Estimation of odds ratio from group testing data with misclassified exposure.

For low prevalence disease, we consider estimation of the odds ratio for two specified groups of individuals using group testing data. Broadly the two groups may be classified as "the exposed" and "the unexposed." Often in observational studies, the exposure status is not correctly recorded. In addition, diagnostic tests are rarely completely accurate. The proposed model accounts for imperfect sensitivity and specificity of diagnostic tests along with the misclassification in the exposure status. For model identifiability, we make use of internal validation data, where a subsample of reasonably small size is selected from the original sample by simple random sampling without replacement. Pseudo-maximum likelihood method is employed for the estimation of the model parameters. The performance of group testing methodology is compared with individual testing for different parametric configurations. A limited data study related to COVID-19 prevalence is performed to illustrate the methodology.

Evaluation of Alzheimer's disease progression based on clinical dementia rating scale with missing responses and covariates.

In clinical trials, patient's disease severity is usually assessed on a Likert-type scale. Patients, however, may miss one or more follow-up visits (non-monotone missing). The statistical analysis of non-Gaussian longitudinal data with non-monotone missingness is difficult to handle, particularly when both response and time-dependent covariates are subject to such missingness. Even when the number of patients with intermittent missing data is small, ignoring those patients from analysis seems to be unsatisfactory. The focus of the current investigation is to study the progression of Alzheimer's disease by incorporating a non-ignorable missing data mechanism for both response and covariates in a longitudinal setup. Combining the cumulative logit longitudinal model for Alzheimer's disease progression with the bivariate binary model for the missing pattern, we develop a joint likelihood. The parameters are then estimated using the Monte Carlo Newton Raphson Expectation Maximization (MCNREM) method. This approach is quite easy to handle and the convergence of the estimates is attained in a reasonable amount of time. The study reveals that apolipo-protein plays a significant role in assessing a patient's disease severity. A detailed simulation has also been carried out for justifying the performance of our approach.

Clustered data analysis under miscategorized ordinal outcomes and missing covariates.

The primary objective in this article is to look into the analysis of clustered ordinal model where complete information on one or more covariates cease to occur. In addition, we also focus on the analysis of miscategorized data that occur in many situations as outcomes are often classified into a category that does not truly reflect its actual state. A general model structure is assumed to accommodate the information that is obtained via surrogate variables. The theoretical motivation actually developed while encountering an orthodontic data to investigate the effects of age, sex and food habit on the extent of plaque deposit. The model we propose is quite flexible and is capable of tackling those additional noises like miscategorization and missingness, which occur in the data most frequently. A new two-step approach has been proposed to estimate the parameters of model framed. A rigorous simulation study has also been carried out to justify the validity of the model taken up for analysis. Copyright © 2015 John Wiley & Sons, Ltd.

On analyzing ordinal data when responses and covariates are both missing at random.

In many occasions, particularly in biomedical studies, data are unavailable for some responses and covariates. This leads to biased inference in the analysis when a substantial proportion of responses or a covariate or both are missing. Except a few situations, methods for missing data have earlier been considered either for missing response or for missing covariates, but comparatively little attention has been directed to account for both missing responses and missing covariates, which is partly attributable to complexity in modeling and computation. This seems to be important as the precise impact of substantial missing data depends on the association between two missing data processes as well. The real difficulty arises when the responses are ordinal by nature. We develop a joint model to take into account simultaneously the association between the ordinal response variable and covariates and also that between the missing data indicators. Such a complex model has been analyzed here by using the Markov chain Monte Carlo approach and also by the Monte Carlo relative likelihood approach. Their performance on estimating the model parameters in finite samples have been looked into. We illustrate the application of these two methods using data from an orthodontic study. Analysis of such data provides some interesting information on human habit.