We propose an allocation method for balancing prognostic variables among treatment groups in clinical trials under the condition that some prognostic variables are continuous and others are categorical. In principle, the proposed method utilizes the sum Sr, with respect to groups, of the Kullback-Leibler information (KLI) from the group-pooled distribution of prognostic variables to the group-specific distribution as the criterion for overall balancing, assuming normal and multinomial distributions, respectively. In the realized procedure, the proposed method allocates sequentially enrolled new subjects to a group with probability Pa so as to achieve the minimum of Sr under the condition that the maximum difference of the number of subjects among groups is in the prespecified allowable range DN . Monte-Carlo simulation studies were conducted in order to compare the performance of the proposed method with the Pocock-Simon method which was the most popular method. The homogeneity test of mean and variance among groups for evaluating the achieved balance showed greater P values in the proposed method than those in the Pocock-Simon method. The parameter estimates of treatment effect adjusted for prognostic variables were also likely to be more stable in the proposed method than in the Pocock-Simon method.
In measuring quality of life (QOL), outcome-dependent missing values are inevitable because of longitudinal nature of the study. In particular, in clinical trials of advanced-stage disease, it is desirable to distinguish differences between reasons for missing, death and drop-out, because QOL scores for death cases are not really missing data, but are nonexistent and are simply undefined. We focus on estimating the local average treatment effect among survivors. Standard randomized treatment comparisons cannot be performed because the QOL scores are only defined in the non-randomly selected subgroup of survivors. We propose a new estimation method of the survivor average causal effect (SACE) in the presence of both death and dropout. The proposed estimator is a weighted average of the standard estimators for survivors where the weight is the probability that the patient would have survived had he/she received the other treatment. For drop-out cases, the multiple imputation method is applied. Two analysis methods (proposed method and analysis based on only observed survivors) were compared by simulation studies. The proposed estimator had smaller biases with smaller MSEs compared with those of the standard estimator. The proposed method was applied to data from a randomized phase III clinical trial for advanced non-small-cell lung cancer patients.
In the field of fish population dynamics, CPUE (catch per unit effort), defined as catch in number (or in weight) per fishing effort, is an important concept which is proportional to stock size. Because there are many areas without any operation in the fishery for southern bluefin tuna, the handling of such parts have a greatly effect on the estimated year trend of CPUE. Therefore, in this paper, we conducted the prediction of CPUE values in these missing cells using neural networks based on the spatial-temporal information. As a result of the reliability check by n-fold cross-validation, it shows rather high values of Pearson's correlation coefficient or low values of absolute error between observed CPUE and the corresponding predicted one based on neural networks. There values of correlation coefficient by neural networks are rather better than those by MCMC based on the multiple imputation method. We suggested the simple way for factorial experiment to extract year trends based on the estimated CPUE by neural networks. It was found that the extracted CPUE year trend based on this simple way using neural networks is rather similar to that by generalized linear models assuming that there exists some fish in the missing cells as well as surrounding areas. The results are consistent with so-called constant square hypothesis using generalize linear models in the CPUE analyses for southern bluefin tuna.
Akaike's information criterion, which is widely used as a criterion of model selection in fish population dynamics, tend to overestimate the number of unknown parameters in several cases. In this paper, we discuss the selection performance to select the true model of various information criteria (AIC, BIC, c-AIC, TIC and HQ etc.) through computer simulations by analysis of variance, linear regression and Gaussian mixture model corresponding to CPUE standardization. As a result, we obtain the following results with regard to the goodness of various information criteria. 1) In small samples or in the case that there are many unknown parameters compared to the sample size, the selection performance of c-AIC (finite correction of AIC) is superior to that of other information criteria. 2) In large samples, the consistent information criteria, BIC and HQ, are better than AIC. 3) In nested ANOVA-type model, the selection performance of TIC, which is exact evaluation of AIC, is almost same as that of AIC and c-AIC, and BIC is slightly good compared to stepwise F-test. 4) In normal mixture model, stepwise chi-square test is not theoretically applicable and the selection performance of Bayes-type information criterion assuming the Dilichlet prior is superior to that of AIC and BIC.
We showed a dynamic allocation procedure to achieve a specified proportion of sample sizes among treatment groups not only within all patients, but also within patients with strata composed of centers and/or prognostic factors in randomized controlled clinical trials. This procedure allocates a patient to one of treatment groups that are within a pre-specified range from the best balance, as well as the treatment groups with the best balance. When target sample sizes of two treatment groups are the same, Zelen (1974) showed a restriction for a range of obtained sample sizes. Proposed method is an extension of Zelen's restriction to the case of different target sample sizes of more than two treatment groups. This method can introduce randomness into a dynamic allocation procedure keeping comparability among treatment groups.