Biomedical subjects

Robert T Magari

Publications and source records attributed to Robert T Magari.

7 recordsLinked to original sources

PubMed2006

Estimation of bias between 2 analytical methods at clinically important ranges.

An approach for estimating bias between 2 analytical methods at different clinical ranges is introduced in this article. The approach models replicated data obtained from the reference and the test method in terms of repeatability and trueness bias. The latter can be partitioned into constant and proportional bias. The approach is based on maximum likelihood estimation and can accommodate normal as well as Poisson and binomial distributions that apply to hematology applications and/or other laboratory methods that count particles per unit of volume and/or time. A full spectrum of statistical inference in the form of confidence intervals for each estimate as well as any statistical hypothesis testing is provided. At the same time these estimates can be practically interpreted and related to any clinical important range or decision point. We recommend this approach as an alternative to the National Committee for Clinical Laboratory Standards (NCCLS) EP9-A2 approach in cases where the application of the NCCLS standard is not appropriate.

Bias↗

PubMed2004

Determining shelf life by comparing degradations at elevated temperatures.

The degradation of a biological product follows a specific pattern that depends on the kinetics of the chemical reaction. For most biological and pharmaceutical products, accelerated stability tests are preferred to establish shelf life. We describe a new approach for accelerated stability tests, for situations in which the Arrhenius equation is not appropriate. This approach consists of estimating stability at elevated temperatures and comparing these results with the stability estimates for a similar product with a known shelf life. In this article, "Test" refers to Immuno-Trol Low Cells, and "Control" (the product with a known shelf life) refers to Immuno-Trol Cells. The degradation rates and stabilities at elevated temperatures of three antigens of the Test are estimated and compared to their respective Control values. Most of the degradation occurs at the beginning of the experimental period and then slows down until it levels off to form a plateau at the minimum level. Both Control and Test showed similar degradation patterns at three elevated temperatures, indicating that they both have the same mechanism of degradation. Thus, it is expected that they will degrade similarly at storage temperature and have nonsignificantly different shelf lives. The approach for accelerated stability testing discussed here is applicable to situations in which the Arrhenius equation is not appropriate, and the chemical properties of both the Test and Control products are similar.

CD3 Complex↗

PubMed2004

Bias estimation in method comparison studies.

A test and a reference analytical method are usually compared for agreement based on paired data obtained from several independent subjects. Bias between two methods can be classified as constant and proportional. In this article, we provide an approach for maximum likelihood estimation of total bias between two methods and partitioning it into constant and proportional bias for each subject. Normal, binomial, or Poisson distribution are the conditional distributions of the response variable that we have considered here, whereas subjects are considered to be random sample from a normally distributed population. Real data on blood cell counts and hemoglobin are used for demonstration. The estimate of biases can be used to test different statistical hypotheses and/or for graphical interpretation of the agreement. The partitioning of total biases in terms of constant and proportional gives an insight on the sources of disagreement between two methods and helps designers and manufacturer's define a remedial strategy.

Algorithms↗

PubMed2004

Linearity evaluation of analytical methods that count particles.

Linearity evaluation of an analytical method is important for both manufacturers of diagnostic devices and laboratory users. Some of the statistical assumptions for estimation and testing in linear regression are violated in analytical methods that count particles per unit of volume or/and time, leading to potential erroneous evaluation of linearity. The objective of this paper is to provide an approach for evaluating linearity in these cases. The number of counts for each concentration level has a Poisson probability distribution that is linear, second-, or higher-order polynomial function of the concentration. Maximum likelihood approach is used to estimate the parameters of the models. Deviance of a particular model and the likelihood ratio test are used to test for linearity. An evaluation of linearity of an analytical method in multiple experiments is also described. No particular changes to the standard testing protocols and data collections are necessary. There are several statistical software packages that can perform the calculations. Formulas and SAS codes presented in this article can also assist in estimation and statistical testing.

Clinical Laboratory Techniques↗

PubMed2002

Accelerated stability model for predicting shelf-life.

Second- and higher-order degradation reactions sometimes cannot be approximated with linear or exponential relationships and need to be appropriately modeled. Events above the COULTER HmX Analyzer white blood cell (WBC) counting threshold were recorded for the HmX PAK reagent system stored at five elevated temperatures. An accelerated stability model for a second-degree polynomial degradation pattern was used. The shelf-life of the reagent, along with 95% lower bound confidence intervals, is predicted using the same pattern of degradation as well as the Arrhenius approximation. Experiments indicated that the degradation of the HmX PAK reagent occurred in two phases, the lag phase and the degradation phase, in all tested temperatures. The phase durations are temperature-dependent, and the Arrhenius approximation is appropriate (P=0.639). The degradation of the reagent during the lag phase was experimentally undetectable. Changes of the reagent were nonsignificant for a predicted period of 164 days at 25 degrees C. The rate of degradation increased significantly later on during the degradation phase. The lower bound of the 95% confidence interval of this prediction indicated that it would take at least 326 days before the HmX PAK reagent would have any performance issue related to aging at storage temperature.

Autoanalysis↗

PubMed2002

Estimating degradation in real time and accelerated stability tests with random lot-to-lot variation: a simulation study.

The effect of different lot-to-lot variability levels on the prediction of stability are studied based on two statistical models for estimating degradation in real time and accelerated stability tests. Lot-to-lot variability is considered as random in both models, and is attributed to two sources-variability at time zero, and variability of degradation rate. Real-time stability tests are modeled as a function of time while accelerated stability tests as a function of time and temperatures. Several data sets were simulated, and a maximum likelihood approach was used for estimation. The 95% confidence intervals for the degradation rate depend on the amount of lot-to-lot variability. When lot-to-lot degradation rate variability is relatively large (CV > or = 8%) the estimated confidence intervals do not represent the trend for individual lots. In such cases it is recommended to analyze each lot individually.

Algorithms↗

PubMedDate not supplied

Establishing tolerance levels for customer complaints.

Customer complaints data are usually expressed as counts for a period of time and are governed by a Poisson process. This process is stationary when the number of complaints is constant, while a change in these numbers would indicate a potential change in the product performance. In this paper we describe an approach for establishing the maximum tolerance level for the number of complaints received within a month. Tolerance level is based on a relatively stable period of time when the Poisson process is stationary. A change-point analysis is performed to the complaints data that exhibit large changes to partition the relatively stable period from the problematic period. Examples that illustrate this approach are provided.

Journal Article↗