Validation of qualitative microbiological test methods

This paper considers a statistical model for the detection mechanism of qualitative microbiological test methods with a parameter for the detection proportion (the probability to detect a single organism) and a parameter for the false positive rate. It is demonstrated that the detection proportion and the bacterial density cannot be estimated separately, not even in a multiple dilution experiment. Only the product can be estimated, changing the interpretation of the most probable number estimator. The asymptotic power of the likelihood ratio statistic for comparing an alternative method with the compendial method, is optimal for a single dilution experiment. The bacterial density should either be close to two CFUs per test unit or equal to zero, depending on differences in the model parameters between the two test methods. The proposed strategy for method validation is to use these two dilutions and test for differences in the two model parameters, addressing the validation parameters specificity and accuracy. Robustness of these two parameters might still be required, but all other validation parameters can be omitted. A confidence interval-based approach for the ratio of the detection proportions for the two methods is recommended, since it is most informative and close to the power of the likelihood ratio test. Keywords: accuracy;detection proportion;specificity;false positives;limit of detection;generalized most probable number estimato

A comparison of spiking experiments to estimate the detection proportion of qualitative microbiological methods

The detection proportion of a qualitative microbiological test method is the probability to detect a single micro-organism. A general expression for the moment estimator of the detection proportion is provided. It depends on the distribution of the spikes used in a validation study through its moment-generating function. Several forms of spiking experiments are compared on their estimation performance using simulations and assuming a generalized Poisson distribution (GPD) for the spikes. The optimal design, which minimizes the mean squared error of our proposed moment estimator, depends on the dispersion parameter of the GPD. The design that uses just one spiked solution instead of multiple solutions is optimal for Poisson and overdispersed Poisson and it is robust against distributions for the spikes

Non-inferiority testing for qualitative microbiological methods: Assessing and improving the approach in USP 1223

The United States Pharmacopoeia (USP) presents two approaches for showing non-inferiority of an alternate qualitative microbiological method versus a compendial method. One approach compares the positive rates for the alternate and compendial methods at one spike level, while the other one compares multiple most probable number (MPN) estimates from a multi-spike design using a t-test. In this paper, we discuss these approaches under certain assumptions and propose a third approach that can be used for both single and multiple dilutions, which we call the generalized MPN (gMPN) approach. Simulations, using Poisson distributed numbers of microorganisms in test samples, confirm that the USP approach based on rates is not suitable, that the USP approach based on MPNs is appropriate for non-inferiority, but the gMPN approach outperforms the MPN-based approach and is therefore recommended

A Phase 2 Randomized Dose-Finding Study With Esmirtazapine in Patients With Primary Insomnia

The antidepressant mirtazapine is an alternative to classical hypnotics, and this study investigated the efficacy and safety of esmirtazapine (Org 50081, the maleic acid salt of S-mirtazapine) in patients given a diagnosis of primary insomnia after acute (2-day) treatment. Patients aged 18 to 65 years with primary insomnia were randomized to receive placebo or 1.5-, 3.0-, or 4.5-mg esmirtazapine in a balanced 4-way crossover study; 2 sleep laboratory nights with polysomnography were separated by 5-day, single-blind placebo washout periods. Polysomnography-determined total sleep time (primary end point) and patient-reported total sleep time improved by at least 25 minutes with all 3 doses of esmirtazapine (P ≤ 0.001 vs placebo). Polysomnography-measured wake time after sleep onset (P ≤ 0.0001) and latency to persistent sleep also improved vs placebo (P ≤ 0.01, 3.0 and 4.5 mg). Patient-reported sleep quality improved with 3.0- and 4.5-mg esmirtazapine (P ≤ 0.01 and P ≤ 0.05, respectively, vs placebo). Morning alertness and contentment were not altered after esmirtazapine, and calmness increased with 4.5-mg esmirtazapine vs placebo. Evening questionnaires showed no difference in duration of daytime naps but reduced energy and ability to work/function after esmirtazapine treatment periods vs placebo (P \u3c 0.05), although this effect was limited to the first night of each 2-night period. There were few adverse events, no serious adverse events, or clinically relevant treatment differences in vital signs, laboratory values, or electrocardiogram. Esmirtazapine doses of 1.5 to 4.5 mg/day significantly improved quantity and quality of sleep and were generally well tolerated, with no evidence of safety concerns or consistent pattern of residual effects

D-Optimal Designs for the Mitscherlich Non-Linear Regression Function

Abstract: Mitscherlich’s function is a well-known three-parameter non-linearregression function that quantifies the relation between astimulus or a time variable and a response. It has manyapplications, in particular in the field of measurementreliability. Optimal designs for estimation of this function havebeen constructed only for normally distributed responses withhomoscedastic variances. In this paper we generalize thisliterature to D-optimal designs for discrete and continuousresponses having their distribution function in the exponentialfamily. We also demonstrate that our D-optimal designs can beidentical to and different from optimal designs for varianceweighted linear regression

Optimal Spiking Experiment for Noninferiority of Qualitative Microbiological Methods on Accuracy With Multiple Microorganisms

The European and United States Pharmacopoeia demand a noninferiority study on the detection of microorganisms when an alternate qualitative microbiological method is intended to replace the compendial microbiological method. However, without imposing any modeling assumptions or constraints, noninferiority studies require large numbers of test samples for a proposed noninferiority criterion of 0.7 or higher for each microorganism. When we can assume that the accuracy of the alternate method with respect to the compendial method is homogeneous across microorganisms, a joint statistical analysis of the data from all microorganisms can be used to help reduce the sample size dramatically. For this situation, we provide a test statistic for noninferiority, an optimal spiking experiment, and a sample size calculation approach under only mild modeling assumptions of the microorganism-specific detection proportions. We illustrate our approach on a real dataset and demonstrate good performance of our method using simulation studies

Optimal Spiking Experiment for Noninferiority of Qualitative Microbiological Methods on Accuracy With Multiple Microorganisms

The European and United States Pharmacopoeia demand a noninferiority study on the detection of microorganisms when an alternate qualitative microbiological method is intended to replace the compendial microbiological method. However, without imposing any modeling assumptions or constraints, noninferiority studies require large numbers of test samples for a proposed noninferiority criterion of 0.7 or higher for each microorganism. When we can assume that the accuracy of the alternate method with respect to the compendial method is homogeneous across microorganisms, a joint statistical analysis of the data from all microorganisms can be used to help reduce the sample size dramatically. For this situation, we provide a test statistic for noninferiority, an optimal spiking experiment, and a sample size calculation approach under only mild modeling assumptions of the microorganism-specific detection proportions. We illustrate our approach on a real dataset and demonstrate good performance of our method using simulation studies