Efficient Multiple Imputation for Sensitivity Analysis of Recurrent Events Data with Informative Censoring.

Missing data are commonly encountered in clinical trials due to dropout or nonadherence to study procedures. In trials in which recurrent events are of interest, the observed count can be an undercount of the events if a patient drops out before the end of the study. In many applications, the data are not necessarily missing at random and it is often not possible to test the missing at random assumption.

Controlling false discovery proportion in identification of drug-related adverse events from multiple system organ classes.

Analyzing safety data from clinical trials to detect safety signals worth further examination involves testing multiple hypotheses, one for each observed adverse event (AE) type. There exists certain hierarchical structure for these hypotheses due to the classification of the AEs into system organ classes, and these AEs are also likely correlated. Many approaches have been proposed to identify safety signals under the multiple testing framework and tried to achieve control of false discovery rate (FDR).

Efficient methods for signal detection from correlated adverse events in clinical trials.

It is an important and yet challenging task to identify true signals from many adverse events that may be reported during the course of a clinical trial. One unique feature of drug safety data from clinical trials, unlike data from post-marketing spontaneous reporting, is that many types of adverse events are reported by only very few patients leading to rare events. Due to the limited study size, the p-values of testing whether the rate is higher in the treatment group across all types of adverse events are in general not uniformly distributed under the null hypothesis that there is no difference between the treatment group and the placebo group.

Improved power of familywise error rate procedures for discrete data under dependency.

In many applications where it is necessary to test multiple hypotheses simultaneously, the data encountered are discrete. In such cases, it is important for multiplicity adjustment to take into account the discreteness of the distributions of the p-values, to assure that the procedure is not overly conservative. In this paper, we review some known multiple testing procedures for discrete data that control the familywise error rate, the probability of making any false rejection.

Multiple testing with discrete data: Proportion of true null hypotheses and two adaptive FDR procedures.

We consider multiple testing with false discovery rate (FDR) control when p values have discrete and heterogeneous null distributions. We propose a new estimator of the proportion of true null hypotheses and demonstrate that it is less upwardly biased than Storey's estimator and two other estimators. The new estimator induces two adaptive procedures, that is, an adaptive Benjamini-Hochberg (BH) procedure and an adaptive Benjamini-Hochberg-Heyse (BHH) procedure.

Periodic benefit-risk assessment using Bayesian stochastic multi-criteria acceptability analysis.

Benefit-risk (BR) assessment is essential to ensure the best decisions are made for a medical product in the clinical development process, regulatory marketing authorization, post-market surveillance, and coverage and reimbursement decisions. One challenge of BR assessment in practice is that the benefit and risk profile may keep evolving while new evidence is accumulating. Regulators and the International Conference on Harmonization (ICH) recommend performing periodic benefit-risk evaluation report (PBRER) through the product's lifecycle.

Control-based imputation for sensitivity analyses in informative censoring for recurrent event data.

In clinical trials, missing data commonly arise through nonadherence to the randomized treatment or to study procedure. For trials in which recurrent event endpoints are of interests, conventional analyses using the proportional intensity model or the count model assume that the data are missing at random, which cannot be tested using the observed data alone. Thus, sensitivity analyses are recommended.

On inference of control-based imputation for analysis of repeated binary outcomes with missing data.

Missing data are common in longitudinal clinical trials. How to handle missing data is critical for both sponsors and regulatory agencies to assess treatment effect from the trials. Recently, a control-based imputation has been proposed, where the missing data are imputed based on the assumption that patients who discontinued the test drug will have a similar response profile to the patients in the control group.

Bayesian probability of success for clinical trials using historical data.

Developing sophisticated statistical methods for go/no-go decisions is crucial for clinical trials, as planning phase III or phase IV trials is costly and time consuming. In this paper, we develop a novel Bayesian methodology for determining the probability of success of a treatment regimen on the basis of the current data of a given trial. We introduce a new criterion for calculating the probability of success that allows for inclusion of covariates as well as allowing for historical data based on the treatment regimen, and patient characteristics.

Medical records-based postmarketing safety evaluation of rare events with uncertain status.

We develop a simple statistic for comparing rates of rare adverse events between treatment groups in postmarketing safety studies where the events have uncertain status. In this setting, the statistic is asymptotically equivalent to the logrank statistic, but the limiting distribution has Poisson and binomial components instead of being Gaussian. We develop two new procedures for computing critical values: a Gaussian approximation and a parametric bootstrap.

Medical records-based postmarketing safety evaluation of rare events with uncertain status.

We develop a simple statistic for comparing rates of rare adverse events between treatment groups in postmarketing safety studies where the events have uncertain status. In this setting, the statistic is asymptotically equivalent to the logrank statistic, but the limiting distribution has Poisson and binomial components instead of being Gaussian. We develop two new procedures for computing critical values, a Gaussian approximation and a parametric bootstrap.

Using proportion of similar response to evaluate correlates of protection for vaccine efficacy.

A question of interest in many vaccine clinical development programmes is whether vaccine-induced serum antibody level can be used as a correlate of vaccine efficacy; that is, whether serum antibody levels induced by a candidate vaccine can reliably predict the risk of breakthrough disease. Traditionally, analyses to answer this question have been based on modelling the incidence of breakthrough disease as a function of antibody level, among vaccinated subjects in clinical trials. The Proportion of Similar Response (PSR) method will be described and explored, and compared to the Receive Operator Characteristics (ROC) curve as a graphical tool and the area under the ROC (AUROC) as a summary measure in the context of evaluating correlates of protection.

cSSMD: assessing collective activity for addressing off-target effects in genome-scale RNA interference screens.

Motivation: Off-target activity commonly exists in RNA interference (RNAi) screens and often generates false positives. Existing analytic methods for addressing the off-target effects are demonstrably inadequate in RNAi confirmatory screens.

Results: Here, we present an analytic method assessing the collective activity of multiple short interfering RNAs (siRNAs) targeting a gene.

The use of SSMD-based false discovery and false nondiscovery rates in genome-scale RNAi screens.

In genome-scale RNA interference (RNAi) screens, it is critical to control false positives and false negatives statistically. Traditional statistical methods for controlling false discovery and false nondiscovery rates are inappropriate for hit selection in RNAi screens because the major goal in RNAi screens is to control both the proportion of short interfering RNAs (siRNAs) with a small effect among selected hits and the proportion of siRNAs with a large effect among declared nonhits. An effective method based on strictly standardized mean difference (SSMD) has been proposed for statistically controlling false discovery rate (FDR) and false nondiscovery rate (FNDR) appropriate for RNAi screens.

Sequential generalized likelihood ratio tests for vaccine safety evaluation.

The evaluation of vaccine safety involves pre-clinical animal studies, pre-licensure randomized clinical trials, and post-licensure safety studies. Sequential design and analysis are of particular interest because they allow early termination of the trial or quick detection that the vaccine exceeds a prescribed bound on the adverse event rate. After a review of the recent developments in this area, we propose a new class of sequential generalized likelihood ratio tests for evaluating adverse event rates in two-armed pre-licensure clinical trials and single-armed post-licensure studies.

Age dependence of the risk of intussusception following [corrected] rhesus-human reassortant rotavirus tetravalent vaccine: is it beyond doubt?

The relation between the risk of intussusception and age at the time of receipt of the first dose of rhesus-human reassortant rotavirus tetravalent vaccine (RRV-TV) has been studied extensively on the basis of Centers for Disease Control and Prevention (CDC) matched case-control study data, using various statistical methods, including conditional logistic regression and quadratic smoothing splines. However, different conclusions have been reported in published analyses regarding the dependence of the risk of intussusception on age at first dose. The authors reanalyzed the CDC matched case-control data set using unrestricted and restricted quadratic smoothing spline methods for various exposure windows (i.

Determination of sample size in genome-scale RNAi screens.

Motivation: For genome-scale RNAi research, it is critical to investigate sample size required for the achievement of reasonably low false negative rate (FNR) and false positive rate.

Results: The analysis in this article reveals that current design of sample size contributes to the occurrence of low signal-to-noise ratio in genome-scale RNAi projects. The analysis suggests that (i) an arrangement of 16 wells per plate is acceptable and an arrangement of 20-24 wells per plate is preferable for a negative control to be used for hit selection in a primary screen without replicates; (ii) in a confirmatory screen or a primary screen with replicates, a sample size of 3 is not large enough, and there is a large reduction in FNRs when sample size increases from 3 to 4.

Analyzing multiple endpoints in clinical trials of pain treatments: IMMPACT recommendations. Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials.

The increasing complexity of randomized clinical trials and the practice of obtaining a wide variety of measurements from study participants have made the consideration of multiple endpoints a critically important issue in the design, analysis, and interpretation of clinical trials. Failure to consider important outcomes can limit the validity and utility of clinical trials; specifying multiple endpoints for the evaluation of treatment efficacy, however, can increase the rate of false positive conclusions about the efficacy of a treatment. We describe the use of multiple endpoints in the design, analysis, and interpretation of pain clinical trials, and review available strategies and methods for addressing multiplicity.

Hit selection with false discovery rate control in genome-scale RNAi screens.

RNA interference (RNAi) is a modality in which small double-stranded RNA molecules (siRNAs) designed to lead to the degradation of specific mRNAs are introduced into cells or organisms. siRNA libraries have been developed in which siRNAs targeting virtually every gene in the human genome are designed, synthesized and are presented for introduction into cells by transfection in a microtiter plate array. These siRNAs can then be transfected into cells using high-throughput screening (HTS) methodologies.

Integrating experimental and analytic approaches to improve data quality in genome-wide RNAi screens.

RNA interference (RNAi) not only plays an important role in drug discovery but can also be developed directly into drugs. RNAi high-throughput screening (HTS) biotechnology allows us to conduct genome-wide RNAi research. A central challenge in genome-wide RNAi research is to integrate both experimental and computational approaches to obtain high quality RNAi HTS assays.

Evaluating the safety of a rotavirus vaccine: the REST of the story.

The Rotavirus Efficacy and Safety Trial (REST) was a blinded, placebo-controlled study of the live pentavalent human-bovine vaccine, RotaTeq (Merck & Co. Inc., West Point, PA).

The use of strictly standardized mean difference for hit selection in primary RNA interference high-throughput screening experiments.

RNA interference (RNAi) high-throughput screening (HTS) has been hailed as the 2nd genomics wave following the 1st genomics wave of gene expression microarrays and single-nucleotide polymorphism discovery platforms. Following an RNAi HTS, the authors are interested in identifying short interfering RNA (siRNA) hits with large inhibition/activation effects. For hit selection, the z-score method and its variants are commonly used in primary RNAi HTS experiments.

Safety and efficacy of a pentavalent human-bovine (WC3) reassortant rotavirus vaccine.

Background: Rotavirus is a leading cause of childhood gastroenteritis and death worldwide.

Methods: We studied healthy infants approximately 6 to 12 weeks old who were randomly assigned to receive three oral doses of live pentavalent human-bovine (WC3 strain) reassortant rotavirus vaccine containing human serotypes G1, G2, G3, G4, and P[8] or placebo at 4-to-10-week intervals in a blinded fashion. Active surveillance was used to identify subjects with serious adverse and other events.

Impact of indinavir on the quality of life in patients with advanced HIV infection treated with zidovudine and lamivudine.

Objective: In AIDS Clinical Trial Group (ACTG) study 320, triple-combination antiretroviral therapy including indinavir significantly slowed progression to acquired immunodeficiency syndrome or death, compared with treatment with dual nucleoside reverse-transcriptase inhibitors (NRTIs) alone, in zidovudine-experienced patients with advanced human immunodeficiency virus (HIV) infection. We examined the impact of indinavir on quality of life in participants from this study.

Methods: A total of 1156 protease inhibitor- and lamivudine-naive patients stratified by CD4 cell count ( View Article and Find Full Text PDF

Use of the false discovery rate for evaluating clinical safety data.

Clinical adverse experience (AE) data are routinely evaluated using between group P values for every AE encountered within each of several body systems. If the P values are reported and interpreted without multiplicity considerations, there is a potential for an excess of false positive findings. Procedures based on confidence interval estimates of treatment effects have the same potential for false positive findings as P value methods.