Ori Davidov

Dropout Rates in Randomized Clinical Trials of Antipsychotics: A Meta-analysis Comparing First- and Second-Generation Drugs and an Examination of the Role of Trial Design Features

Dropout is often used as an outcome measure in clinical trials of antipsychotic medication. Previous research is inconclusive regarding (a) differences in dropout rates between first- and second-generation antipsychotic medications and (b) how trial design features reduce dropout. Meta-analysis of randomized controlled trials (RCTs) of antipsychotic medication was conducted to compare dropout rates for first- and second-generation antipsychotic drugs and to examine how a broad range of design features effect dropout. Ninety-three RCTs that met inclusion criteria were located (n = 26 686). Meta-analytic random effects models showed that dropout was higher for first- than second-generation drugs (odds ratio = 1.49, 95% confidence interval: 1.31-1.66). This advantage persisted after removing study arms with excessively high dosages, in flexible dose studies, studies of patients with symptom exacerbation, nonresponder patients, inpatients, and outpatients. Mixed effects models for meta-analysis were used to identify design features that effected dropout and develop formulae to derive expected dropout rates based on trial design features, and these assigned a pivotal role to duration. Collectively, dropout rates are lower for second- than first-generation antipsychotic drugs and appear to be partly explained by trial design features thus providing direction for future trial design.

The Association of Dropout and Outcome in Trials of Antipsychotic Medication and Its Implications for Dealing With Missing Data

OBJECTIVE The extent to which noncompletion of a clinical trial relates to outcomes has implications for choosing the most appropriate method for contending with missing data due to dropout. We examined whether dropout relates to outcome in clinical trials of antipsychotic medication. METHODS Data from 5 large clinical trials of schizophrenia (n=3483) were examined separately. Patients were aggregated into groups based on their final study visit. Group mean Positive and Negative Syndrome Scale (PANSS) total scores for each visit were computed and graphed. Change from baseline to end point for each group was computed and examined using ANCOVA. Cox regression modeling was used to examine baseline PANSS total and change as predictors of time to dropout. RESULTS In all 5 trials there was a statistically significantly relationship between time in trial and improvement. The longer the patients remained in the trial the more that they improved, with trial completers showing the most improvement at each time point. Higher baseline PANSS scores and symptom deterioration indicated by increased PANSS preceding the final study visit prior to dropout corresponded significantly with a greater likelihood of dropout. CONCLUSIONS Dropout in clinical trials of antipsychotic medications corresponds with efficacy outcomes, the dynamics of symptom change and baseline symptom severity. Therefore, methods for statistical analysis should examine both efficacy and dropout and cannot assume that missing data due to dropout are completely at random.

Order-Restricted Inference for Multivariate Binary Data With Application to Toxicology

In many applications, researchers collect multivariate binary response data under two or more naturally ordered experimental conditions. In such situations, one is often interested in using all binary outcomes simultaneously to detect an ordering among the experimental conditions. To make such comparisons, we develop a general methodology for testing for the multivariate stochastic order between K ≥ 2 multivariate binary distributions. Our proposed test uses order-restricted estimators, which, according to our simulation study, are more efficient than the unrestricted estimators in terms of their mean squared error. We compared the power of the proposed test with that of several alternative tests, including procedures that combine individual univariate tests for order, such as union-intersection tests and a Bonferroni-based test. We also compared the proposed test with an unrestricted Hotelling T2-type test. Our simulations suggest that the proposed method competes well with these alternatives. The gain in power is often substantial. The proposed methodology is illustrated by applying it to a two-year rodent cancer bioassay data obtained from the U.S. National Toxicology Program. Supplemental materials are available online.

Constrained inference in mixed-effects models for longitudinal data with application to hearing loss.

In medical studies, endpoints are often measured for each patient longitudinally. The mixed-effects model has been a useful tool for the analysis of such data. There are situations in which the parameters of the model are subject to some restrictions or constraints. For example, in hearing loss studies, we expect hearing to deteriorate with time. This means that hearing thresholds which reflect hearing acuity will, on average, increase over time. Therefore, the regression coefficients associated with the mean effect of time on hearing ability will be constrained. Such constraints should be accounted for in the analysis. We propose maximum likelihood estimation procedures, based on the expectation-conditional maximization either algorithm, to estimate the parameters of the model while accounting for the constraints on them. The proposed methods improve, in terms of mean square error, on the unconstrained estimators. In some settings, the improvement may be substantial. Hypotheses testing procedures that incorporate the constraints are developed. Specifically, likelihood ratio, Wald, and score tests are proposed and investigated. Their empirical significance levels and power are studied using simulations. It is shown that incorporating the constraints improves the mean squared error of the estimates and the power of the tests. These improvements may be substantial. The methodology is used to analyze a hearing loss study.

Order-Restricted Semiparametric Inference for the Power Bias Model

The power bias model, a generalization of length-biased sampling, is introduced and investigated in detail. In particular, attention is focused on order-restricted inference. We show that the power bias model is an example of the density ratio model, or in other words, it is a semiparametric model that is specified by assuming that the ratio of several unknown probability density functions has a parametric form. Estimation and testing procedures under constraints are developed in detail. It is shown that the power bias model can be used for testing for, or against, the likelihood ratio ordering among multiple populations without resorting to any parametric assumptions. Examples and real data analysis demonstrate the usefulness of this approach.

Designing cancer prevention trials: a stochastic model approach.

There is growing interest in the design and implementation of cancer prevention trials. The key idea is to have agents which interfere with carcinogenesis and/or the preclinical stage. In this article we develop multi-stage stochastic models for the planning of cancer prevention trials. For known inputs it is possible to calculate the incidence of disease for the control and intervention groups. Consequently we find designs that balance the required sample size and follow-up time while guaranteeing prespecified error probabilities. Moreover such models can incorporate the mode of action of the intervention as well as compliance. The model has been applied to breast cancer to determine the implications for planning breast cancer intervention trials. Although the model addresses issues in cancer prevention, it is quite general and may be suitable for other chronic diseases.

Short communication: Combining p-values using order-based methods

Statistical practice often requires combining evidence from independent sources. A popular approach is to combine p-values. Motivated by the observation that p-values under the alternative are stochastically smaller than p-values under the null, we develop new combination rules that explicitly account for this ordering. The new combination rules are broadly applicable, optimal against some alternatives, and highly efficient against optimal procedures.

The linear stochastic order and directed inference for multivariate ordered distributions

Researchers are often interested in drawing inferences regarding the order between two experimental groups on the basis of multivariate response data. Since standard multivariate methods are designed for two sided alternatives they may not be ideal for testing for order between two groups. In this article we introduce the notion of the linear stochastic order and investigate its properties. Statistical theory and methodology are developed to both estimate the direction which best separates two arbitrary ordered distributions and to test for order between the two groups. The new methodology generalizes Roys classical largest root test to the nonparametric setting and is applicable to random vectors with discrete and/or continuous components. The proposed methodology is illustrated using data obtained from a 90-day pre-chronic rodent cancer bioassay study conducted by the National Toxicology Program (NTP).

A note on an iterative algorithm for nonparametric estimation in biased sampling models

A simple iterative estimation procedure for computing the nonparametric maximum likelihood estimator (NPMLE) in biased sampling models is discussed and studied in detail. A proof of convergence is provided. Numerical experiments show that the algorithm is significantly faster in terms of CPU time compared with the standard procedure.

Optimal design for double sampling with continuous outcomes

We provide a method for finding the optimal double sampling plan for estimating the mean value of a continuous outcome. It is assumed that the fallible and true outcome data are related by a simple linear regression model. The design parameters are the total sample size, N, and the number of doubly sampled units n. We show that under certain conditions the efficiency gains, relative to standard sampling plans are considerable.