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J L Ciminera

Publications and source records attributed to J L Ciminera.

10 recordsLinked to original sources
PubMed1993

Tests for qualitative treatment-by-centre interaction using a 'pushback' procedure.

In multicentre clinical trials using a common protocol, the centres are usually regarded as being a fixed factor, thus allowing any treatment-by-centre interaction to be omitted from the error term for the effect of treatment. However, we feel it necessary to use the treatment-by-centre interaction as the error term if there is substantial evidence that the interaction with centres is qualitative instead of quantitative. To make allowance for the estimated uncertainties of the centre means, we propose choosing a reference value (for example, the median of the ordered array of centre means) and converting the individual centre results into standardized deviations from the reference value. The deviations are then reordered, and the results 'pushed back' by amounts appropriate for the corresponding order statistics in a sample from the relevant distribution. The pushed-back standardized deviations are then restored to the original scale. The appearance of opposite signs among the destandardized values for the various centres is then taken as 'substantial evidence' of qualitative interaction. Procedures are presented using, in any combination: (i) Gaussian, or Student's t-distribution; (ii) order-statistic medians or outward 90 per cent points of the corresponding order statistic distributions; (iii) pooling or grouping and pooling the internally estimated standard deviations of the centre means. The use of the least conservative combination--Student's t, outward 90 per cent points, grouping and pooling--is recommended.

Clinical Trials as Topic↗
PubMed1993

Evaluation of multicentre clinical trial data using adaptations of the Mosteller-Tukey procedure.

Two procedures, based on proposals discussed by Mosteller and Tukey, are described for obtaining a combined estimate of the difference between two treatment means and its confidence interval from multicentre clinical trial data. Both procedures provide estimates in the possible presence of heteroscedasticity. The first procedure is designated the primary analysis for efficacy assessment. It omits treatment-by-centre interaction from the error term for treatment, unless there is substantial evidence of qualitative interaction (Ciminera et al.) or other special circumstances. The second procedure is the primary analysis whenever there is substantial evidence of qualitative interaction, and can be used whenever there are other reasons to make an analysis allowing for interaction.

Asthma↗
PubMed1992

Developing control charts to review and monitor medication errors.

There is a need to monitor reported medication errors in a hospital setting. Because the quantity of errors vary due to external reporting, quantifying the data is extremely difficult. Typically, these errors are reviewed using classification systems that often have wide variations in the numbers per class per month. The authors recommend the use of control charts to review historical data and to monitor future data. The procedure they have adopted is a modification of schemes using absolute (i.e., positive) values of successive differences to estimate the standard deviation when only single incidence values are available in time rather than sample averages, and when many successive differences may be zero.

Data Collection↗
PubMed1987

The statistical evaluation of a three-period two-treatment crossover pharmacokinetic drug interaction study.

In a pharmacokinetic drug interaction study, the purpose is to determine whether the coadministration of a drug A with a second drug B alters the absorption/distribution/metabolism/elimination profile of either drug. While the usual design for such studies is a three-period crossover, it cannot be analyzed as such, because the plasma-level data of drug B will be 0 when drug A is given alone, and vice versa. The easiest way to proceed is to do two sets of paired analyses, one on the absorption profile of A (A vs AB), and the other on the absorption profile of B (B vs AB). A complete separation of the total sources of variation and degrees of freedom is presented along with a numerical example.

Angiotensin-Converting Enzyme Inhibitors↗
PubMed1985

Testing the statistical certainty of a response to increasing doses of a drug.

Experiments in which the treatments are composed of a series of doses of a compound and a zero dose control are often used in animal toxicity studies. A test procedure is proposed to assess trends in the response variable. The notion of a no-statistical-significance-of-trend (NOSTASOT) dose is introduced, and questions of multiplicity of statistical tests in this context are addressed.

Animals↗
PubMed1977

Mantel-Haenszel analyses of litter-matched time-to-response data, with modifications for recovery of interlitter information.

The Mantel-Haenszel procedure for comparing sets of time-to-response data is adaptable to data that can be stratified on other variables. A particular adaptation, which we have used, is one in which animals from the same litter have been assigned to different treatment groups, e.g., some to a control group and some to a drug treatment group. The time to response used was that of tumor appearance; death from other causes was considered a loss to observation. The initial litter-adjusted analysis seemed to have only limited advantages compared to analysis that ignored litters and could be interpreted as suggesting that litter matchihg was not advantageous. Contributing to the difficulty was the fact that in the litter-matched analysis no further information was forthcoming from the remaining similarly treated animals in a litter when there were no remaining contrastingly treated littermates. Several devices for recovering interlitter information from such remnants and for combining it with intralitter information are examined and applied.

Animals↗
PubMed1975

An improved Mantel-Bryan procedure for "safety" testing of carcinogens.

A published method by Mantel and Bryan for calculating "safe" doses of carcinogens is updated by incorporating several improvements. These improvements include more effective procedures for taking into account any spontaneous tumor rate and for combining data at several dose levels. An added feature is that it permits the combining of data from several experiments by postulating that it is only the spontaneous rate that differs between experiments. The improved method is illustrated with data from five hypothetical experiments, using a risk level of 10-8, a conservative slope of one probit or normal deviate per tenfold dose increase, and a nominal assurance level of 99%. The hypothetical experiments were geared to bring out particular pointsas, for example, the applicability of the model in the absence of control data. A large variety of issues involved in the determination of "safe" doses are discussed, including questions of experiment design and extrapolitan between species. A statistical appendix is provided, laying the framework for the calculating procedure and detailing complications therein. The "safe" dose approach helps resolve certain dilemmas in questions relating to food additives. A "no-detectable-level" prescription for chemical residues may be dangerous to the public where detection techniques are insufficiently sensitive, but it can become far too restrictive as exquisitely sensitive detection techniquesare developed. Only levels in excess of the "safe" dose would require detection. Calculated values for the "safe" dose could be updated and increased as more clear evidence of safety becomes available.

Animals↗