Robust analyzes for longitudinal clinical trials with missing and non-normal continuous outcomes.

Missing data is unavoidable in longitudinal clinical trials, and outcomes are not always normally distributed. In the presence of outliers or heavy-tailed distributions, the conventional multiple imputation with the mixed model with repeated measures analysis of the average treatment effect (ATE) based on the multivariate normal assumption may produce bias and power loss. Control-based imputation (CBI) is an approach for evaluating the treatment effect under the assumption that participants in both the test and control groups with missing outcome data have a similar outcome profile as those with an identical history in the control group.

Multiply robust estimators in longitudinal studies with missing data under control-based imputation.

Longitudinal studies are often subject to missing data. The recent guidance from regulatory agencies, such as the ICH E9(R1) addendum addresses the importance of defining a treatment effect estimand with the consideration of intercurrent events. Jump-to-reference (J2R) is one classical control-based scenario for the treatment effect evaluation, where the participants in the treatment group after intercurrent events are assumed to have the same disease progress as those with identical covariates in the control group.

Safety signal detection with control of latent factors.

Postmarket drug safety database like vaccine adverse event reporting system (VAERS) collect thousands of spontaneous reports annually, with each report recording occurrences of any adverse events (AEs) and use of vaccines. We hope to identify signal vaccine-AE pairs, for which certain vaccines are statistically associated with certain adverse events (AE), using such data. Thus, the outcomes of interest are multiple AEs, which are binary outcomes and could be correlated because they might share certain latent factors; and the primary covariates are vaccines.

A novel power prior approach for borrowing historical control data in clinical trials.

There has been an increased interest in borrowing information from historical control data to improve the statistical power for hypothesis testing, therefore reducing the required sample sizes in clinical trials. To account for the heterogeneity between the historical and current trials, power priors are often considered to discount the information borrowed from the historical data. However, it can be challenging to choose a fixed power prior parameter in the application.

Sensitivity analyses in longitudinal clinical trials via distributional imputation.

Missing data is inevitable in longitudinal clinical trials. Conventionally, the missing at random assumption is assumed to handle missingness, which however is unverifiable empirically. Thus, sensitivity analyses are critically important to assess the robustness of the study conclusions against untestable assumptions.

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.

Mixture survival trees for cancer risk classification.

In oncology studies, it is important to understand and characterize disease heterogeneity among patients so that patients can be classified into different risk groups and one can identify high-risk patients at the right time. This information can then be used to identify a more homogeneous patient population for developing precision medicine. In this paper, we propose a mixture survival tree approach for direct risk classification.

SMIM: A unified framework of survival sensitivity analysis using multiple imputation and martingale.

Censored survival data are common in clinical trial studies. We propose a unified framework for sensitivity analysis to censoring at random in survival data using multiple imputation and martingale, called SMIM. The proposed framework adopts the δ-adjusted and control-based models, indexed by the sensitivity parameter, entailing censoring at random and a wide collection of censoring not at random assumptions.

Inferring latent heterogeneity using many feature variables supervised by survival outcome.

In cancer studies, it is important to understand disease heterogeneity among patients so that precision medicine can particularly target high-risk patients at the right time. Many feature variables such as demographic variables and biomarkers, combined with a patient's survival outcome, can be used to infer such latent heterogeneity. In this work, we propose a mixture model to model each patient's latent survival pattern, where the mixing probabilities for latent groups are modeled through a multinomial distribution.

Analysis of time-to-event data using a flexible mixture model under a constraint of proportional hazards.

Cox proportional hazards (PH) model evaluates the effects of interested covariates under PH assumption without specified the baseline hazard. In clinical trial applications, however, the explicitly estimated hazard or cumulative survival function for each treatment group helps to assess and interpret the meaning of treatment difference. In this paper, we propose to use a flexible mixture model under the PH constraint to fit the underline survival functions.

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).

A flexible parametric survival model for fitting time to event data in clinical trials.

Time-to-event data are common in clinical trials to evaluate survival benefit of a new drug, biological product, or device. The commonly used parametric models including exponential, Weibull, Gompertz, log-logistic, log-normal, are simply not flexible enough to capture complex survival curves observed in clinical and medical research studies. On the other hand, the nonparametric Kaplan Meier (KM) method is very flexible and successful on catching the various shapes in the survival curves but lacks ability in predicting the future events such as the time for certain number of events and the number of events at certain time and predicting the risk of events (eg, death) over time beyond the span of the available data from clinical trials.

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.

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.

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.

A note on effective sample size for constructing confidence intervals for the difference of two proportions.

Proportion differences are often used to estimate and test treatment effects in clinical trials with binary outcomes. In order to adjust for other covariates or intra-subject correlation among repeated measures, logistic regression or longitudinal data analysis models such as generalized estimating equation or generalized linear mixed models may be used for the analyses. However, these analysis models are often based on the logit link which results in parameter estimates and comparisons in the log-odds ratio scale rather than in the proportion difference scale.

Handling missing data in vaccine clinical trials for immunogenicity and safety evaluation.

In clinical trials, study subjects are usually followed for a period of time after treatment, and the missing data issue is almost inevitable due to various reasons, including early dropout or lost-to-follow-up. It is important to take the missing data into consideration at the study design stage to minimize its occurrence throughout the study and to prospectively account for it in the analyses. There are many methods available in the literature that are designed to handle the missing data issue under various settings.

Assessing non-inferiority to an aggregate response with an application to development of pneumococcal conjugate vaccines.

The development of a new pneumococcal conjugate vaccine involves assessing the responses of the new serotypes included in the vaccine. The World Health Organization guidance states that the response from each new serotype in the new vaccine should be compared with the aggregate response from the existing vaccine to evaluate non-inferiority. However, no details are provided on how to define and estimate the aggregate response and what methods to use for non-inferiority comparisons.

Should baseline be a covariate or dependent variable in analyses of change from baseline in clinical trials?

In randomized clinical trials, a pre-treatment measurement is often taken at baseline, and post-treatment effects are measured at several time points post-baseline, say t=1, ...

Concomitant use of the 3-dose oral pentavalent rotavirus vaccine with a 3-dose primary vaccination course of a diphtheria-tetanus-acellular pertussis-hepatitis B-inactivated polio-Haemophilus influenzae type b vaccine: immunogenicity and reactogenicity.

Background: The pentavalent rotavirus vaccine (PRV), RotaTeq, can be concomitantly administered with most routine childhood vaccines. This study evaluated the immunogenicity and reactogenicity of PRV when used concomitantly with a hexavalent vaccine containing diphtheria, tetanus, acellular pertussis, hepatitis B, inactivated poliovirus, and Haemophilus influenzae type b.

Methods: Healthy infants (N = 403) received hexavalent vaccine concomitantly with either PRV or placebo at 2, 3, and 4 months of age.

Concomitant use of the oral pentavalent human-bovine reassortant rotavirus vaccine and oral poliovirus vaccine.

Objectives: The live oral pentavalent rotavirus vaccine (PRV) is well tolerated and highly efficacious against rotavirus gastroenteritis. This open-label, multicenter study evaluated the immunogenicity and safety of coadministering oral poliovirus vaccine (OPV) with PRV.

Methods: From 2005 to 2006, healthy 6- to 12-week-old Latin American infants were randomized to PRV and OPV concomitantly or PRV 2-4 weeks before OPV.