On meta-analytic assessment of surrogate outcomes

We discuss the strengths and weaknesses of the meta-analytic approach to estimating the effect of a new treatment on a true clinical outcome measure, T, from the effect of treatment on a surrogate response, S. The meta-analytic approach (see Daniels and Hughes (1997) 16, 1965-1982) uses data from a series of previous studies of interventions similar to the new treatment. The data are used to estimate relationships between summary measures of treatment effects on T and S that can be used to infer the magnitude of the effect of the new treatment on T from its effects on S. We extend the class of models to cover a broad range of applications in which the parameters define features of the marginal distribution of (T, S). We present a new bootstrap procedure to allow for the variability in estimating the distribution that governs the between-study variation. Ignoring this variability can lead to confidence intervals that are much too narrow. The meta-analytic approach relies on quite different data and assumptions than procedures that depend, for example, on the conditional independence, at the individual level, of treatment and T, given S (see Prentice (1989) 8, 431-440). Meta-analytic calculations in this paper can be used to determine whether a new study, based only on S, will yield estimates of the treatment effect on T that are precise enough to be useful. Compared to direct measurement on T, the meta-analytic approach has a number of limitations, including likely serious loss of precision and difficulties in defining the class of previous studies to be used to predict the effects on T for a new intervention.     

Temporal spread of cassava mosaic virus disease in a range of cassava cultivars in different agro-ecological regions of Uganda

The spread of cassava mosaic disease (CMD) in a range of cassava cultivars was studied in experiments and on-farm trials in different agro-ecological regions of Uganda in 1989–1990 and 1990–1991. No spread occurred in either experiment at the southernmost site near Kampala, but there was considerable spread at the four sites elsewhere and also in the on-farm trials in Luwero district. There were significant differences in the final incidence of disease between locations and between cultivars at each location. Where spread occurred it was more rapid in the Ugandan cvs Ebwanateraka, Senyonjo and Bao than in four of the five improved TMS cultivars introduced from Nigeria. These usually showed an apparent decline in incidence of CMD after reaching maxima 4 to 8 months after planting (MAP). The areas under the disease progress curves (AUDPCs) differed significantly between locations and cultivars and were less for cvs TMS 30572, TMS 30395, TMS 30337 and TMS 60142 than for cvs Ebwanateraka, Senyonjo, Bao and TMS 30786. Overall, the mean AUDPCs were greatest at Migyera in Luwero district in 1989–1990 and at Kagando in Kasese district in 1990–1991. They were significantly less at Mubuku in Kasese district in 1989–1990 than at the other two experimental sites where spread occurred. Adult whitefly vector populations were highest at Migyera and Kagando in the 1989–1990 and 1990–1991 trials, respectively, and they were higher on cvs Bao, Ebwanateraka and TMS 30786 than on other varieties. Mean numbers of adults increased until 3–5 MAP and then declined, but CMD incidence increased progressively to reach maxima at or near crop maturity. Locations with the largest numbers of adults also had a relatively high incidence of CMD. Symptoms of CMD were usually more severe on cvs Ebwanateraka, Bao and Bukalasa 11 than on the TMS cultivars, on which symptoms remained slight throughout growth and usually decreased from 5 MAP. The differences between sites, the resistance of the cultivars and the relationship between CMD incidence and whitefly populations are discussed.     

Response Adaptive Designs with a Variance‐penalized Criterion

We consider a response adaptive design of clinical trials with a variance‐penalized criterion. It is shown that this criterion evaluates the performance of a response adaptive design based on both the number of patients assigned to the better treatment and the power of the statistical test. A new proportion of treatment allocation is proposed and the doubly biased coin procedure is used to target the proposed proportion. Under reasonable assumptions, the proposed design is demonstrated to generate an asymptotic variance of allocation proportions, which is smaller than that of the drop‐the‐loser design. Simulation comparisons of the proposed design with some existing designs are presented.     

Sample size re‐estimation in clinical trials

Adaptive clinical trials are becoming very popular because of their flexibility in allowing mid‐stream changes of sample size, endpoints, populations, etc. At the same time, they have been regarded with mistrust because they can produce bizarre results in very extreme settings. Understanding the advantages and disadvantages of these rapidly developing methods is a must. This paper reviews flexible methods for sample size re‐estimation when the outcome is continuous.     

Improved Logrank-Type Tests for Survival Data Using Adaptive Weights

Summary .  For testing for treatment effects with time-to-event data, the logrank test is the most popular choice and has some optimality properties under proportional hazards alternatives. It may also be combined with other tests when a range of nonproportional alternatives are entertained. We introduce some versatile tests that use adaptively weighted logrank statistics. The adaptive weights utilize the hazard ratio obtained by fitting the model of Yang and Prentice (2005,  Biometrika   92 , 1–17). Extensive numerical studies have been performed under proportional and nonproportional alternatives, with a wide range of hazard ratios patterns. These studies show that these new tests typically improve the tests they are designed to modify. In particular, the adaptively weighted logrank test maintains optimality at the proportional alternatives, while improving the power over a wide range of nonproportional alternatives. The new tests are illustrated in several real data examples.     

Informed Consent in Cross-cultural Perspective: Clinical Research in the Tibetan Autonomous Region, PRC

Procedures of Informed Consent are considered a high priority for international biomedical research. However, informed consent protocols are not necessarily transferable across cultural, national or ethnic groups. Recent debates identify the need for balancing ethical universals with practical and local conditions and paying attention to questions of cultural competence when it comes to the Informed Consent process for clinical biomedical research. This article reports on the results of a two-year effort to establish a culturally appropriate Informed Consent process for biomedical research in the Tibet Autonomous Region in the People's Republic of China. A team of Tibetan and American researchers, physicians, health professionals and medical anthropologists conducted the research. The Informed Consent was specifically for undertaking a triple-blind, double placebo-controlled randomized clinical trial of a Tibetan medicine compared with Misoprostol for reducing postpartum blood loss. The findings suggest greater need for flexibility and cooperation in establishing Informed Consent protocols across cultures and nations.     

Quantile regression for longitudinal data using the asymmetric Laplace distribution

In longitudinal studies, measurements of the same individuals are taken repeatedly through time. Often, the primary goal is to characterize the change in response over time and the factors that influence change. Factors can affect not only the location but also more generally the shape of the distribution of the response over time. To make inference about the shape of a population distribution, the widely popular mixed-effects regression, for example, would be inadequate, if the distribution is not approximately Gaussian. We propose a novel linear model for quantile regression (QR) that includes random effects in order to account for the dependence between serial observations on the same subject. The notion of QR is synonymous with robust analysis of the conditional distribution of the response variable. We present a likelihood-based approach to the estimation of the regression quantiles that uses the asymmetric Laplace density. In a simulation study, the proposed method had an advantage in terms of mean squared error of the QR estimator, when compared with the approach that considers penalized fixed effects. Following our strategy, a nearly optimal degree of shrinkage of the individual effects is automatically selected by the data and their likelihood. Also, our model appears to be a robust alternative to the mean regression with random effects when the location parameter of the conditional distribution of the response is of interest. We apply our model to a real data set which consists of self-reported amount of labor pain measurements taken on women repeatedly over time, whose distribution is characterized by skewness, and the significance of the parameters is evaluated by the likelihood ratio statistic.