Survival analysis in clinical trials: past developments and future directions

The field of survival analysis emerged in the 20th century and experienced tremendous growth during the latter half of the century. The developments in this field that have had the most profound impact on clinical trials are the Kaplan-Meier (1958, Journal of the American Statistical Association 53, 457-481) method for estimating the survival function, the log-rank statistic (Mantel, 1966, Cancer Chemotherapy Report 50, 163-170) for comparing two survival distributions, and the Cox (1972, Journal of the Royal Statistical Society, Series B 34, 187-220) proportional hazards model for quantifying the effects of covariates on the survival time. The counting-process martingale theory pioneered by Aalen (1975, Statistical inference for a family of counting processes, Ph.D. dissertation, University of California, Berkeley) provides a unified framework for studying the small- and large-sample properties of survival analysis statistics. Significant progress has been achieved and further developments are expected in many other areas, including the accelerated failure time model, multivariate failure time data, interval-censored data, dependent censoring, dynamic treatment regimes and causal inference, joint modeling of failure time and longitudinal data, and Baysian methods.     

On a likelihood-based goodness-of-fit test of the beta-binomial model

When faced with proportion data that exhibit extra-binomial variation, data analysts often consider the beta-binomial distribution as an alternative model to the more common binomial distribution. A typical example occurs in toxicological experiments with laboratory animals, where binary observations on fetuses within a litter are often correlated with each other. In such instances, it may be of interest to test for the goodness of fit of the beta-binomial model; this effort is complicated, however, when there is large variability among the litter sizes. We investigate a recent goodness-of-fit test proposed by Brooks et al. (1997, Biometrics 53, 1097-1115) but find that it lacks the ability to distinguish between the beta-binomial model and some severely non-beta-binomial models. Other tests and models developed in their article are quite useful and interesting but are not examined herein.     

Sorption isotherms and kinetics in the primary biodegradation of anionic surfactants by immobilized bacteria: I. Pseudomonas C12B

The surfactant-degrading biocatalyst Pseudomonas C12B was immobilized by covalent linking on silanized inorganic supports and by physical entrapment of cells within reticulated polyurethane foam. Both immobilized biocatalysts have been shown to be appropriate for the effective primary biodegradation of the anionic surfactants sodium dodecyl sulphate (SDS), dodecylbenzene sulphonic acid (DBS), dioctyl sulphosuccinate (DOSS) and dihexyl sulphosuccinate (DHSS). The overall surfactant removal from water by cells entrapped in reticulated polyurethane foam exhibits a biphasic process, a rapid sorption step of the surfactant onto the cell-loaded support and the intrinsic primary biodegradation slower step, both acting cooperatively. The optimization of variables for the adsorption and the biodegradation processes (flow rate, particle size, substrate concentration) have been studied. Sorption isotherms for the surfactants on reticulated polyurethane foam have been established as type II of the Brunauer, Deming, Deming and Teller (BDDT) classification. The kinetics of the primary biodegradation of SDS by cells covalent linked on sepiolite treated with 3-aminopropyl triethoxysilane (APTS) were found to be first-order. In this case, surfactant adsorption does not exist.     

大花黄牡丹分类学地位的研究

通过野外调查、引种试验,对大花黄牡丹与黄牡丹的形态特征、生长和繁殖特性进行了综合比较研究,结果表明,二者之间在植株高度、心皮数目、花色、花瓣基部有无棕红斑及繁殖特性等存在明显差异;同时,染色体组型、带型及蛋白亚基构成分析也表明二者之间有明显差异,因此我们认为,大花黄牡丹应上升为种的等级：Ｐａｅｏｎｉａ ｌｕｄｌｏｗｉｉ（Ｓｔｅｒｎ ｅｔ Ｔａｙｌｏｒ）Ｊ．Ｊ．Ｌｉ ｅｔ Ｄ．Ｚ．Ｃｈｅｎ,ｓｔａｔ     

Differences Between Vervets (Cercopithecus aethiops) and Patas Monkeys (Erythrocebus patas) in Agonistic Interactions Between Adult Females

We examined agonistic interactions between adult females in wild, unprovisioned patas monkeys (Erythrocebus patas) and vervets (Cercopithecus aethiops). The dominance hierarchy of patas is far less clear than that of vervets. Patas had fewer interactions per dyad, fewer dyads with interactions, and a high percentage (18%) of reversals in which lower-ranking females won in agonistic interactions with higher-ranking females. Although the rank ordering of the kinds of interactions patas and vervets displayed is similar, with avoidance being the most frequently observed agonistic response to approaches by other females, patas were chased and supplanted more often than vervets were. The resources over which females were supplanted also differ between species. Supplants over food comprise smaller proportion of total supplants patas than for vervets. Patas appear to feed on less usurpable foods than vervets. We conclude that (1) Erythrocebus and Cercopithecus spp., except C. aethiops, should not be categorized with other Cercopithecinae, and C. aethiops should not be categorized with other Cercopithecus spp. and Erythrocebus, in discussions and analyses of relationships between females within groups and (2) ecological conditions, i.e., usurpability of foods, can override phylogenetic history as the selective pressure determining the nature of female competitive relationships within groups.     

Comparison of Linear,Nonlinear and Semiparametric Mixed‐effects Models for Estimating HIV Dynamic Parameters

The potency of antiretroviral agents in AIDS clinical trials can be assessed on the basis of an early viral response such as viral decay rate or change in viral load (number of copies of HIV RNA) of the plasma. Linear, parametric nonlinear, and semiparametric nonlinear mixed‐effects models have been proposed to estimate viral decay rates in viral dynamic models. However, before applying these models to clinical data, a critical question that remains to be addressed is whether these models produce coherent estimates of viral decay rates, and if not, which model is appropriate and should be used in practice. In this paper, we applied these models to data from an AIDS clinical trial of potent antiviral treatments and found significant incongruity in the estimated rates of reduction in viral load. Simulation studies indicated that reliable estimates of viral decay rate were obtained by using the parametric and semiparametric nonlinear mixed‐effects models. Our analysis also indicated that the decay rates estimated by using linear mixed‐effects models should be interpreted differently from those estimated by using nonlinear mixed‐effects models. The semiparametric nonlinear mixed‐effects model is preferred to other models because arbitrary data truncation is not needed. Based on real data analysis and simulation studies, we provide guidelines for estimating viral decay rates from clinical data. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Adjusting for Ordinal Covariates by Inducing a Partial Ordering

In the context of randomized clinical trials, multiplicity arises in many forms. One prominent example is when a key endpoint is measured and analyzed both at baseline and after treatment. It is common to analyze each separately, but more efficient to adjust the post‐treatment comparisons for the baseline values. Adjustment techniques generally treat the covariate (baseline value, in this case) as either nominal or continuous. Either is problematic when applied to an ordinal covariate, the former because it fails to exploit the natural ordering and the latter because it relies on an artifical notion of linear prediction and differences between values. We propose new methods for adjusting for ordinal covariates without having to treat them as nominal or continuous. Specifically, the information‐preserving composite endpoint consists of the pair of values for each patient, one at baseline and one after treatment. Some of these patterns will indicate more improvement than others, yet some pairs of patterns are not comparable. Hence, the ordering is only partial. We develop an approach to testing and deriving estimators of magnitudes of the treatment effect based on comparing each observation in one group to each observation in the other group to which it is comparable. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Measurement and comparison of head scatter factor for 7 MV unflattened (FFF) and 6 MV flattened photon beam using indigenously designed columnar mini phantom

Aim

To measure and compare the head scatter factor for 7 MV unflattened and 6 MV flattened photon beam using a home-made designed mini phantom.

Background

The head scatter factor (Sc) is one of the important parameters for MU calculation. There are multiple factors that influence the Sc values, like accelerator head, flattening filter, primary and secondary collimators.

Materials and methods

A columnar mini phantom was designed as recommended by AAPM Task Group 74 with high and low atomic number material for measurement of head scatter factors at 10 cm and d_max dose water equivalent thickness.

Results

The Sc values measured with high-Z are higher than the low-Z mini phantoms observed for both 6MV-FB and 7MV-UFB photon energies. Sc values of 7MV-UFB photon beams were smaller than those of the 6MV-FB photon beams (0.6–2.2% (Primus), 0.2–1.4% (Artiste) and 0.6–3.7% (Clinac iX (2300CD))) for field sizes ranging from 10 cm × 10 cm to 40 cm × 40 cm. The SSD had no influence on head scatter for both flattened and unflattened beams. The presence of wedge filters influences the Sc values. The collimator exchange effects showed that the opening of the upper jaw increases Sc irrespective of FF and FFF.

Conclusions

There were significant differences in Sc values measured for 6MV-FB and unflattened 7MV-UFB photon beams over the range of field sizes from 10 cm × 10 cm to 40 cm × 04 cm. Different results were obtained for measurements performed with low-Z and high-Z mini phantoms.