Monitoring for Adverse Events Post Marketing Approval of Drugs

This brief communication provides information to those developing monitoring plans for serious adverse events (SAE’s) following regulatory approval of a new drug. In addition, we (1) illustrate how many patients would need to be treated in order to have high confidence of seeing at least 1 pre-specified SAE, (2) show that absence of proof of a SAE is not proof of absence of that SAE, and (3) identify statistical methodology that could be used for formal statistical monitoring of SAE’s

Size and Power of Tests of Hypotheses on Survival Parameters from the Lindley Distribution with Covariates

The Lindley model is considered as an alternative model facilitating analyses of time-to-event data with covariates. Covariate information is incorporated using the Cox’s proportional hazard model with the Lindley model at the timedependent component. Simulation studies are performed to assess the size and power of tests of hypotheses on parameters arising from maximum likelihood estimators of parameters in the Lindley model. Results are contrasted with that arising from Cox’s partial maximum likelihood estimator. The Linley model is used to analyze a publicly available data set and contrasted with other models

Inequalities and Approximations of Weighted Distributions by Lindley Reliability Measures, and the Lindley-Cox Model with Applications

In this note, stochastic comparisons and results for weighted and Lindley models are presented. Approximation of weighted distributions via Lindley distribution in the class of increasing failure rate (IFR) and decreasing failure rate (DFR) weighted distributions with monotone weight functions are obtained including approximations via the length-biased Lindley distribution. Some useful bounds and moment-type inequality for weighted life distributions and applications are presented. Incorporation of covariates into Lindley model is considered and an application to illustrate the usefulness and applicability of the proposed Lindley-Cox model is given

Size and Power of Tests of Hypotheses on Parameters When Modeling Time-to-Event Data with the Lindley Distribution

Covariates on subjects are collected in addition to times-to-event in time-to-event studies. Such data are often analyzed by choosing a model that allows the covariate information to be utilized in the analyses. The analysis proceeds by estimating parameters in the model and testing hypotheses about the parameters based on their estimates; validity of inferences from tests of hypotheses about the parameters depends on size and power of the tests. The Lindley model is considered, in this dissertation, as an alternative model facilitating the analysis of time-to-event data with or without covariates for complete or incomplete data. Covariate information is incorporated using the form of Cox\u27s proportional hazard\u27s model with the Lindley model as the time dependent component (called Lindley-Cox model). Results suggest that size of tests on parameters arising from their maximum likelihood estimates (MLEs) in the Lindley-Cox model is -level and power of tests on parameters arising from their MLEs in this model compares to that from MLEs in Cox\u27s

A Comparison of Two Methods for Generating Data That Follow The Lindley Distribution

The composition approach is a convenient method for generating data that follows the Lindley distribution, but it might be unsuitable in some settings such as when incorporating covariate information

A Comparison of Size and Power of Tests of Hypotheses on Parameters Based on Two Generalized Lindley Distributions

This study compares two generalized Lindley distributions and assesses consistency between theoretical and analytical results. Data (complete and censored) assumed to follow the Lindley distribution are generated and analyzed using two generalized Lindley distributions, and maximum likelihood estimates of parameters from the generalized distributions are obtained. Size and power of tests of hypotheses on the parameters are assessed drawing on asymptotic properties of the maximum likelihood estimates. Results suggest that whereas size of some of the tests of hypotheses based on the considered generalized distributions are essentially α-level, some are possibly not; power of tests of hypotheses on the Lindley distribution parameter from the two distributions differs

Discrete Time-to-Event and Score-Based Methods with Application to Composite Endpoint for Assessing Evidence of Disease Activity-Free

This presentation was given at the DO-Touch.NET Annual Meeting and Educational Seminar

Closest Similar Subset Imputation for Missing Data Analysis

Classifying patients based on stated reasons for missing outcome from different intercurrent events induces patients’ subsets in data from clinical trials. Often, data imputation disregards these patients’ subsets. We discuss a non-parametric data imputation method that reflects reasons stated for missing data and hence patients’ subsets. This subset imputation method is based on a similarity measure between baseline covariates of patients’ subset with missing data and a random closest subset without missing data. An illustration using imputation of gadolinium enhancing lesions in multiple sclerosis is provided

A SASR Algorithm for Imputing Discrete Missing Outcomes Based on Minimum Distance

Missing outcome data are encountered in many clinical trials and public health studies and present challenges in imputation. We present a simple and easy to use SAS-based imputation method for missing discrete outcome data. The method is based on minimum distance between baseline covariates of those with missing data and those without missing data. The imputation algorithm, a method that may be viewed as a variant of the hot dec imputation method, imputes missing values that are close to the observed values, implying that had there been data on those missing, it would have been similar to those non-missing. An illustrative example is presented

Adaptive Design and the Estimand Framework

Adaptive designs allow prospectively planned modifications of a study without undermining the integrity and validity of the study. The draft ICH E9 addendum recommends the estimand framework in design, conduct, analysis, and interpretation of clinical trials for assessment of effectiveness of therapies. Herein, we discuss the possible impact and scientific implications when incorporating the estimand framework to adaptively designed clinical trials. It is hoped that this will elucidate how this framework provides a language for discussing relevant questions, related to the attributes of the estimand, that may arise from study adaptations