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Biomedical subjects

M J Meisner

Publications and source records attributed to M J Meisner.

4 recordsLinked to original sources
PubMed1992

Nonparametric estimation and testing in a cure model.

Nonparametric generalized maximum likelihood product limit point estimators and confidence intervals are given for a cure model with random censorship. One-, two-, and K-sample likelihood ratio tests for inference on the cure rates are developed. In the two-sample case its power is compared to the power of several alternatives, including the log-rank and Gray and Tsiatis (1989, Biometrics 45, 899-904) tests. Implications for the use of the likelihood ratio test in a clinical trial designed to compare cure rates are discussed.

Biometry↗
PubMed1989

Testing whether an identified treatment is best.

We consider the problem of testing whether an identified treatment is better than each of K treatments. Suppose there are univariate test statistics Si that contrast the identified treatment with treatment i for i = 1, 2,...., K. The min test is defined to be the alpha-level procedure that rejects the null hypothesis that the identified treatment is not best when, for all i, Si rejects the one-sided hypothesis, at the alpha-level, that the identified treatment is not better than the ith treatment. In the normal case where Si are t statistics the min test is the likelihood ratio test. For distributions satisfying mild regularity conditions, if attention is restricted to test statistics that are monotone nondecreasing functions of Si, then regardless of their covariance structure the min test is an optimal alpha-level test. Tables of the sample size needed to achieve power .5, .8, .90, and .95 are given for the min test when the Si are Student's t and Wilcoxon.

Biometry↗
PubMedDate not supplied

Quantitative differences in aspirin analgesia in three models of clinical pain.

An analysis was made of data from over 4000 postepisiotomy, uterine cramping, and postsurgical patients complaining of moderate or severe pain. They had received 325, 650, or 1300 mg aspirin or placebo while they were subjects in 10 analgesic clinical trials. On the average, for the same verbally expressed pain intensity level and the same treatment, more relief was obtained by a patient with uterine cramping than one with episiotomy pain, who in turn obtained more relief than a patient with surgical pain. A new mathematical model which characterizes the probability that an analgesic provides complete relief as a function of dose, severity of pain intensity, and pain etiology is developed. The model utilizes the data itself to estimate the numerical score corresponding to verbal pain intensities. The results indicate that the numerical score quantifying severe surgical pain is 1.4 times greater than the score for severe episiotomy pain, which in turn is 3.2 times greater than the score for severe uterine cramping. Clinical trials must be designed to take these differences into account. Also, clinicians must be cognizant of such differences when choosing among drugs and dosages for patients with different pain intensity and etiology.

Analysis of Variance↗