the cluster bootstrap and primary unit splitting methods, called split bootstrap, which is a novel combination that can be used as an alternative when analyzing data pertaining to the abuse of antibiotics in China with small cluster sizes. The split bootstrap method provides accurate estimations with a minimal reduction in precision.

Partitioned GMM logistic regression models for longitudinal data.

Correlation is inherent in longitudinal studies due to the repeated measurements on subjects, as well as due to time-dependent covariates in the study. In the National Longitudinal Study of Adolescent to Adult Health (Add Health), data were repeatedly collected on children in grades 7-12 across four waves. Thus, observations obtained on the same adolescent were correlated, while predictors were correlated with current and future outcomes such as obesity status, among other health issues. Previous methods, such as the generalized method of moments (GMM) approach have been proposed to estimate regression coefficients for time-dependent covariates. However, these approaches combined all valid moment conditions to produce an averaged parameter estimate for each covariate and thus assumed that the effect of each covariate on the response was constant across time. This assumption is not necessarily optimal in applications such as Add Health or health-related data. Thus, we depart from this assumption and instead use the Partitioned GMM approach to estimate multiple coefficients for the data based on different time periods. These extra regression coefficients are obtained using a partitioning of the moment conditions pertaining to each respective relationship. This approach offers a deeper understanding and appreciation into the effect of each covariate on the response. We conduct simulation studies, as well as analyses of obesity in Add Health, rehospitalization in Medicare data, and depression scores in a clinical study. The Partitioned GMM methods exhibit benefits over previously proposed models with improved insight into the nonconstant relationships realized when analyzing longitudinal data.

Impact of communities, health, and emotional-related factors on smoking use: comparison of joint modeling of mean and dispersion and Bayes' hierarchical models on add health survey.

BACKGROUND: The analysis of correlated binary data is commonly addressed through the use of conditional models with random effects included in the systematic component as opposed to generalized estimating equations (GEE) models that addressed the random component. Since the joint distribution of the observations is usually unknown, the conditional distribution is a natural approach. Our objective was to compare the fit of different binary models for correlated data in Tabaco use. We advocate that the joint modeling of the mean and dispersion may be at times just as adequate. We assessed the ability of these models to account for the intraclass correlation. In so doing, we concentrated on fitting logistic regression models to address smoking behaviors. METHODS: Frequentist and Bayes' hierarchical models were used to predict conditional probabilities, and the joint modeling (GLM and GAM) models were used to predict marginal probabilities. These models were fitted to National Longitudinal Study of Adolescent to Adult Health (Add Health) data for Tabaco use. RESULTS: We found that people were less likely to smoke if they had higher income, high school or higher education and religious. Individuals were more likely to smoke if they had abused drug or alcohol, spent more time on TV and video games, and been arrested. Moreover, individuals who drank alcohol early in life were more likely to be a regular smoker. Children who experienced mistreatment from their parents were more likely to use Tabaco regularly. CONCLUSIONS: The joint modeling of the mean and dispersion models offered a flexible and meaningful method of addressing the intraclass correlation. They do not require one to identify random effects nor distinguish from one level of the hierarchy to the other. Moreover, once one can identify the significant random effects, one can obtain similar results to the random coefficient models. We found that the set of marginal models accounting for extravariation through the additional dispersion submodel produced similar results with regards to inferences and predictions. Moreover, both marginal and conditional models demonstrated similar predictive power.

Predicted probabilities' relationship to inclusion probabilities.

It has been shown that under a general multiplicative intercept model for risk, case-control (retrospective) data can be analyzed by maximum likelihood as if they had arisen prospectively, up to an unknown multiplicative constant, which depends on the relative sampling fraction. (1) With suitable auxiliary information, retrospective data can also be used to estimate response probabilities. (2) In other words, predictive probabilities obtained without adjustments from retrospective data will likely be different from those obtained from prospective data. We highlighted this using binary data from Medicare to determine the probability of readmission into the hospital within 30 days of discharge, which is particularly timely because Medicare has begun penalizing hospitals for certain readmissions. (3).

GMM logistic regression models for longitudinal data with time-dependent covariates and extended classifications.

When analyzing longitudinal data, it is essential to account both for the correlation inherent from the repeated measures of the responses as well as the correlation realized on account of the feedback created between the responses at a particular time and the predictors at other times. As such one can analyze these data using generalized estimating equation with the independent working correlation. However, because it is essential to include all the appropriate moment conditions as you solve for the regression coefficients, we explore an alternative approach using a generalized method of moments for estimating the coefficients in such data. We develop an approach that makes use of all the valid moment conditions necessary with each time-dependent and time-independent covariate. This approach does not assume that feedback is always present over time, or if present occur at the same degree. Further, we make use of continuously updating generalized method of moments in obtaining estimates. We fit the generalized method of moments logistic regression model with time-dependent covariates using SAS PROC IML and also in R. We used p-values adjusted for multiple correlated tests to determine the appropriate moment conditions for determining the regression coefficients. We examined two datasets for illustrative purposes. We looked at re-hospitalization taken from a Medicare database. We also revisited data regarding the relationship between the body mass index and future morbidity among children in the Philippines. We conducted a simulated study to compare the performances of extended classifications.

Complications in robotic-assisted gynecologic surgery according to case type: a 6-year retrospective cohort study using Clavien-Dindo classification.

STUDY OBJECTIVE: To estimate the risk of postoperative complications in robotic-assisted gynecologic surgery according to case type. STUDY DESIGN: Retrospective cohort study (Canadian Task Force classification II-2). SETTING: Mayo Clinic Arizona. PATIENTS: All 1155 patients who underwent robotic-assisted gynecologic surgery between March 2004 and December 2009 were included. Patients were primarily white (94.3%), with a mean (SD) age of 51.5 (15.4) years, and were overweight, with body mass index (BMI) of 27.2 (6.8). INTERVENTIONS: Risk of complications, overall and according to Clavien-Dindo grade, and incidence of specific complications were analyzed. Robotic-assisted gynecologic surgical procedures were categorized postoperatively according to case type as benign simple (e.g., oophorectomy, simple hysterectomy) in 552 (47.8%) patients, benign complex (e.g., excision of invasive endometriosis) in 262 (22.7%), urogynecologic in 121 (10.5%), and oncologic in 220 (19.1%). MEASUREMENTS AND MAIN RESULTS: Intraoperative complications occurred in 3.2% of patients. Postoperative complications of any type occurred in 18.4% of patients. Conversion to laparotomy was necessary in 2.7%. Urologic complications were more common in urogynecologic cases (5.8%) as compared with benign simple (0.5%), benign complex (2.7%), and oncologic (3.2%). Bleeding complications were most common in oncologic cases (5%). Clavien-Dindo grade ≥ 3 complications occurred in 5.2% of patients overall, and were >3-fold likely to occur in benign complex, urogynecologic, and oncologic cases than in benign simple cases. When adjusted for age, BMI, estimated blood loss, operative time, length of stay, and previous pelvic surgery, complications were nearly twice as common for benign complex (odds ratio [OR] 1.7; 95% confidence interval [CI], 1.1-2.7), urogynecologic (OR 1.9; 95% CI, 1.0-3.4), and oncologic (OR 1.9; 95% CI, 1.1-3.1) cases as for benign simple cases, although weakly significant. Case type, BMI, estimated blood loss, and length of stay remained important factors in predicting postoperative complications. CONCLUSION: The incidence of complications in robotic-assisted gynecologic surgery varies according to case type. Defining the role of patient and surgical variables such as case type in the occurrence of complications may help in identification of cases with increased risk, to improve patient counseling and surgical outcome.

Statistical analysis of data from dilution assays with censored correlated counts.

Frequently, count data obtained from dilution assays are subject to an upper detection limit, and as such, data obtained from these assays are usually censored. Also, counts from the same subject at different dilution levels are correlated. Ignoring the censoring and the correlation may provide unreliable and misleading results. Therefore, any meaningful data modeling requires that the censoring and the correlation be simultaneously addressed. Such comprehensive approaches of modeling censoring and correlation are not widely used in the analysis of dilution assays data. Traditionally, these data are analyzed using a general linear model on a logarithmic-transformed average count per subject. However, this traditional approach ignores the between-subject variability and risks, providing inconsistent results and unreliable conclusions. In this paper, we propose the use of a censored negative binomial model with normal random effects to analyze such data. This model addresses, in addition to the censoring and the correlation, any overdispersion that may be present in count data. The model is shown to be widely accessible through the use of several modern statistical software.

Effect of olfactory bulb pathology on olfactory function in normal aging.

Decline of olfactory function is frequently observed in aging and is an early symptom of neurodegenerative diseases. As the olfactory bulb (OB) is one of the first regions involved by pathology and may represent an early disease stage, we specifically aimed to evaluate the contribution of OB pathology to olfactory decline in cognitively normal aged individuals without parkinsonism or dementia. This clinicopathological study correlates OB tau, amyloid ß (Aß) and α-synuclein (αSyn) pathology densities and whole brain pathology load to olfactory identification function as measured with the University of Pennsylvania Smell Identification Test (UPSIT) and clinical data measured proximate to death in a large autopsy study including 138 cases considered non-demented controls during life. Tau pathology was frequently observed in the OB (95% of cases), while both Aß (27% of cases) and αSyn (20% of cases) OB pathologies were less commonly observed. A weak correlation was only observed between OB tau and olfactory performance, but when controlled for age, neither OB tau, Aß or αSyn significantly predict olfactory performance. Moreover, whole brain tau and αSyn pathology loads predicted olfactory performance; however, only αSyn pathology loads survived age correction. In conclusion, OB tau pathology is frequently observed in normally aging individuals and increases with age but does not appear to independently contribute to age-related olfactory impairment suggesting that further involvement of the brain seems necessary to contribute to age-related olfactory decline.

Exact logistic models for nested binary data.

The use of logistic models for independent binary data has relied first on asymptotic theory and later on exact distributions for small samples. However, the use of logistic models for dependent analysis based on exact analysis is not as common. Moreover, attention is usually given to one-stage clustering. In this paper, we extend the exact techniques to address hypothesis testing (estimation is not addressed) for data with second-stage and probably higher levels of clustering. The methods are demonstrated through a somewhat generic example using C+ + program.

Is diabetes associated with increased pathological burden in Alzheimer's disease?

INTRODUCTION: We examined the association between Alzheimer's disease (AD) and type 2 diabetes mellitus (DM) and hypothesized that diabetes is associated with an increased pathological burden in clinically and pathologically diagnosed AD. METHODS: All data were obtained from the Uniform Data Set (UDS) v3, the Neuropathology Data Set, and the Researcher's Data Dictionary-Genetic Data from the National Alzheimer's Coordinating Center. The dataset (37 cases with diabetes and 1158 cases without) relies on autopsy-confirmed data in clinically diagnosed AD patients who were assessed for diabetes type in form A5 or D2 during at least one visit. Differences in scores were explored using a general linear model. Effect sizes were calculated using sample means and standard deviations (Cohen's d). RESULTS: The presence of diabetes was associated with a lower Thal phase of amyloid plaques (A score; 4.6 ± 0.79 vs. 4.3 ± 0.85, P < .05) and lower Braak stage for neurofibrillary degeneration (B score; 5.58 ± 0.72 vs. 5.16 ± 0.96, P < 0.05) but not for density of neocortical neuritic plaques (CERAD score-C score). The National Institute on Aging-Alzheimer's Association Alzheimer's disease neuropathologic change (ABC score) was not different between AD+DM and AD-DM. DISCUSSION: This pilot study found a significantly lower Thal phase of amyloid plaques and Braak stage for neurofibrillary degeneration in AD-confirmed individuals with diabetes compared to those without. Thus type 2 DM is not associated with increased AD pathology in clinically and pathologically confirmed cases of AD.

HIV survey in Mozambique: analysis with simultaneous model in contrast to separate hierarchical models.

BACKGROUND: The analysis of correlated responses obtained one at a time in survey data is not as informative or as useful as modeling them simultaneously. Simultaneous modeling allows for the opportunity to evaluate the system in a more pragmatic form rather than to allow for responses that assumedly originated in isolation. METHODS: This research uses the Mozambique National Survey data to demonstrate the benefits of simultaneous modeling on blood test results, knowledge of HIV/AIDS, and awareness of an HIV/AIDS campaign. This simultaneous modeling also addresses the correlation inherent due to the hierarchical structure in the data collection. RESULTS: Employment and self-perceived risk of HIV/AIDS have different impact on blood test, awareness of an HIV/AIDS campaign, and knowledge of HIV/AIDS when examined simultaneously as opposed to separate modeling. CONCLUSION: Simultaneous modeling of correlated responses improves the reliability of the estimates. More importantly, it provides an opportunity to engage in cost-saving decisions when designing future surveys and make better health policies.

Bayesian multiple membership multiple classification logistic regression model on student performance with random effects in university instructors and majors.

Educational success measured by retention leading to graduation is an essential component of any academic institution. As such, identifying the factors that contribute significantly to success and addressing those factors that result in poor performances are important exercises. By success, we mean obtaining a semester GPA of 3.0 or better and a GPA of 2.0 or better. We identified these factors and related challenges through analytical models based on student performance. A large dataset obtained from a large state university over three consecutive semesters was utilized. At each semester, GPAs were nested within students and students were taking classes from multiple instructors and pursuing a specific major. Thus, we used multiple membership multiple classification (MMMC) Bayesian logistic regression models with random effects for instructors and majors to model success. The complexity of the analysis due to multiple membership modeling and a large number of random effects necessitated the use of Bayesian analysis. These Bayesian models identified factors affecting academic performance of college students while accounting for university instructors and majors as random effects. In particular, the models adjust for residency status, academic level, number of classes, student athletes, and disability residence services. Instructors and majors accounted for a significant proportion of students' academic success, and served as key indicators of retention and graduation rates. They are embedded within the processes of university recruitment and competition for the best students.

Modification of the generalized quasi-likelihood model in the analysis of the Add Health study.

The relationship between the mean and variance is an implicit assumption of parametric modeling. While many distributions in the exponential family have a theoretical mean-variance relationship, it is often the case that the data under investigation are correlated, thus varying from the relation. We present a generalized method of moments estimation technique for modeling certain correlated data by adjusting the mean-variance relationship parameters based on a canonical parameterization. The proposed mean-variance form describes overdispersion using two parameters and implements an adjusted canonical parameter which makes this approach feasible for all distributions in the exponential family. Test statistics and confidence intervals are used to measure the deviations from the mean-variance relation parameters. We use the modified relation as a means of fitting generalized quasi-likelihood models to correlated data. The performance of the proposed modified generalized quasi-likelihood model is demonstrated through a simulation study and the importance of accounting for overdispersion is highlighted through the evaluation of adolescent obesity data collected from a U.S. longitudinal study.