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. Appropriately accounting for the complex correlation among AEs could improve the sensitivity and specificity of identifying signal vaccine-AE pairs. We propose a two-step approach in which we first estimate the shared latent factors among AEs using a working multivariate logistic regression model, and then use univariate logistic regression model to examine the vaccine-AE associations after controlling for the latent factors. Our simulation studies show that this approach outperforms current approaches in terms of sensitivity and specificity. We apply our approach in analyzing VAERS data and report our findings.

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. Consequently, it is critical to conduct sensitivity analysis. We develop a control-based multiple imputation method for recurrent events data, where patients who drop out of the study are assumed to have a similar response profile to those in the control group after dropping out. Specifically, we consider the copy reference approach and the jump to reference approach. We model the recurrent event data using a semiparametric proportional intensity frailty model with the baseline hazard function completely unspecified. We develop nonparametric maximum likelihood estimation and inference procedures. We then impute the missing data based on the large sample distribution of the resulting estimators. The variance estimation is corrected by a bootstrap procedure. Simulation studies demonstrate the proposed method performs well in practical settings. We provide applications to two clinical trials.

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. We assume that the patients can be classified into a pre-specified number of risk groups, where each group has distinct survival profile. Our proposed tree-based methods are devised to estimate latent group membership using an EM algorithm. The observed data log-likelihood function is used as the splitting criterion in recursive partitioning. The finite sample performance is evaluated by extensive simulation studies and the proposed method is illustrated by a case study in breast cancer.

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. The Bayesian information criterion is used for selecting the number of latent groups. Furthermore, we incorporate variable selection with the adaptive lasso into inference so that only a few feature variables will be selected to characterize the latent heterogeneity. We show that our adaptive lasso estimator has oracle properties when the number of parameters diverges with the sample size. The finite sample performance is evaluated by the simulation study, and the proposed method is illustrated by two datasets.

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). The FDR control concerns the expectation of the false discovery proportion (FDP). In practice, the control of the actual random variable FDP could be more relevant and has recently drawn much attention. In this paper, we proposed a two-stage procedure for safety signal detection with direct control of FDP, through a permutation-based approach for screening groups of AEs and a permutation-based approach of constructing simultaneous upper bounds for false discovery proportion. Our simulation studies showed that this new approach has controlled FDP. We demonstrate our approach using data sets derived from a drug clinical trial.

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. A consequence is that typically fewer than 100α percent of the hypotheses are rejected under the null at the nominal significance level of α . The other challenge is multiplicity control. Adverse events from the same body system may be correlated. There may also be correlations between adverse events from different body systems. To tackle these challenging issues, we develop Monte-Carlo-based methods for the signal identification from patient-reported adverse events in clinical trials. The proposed methodologies account for the rare events and arbitrary correlation structures among adverse events within and/or between body systems. Extensive simulation studies demonstrate that the proposed method can accurately control the family-wise error rate and is more powerful than existing methods under many practical situations. Application to two real examples is provided.

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. We implement the control-based multiple imputation as sensitivity analyses for the recurrent event data. We model the recurrent event using a piecewise exponential proportional intensity model with frailty and sample the parameters from the posterior distribution. We impute the number of events after dropped out and correct the variance estimation using a bootstrap procedure. We apply the method to an application of sitagliptin study.

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 new class of prior distributions and covariate distributions is developed to achieve this goal. The methodology is quite general and can be used with univariate or multivariate continuous or discrete data, and it generalizes Chuang-Stein's work. This methodology will be invaluable for informing the scientist on the likelihood of success of the compound, while including the information of covariates for patient characteristics in the trial population for planning future pre-market or post-market trials.

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. A two-step method is proposed in the literature to approximate the calculation of confidence intervals for the proportion difference using a concept of effective sample sizes. However, the performance of this two-step method has not been investigated in their paper. On this note, we examine the properties of the two-step method and propose an adjustment to the effective sample size formula based on Bayesian information theory. Simulations are conducted to evaluate the performance and to show that the modified effective sample size improves the coverage property of the confidence intervals.

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. Vaccines are biological products that are primarily designed to prevent infectious diseases, and are different from pharmaceutical products, which traditionally have been chemical products designed to treat or cure diseases. While a lot of similarities exist between clinical trials for vaccines and those for pharmaceutical products, there are some unique issues in vaccine trials, including how to handle the missing data, which calls for special considerations. In this report we present a variety of statistical approaches for analyses of vaccine immunogenicity and safety trials in the presence of missing data. The methods are illustrated with numerical simulations and vaccine trial examples.

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. We investigate several methods to estimate the aggregate response based on binary data including simple average, model-based, and lowest response methods. The response of each new serotype is then compared with the estimated aggregate response for non-inferiority. The non-inferiority test p-value and confidence interval are obtained from Miettinen and Nurminen's method, using an effective sample size. The methods are evaluated using simulations and demonstrated with a real clinical trial example.

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, ..., T. At the end of the trial, it is of interest to assess the treatment effect based on the mean change from baseline at the last time point T. We consider statistical methods for (i) a point estimate and 95 per cent confidence interval for the mean change from baseline at time T for each treatment group, and (ii) a p-value and 95 per cent confidence interval for the between-group difference in the mean change from baseline. The manner in which the baseline responses are used in the analysis influences both the accuracy and the efficiency of items (i) and (ii). In this paper, we will consider the ANCOVA approach with change from baseline as a dependent variable and compare that with a constrained longitudinal data analysis (cLDA) model proposed by Liang and Zeger (Sankhya: Indian J. Stat. (Ser B) 2000; 62:134-148), in which the baseline is modeled as a dependent variable in conjunction with the constraint of a common baseline mean across the treatment groups. Some drawbacks of the ANCOVA model and potential advantages of the cLDA approach are discussed and illustrated using numerical simulations.

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. Antibody responses were measured immediately before and 42 +/- 3 days after vaccination. Parents/legal guardians recorded all adverse events for 14 days after vaccination. RESULTS: Seroprotective titers for hepatitis B (hepatitis B surface antigen > or =10 mIU/mL) were achieved by 97.8% of subjects in both vaccine treatment groups. Seroprotective titers to H. influenzae type b (polyribosylribitol phosphate > or =0.15 microg/mL) were achieved by 91.4% of subjects receiving both vaccines and 95.1% of subjects receiving only hexavalent vaccine. Seroprotective titers to diphtheria, tetanus, and poliovirus were also comparable between the vaccine treatment groups, as were geometric mean antibody titers to the pertussis antigens. Among PRV recipients, 92% had a > or =3-fold rise in serum antirotavirus immunoglobulin A levels. Concomitant administration was well tolerated. The incidence of adverse events was similar for both groups, with no statistically significant increases in fever, vomiting, diarrhea, or irritability. CONCLUSIONS: In this study, concomitant administration of PRV with hexavalent vaccine was well tolerated and the immune responses to the antigens of the hexavalent vaccine were noninferior when compared with those of the control group. In addition, PRV was immunogenic when administered concomitantly with hexavalent vaccine.

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. Three doses of each vaccine were administered 8-10 weeks apart. Subjects did not receive OPV at birth. Routine licensed pediatric vaccines were allowed. Antibody responses to PRV and OPV were evaluated 42 days after the last dose of each vaccine. Adverse events were recorded for 14 days after each study visit. RESULTS: In the concomitant-use group (n = 372), more than 98% of subjects achieved serum-neutralizing antibody titer > or = 1:8 against poliovirus types 1, 2, and 3. The poliovirus seroprotection rate in the concomitant-use group was statistically noninferior to the staggered-use group (n = 363). The immunoglobulin A (IgA) antirotavirus geometric mean titer was 46% lower in the concomitant-use group than in the staggered-use group. However, concomitant use elicited a > or = 3-fold increase (from predose 1 to postdose 3) in serum antirotavirus IgA in 93% of subjects and achieved the definition of noninferiority. Both regimens were similarly well tolerated. CONCLUSIONS: PRV did not interfere with immune responses to OPV. Although coadministration with OPV reduced serum antirotavirus IgA geometric mean titer, seroresponse rates were high and consistent with those observed in previous studies showing high vaccine efficacy. These results support including PRV in vaccination schedules involving OPV.