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

Measurement Error Correction in Exploiting Gene-Environment Independence in Family-Based Case-Control Studies.

Family-based case-control designs are commonly used in epidemiological studies for evaluating the role of genetic susceptibility and environmental exposure to risk factors in the etiology of rare diseases. Within this framework, it is often reasonable to assume genetic susceptibility and environmental exposure being conditionally independent of each other within families in the source population. We focus on this setting to consider the common situation of measurement error aecting the assessment of the environmental exposure. We propose to correct for measurement error through a likelihood-based method, by exploiting the conditional likelihood of Chatterjee, Kalaylioglu and Carroll (2005) to relate the probability of disease to the genetic and the mismeasured environmental risk factors. Simulation studies show that this approach provides less biased and more ecient results than that based on traditional logistic regression. The likelihood approach for measurement error correction is also compared to regression calibration, the last resulting in severely biased estimators of the parameters of interest

Robust Techniques for Measurement Error Correction in Case-Control Studies: 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 underlying modeling assumptions and 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. The focus will be mainly on case-control studies

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

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\u2009<\u20090) 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 (-\u200956.94\u2009\ub1\u200931.87 HU) while 100 lesions presented positive density (44.87\u2009\ub1\u200923.89 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\u2009\ub1\u2009154.38\u2009 7\u200910-6 mm2/s) and low SUV max values (1.69\u2009\ub1\u20090.56), consistent with fatty marrow, whereas positive HU LBLs showed an infiltrative pattern, characterized by higher ADC (mean 868.46\u2009\ub1\u2009207.67\u2009 7\u200910-6 mm2/s) and SUV max (mean 5.04\u2009\ub1\u20091.94) 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

Robust Techniques for Measurement Error Correction in Case-Control Studies: 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 underlying modeling assumptions and 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. The focus will be mainly on case-control studies

A Flexible Approach to Measurement Error Correction in Case-Control Studies

We investigate the use of prospective likelihood methods to analyze retrospective case-control data where some of the covariates are measured with error. We show that prospective methods can be applied and the case-control sampling scheme can be ignored if one adequately models the distribution of the error-prone covariates in the case-control sampling scheme. Indeed, subject to this, the prospective likelihood methods result in consistent estimates and information standard errors are asymptotically correct. However, the distribution of such covariates is not the same in the population and under case-control sampling, dictating the need to model the distribution flexibly. In this paper, we illustrate the general principle by modeling the distribution of the error-prone covariates using the skewnormal distribution. The performance of the method is evaluated through simulation studies, which show satisfactory results in terms of bias and coverage. Finally, the method is applied to the analysis of two data sets which refer, respectively, to a cholesterol study and a study on breast cancer