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.

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 100 α 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.

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. Taking advantage of the fact that the exact permutation or exact pairwise permutation distributions of the p-values can often be determined when the sample size is small, we investigate procedures that incorporate the dependence structure through the exact permutation distribution and propose two new procedures that incorporate the exact pairwise permutation distributions. A step-up procedure is also proposed that accounts for the discreteness of the data. The performance of the proposed procedures is investigated through simulation studies and two applications. The results show that by incorporating both discreteness and dependency of p-value distributions, gains in power can be achieved.

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. We prove that the adaptive BH (aBH) procedure is conservative nonasymptotically. Through simulation studies, we show that these procedures are usually more powerful than their nonadaptive counterparts and that the adaptive BHH procedure is usually more powerful than the aBH procedure and a procedure based on randomized p-value. The adaptive procedures are applied to a study of HIV vaccine efficacy, where they identify more differentially polymorphic positions than the BH procedure at the same FDR level.

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. In this paper, we propose a general statistical framework for periodic benefit-risk assessment, in which Bayesian meta-analysis and stochastic multi-criteria acceptability analysis (SMAA) will be combined to synthesize the accumulating evidence. The proposed approach allows us to compare the acceptability of different drugs dynamically and effectively and accounts for the uncertainty of clinical measurements and imprecise or incomplete preference information of decision makers. We apply our approaches to two real examples in a post-hoc way for illustration purpose. The proposed method may easily be modified for other pre and post market settings, and thus be an important complement to the current structured benefit-risk assessment (sBRA) framework to improve the transparent and consistency of the decision-making process.

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. Under control-based imputation, the variance estimation may be biased using Rubin's formula which could produce biased statistical inferences. We evaluate several statistical methods for obtaining appropriate variances under control-based imputation for analysis of repeated binary outcomes with monotone missing data and show that both the analytical method developed by Robins & Wang and the nonparametric bootstrap method provide more appropriate variance estimates under various simulation settings. We use the methods in an application of an antidepressant Phase III clinical trial and give discussion and recommendations on method performance and preference.

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. Excess false positive findings can needlessly complicate the safety profile of a safe drug or vaccine. Accordingly, we propose a novel method for addressing multiplicity in the evaluation of adverse experience data arising in clinical trial settings. The method involves a two-step application of adjusted P values based on the Benjamini and Hochberg1 false discovery rate (FDR). Data from three moderate to large vaccine trials are used to illustrate our proposed ‘Double FDR’ approach, and to reinforce the potential impact of failing to account for multiplicity. This work was in collaboration with the late Professor John W. Tukey who coined the term ‘Double FDR’.

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.

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. Both numerical and asymptotic properties of the procedures are studied. The test procedures are demonstrated on a postmarketing safety study of the RotaTeq vaccine. This vaccine was developed to reduce the incidence of severe diarrhea in infants.

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. Using this method, we can not only reduce the impact of off-target activities, but also evaluate the specific effect of an siRNA, thus providing information about potential off-target effects. Using in-house RNAi screens, we demonstrate that our method obtains more reasonable and sensible results than current methods such as the redundant siRNA activity (RSA) method, the RNAi gene enrichment ranking (RIGER) method, the frequency approach and the t-test.

CONTACT

xiaohua_zhang@merck.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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. The proposed approach is illustrated using data from the Rotavirus Efficacy and Safety Trial. Simulation studies of the performance of the proposed approach and other methods are also given.

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. In this article, the authors explore the utility of the SSMD-based method for hit selection in RNAi screens. As demonstrated in 2 genome-scale RNAi screens, the SSMD-based method addresses the unmet need of controlling for the proportion of siRNAs with a small effect among selected hits, as well as controlling for the proportion of siRNAs with a large effect among declared nonhits. Furthermore, the SSMD-based method results in reasonably low FDR and FNDR for selecting inhibition or activation hits. This method works effectively and should have a broad utility for hit selection in RNAi screens with replicates.

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.e., intervals postvaccination). These analyses indicated that the use of different models may lead to different conclusions. The restricted quadratic smoothing spline with appropriately chosen knot locations showed a statistically significant increased risk of intussusception associated with RRV-TV for the exposure window 3-14 days after the first dose at an age as young as 49 days, the youngest age in the data set at which vaccine was administered; this implies an increased risk of intussusception associated with RRV-TV at all ages studied.

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. To search a tradeoff between benefit and cost, any sample size between 4 and 11 is a reasonable choice. If the main focus is the selection of siRNAs with strong effects, a sample size of 4 or 5 is a good choice. If we want to have enough power to detect siRNAs with moderate effects, sample size needs to be 8, 9, 10 or 11. These discoveries about sample size bring insight to the design of a genome-scale RNAi screen experiment.

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). REST was noteworthy because its primary objective was to evaluate the safety of RotaTeq® with regard to intussusception, a rare intestinal illness that occurs with a background incidence of approximately 50 cases per 100 000 infant years. The study involved approximately 70 000 infants at over 500 study sites in 11 countries. The study demonstrated that the risk of intussusception was similar in vaccine and placebo recipients and that the vaccine prevented rotavirus gastroenteritis, ameliorated the severity of disease in those who had any disease, and substantially reduced rotavirus-associated hospitalizations and other health care contacts. This report provides an in-depth review of the background, statistical and regulatory considerations, and execution of REST. We describe the rationale and methods used for sample size, continuous safety monitoring, group sequential design, and detailed study execution. The results of the study have been reported elsewhere. The design and conduct of this study may serve as a useful model for planning other future large-scale clinical trials, especially those evaluating uncommon adverse events.

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. The goal of RNAi HTS is to identify a set of siRNAs that inhibit or activate defined cellular phenotypes. The commonly used analysis methods including median +/- kMAD have issues about error rates in multiple hypothesis testing and plate-wise versus experiment-wise analysis. We propose a methodology based on a Bayesian framework to address these issues. Our approach allows for sharing of information across plates in a plate-wise analysis, which obviates the need for choosing either a plate-wise or experimental-wise analysis. The proposed approach incorporates information from reliable controls to achieve a higher power and a balance between the contribution from the samples and control wells. Our approach provides false discovery rate (FDR) control to address multiple testing issues and it is robust to outliers.

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. Based on our daily practice in RNAi HTS experiments, we propose the implementation of 3 experimental and analytic processes to improve the quality of data from RNAi HTS biotechnology: (1) select effective biological controls; (2) adopt appropriate plate designs to display and/or adjust for systematic errors of measurement; and (3) use effective analytic metrics to assess data quality. The applications in 5 real RNAi HTS experiments demonstrate the effectiveness of integrating these processes to improve data quality. Due to the effectiveness in improving data quality in RNAi HTS experiments, the methods and guidelines contained in the 3 experimental and analytic processes are likely to have broad utility in genome-wide RNAi research.

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. Recently, strictly standardized mean difference (SSMD) has been proposed to measure the siRNA effect represented by the magnitude of difference between an siRNA and a negative reference group. The links between SSMD and d+-probability offer a clear interpretation of siRNA effects from a probability perspective. Hence, SSMD can be used as a ranking metric for hit selection. In this article, the authors investigated both the SSMD-based testing process and the use of SSMD as a ranking metric for hit selection in 2 primary siRNA HTS experiments. The analysis results showed that, as a ranking metric, SSMD was more stable and reliable than percentage inhibition and led to more robust hit selection results. Using the SSMD -based testing method, the false-negative rate can more readily be obtained. More important, the use of the SSMD-based method can result in a reduction in both the false-negative and false-positive rates. The applications presented in this article demonstrate that the SSMD method addresses scientific questions and fills scientific needs better than both percentage inhibition and the commonly used z-score method for hit selection.

Statistical considerations for noninferiority/equivalence trials in vaccine development

Noninferioritylequivalence designs are often used in vaccine clinical trials. The goal of these designs is to demonstrate that a new vaccine, or new formulation or regimen of an existing vaccine, is similar in terms of effectiveness to the existing vaccine, while offering such advantages as easier manufacturing, easier administration, lower cost, or improved safety profile. These noninferioritylequivalence designs are particularly useful in four common types of immunogenicity trials: vaccine bridging trials, combination vaccine trials, vaccine concomitant use trials, and vaccine consistency lot trials. In this paper, we give an overview of the key statistical issues and recent developments for noninferioritylequivalence vaccine trials. Specifically, we cover the following topics: (i) selection of study endpoints; (ii) formulation of the null and alternative hypotheses; (iii) determination of the noninferioritylequivalence margin; (iv) selection of efficient statistical methods for the statistical analysis of noninferioritylequivalence vaccine trials, with particular emphasis on adjustment for stratification factors and missing pre-or post-vaccination data; and (v) the calculation of sample size and power.

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.

RESULTS

The 34,035 infants in the vaccine group and 34,003 in the placebo group were monitored for serious adverse events. Intussusception occurred in 12 vaccine recipients and 15 placebo recipients within one year after the first dose including six vaccine recipients and five placebo recipients within 42 days after any dose (relative risk, 1.6; 95 percent confidence interval, 0.4 to 6.4). The vaccine reduced hospitalizations and emergency department visits related to G1-G4 rotavirus gastroenteritis occurring 14 or more days after the third dose by 94.5 percent (95 percent confidence interval, 91.2 to 96.6 percent). In a nested substudy, efficacy against any G1-G4 rotavirus gastroenteritis through the first full rotavirus season after vaccination was 74.0 percent (95 percent confidence interval, 66.8 to 79.9 percent); efficacy against severe gastroenteritis was 98.0 percent (95 percent confidence interval, 88.3 to 100 percent). The vaccine reduced clinic visits for G1-G4 rotavirus gastroenteritis by 86.0 percent (95 percent confidence interval, 73.9 to 92.5 percent).

CONCLUSIONS

This vaccine was efficacious in preventing rotavirus gastroenteritis, decreasing severe disease and health care contacts. The risk of intussusception was similar in vaccine and placebo recipients. (ClinicalTrials.gov number, NCT00090233.)

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 (<or=50 and 51-200 cells/mm(3)) were randomized to receive zidovudine (or stavudine) and lamivudine, with or without indinavir. Health-related quality of life was measured using the ACTG QoL601-602 questionnaire, which assesses general health status measured on a visual analogue scale and 8 specific health-related domains. Quality-adjusted survival time was estimated using the visual analogue scale for general health.

RESULTS

Mean changes in general health scores after 24 weeks were +2.9 in the triple-therapy group versus -0.2 in the dual-therapy group (P=.018). By week 24, scores in all specific domains were higher with triple-drug therapy than dual-drug therapy, with statistically significant differences in role function, energy, and pain scores. Benefits of triple-drug therapy were largely confined to patients with CD4 cell counts of <or=50 cells/mm(3). Quality-adjusted survival time did not differ significantly between the 2 treatment groups.

CONCLUSIONS

Triple-drug therapy with indinavir and 2 NRTIs resulted in a significant improvement in general health status after 24 weeks, especially in patients with low CD4 cell counts. Patients receiving triple-drug therapy also had significantly better role function, energy, and pain scores than did patients treated with dual-drug therapy.

Economic endpoints in clinical trials

Healthcare decision makers are increasingly requesting information on the cost and cost-effectiveness of new medicines at the time of product launch. In order to provide this information, data on healthcare resource utilization and, in some cases, costs, may be collected in clinical trials. In this paper, we discuss some of the issues statisticians need to address when it is appropriate to include these economic endpoints in the trial. Several design issues are discussed, including the alternative types of and methods for collecting economic endpoint data, sample size and generalizability. Alternative approaches in the analysis of resource utilization, cost and cost-effectiveness are also presented. Finally, several of the analytic approaches are applied to actual data from a clinical trial.

Assessing the appropriateness of combining economic data from multinational clinical trials

Because of the potential for large variability among countries in the utilization and cost of health care resources, it is important to assess the appropriateness of combining economic data across the countries in a multinational clinical economic trial. We show how available tests for interaction can be applied to economic endpoints, including cost-effectiveness ratios and net health benefits. This analysis includes a characterization of possible interactions being quantitative or qualitative in nature. In the absence of interaction, a pooled estimate of the economic endpoint should be representative of the participating countries. We explore the analytic issues by further analysing data from the Scandinavian Simvastatin Survival Study (4S).

Testing for interaction in studies of noninferiority

We consider the role of interaction tests in the context of active-controlled clinical trials that aim to demonstrate the noninferiority of an experimental treatment compared to a standard (control) treatment. When the subjects can be grouped into strata (e.g., study sites, gender, race, etc.), there may be a desire to determine whether the experimental treatment is noninferior to the standard in each of the strata. We present five possible analysis strategies to test for heterogeneity of relative treatment effects among strata. These strategies are either identical to or straightforward modifications of strategies that can be used to test for interaction when the objective of the study is to show differences rather than noninferiority. The various analysis strategies implicitly depend on different definitions of interaction. Power of the various tests will be low, a phenomenon that often occurs when testing for interaction. We present simulation results to quantify the power and type I error rates under different scenarios and an example to demonstrate the proposed tests. None of the analysis strategies is best under every parameter configuration. The tests may be best used in a descriptive or exploratory manner. Extensions to two-sided equivalence testing are also discussed.

Inverse relationship between six week postvaccination varicella antibody response to vaccine and likelihood of long term breakthrough infection

BACKGROUND

We used the large clinical database that supported the development of Oka/ Merck varicella vaccine to study the relationship between the primary varicella antibody response, as determined by gpELISA, an enzyme-linked immunosorbent assay that detects antibodies to varicella-zoster virus (VZV) glycoprotein, and the subsequent risk of postvaccination breakthrough varicella.

METHODS

We vaccinated 1,164 healthy children with a single dose of varicella vaccine containing 2900 to 9000 plaque-forming units/dose. The primary immune response to vaccination was determined by gpELISA 6 weeks after vaccination. Subjects were followed annually for 7 years to ascertain cases of breakthrough varicella.

RESULTS

The estimated vaccine efficacy among children with a 6-week postvaccination antibody titer of > or = 5 gpELISA units was 95.5% (95% confidence interval, 94.2%, 96.8%) compared with 83.5% (95% confidence interval, 76.9%, 89.5%) for subjects with a titer of <5 gpELISA units. Children with a 6-week postvaccination antibody titer of <5 gpELISA units were 3.5 times more likely than those with a titer of > or = 5 gpELISA units to develop breakthrough varicella.

CONCLUSIONS

We identified a 6-week postvaccination antibody titer of > or = 5 gpELISA units as an approximate correlate of protection. In addition we established an accelerated failure time model based on log normal hazard that predicted varicella breakthrough rates based on the distribution of 6-week postvaccination varicella antibody titers.

Childhood vaccination against varicella: persistence of antibody, duration of protection, and vaccine efficacy

OBJECTIVE

To document the duration of protection afforded by Oka/Merck varicella vaccine over a 7-year period.

STUDY DESIGN

The subjects were healthy children 1 to 12 years of age originally enrolled in clinical studies to evaluate the primary immune response to varicella vaccine 6 weeks after vaccination. Each was monitored for antibody persistence, breakthrough infection, and household exposure to varicella to produce estimates of vaccine efficacy.

RESULTS

The 6-year cumulative varicella antibody persistence rate was 99.5% (95% CI: 98.9%, 100.0%). The annual breakthrough rate through 7 years ranged from 0.2% to 2.3% per year; the estimated cumulative event rate was 6.5%. Comparison of the observed average annual breakthrough rate with the age-adjusted expected annual incidence rate of varicella in unvaccinated children corresponded to an estimated vaccine efficacy of 93.8% to 94.6%. Eighty vaccinated children were exposed to varicella in the household, resulting in 8 (10%) cases of infection. When compared with the historical attack rate of 86.8% in unvaccinated susceptible persons exposed to varicella in the household, this yields an estimated vaccine efficacy of 88.5% (95% CI: 80.9%, 96.1%). Varicella cases in vaccinated children generally were mild.

CONCLUSION

The live attenuated varicella vaccine is highly effective in inducing persistent immunity and long-term protection against breakthrough varicella infection.

Non-parametric estimates of overlap

Kernel densities provide accurate non-parametric estimates of the overlapping coefficient or the proportion of similar responses (PSR) in two populations. Non-parametric estimates avoid strong assumptions on the shape of the populations, such as normality or equal variance, and possess sampling variation approaching that of parametric estimates. We obtain accurate standard error estimates by bootstrap resampling. We illustrate the practical use of these methods in two examples and use simulations to explore the properties of the estimators under various sampling situations.

Use of an angular transformation for ratio estimation in cost-effectiveness analysis

Economic evaluations of medical technologies involve a consideration of both costs and clinical benefits, and an increasing number of clinical studies include a specific objective of assessing cost-effectiveness. These studies measure the trade-off between costs and benefits using the cost-effectiveness ratio (CE ratio), which is defined as the net incremental cost per unit of benefit provided by the candidate therapy. In this paper we review the statistical methods which have been proposed for estimating 95 per cent confidence intervals for cost-effectiveness ratios. We show that the use of an angular transformation of the standardized ratio stabilizes the variance of the estimated CE ratio, and provides a clearer interpretation of study results. An estimate of the 95 per cent confidence interval for the CE ratio in the transformed scale is easily made using the jack-knife or bootstrap. The available methods are compared using data from a long term study of mortality in patients with congestive heart failure.

A regression-based method for estimating mean treatment cost in the presence of right-censoring

In many clinical trials and evaluations using medical care administrative databases it is of interest to estimate not only the survival time of a given treatment modality but also the total associated cost. The most widely used estimator for data subject to censoring is the Kaplan-Meier (KM) or product-limit (PL) estimator. The optimality properties of this estimator applied to time-to-event data (consistency, etc.) under the assumptions of random censorship have been established. However, whenever the relationship between cost and survival time includes an error term to account for random differences among patients' costs, the dependency between cumulative treatment cost at the time of censoring and at the survival time results in KM giving biased estimates. A similar phenomenon has previously been noted in the context of estimating quality-adjusted survival time. We propose an estimator for mean cost which exploits the underlying relationship between total treatment cost and survival time. The proposed method utilizes either parametric or nonparametric regression to estimate this relationship and is consistent when this relationship is consistently estimated. We then present simulation results which illustrate the gain in finite-sample efficiency when compared with another recently proposed estimator. The methods are then applied to the estimation of mean cost for two studies where right-censoring was present. The first is the heart failure clinical trial Studies of Left Ventricular Dysfunction (SOLVD). The second is a Health Maintenance Organization (HMO) database study of the cost of ulcer treatment.

Efficacy estimates from parasite count data that include zero counts

Because of the positive skewness of parasite distributions and the greater constancy of percentage of response of therapy in animal populations, parasite count data are conventionally transformed logarithmically before combining results from different animals, either all controls or all treated. Observations of zero counts raise difficulties, since the logarithm of zero is not useful. In this study, several types of zero count adjustments are compared. Two systems for assigning values to zero counts were considered: a fixed system, which assigns the same value to all zero counts regardless of the proportion of such counts in a treatment group, and a variable system, which replaces zero counts with a value based on the proportion of zero counts in the group. The values assigned by either system are then adjusted to reflect aliquot size. An evaluation was performed by using 32 compound Poisson lognormal distributions, three sample sizes, and three representatives of each zero count adjustment system. The Poisson lognormal distribution provides a convenient method with which to provide variability greater than Poisson. Expected values of the sample estimate of the (known) population mean were calculated for each of the 576 combinations of these factors, and the bias associated with each combination was derived. The bias associated with the three representatives of the variable adjustment system was similar. The variable adjustment system had a lower overall bias than any representatives of the fixed adjustment system.

Cholesterol reduction yields clinical benefit: impact of statin trials

BACKGROUND

We determined the effect of incorporating the results of eight recently published trials of Hmg CoA reductase inhibitors ("statins") on the conclusions from our previously published meta-analysis regarding the clinical benefit of cholesterol lowering.

METHODS AND RESULTS

We used the same analytic approach as in our previous investigation, separating the specific effects of cholesterol lowering from the effects attributable to the different types of intervention studied. The reductions in coronary heart disease (CHD) and total mortality risk observed for the statins fell near the predictions from our earlier meta-analysis. Including the statin trial findings into the calculations led to a prediction that for every 10 percentage points of cholesterol lowering, CHD mortality risk would be reduced by 15% (P<.001), and total mortality risk would be reduced by 11% (P<.001), as opposed to the values of 13% and 10%, respectively, reported previously. Cholesterol lowering in general and by the statins in particular does not increase non-CHD mortality risk.

CONCLUSIONS

Adding the results from the statin trials confirmed our original conclusion that lowering cholesterol is clinically beneficial. The relationships (slope) between cholesterol lowering and reduction in CHD and total mortality risk became stronger, and the standard error of the estimated slopes decreased by about half. Use of statins does not increase non-CHD mortality risk. The effect of the statins on CHD and total mortality risk can be explained by their lipid-lowering ability and appears to be directly proportional to the degree to which they lower lipids.

Cholesterol-reduction intervention study (CRIS): a randomized trial to assess effectiveness and costs in clinical practice

BACKGROUND

The 1988 US National Cholesterol Education Program Expert Panel Report recommended initial treatment with niacin or bile acid sequestrants, followed by other agents if needed, to lower low-density lipoprotein cholesterol (LDL-C) levels in hypercholesterolemic patients who require drug therapy. It is unknown how the effectiveness and costs of such an approach ("stepped care") compare in typical clinical practice to those of initial therapy with lovastatin.

PATIENTS AND METHODS

We randomly assigned 612 patients, aged 20 to 70 years, who met 1988 National Cholesterol Education Program guidelines for drug treatment of elevated LDL-C level and had not previously used cholesterol-lowering medication, to either a stepped-care regimen or initial therapy with lovastatin (both n=306). The study, conducted at Southern California Kaiser Permanente, was designed to approximate typical practice: provider compliance with treatment plans was encouraged but not enforced, and patients paid for medication as they customarily would.

RESULTS

At 1 year, the decline in mean LDL-C level was significantly greater among patients assigned to initial treatment with lovastatin (22% vs 15% for stepped care; P<.001), as was the number who attained goal LDL-C level (</= 4.14 mmol/L [</= 160 mg/dL], or </= 3.36 mmol/L [</= 130 mg/dL] if coronary heart disease or two or more risk factors were present) (40% vs 24%; P<.001). The increase in mean high-density lipoprotein cholesterol levels was significantly greater in the stepped-care group, however (8% vs 1% for lovastatin; P<.001). Patients who were randomized to stepped care were more likely to report substantial bother caused by side effects (30% vs 16% for lovastatin; P<.001) and discontinuation of therapy at 1 year (28% vs 18%, respectively; P<.01). Costs of care were $333 higher per patient in the lovastatin group ($786 vs $453; P<.001).

CONCLUSIONS

A stepped-care regimen beginning with niacin is less costly than initial therapy with lovastatin, but also less effective in lowering LDL-C level. While it is more effective in increasing high-density lipoprotein cholesterol levels, the tolerability of such a regimen may be a problem.

Cholesterol reduction yields clinical benefit. A new look at old data

BACKGROUND

There has been a continuing debate about the overall benefit of cholesterol lowering. We performed a novel meta-analysis of all randomized trials of more than 2 years' duration (n = 35 trials) to describe how coronary-heart-disease (CHD), non-CHD, and total mortality are related to cholesterol lowering and to type of intervention.

METHODS AND RESULTS

The analytic approach was designed to separate the effects of cholesterol lowering itself from the other effects of the different types of intervention used. For every 10 percentage points of cholesterol lowering, CHD mortality was reduced by 13% (P < .002) and total mortality by 10% (P < .03). Cholesterol lowering had no effect on non-CHD mortality. Certain types of intervention had specific effects independent of cholesterol lowering. Fibrates (clofibrates, 7 trials; gemfibrozil, 2 trials) increased non-CHD mortality by about 30% (P < .01) and total mortality by about 17% (P < .02). Hormones (estrogen, 2 trials; dextrothyroxin, 2 trials) increased CHD mortality in men by about 27% (P < .04), non-CHD mortality by about 55% (P < .03), and total mortality by about 33% (P < .01). No specific effects independent of cholesterol lowering were found due to diet (n = 11) or other interventions (resins, 5; niacin, 3; statins, 2; partial ileal bypass, 1).

CONCLUSIONS

The results suggest that cholesterol lowering itself is beneficial but that specific adverse effects of fibrates and hormones increase the risk of CHD (hormones only), non-CHD, and total mortality.

Reduction in burden of illness: a new efficacy measure for prevention trials

A new efficacy measure is developed for use in prevention trials of interventions which may affect both disease incidence and disease severity. We assign a severity score to each incident case and sum severity scores over all incident cases within each treatment group to create a burden-of-illness score for each treatment group. Efficacy is evaluated by the difference between the burden-of-illness per randomized subject in the two randomized treatment groups. Since the numbers of summands in each burden-of-illness score is a random variable, standard methods of analysis are not directly applicable. The asymptotic distribution and sampling properties of the net reduction in the burden-of-illness score are derived for trials designed to stop either after a fixed length of follow-up or after the occurrence of a fixed number of cases. We illustrate the method with data from a clinical trial of a human rotavirus vaccine.

Effect of finasteride on serum PSA concentration in men with benign prostatic hyperplasia. Results from the North American phase III clinical trial

Finasteride, a 5-alpha reductase inhibitor recently introduced for the treatment of symptomatic benign prostatic hyperplasia, reduces prostate size and decreases serum PSA concentration. To interpret PSA in men treated with finasteride, it is necessary to take the reduction into account. This article describes the effect of finasteride on the serum PSA concentration in the North American Phase III clinical trial and discusses implications of these findings for the interpretation of serum PSA in men treated with finasteride.

Time-dependent variability in repeated measurements of cholesterol levels: clinical implications for risk misclassification and intervention monitoring

Intraindividual variability (IIV) in total cholesterol levels based on measurements taken 1 week apart is compared with an estimate based on measurements taken 2 years apart. Single-subject 95% confidence intervals around the mean of two repeated measurements were Xi +/- 21 and +/- 28 mg/dl, respectively, and Xi +/- 30 and +/- 40 mg/dl for a single measurement. Comparing these results with published estimates over varying time intervals shows a trend of decreasing IIV with shorter intervals, suggesting that confidence interval widths based on short-term repeated measurements and those based on longer-term repeated measurements may differ more than previously assumed. The practical consequences are that: (1) the level of misclassification inherent in the National Cholesterol Education Program (NCEP) guidelines may be less than had been estimated; and (2) reliable cholesterol reductions resulting from dietary or other interventions may be somewhat easier to detect. These findings have implications for the cost-effectiveness of cholesterol screening strategies and interventions to reduce cholesterol.