A regression-based method for estimating mean treatment cost in the presence of right-censoring

In many clinical trials and evaluations using medical care administrative databases it is of interest to estimate not only the survival time of a given treatment modality but also the total associated cost. The most widely used estimator for data subject to censoring is the Kaplan-Meier (KM) or product-limit (PL) estimator. The optimality properties of this estimator applied to time-to-event data (consistency, etc.) under the assumptions of random censorship have been established. However, whenever the relationship between cost and survival time includes an error term to account for random differences among patients' costs, the dependency between cumulative treatment cost at the time of censoring and at the survival time results in KM giving biased estimates. A similar phenomenon has previously been noted in the context of estimating quality-adjusted survival time. We propose an estimator for mean cost which exploits the underlying relationship between total treatment cost and survival time. The proposed method utilizes either parametric or nonparametric regression to estimate this relationship and is consistent when this relationship is consistently estimated. We then present simulation results which illustrate the gain in finite-sample efficiency when compared with another recently proposed estimator. The methods are then applied to the estimation of mean cost for two studies where right-censoring was present. The first is the heart failure clinical trial Studies of Left Ventricular Dysfunction (SOLVD). The second is a Health Maintenance Organization (HMO) database study of the cost of ulcer treatment.

Simultaneous testing strategy for comparing two treatments in a stratified binomial trial

The Mantel-Haenszel (M-H) procedure is commonly used to compare two treatments in a stratified binomial trial. However, this procedure is asymptotically optimal only if the odds ratio is constant across strata. We propose an alternative analytic strategy based on the simultaneous use of two statistics, ZS and ZI, each involving a weighted averaging of within-stratum differences between proportions. The two treatments are declared significantly different at overall level alpha if either min(ZS, ZI) > Zalpha/2 or max(ZS, ZI) > Zalpha*/2, where alpha* is data dependent. Our strategy is shown to be more powerful than the M-H and other related procedures. Numerical examples are provided for illustration.

Design and analysis of three treatment equivalence trials

We consider design and analysis of clinical trials aimed at demonstrating equivalence of three treatments. We discuss analysis methods that require demonstrating that each pair of treatments has an unimportant difference. The critical values that we provide are smaller than the standard normal critical values, pointing out that the adjustment is counter to the usual multiplicity adjustments. Special issues in demonstrating equivalence of three rather than two treatments are discussed. We propose some conservative criteria for estimating sample size. Procedures for choosing pairs of treatments that are equivalent to each other, even when all three treatments are not shown equivalent, are also discussed along with implications for control of type I errors. We also demonstrate the methods with data from the literature.

On testing equivalence of three populations

We consider analysis of clinical trials in which the objective is to show that three populations are equivalent. Equivalence is defined in terms of delta, the maximum difference in population means; a one-sided hypothesis test of delta is considered. We provide the distribution of the maximum pairwise difference in sample means, and we use this distribution to find critical values for tests of size 0.100, 0.050, 0.025, and 0.010. When standard errors are not equal among the three treatments, a simple adjustment is proposed to control the type I error rate. These tests are applied to studying the equivalence of three binomial proportions. Test-based confidence intervals are discussed. Two examples illustrate the proposed methods.

Robust and bootstrap testing procedures for bioequivalence

A common problem encountered in bioequivalence studies is the presence of outliers. In this situation, the two one-sided t-tests proposed by Schuirmann fail to provide reasonable power for concluding bioequivalence. In contrast, our proposed 2 beta trimmed-t procedure has the following advantages: (1) it has higher efficiency for nonnormal symmetric distributions, (2) it is resistant to outliers, and (3) it is relatively easy to compute. Two bootstrap procedures introduced here provide further justification for the proposed trimmed t-test procedure. Results from Monte Carlo studies illustrate the power of the proposed procedures under various distributional assumptions for a 2 x 2 crossover trial.

The influence of uninterpretability on the assessment of diagnostic tests

A frequent problem faced by physicians utilizing diagnostic tests is the occurrence of uninterpretable test results. Such results, if they occur commonly, can seriously impair the diagnostic performance of the test. Moreover, in assessing the characteristics of the test, i.e. sensitivity, specificity, etc. failure to consider the impact of uninterpretability will artificially inflate the test characteristics. In this paper we explore the implications of this issue. We observe that a relevant factor is the potential repeatability of the test, i.e. whether the cause of uninterpretability is a transient phenomenon or an inherent property of the subject. We distinguish uninterpretable results, in which no result is obtained, from indeterminate results, in which the result is equivocal, or for which predisposing concomitant factors limit the interpretability of the result. Our results demonstrate that the naive approach of ignoring uninterpretable results in test assessments may indeed be unbiased in certain circumstances. However, if the cause of uninterpretability is related to disease status or to the potentially observable test result, then this approach will lead to bias. In either case, the frequency of uninterpretability is an important consideration in the cost-effectiveness of the test.

Elevated levels of carbon monoxide in the patient compartment of ambulances

Six hundred and ninety New Jersey ambulances were monitored for carbon monoxide (CO); 27 per cent had CO levels of 10 ppm or more greater than ambient air in the breathing zone of the patient. Twenty-nine of these ambulances had levels of at least 35 ppm greater than ambient air. Results indicate that a CO exposure problem exists in ambulances.

A treatment allocation procedure for sequential clinical trials

A dynamic treatment allocation procedure is proposed for clinical trials which require balancing across several prognostic factors. The treatment allocation decision is based on the minimization of a function which is an easily evaluated approximation to the variance of the treatment effect in a linear model relating the outcome variable to the chosen prognostic factors and selected interactions. By use of simulations, the procedure is shown to be superior to ad hoc procedures proposed by Pocock and Simon (1975, Biometrics 31, 103-115), over a variety of reasonable experimental situations. It is shown that it is feasible to evaluate the procedure by hand calculations and that it is extremely easy if a small programmable calculator is available. Practical problems relating to implementation of the procedure are discussed with special reference to multi-institutional clinical trials.