Methodological standards for randomised controlled trials of interventions for preventing recurrence of child physical abuse and neglect

Despite the significant financial and human resources invested in child protection services (CPS), it is unknown whether these services are effective in preventing recurrence of child physical abuse and neglect. This paper reviews available studies evaluating the effectiveness of these interventions and identifies methodological limitations and factors that may contribute to these limitations. We searched databases to identify randomised controlled trials published in peer‐reviewed journals in the past five decades that evaluated interventions to reduce recurrence of physical abuse and neglect. We outlined ten methodological standards that are important for rigorous testing of psychosocial interventions and applied them in critically appraising identified studies. Thirteen randomised controlled trials were reviewed. This review identified methodological limitations (e.g. small sample size, lack of standardisations, contamination) that made it difficult to draw reliable conclusions as to the effectiveness of interventions. Field‐specific factors that contributed to methodological limitations (e.g. heterogeneity of sample, multiple family problems, psychosocial nature of interventions) were identified and recommendations were provided for improvement. It was concluded that it is possible to implement high‐quality trials that are ethical and feasible in the child welfare field. Copyright © 2009 John Wiley & Sons, Ltd.     

Efficiency of tests based on weighted rankings for equality of treatment effects in a complete randomized blocks layout

Quade (1972,1979) proposed a family of nonparametric tests based on a method of weighted within-block rankings, for testing the hypothesis of no treatment effects in a complete randomized blocks layout.

In this paper we obtain an expression for the Pitman asymp-totic relative efficiency of these tests with respect to the Friedman test.     

A Procedure for Identification of Principal Variables by Least Generalized Dependence

Principal components are often used for reducing dimensions in multivariate data, but they frequently fail to provide useful results and their interpretation is rather difficult. In this article, the use of entropy optimization principles for dimensional reduction in multivariate data is proposed. Under the assumptions of multivariate normality, a four-step procedure is developed for selecting principal variables and hence discarding redundant variables. For comparative performance of the information theoretic procedure, we use simulated data with known dimensionality. Principal variables of cluster bean (Guar) are identified by applying this procedure to a real data set generated in a plant breeding experiment.     

Testing the proportional odds assumption in multiply imputed ordinal longitudinal data

A popular choice when analyzing ordinal data is to consider the cumulative proportional odds model to relate the marginal probabilities of the ordinal outcome to a set of covariates. However, application of this model relies on the condition of identical cumulative odds ratios across the cut-offs of the ordinal outcome; the well-known proportional odds assumption. This paper focuses on the assessment of this assumption while accounting for repeated and missing data. In this respect, we develop a statistical method built on multiple imputation (MI) based on generalized estimating equations that allows to test the proportionality assumption under the missing at random setting. The performance of the proposed method is evaluated for two MI algorithms for incomplete longitudinal ordinal data. The impact of both MI methods is compared with respect to the type I error rate and the power for situations covering various numbers of categories of the ordinal outcome, sample sizes, rates of missingness, well-balanced and skewed data. The comparison of both MI methods with the complete-case analysis is also provided. We illustrate the use of the proposed methods on a quality of life data from a cancer clinical trial.     

Sample size planning for phase II trials based on success probabilities for phase III

In recent years, high failure rates in phase III trials were observed. One of the main reasons is overoptimistic assumptions for the planning of phase III resulting from limited phase II information and/or unawareness of realistic success probabilities. We present an approach for planning a phase II trial in a time‐to‐event setting that considers the whole phase II/III clinical development programme. We derive stopping boundaries after phase II that minimise the number of events under side conditions for the conditional probabilities of correct go/no‐go decision after phase II as well as the conditional success probabilities for phase III. In addition, we give general recommendations for the choice of phase II sample size. Our simulations show that unconditional probabilities of go/no‐go decision as well as the unconditional success probabilities for phase III are influenced by the number of events observed in phase II. However, choosing more than 150 events in phase II seems not necessary as the impact on these probabilities then becomes quite small. We recommend considering aspects like the number of compounds in phase II and the resources available when determining the sample size. The lower the number of compounds and the lower the resources are for phase III, the higher the investment for phase II should be. Copyright © 2015 John Wiley & Sons, Ltd.     

Strengthening the understanding of the relationship between survival designs and test statistics

In the planning of randomized survival trials, the role of follow‐up time of trial participants introduces a level of complexity not encountered in non‐survival trials. Of the two commonly used survival designs, one design fixes the follow‐up time whereas the other allows it to vary. When the follow‐up time is fixed the number of events varies. Conversely, when the number of events is fixed, the follow‐up time varies. These two designs influence test statistics in ways that have not been fully explored resulting in a misunderstanding of the design–test statistic relationship. We use examples from the literature to strengthen the understanding of this relationship. Group sequential trials are briefly discussed. When the number of events is fixed, we demonstrate why a two‐sample risk difference test statistic reduces to a one‐sample test statistic which is nearly equal to the risk ratio test statistic. Some aspects of fixed event designs that need further consideration are also discussed. Copyright © 2009 John Wiley & Sons, Ltd.     

Tests based on weighted rankings in complete blocks: exact distribution and monte carlo simulation

Quade (1972, 1979) proposed a family of nonparametric tests based on weighted within-block rankings, for testing the hypothesis of no treatment effects in a complete randomized blocks layout. In this paper we give a table of the exact null distribution of these tests when the number of treatments is 3, the number of blocks is less than or equal to 14 and the block scores are linear. Moreover, a Monte Carlo study was performed to compare the powers of these tests with parametric and nonparametric competitors     

A COMPARISON OF THE IMPRECISE BETA CLASS, THE RANDOMIZED PLAY-THE-WINNER RULE AND THE TRIANGULAR TEST FOR CLINICAL TRIALS WITH BINARY RESPONSES

This paper develops clinical trial designs that compare two treatments with a binary outcome. The imprecise beta class (IBC), a class of beta probability distributions, is used in a robust Bayesian framework to calculate posterior upper and lower expectations for treatment success rates using accumulating data. The posterior expectation for the difference in success rates can be used to decide when there is sufficient evidence for randomized treatment allocation to cease. This design is formally related to the randomized play‐the‐winner (RPW) design, an adaptive allocation scheme where randomization probabilities are updated sequentially to favour the treatment with the higher observed success rate. A connection is also made between the IBC and the sequential clinical trial design based on the triangular test. Theoretical and simulation results are presented to show that the expected sample sizes on the truly inferior arm are lower using the IBC compared with either the triangular test or the RPW design, and that the IBC performs well against established criteria involving error rates and the expected number of treatment failures.     

Unbiasedly estimating the total of a stigmatizing variable from a complex survey on permitting options for direct or randomized responses

Following the approach of Mangat and Singh (in J Ind Statist Assoc 32(3): 71–75, 1994) we consider estimating the population total of a sensitive variable like earning through gambling, amount of tax evaded etc. on generating sample responses allowing options for direct or randomized responses according to a specified device. Gathering two independent responses from each individual chosen according to any general sampling scheme we derive unbiased estimators for the total as well as for the variances of these estimators.     

Adaptive designs for selecting drug combinations based on efficacy–toxicity response

We propose a new adaptive procedure for dose-finding in clinical trials with combination of two drugs when both efficacy and toxicity responses are available. We model the distribution of this bivariate binary endpoint using the bivariate probit model. The analytic formulae for the Fisher information matrix are obtained, that form the basis for derivation of the locally optimal, minimax, Bayesian, and adaptive designs in the framework of optimal design theory.