Plotting regression coefficients and other estimates in Stata

Graphical presentation of regression results has become increasingly popular in the scientific literature, as graphs are much easier to read than tables in many cases. In Stata such plots can be produced by the -marginsplot- command. However, while -marginsplot- is very versatile and flexible, it has two major limitations: it can only process results left behind by -margins- and it can only handle one set of results at the time. In this article I introduce a new command called -coefplot- that overcomes these limitations. It plots results from any estimation command and combines results from several models into a single graph. The default behavior of -coefplot- is to plot markers for coefficients and horizontal spikes for confidence intervals. However, -coefplot- can also produce various other types of graphs. The capabilities of -coefplot- are illustrated in this article using a series of examples.

A randomized, double-blind, placebo-controlled pilot study of naltrexone in outpatients with bipolar disorder and alcohol dependence.

BACKGROUND: Alcohol dependence is extremely common in patients with bipolar disorder and is associated with unfavorable outcomes including treatment nonadherence, violence, increased hospitalization, and decreased quality of life. While naltrexone is a standard treatment for alcohol dependence, no controlled trials have examined its use in patients with co-morbid bipolar disorder and alcohol dependence. In this pilot study, the efficacy of naltrexone in reducing alcohol use and on mood symptoms was assessed in bipolar disorder and alcohol dependence. METHODS: Fifty adult outpatients with bipolar I or II disorders and current alcohol dependence with active alcohol use were randomized to 12 weeks of naltrexone (50 mg/d) add-on therapy or placebo. Both groups received manual-driven cognitive behavioral therapy designed for patients with bipolar disorder and substance-use disorders. Drinking days and heavy drinking days, alcohol craving, liver enzymes, and manic and depressed mood symptoms were assessed. RESULTS: The 2 groups were similar in baseline and demographic characteristics. Naltrexone showed trends (p < 0.10) toward a greater decrease in drinking days (binary outcome), alcohol craving, and some liver enzyme levels than placebo. Side effects were similar in the 2 groups. Response to naltrexone was significantly related to medication adherence. CONCLUSIONS: Results suggest the potential value and acceptable tolerability of naltrexone for alcohol dependence in bipolar disorder patients. A larger trial is needed to establish efficacy.

COEFPLOT: Stata module to plot regression coefficients and other results

coefplot plots results from estimation commands or Stata matrices. Results from multiple models or matrices can be combined in a single graph. The default behavior of coefplot is to draw markers for coefficients and horizontal spikes for confidence intervals. However, coefplot can also produce various other types of graphs.

Development of a Bayesian Joint Logistic Model to Better Study the Association between Haplotypes and Disease

In 2011, there will be an estimated 1,596,670 new cancer cases and 571,950 cancer-related deaths in the US. With the ever-increasing applications of cancer genetics in epidemiology, there is great potential to identify genetic risk factors that would help identify individuals with increased genetic susceptibility to cancer, which could be used to develop interventions or targeted therapies that could hopefully reduce cancer risk and mortality. In this dissertation, I propose to develop a new statistical method to evaluate the role of haplotypes in cancer susceptibility and development. This model will be flexible enough to handle not only haplotypes of any size, but also a variety of covariates. I will then apply this method to three cancer-related data sets (Hodgkin Disease, Glioma, and Lung Cancer). I hypothesize that there is substantial improvement in the estimation of association between haplotypes and disease, with the use of a Bayesian mathematical method to infer haplotypes that uses prior information from known genetics sources. Analysis based on haplotypes using information from publically available genetic sources generally show increased odds ratios and smaller p-values in both the Hodgkin, Glioma, and Lung data sets. For instance, the Bayesian Joint Logistic Model (BJLM) inferred haplotype TC had a substantially higher estimated effect size (OR=12.16, 95% CI = 2.47-90.1 vs. 9.24, 95% CI = 1.81-47.2) and more significant p-value (0.00044 vs. 0.008) for Hodgkin Disease compared to a traditional logistic regression approach. Also, the effect sizes of haplotypes modeled with recessive genetic effects were higher (and had more significant p-values) when analyzed with the BJLM. Full genetic models with haplotype information developed with the BJLM resulted in significantly higher discriminatory power and a significantly higher Net Reclassification Index compared to those developed with haplo.stats for lung cancer. Future analysis for this work could be to incorporate the 1000 Genomes project, which offers a larger selection of SNPs can be incorporated into the information from known genetic sources as well. Other future analysis include testing non-binary outcomes, like the levels of biomarkers that are present in lung cancer (NNK), and extending this analysis to full GWAS studies.

Design and analysis for three level hierarchical structures with count response

In numerous intervention studies and education field trials, random assignment to treatment occurs in clusters rather than at the level of observation. This departure of random assignment of units may be due to logistics, political feasibility, or ecological validity. Data within the same cluster or grouping are often correlated. Application of traditional regression techniques, which assume independence between observations, to clustered data produce consistent parameter estimates. However such estimators are often inefficient as compared to methods which incorporate the clustered nature of the data into the estimation procedure (Neuhaus 1993).1 Multilevel models, also known as random effects or random components models, can be used to account for the clustering of data by estimating higher level, or group, as well as lower level, or individual variation. Designing a study, in which the unit of observation is nested within higher level groupings, requires the determination of sample sizes at each level. This study investigates the design and analysis of various sampling strategies for a 3-level repeated measures design on the parameter estimates when the outcome variable of interest follows a Poisson distribution. ^ Results study suggest that second order PQL estimation produces the least biased estimates in the 3-level multilevel Poisson model followed by first order PQL and then second and first order MQL. The MQL estimates of both fixed and random parameters are generally satisfactory when the level 2 and level 3 variation is less than 0.10. However, as the higher level error variance increases, the MQL estimates become increasingly biased. If convergence of the estimation algorithm is not obtained by PQL procedure and higher level error variance is large, the estimates may be significantly biased. In this case bias correction techniques such as bootstrapping should be considered as an alternative procedure. For larger sample sizes, those structures with 20 or more units sampled at levels with normally distributed random errors produced more stable estimates with less sampling variance than structures with an increased number of level 1 units. For small sample sizes, sampling fewer units at the level with Poisson variation produces less sampling variation, however this criterion is no longer important when sample sizes are large. ^ 1Neuhaus J (1993). “Estimation efficiency and Tests of Covariate Effects with Clustered Binary Data”. Biometrics , 49, 989–996^

A comparison of Markov and generalized linear models of hospital mortality

This paper reports a comparison of three modeling strategies for the analysis of hospital mortality in a sample of general medicine inpatients in a Department of Veterans Affairs medical center. Logistic regression, a Markov chain model, and longitudinal logistic regression were evaluated on predictive performance as measured by the c-index and on accuracy of expected numbers of deaths compared to observed. The logistic regression used patient information collected at admission; the Markov model was comprised of two absorbing states for discharge and death and three transient states reflecting increasing severity of illness as measured by laboratory data collected during the hospital stay; longitudinal regression employed Generalized Estimating Equations (GEE) to model covariance structure for the repeated binary outcome. Results showed that the logistic regression predicted hospital mortality as well as the alternative methods but was limited in scope of application. The Markov chain provides insights into how day to day changes of illness severity lead to discharge or death. The longitudinal logistic regression showed that increasing illness trajectory is associated with hospital mortality. The conclusion is reached that for standard applications in modeling hospital mortality, logistic regression is adequate, but for new challenges facing health services research today, alternative methods are equally predictive, practical, and can provide new insights. ^

Evaluation of the accuracy of multilevel analysis parameter estimates for intra-cluster correlation in correlated binary data

Many studies in biostatistics deal with binary data. Some of these studies involve correlated observations, which can complicate the analysis of the resulting data. Studies of this kind typically arise when a high degree of commonality exists between test subjects. If there exists a natural hierarchy in the data, multilevel analysis is an appropriate tool for the analysis. Two examples are the measurements on identical twins, or the study of symmetrical organs or appendages such as in the case of ophthalmic studies. Although this type of matching appears ideal for the purposes of comparison, analysis of the resulting data while ignoring the effect of intra-cluster correlation has been shown to produce biased results.^ This paper will explore the use of multilevel modeling of simulated binary data with predetermined levels of correlation. Data will be generated using the Beta-Binomial method with varying degrees of correlation between the lower level observations. The data will be analyzed using the multilevel software package MlwiN (Woodhouse, et al, 1995). Comparisons between the specified intra-cluster correlation of these data and the estimated correlations, using multilevel analysis, will be used to examine the accuracy of this technique in analyzing this type of data. ^

Thrombus imaging in acute stroke: correlation of thrombus length on susceptibility-weighted imaging with endovascular reperfusion success

OBJECTIVES Susceptibility-weighted imaging (SWI) enables visualization of thrombotic material in acute ischemic stroke. We aimed to validate the accuracy of thrombus depiction on SWI compared to time-of-flight MRA (TOF-MRA), first-pass gadolinium-enhanced MRA (GE-MRA) and digital subtraction angiography (DSA). Furthermore, we analysed the impact of thrombus length on reperfusion success with endovascular therapy. METHODS Consecutive patients with acute ischemic stroke due to middle cerebral artery (MCA) occlusions undergoing endovascular recanalization were screened. Only patients with a pretreatment SWI were included. Thrombus visibility and location on SWI were compared to those on TOF-MRA, GE-MRA and DSA. The association between thrombus length on SWI and reperfusion success was studied. RESULTS Eighty-four of the 88 patients included (95.5 %) showed an MCA thrombus on SWI. Strong correlations between thrombus location on SWI and that on TOF-MRA (Pearson's correlation coefficient 0.918, P < 0.001), GE-MRA (0.887, P < 0.001) and DSA (0.841, P < 0.001) were observed. Successful reperfusion was not significantly related to thrombus length on SWI (P = 0.153; binary logistic regression). CONCLUSIONS In MCA occlusion thrombus location as seen on SWI correlates well with angiographic findings. In contrast to intravenous thrombolysis, thrombus length appears to have no impact on reperfusion success of endovascular therapy. KEY POINTS • SWI helps in assessing location and length of thrombi in the MCA • SWI, MRA and DSA are equivalent in detecting the MCA occlusion site • SWI is superior in identifying the distal end of the thrombus • Stent retrievers should be deployed over the distal thrombus end • Thrombus length did not affect success of endovascular reperfusion guided by SWI.

Regression of the inflammatory microenvironment of the peritoneal cavity in women with endometriosis by GnRHa treatment.

OBJECTIVE To investigate the effect of gonadotropin-releasing hormone analogues (GnRHa) on the peritoneal fluid microenvironment in women with endometriosis. STUDY DESIGN Peritoneal fluid was collected from 85 women with severe endometriosis (rAFS stage III and IV) during laparoscopic surgery during the proliferative phase. Prior to surgery clinical data were collected. The concentrations of specific markers for endometriosis in the peritoneal fluid were determined using an ELISA and a comparison between peritoneal fluid markers in women using GnRHa and no hormonal treatment was performed using a non-parametric Mann-Whitney U test. RESULTS The study included peritoneal fluid from 39 patients who had been administered GnRHa (Zoladex(®)) in the three months prior to surgery and 46 from women with no hormonal treatment in this period. Concentrations of IL-8, PAPP-A, glycodelin-A and midkine were significantly reduced in the GnRHa treatment group compared to women receiving no hormonal treatment. RANTES, MCP-1, ENA-78, TNF-α, OPG, IP-10 and defensin showed no significant change between the two groups. CONCLUSIONS GnRHa mediate a significant regression in the inflammatory nature of the peritoneal microenvironment in women with endometriosis.

Independent measurement of biogenic silica in sediments by FTIR spectroscopy and PLS regression

We present an independent calibration model for the determination of biogenic silica (BSi) in sediments, developed from analysis of synthetic sediment mixtures and application of Fourier transform infrared spectroscopy (FTIRS) and partial least squares regression (PLSR) modeling. In contrast to current FTIRS applications for quantifying BSi, this new calibration is independent from conventional wet-chemical techniques and their associated measurement uncertainties. This approach also removes the need for developing internal calibrations between the two methods for individual sediments records. For the independent calibration, we produced six series of different synthetic sediment mixtures using two purified diatom extracts, with one extract mixed with quartz sand, calcite, 60/40 quartz/calcite and two different natural sediments, and a second extract mixed with one of the natural sediments. A total of 306 samples—51 samples per series—yielded BSi contents ranging from 0 to 100 %. The resulting PLSR calibration model between the FTIR spectral information and the defined BSi concentration of the synthetic sediment mixtures exhibits a strong cross-validated correlation ( R2cv = 0.97) and a low root-mean square error of cross-validation (RMSECV = 4.7 %). Application of the independent calibration to natural lacustrine and marine sediments yields robust BSi reconstructions. At present, the synthetic mixtures do not include the variation in organic matter that occurs in natural samples, which may explain the somewhat lower prediction accuracy of the calibration model for organic-rich samples.

Sparse computation for large-scale binary classification

Well-known data mining algorithms rely on inputs in the form of pairwise similarities between objects. For large datasets it is computationally impossible to perform all pairwise comparisons. We therefore propose a novel approach that uses approximate Principal Component Analysis to efficiently identify groups of similar objects. The effectiveness of the approach is demonstrated in the context of binary classification using the supervised normalized cut as a classifier. For large datasets from the UCI repository, the approach significantly improves run times with minimal loss in accuracy.

Factors influencing fixed retention practices in German-speaking Switzerland : A survey.

AIM Several surveys evaluate different retention approaches among orthodontists, but none exist for general dentists. The primary aim of this survey was to record the preferred fixed retainer designs and retention protocols amongst general dentists and orthodontists in Switzerland. A secondary aim was to investigate whether retention patterns were associated with parameters such as gender, university of graduation, time in practice, and specialist status. METHODS An anonymized questionnaire was distributed to general dentists (n = 401) and orthodontists (n = 398) practicing in the German-speaking part of Switzerland. A total of 768 questionnaires could be delivered, 562 (73.2 %) were returned and evaluated. Descriptive statistics were performed and responses to questions of interest were converted to binary outcomes and analyzed using multiple logistic regression. Any associations between the answers and gender, university of graduation (Swiss or foreign), years in practice, and specialist status (orthodontist/general dentist) were assessed. RESULTS Almost all responding orthodontists (98.0 %) and nearly a third of general dentists (29.6 %) reported bonding fixed retainers regularly. The answers were not associated with the practitioner's gender. The university of graduation and number of years in practice had a moderate impact on the responses. The answers were mostly influenced by specialist status. CONCLUSION Graduation school, years in practice, and specialist status influence retention protocol, and evidence-based guidelines for fixed retention should be issued to minimize these effects. Based on the observation that bonding and maintenance of retainers are also performed by general dentists, these guidelines should be taught in dental school and not during post-graduate training.