Approximate likelihood and pseudo-likelihood inference in meta-analysis of diagnostic accuracy studies accounting for disease prevalence and study design.

Bivariate random-effects models represent a recommended approach for meta-analysis of diagnostic test accuracy, jointly modeling study-specific sensitivity and specificity. As the severity of the disease status can vary across studies, a proper analysis should account for the dependence of the accuracy measures on the disease prevalence. To this aim, trivariate generalized linear mixed-effects models have been proposed in the literature, although computational difficulties strongly limit their applicability. In addition, the attention has been mainly paid to cohort studies, where the study-specific disease prevalence can be estimated from, while information from case-control studies is often neglected. To overcome such limits, this article introduces a trivariate approximate normal model, which accounts for disease prevalence along with accuracy measures in cohort studies and sensitivity and specificity in case-control studies. The model represents an extension of the bivariate normal mixed-effects model originally developed for meta-analysis not accounting for disease prevalence, under an approximate normal within-study distribution for the logit of estimated sensitivity and specificity. The components of the approximate within-study covariance matrix are derived and the likelihood function is obtained in closed-form. The approximate likelihood approach is compared to that based on the exact within-study distribution and to its modifications following a pseudo-likelihood strategy aimed at reducing the computational effort. The comparison is based on simulation studies in a variety of scenarios, and illustrated in a meta-analysis about the accuracy of a test to diagnose fungal infection and a meta-analysis of a noninvasive test to detect colorectal cancer.

Measurement errors in control risk regression: A comparison of correction techniques.

Control risk regression is a diffuse approach for meta-analysis about the effectiveness of a treatment, relating the measure of risk with which the outcome occurs in the treated group to that in the control group. The severity of illness is a source of between-study heterogeneity that can be difficult to measure. It can be approximated by the rate of events in the control group. Since the estimate is a surrogate for the underlying risk, it is prone to measurement error. Correction methods are necessary to provide reliable inference. This article illustrates the extent of measurement error effects under different scenarios, including departures from the classical normality assumption for the control risk distribution. The performance of different measurement error corrections is examined. Attention will be paid to likelihood-based structural methods assuming a distribution for the control risk measure and to functional methods avoiding the assumption, namely, a simulation-based method and two score function methods. Advantages and limits of the approaches are evaluated through simulation. In case of large heterogeneity, structural approaches are preferable to score methods, while score methods perform better for small heterogeneity and small sample size. The simulation-based approach has a satisfactory behavior whichever the examined scenario, with no convergence issues. The methods are applied to a meta-analysis about the association between diabetes and risk of Parkinson disease. The study intends to make researchers aware of the measurement error problem occurring in control risk regression and lead them to the use of appropriate correction techniques to prevent fallacious conclusions.

Whole-body low-dose CT recognizes two distinct patterns of lytic lesions in multiple myeloma patients with different disease metabolism at PET/MRI.

We evaluated differences in density and 18F-FDG PET/MRI features of lytic bone lesions (LBLs) identified by whole-body low-dose CT (WB-LDCT) in patients affected by newly diagnosed multiple myeloma (MM). In 18 MM patients, 135 unequivocal LBLs identified by WB-LDCT were characterized for inner density (negative or positive Hounsfield unit (HU)), where negative density (HU < 0) characterizes normal yellow marrow whereas positive HU correlates with tissue-like infiltrative pattern. The same LBLs were analyzed by 18F-FDG PET/DWI-MRI, registering DWI signal with ADC and SUV max values. According to HU, 35 lesions had a negative density (- 56.94 ± 31.87 HU) while 100 lesions presented positive density (44.87 ± 23.89 HU). In seven patients, only positive HU LBLs were demonstrated whereas in eight patients, both positive and negative HU LBLs were detected. Intriguingly, in three patients (16%), only negative HU LBLs were shown. At 18F-FDG PET/DWI-MRI analysis, negative HU LBLs presented low ADC values (360.69 ± 154.38 × 10-6 mm2/s) and low SUV max values (1.69 ± 0.56), consistent with fatty marrow, whereas positive HU LBLs showed an infiltrative pattern, characterized by higher ADC (mean 868.46 ± 207.67 × 10-6 mm2/s) and SUV max (mean 5.04 ± 1.94) values. Surprisingly, histology of negative HU LBLs documented infiltration by neoplastic plasma cells scattered among adipocytes. In conclusion, two different patterns of LBLs were detected by WB-LDCT in MM patients. Both types of lesions were indicative for active disease, although only positive HU LBL were captured by 18F-FDG PET/DWI-MRI imaging, indicating that WB-LDCT adds specific information.

Improving likelihood-based inference in control rate regression.

Control rate regression is a diffuse approach to account for heterogeneity among studies in meta-analysis by including information about the outcome risk of patients in the control condition. Correcting for the presence of measurement error affecting risk information in the treated and in the control group has been recognized as a necessary step to derive reliable inferential conclusions. Within this framework, the paper considers the problem of small sample size as an additional source of misleading inference about the slope of the control rate regression. Likelihood procedures relying on first-order approximations are shown to be substantially inaccurate, especially when dealing with increasing heterogeneity and correlated measurement errors. We suggest to address the problem by relying on higher-order asymptotics. In particular, we derive Skovgaard's statistic as an instrument to improve the accuracy of the approximation of the signed profile log-likelihood ratio statistic to the standard normal distribution. The proposal is shown to provide much more accurate results than standard likelihood solutions, with no appreciable computational effort. The advantages of Skovgaard's statistic in control rate regression are shown in a series of simulation experiments and illustrated in a real data example. R code for applying first- and second-order statistic for inference on the slope on the control rate regression is provided.

A double SIMEX approach for bivariate random-effects meta-analysis of diagnostic accuracy studies.

BACKGROUND: Bivariate random-effects models represent a widely accepted and recommended approach for meta-analysis of test accuracy studies. Standard likelihood methods routinely used for inference are prone to several drawbacks. Small sample size can give rise to unreliable inferential conclusions and convergence issues make the approach unappealing. This paper suggests a different methodology to address such difficulties. METHODS: A SIMEX methodology is proposed. The method is a simulation-based technique originally developed as a correction strategy within the measurement error literature. It suits the meta-analysis framework as the diagnostic accuracy measures provided by each study are prone to measurement error. SIMEX can be straightforwardly adapted to cover different measurement error structures and to deal with covariates. The effortless implementation with standard software is an interesting feature of the method. RESULTS: Extensive simulation studies highlight the improvement provided by SIMEX over likelihood approach in terms of empirical coverage probabilities of confidence intervals under different scenarios, independently of the sample size and the values of the correlation between sensitivity and specificity. A remarkable amelioration is obtained in case of deviations from the normality assumption for the random-effects distribution. From a computational point of view, the application of SIMEX is shown to be neither involved nor subject to the convergence issues affecting likelihood-based alternatives. Application of the method to a diagnostic review of the performance of transesophageal echocardiography for assessing ascending aorta atherosclerosis enables overcoming limitations of the likelihood procedure. CONCLUSIONS: The SIMEX methodology represents an interesting alternative to likelihood-based procedures for inference in meta-analysis of diagnostic accuracy studies. The approach can provide more accurate inferential conclusions, while avoiding convergence failure and numerical instabilities. The application of the method in the R programming language is possible through the code which is made available and illustrated using the real data example.

A SIMEX approach for meta-analysis of diagnostic accuracy studies with attention to ROC curves.

Bivariate random-effects models represent an established approach for meta-analysis of accuracy measures of a diagnostic test, which are typically given by sensitivity and specificity. A recent formulation of the classical model describes the test accuracy in terms of study-specific Receiver Operating Characteristics curves. In this way, the resulting summary curve can be thought of as an average of the study-specific Receiver Operating Characteristics curves. Within this framework, the paper shows that the standard likelihood approach for inference is prone to several issues. Small sample size can give rise to unreliable conclusions and convergence problems deeply affect the analysis. The proposed alternative is a simulation-extrapolation method, called SIMEX, developed within the measurement error literature. It suits the meta-analysis framework, as the accuracy measures provided by the studies are estimates rather than true values, and thus are prone to error. The methods are compared in a series of simulation studies, covering different scenarios of interest, including deviations from normality assumptions. SIMEX reveals a satisfactory strategy, providing more accurate inferential results if compared to the likelihood approach, while avoiding convergence failure. The approaches are applied to a meta-analysis of the accuracy of the ultrasound exam for diagnosing abdominal tuberculosis in HIV-positive subjects.

ß-Cell Glucose Sensitivity to Assess Changes in ß-Cell Function in Recent-Onset Stage 3 Type 1 Diabetes.

Following a diagnosis of type 1 diabetes (T1D), persisting C-peptide secretion leads to improved glycemic control and outcomes. Residual ß-cell function is often assessed with serial mixed-meal tolerance tests, but these tests do not correlate well with clinical outcomes. Herein, we instead use ß-cell glucose sensitivity (ßGS) to assess changes in ß-cell function, incorporating insulin secretion for a given serum glucose into the assessment of ß-cell function. We evaluated changes in ßGS in individuals enrolled in the placebo arm of 10 T1D trials performed at diabetes onset. We found that ßGS showed a more rapid decline in children, as compared with adolescents and adults. Individuals in the top quartile of ßGS baseline distribution had a slower rate in loss of glycemic control time over time. Notably, half of this group were children and adolescents. Finally, to identify predictors of glycemic control throughout follow-up, we ran multivariate Cox models and found that incorporating ßGS significantly improved the overall model. Taken together, these data suggest that ßGS may be of great utility in predicting those more likely to have a more robust clinical remission and may be of use in design of new-onset diabetes clinical trials and in evaluating response to therapies. ARTICLE HIGHLIGHTS: We undertook this study to better predict ß-cell loss following type 1 diabetes diagnosis. We set out to answer whether ß-cell glucose sensitivity (ßGS) improves means to evaluate ß-cell function postdiagnosis and whether ßGS correlates with clinical outcomes. We found that ßGS declines faster in children, subjects in the top baseline quartile of ßGS exhibit slower ß-cell decline (half are children), and incorporating ßGS into multivariate Cox models for glycemic improves the model. The implications of our findings are that ßGS predicts those likely to have robust clinical remissions and may help with clinical trials design.

Higher-order likelihood inference in meta-analysis and meta-regression.

This paper investigates the use of likelihood methods for meta-analysis, within the random-effects models framework. We show that likelihood inference relying on first-order approximations, while improving common meta-analysis techniques, can be prone to misleading results. This drawback is very evident in the case of small sample sizes, which are typical in meta-analysis. We alleviate the problem by exploiting the theory of higher-order asymptotics. In particular, we focus on a second-order adjustment to the log-likelihood ratio statistic. Simulation studies in meta-analysis and meta-regression show that higher-order likelihood inference provides much more accurate results than its first-order counterpart, while being of a computationally feasible form. We illustrate the application of the proposed approach on a real example.

Loss of the Incretin Effect in Type 2 Diabetes: A Systematic Review and Meta-analysis.

CONTEXT: Loss of the incretin effect (IE) in type 2 diabetes (T2D) contributes to hyperglycemia and the mechanisms underlying this impairment are unclear. OBJECTIVE: To quantify the IE impairment in T2D and to investigate the factors associated with it using a meta-analytic approach. METHODS: PubMed, Scopus, and Web-of-Science were searched. Studies measuring IE by the gold-standard protocol employing an oral glucose tolerance test (OGTT) and an intravenous glucose infusion at matched glucose levels were selected. We extracted IE, sex, age, body mass index (BMI), and hemoglobin A1c, fasting values, and area under curve (AUC) of glucose, insulin, C-peptide, glucose-dependent insulinotropic peptide (GIP) and glucagon-like peptide 1 (GLP-1). In subjects with T2D, we also recorded T2D duration, age at diagnosis, and the percentage of subjects taking antidiabetic medications. RESULTS: The IE weighted mean difference between subjects with T2D and those with normal glucose tolerance (NGT) was -27.3% (CI -36.5% to -18.1%; P < .001; I2 = 86.6%) and was affected by age (P < .005). By meta-regression of combined NGT and T2D data, IE was inversely associated with glucose tolerance (lower IE in T2D), BMI, and fasting GIP (P < .05). By meta-regression of T2D studies only, IE was associated with the OGTT glucose dose (P < .0001). IE from insulin was larger than IE from C-peptide (weighted mean difference 11.2%, CI 9.2-13.2%; P < .0001; I2 = 28.1%); the IE difference was inversely associated with glucose tolerance and fasting glucose. CONCLUSION: The IE impairment in T2D vs NGT is consistent though considerably variable, age being a possible factor affecting the IE difference. Glucose tolerance, BMI, and fasting GIP are independently associated with IE; in subjects with T2D only, the OGTT dose is a significant covariate.

A pseudo-likelihood approach for multivariate meta-analysis of test accuracy studies with multiple thresholds.

Multivariate meta-analysis of test accuracy studies when tests are evaluated in terms of sensitivity and specificity at more than one threshold represents an effective way to synthesize results by fully exploiting the data, if compared to univariate meta-analyses performed at each threshold independently. The approximation of logit transformations of sensitivities and specificities at different thresholds through a normal multivariate random-effects model is a recent proposal that straightforwardly extends the bivariate models well recommended for the one threshold case. However, drawbacks of the approach, such as poor estimation of the within-study correlations between sensitivities and between specificities, and severe computational issues can make it unappealing. We propose an alternative method for inference on common diagnostic measures using a pseudo-likelihood constructed under a working independence assumption between sensitivities and between specificities at different thresholds in the same study. The method does not require within-study correlations, overcomes the convergence issues and can be effortlessly implemented. Simulation studies highlight a satisfactory performance of the method, remarkably improving the results from the multivariate normal counterpart under different scenarios. The pseudo-likelihood approach is illustrated in the evaluation of a test used for diagnosis of preeclampsia as a cause of maternal and perinatal morbidity and mortality.

Detection of silica particles in lung tissue by environmental scanning electron microscopy.

For pathologists, pneumologists, and occupational and environmental physicians it is relevant to know silica levels in lung tissue to better define limits of exposure. Environmental Scanning Electron Microscopy (ESEM) has been employed to detect silica particles and to compare silica levels in subjects with and without Lung Cancer (LC). We investigated 25 paraffin-embedded tissue samples of patients with LC adenocarcinoma, and 20 fresh samples of subjects without LC deceased for extra-pulmonary diseases. Silica levels were quantified considering the Number of Spots of silica particles (NS), and the Number of Positive Zones (NPZ) in which there was at least one spot. Levels of NS and NPZ were assessed with Poisson-type regression models, and in two samples of silica-exposed workers with LC the performance of models were evaluated. LC patients displayed higher silica levels, as compared to controls; smoking, age and gender had no significant effects on this relationship. Values of NS and NPZ for the exposed workers were in agreement with model estimates. The fitted model between NS and NPZ might be useful in evaluating new observations and in the development of threshold limit values of silica in biological tissues. ESEM is a rapid, simple and valid tool for the determination of silica levels in lung tissues.

Robust techniques for measurement error correction: a review.

Measurement error affecting the independent variables in regression models is a common problem in many scientific areas. It is well known that the implications of ignoring measurement errors in inferential procedures may be substantial, often turning out in unreliable results. Many different measurement error correction techniques have been suggested in literature since the 80's. Most of them require many assumptions on the involved variables to be satisfied. However, it may be usually very hard to check whether these assumptions are satisfied, mainly because of the lack of information about the unobservable and mismeasured phenomenon. Thus, alternatives based on weaker assumptions on the variables may be preferable, in that they offer a gain in robustness of results. In this paper, we provide a review of robust techniques to correct for measurement errors affecting the covariates. Attention is paid to methods which share properties of robustness against misspecifications of relationships between variables. Techniques are grouped according to the kind of the underlying modeling assumptions and the inferential methods. Details about the techniques are given and their applicability is discussed. The basic framework is the epidemiological setting, where literature about the measurement error phenomenon is very substantial.

99Tc Nanocolloid sentinel node procedure in thyroid carcinoma.

PURPOSE: The purposes of this study were to investigate the efficacy of radiocolloid lymphoscintigraphy and of handheld gamma probe procedure for sentinel lymph node biopsy (SLNB) in papillary thyroid carcinoma (PTC) and to evaluate its results in clinical staging. MATERIALS AND METHODS: Sixty-five PTC consecutive patients entered the study. Patients underwent radiocolloid lymphoscintigraphy before surgery. Intraoperative sentinel lymph node (SLN) localization was performed using a handheld gamma probe. They were followed up at 2, 6 months, and yearly. RESULTS: SLN metastases were diagnosed in 52%. Fifty-one patients underwent ablative 131-I therapy. The mean Tireoglobulin level in N0 vs N1 cases was 2.2 ng/ml vs 4.73 (p = 0.03) and 0.68 vs 2.1 ng/ml (p = 0.005) before and after 131-I therapy, respectively. CONCLUSIONS: In patients classified N0 by SLNB, ablative 131-I therapy could be avoided.

Is the laparoscopic adrenalectomy for pheochromocytoma the best treatment?

BACKGROUND: Laparoscopic adrenalectomy has become the gold standard for removing adrenal masses, but several authors still debate the role of laparoscopic adrenalectomy in pheochromocytoma. The purpose of this study was to evaluate the short- and long-term outcomes of laparoscopic versus open adrenalectomy for pheochromocytomas and to compare the feasibility and safety of laparoscopic adrenalectomy for neoplasms that are smaller than 6 cm versus those that are larger than 6 cm. METHODS: From January 1990 to December 2005, the same team in our department carried out 221 adrenalectomies in 211 patients. A total of 64 of these patients underwent 71 adrenalectomies for pheochromocytoma, 24 patients (37%) had open adrenalectomy, and 40 patients (63%) had laparoscopic adrenalectomy. Sex, age, side and size of lesion, operating time, duration of hospital stay, need for intensive care, intraoperative blood pressure variations, blood loss, postoperative analgesia, return to oral nutrition, and complications were compared among groups. RESULTS: An advantage of laparoscopic adrenalectomy over open adrenalectomy was observed in mean operating time, hospital stay, need for intensive care, intraoperative hypertension, intraoperative blood loss, postoperative analgesia, and return to oral nutrition (P 6 cm) in laparoscopic adrenalectomy showed that none of the variables differed significantly, except for intraoperative blood loss, which was greater for the larger neoplasms (P = .007). CONCLUSIONS: Laparoscopic adrenalectomy, when performed by experienced laparoscopic surgeons, is preferable to open adrenalectomy for the majority of pheochromocytomas, and as long as there is no evidence of invasion of surrounding structures, tumor size does not appear to have a profound effect on surgical outcome.

Random-effects meta-analysis: the number of studies matters.

This paper investigates the impact of the number of studies on meta-analysis and meta-regression within the random-effects model framework. It is frequently neglected that inference in random-effects models requires a substantial number of studies included in meta-analysis to guarantee reliable conclusions. Several authors warn about the risk of inaccurate results of the traditional DerSimonian and Laird approach especially in the common case of meta-analysis involving a limited number of studies. This paper presents a selection of likelihood and non-likelihood methods for inference in meta-analysis proposed to overcome the limitations of the DerSimonian and Laird procedure, with a focus on the effect of the number of studies. The applicability and the performance of the methods are investigated in terms of Type I error rates and empirical power to detect effects, according to scenarios of practical interest. Simulation studies and applications to real meta-analyses highlight that it is not possible to identify an approach uniformly superior to alternatives. The overall recommendation is to avoid the DerSimonian and Laird method when the number of meta-analysis studies is modest and prefer a more comprehensive procedure that compares alternative inferential approaches. R code for meta-analysis according to all of the inferential methods examined in the paper is provided.

Progression to type 2 diabetes in women with former gestational diabetes: time trajectories of metabolic parameters.

Aim of this study was analyzing the time trajectories of the metabolic parameters in European women with former gestational diabetes (fGDM), and determining predictors of type 2 diabetes onset. A group of seventy-six fGDM women were studied at the outpatient department of the University Clinic of Vienna. They were evaluated yearly with a 3 h-oral glucose tolerance test (OGTT) up to 7-years from delivery. At baseline, women also underwent an intravenous glucose tolerance test (IVGTT). Insulin sensitivity and beta-cell function were assessed by both OGTT and IVGTT. Women were divided into progressors (PROG) to diabetes (n = 19) and non-progressors (n = 57). Time trajectories of glycemia and other parameters were analyzed after synchronization to time of diabetes onset or last OGTT. Then, Cox proportional hazard regression analysis was performed to assess the predictive power of studied variables for diabetes onset. We found that, in PROG, time trajectories of glycemia were flat until diabetes onset, when they showed a marked increase (P<0.0001). Insulin sensitivity showed similar marked decrease (P<0.0001) at diabetes onset, together with a tendency to continuous slow decline in the previous years. At contrast, beta-cell function showed only continuous slow decline. Major predictors of diabetes onset were glycemic levels, BMI, insulin resistance, and condition of impaired glucose tolerance. In conclusion, in fGDM, marked deterioration of insulin sensitivity is associated with diabetes onset. Prevention strategies aimed at opposing to the insulin sensitivity derangement may be particularly beneficial.

Complications in thyroid surgery for carcinoma: one institution's surgical experience.

BACKGROUND: The purpose of this study was to evaluate the factors influencing morbidity after total thyroidectomy for carcinoma, such as the histotype, the extension of surgery, the primary surgery versus reoperation, and the surgeon's experience. METHODS: We performed a retrospective analysis on inferior laryngeal nerve (ILN) injury and permanent hypoparathyroidism (HPT) rates in 504 consecutive patients with thyroid carcinoma who were operated on by the same surgeon from 1999 to 2006. The following parameters were assessed at univariate analysis: histotype, total thyroidectomy with or without central node dissection (level VI), primary surgery versus reoperation, and early (group 1: 1999-2002, 143 patients) and late (group 2: 2003-2006, 361 patients) experience. RESULTS: The global incidence rates of ILN palsy and permanent HPT were 2.18% and 6.3%, respectively. The incidence of ILN damage after total thyroidectomy plus node dissection versus total thyroidectomy without node excision was 2.87% vs. 0.36% (p = 0.029). The incidence of permanent HPT in group 1 was 13.2% vs. 3.6% in group 2 (p = 0.0001). Moreover, the incidence rate of ILN palsy resulted higher in group 1 (2.8%) and in reoperation (3.4%), while the permanent HPT resulted higher in thyroidectomy with node dissection (6.8%) and reoperation (6.9%), although the difference was not significant. CONCLUSIONS: The complications after total thyroidectomy were progressively reduced as a result of a more accurate technique. Nevertheless, our study showed that the incidence of complications is mostly related to the dissection of central lymph node (level VI) and the surgeon's experience.

Shared uncertainty in measurement error problems, with application to Nevada Test Site fallout data.

In radiation epidemiology, it is often necessary to use mathematical models in the absence of direct measurements of individual doses. When complex models are used as surrogates for direct measurements to estimate individual doses that occurred almost 50 years ago, dose estimates will be associated with considerable error, this error being a mixture of (a) classical measurement error due to individual data such as diet histories and (b) Berkson measurement error associated with various aspects of the dosimetry system. In the Nevada Test Site(NTS) Thyroid Disease Study, the Berkson measurement errors are correlated within strata. This article concerns the development of statistical methods for inference about risk of radiation dose on thyroid disease, methods that account for the complex error structure inherence in the problem. Bayesian methods using Markov chain Monte Carlo and Monte-Carlo expectation-maximization methods are described, with both sharing a key Metropolis-Hastings step. Regression calibration is also considered, but we show that regression calibration does not use the correlation structure of the Berkson errors. Our methods are applied to the NTS Study, where we find a strong dose-response relationship between dose and thyroiditis. We conclude that full consideration of mixtures of Berkson and classical uncertainties in reconstructed individual doses are important for quantifying the dose response and its credibility/confidence interval. Using regression calibration and expectation values for individual doses can lead to a substantial underestimation of the excess relative risk per gray and its 95% confidence intervals.