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J S Krouwer

Publications and source records attributed to J S Krouwer.

At least 19 recordsLinked to original sources
PubMed1995

A simple, graphical method to evaluate laboratory assays.

Evaluation methods of laboratory assays often fail to predict the large, infrequent errors that are a major source of clinician complaints. We present a simple, graphical method to evaluate laboratory assays, which focuses on detecting large, infrequent errors. Our method, the folder empirical cumulative distribution plot or, more simply, mountain plot, is prepared by computing a percentile for each ranked difference between the new and reference method. To get a folded plot, one performs the following subtraction for all percentiles over 50: percentile = 100 - percentile. Percentiles (y axis) are then plotted against differences or percent differences (x axis). The calculations and plots are simple enough to perform in a spreadsheet. We also offer Windows based software to perform all calculations and plots. The mountain plot compared to the difference plot focuses attention on two features of the data: the center and the tails. We prefer the mountain plot over other graphical techniques because: 1. It is easier to find the central 95% of the data. 2. It is easier to estimate percentile for large differences (e.g., percentiles greater than 95%). 3. Unlike a histogram, the plot shape is not a function of the intervals. 4. Comparing different distributions is easier. 5. The plot is easier to interpret than a standard empirical cumulative distribution plot. Difference and mountain plots each provide complementary perspectives on the data. We recommend both plots. This method can also be used with data from a wide variety of other applications such as clinical trials and quality control.

Chemistry, Clinical↗
PubMed1993

A method to quantify deviations from assay linearity.

We present a statistical method to quantify deviations from linearity for assays that veer from linear assay responses. Our procedure handles the common case of unequally spaced analyte levels and nonconstant variance and provides a least-squares estimate with a confidence interval for the amount of deviation from assay linearity at a specified analyte concentration. This estimate of assay bias due to nonlinearity goes beyond the NCCLS EP6 lack-of-fit test, which tests for only the presence of nonlinearity. Knowing that nonlinearity is present is insufficient; users need to know the magnitude of the bias caused by nonlinearity. Our method can also be used with multifactor designs that estimate other systematic assay effects such as drift and carryover, thus obviating the need for a separate protocol to assess linearity. The procedure is carried out by adding extra columns to the design matrix corresponding to the concentration level(s) of interest. The extra columns, which replace the quadratic column, are orthogonal to all other columns. We describe a general method of constructing the new columns, and illustrate the procedure with a manual ammonia assay example dataset from EP6.

Ammonia↗
PubMed1992

Estimating total analytical error and its sources. Techniques to improve method evaluation.

The process of method evaluation starts with identifying goals either to demonstrate the clinical validity of an assay or to identify assay error sources that require improvement. Taguchi's idea of continual quality improvement vs the notion of meeting or failing specification has been applied to clinical chemistry. In this article, I propose a model of assay performance that includes the terms random interferences and protocol-specific biases (a series of systematic errors). I explain these terms, as well as the consequences of failing to consider them. To validate an assay clinically, I recommend direct estimation of total analytical error from a method comparison. To identify assay error sources that require improvement, I recommend a multifactor protocol (in addition to a method comparison). Individual error sources are related to total analytical error with the use of an error propagation technique. Much of the proposed data analysis techniques are straightforward but not routinely practiced. I demonstrate principles with the use of a cholesterol assay.

Chemistry, Clinical↗
PubMed1991

Multi-factor designs. IV. How multi-factor designs improve the estimate of total error by accounting for protocol-specific biases.

Total error is often calculated as a combination of random error and fixed bias. However, the specific protocols used to estimate random error and fixed bias are themselves variable factors that can affect the estimate of total error. We refer to biases such as assay drift, sample-to-sample carryover, and reagent carryover as examples of fixed biases that are protocol-specific and distinguish them from other fixed biases. Failing to account for protocol-specific biases that are present will lead to incorrect estimates of total error when routine use of the assay involves a protocol different from that used to estimate total error. Multi-factor protocols are recommended to determine protocol-specific biases, which, if present, should be included in the estimate of total error.

Carcinoembryonic Antigen↗
PubMed1989

Multi-factor designs. II. A design for identifying instruments with sample-to-sample carryover and drift.

We describe a nearly orthogonal two-level design that involves use of a weighted analysis to estimate drift and very low amounts of sample-to-sample carryover simultaneously. Identifying systematic errors from these sources is especially important for assays of analytes presenting a large range and with a medical decision point close to zero. The design is illustrated with data for thyrotropin, where, in one run with 32 samples, 0.08% carryover was detected in the presence of concentration-dependent negative drift.

Autoanalysis↗
PubMed1988

A multi-factor experimental design for evaluating random-access analyzers.

A multi-factor experimental design for evaluating random-access analyzers has been developed and tested for the Ciba Corning "550 Express" random-access analyzer. The 12-sample design estimates imprecision, slope, nonlinearity, linear drift, and reagent carryover to the next assay. The design was constructed so that estimates of the factors' effects are almost entirely uninfluenced by each other. Use of the design is illustrated by an example in which reagent-to-assay carryover was pinpointed as an apparent cause of high imprecision. This led to a modification of the analyzer such that carryover was insignificant. The appendix contains a 27-sample design that provides additional estimates. Software to perform such calculations is available on request.

Aspartate Aminotransferases↗
PubMed1984

How to improve estimates of imprecision.

Authors sometimes report total imprecision as being less than within-run imprecision. This leads one to question how well the underlying statistical concepts are understood. This article explains statistical concepts relevant to imprecision studies and reviews proper use of the relevant statistical terms. Calculation of total imprecision and its components is illustrated by a numerical example. Two of the most common calculation mistakes are pointed out. Finally, we recommend improvements in experimental design that will result in more precise estimates.

Chemistry, Clinical↗
PubMed1980

The mechanism of action of ethanolamine ammonia-lyase, an adenosylcobalamin-dependent enzyme. Studies with isopropanolamine, a true substrate.

Ethanolamine ammonia-lyase catalyzes the adenosylcobalamin (AdoCbl)-dependent deamination of 1-amino-3-propanol (isopropanolamine) to acetone and NH3. During the course of the reaction a hydrogen is transferred from the carbinol carbon of the substrate to one of the methyl carbons of the product, the cofactor serving as intermediate hydrogen carrier. This result, together with previous results obtained with this and other AdoCbl-requiring enzymes, suggests that an initial step in the catalytic mechanism is the abstraction of a hydrogen atom from the substrate to produce the 1-amino-2-hydroxyprop-2-yl radical. This is a bulky species which for steric reasons is unlikely to alkylate cob(II)alamin. Thus, an organocobalamin formed by a reaction between the substrate radical and cob(II)alanin is probably not involved in the mechanism of deamination of isopropanolamine by ethanolamine ammonia-lyase.

Ammonia-Lyases↗
PubMed1980

The mechanisms of action of ethanolamine ammonia-lyase, an adenosylcobalamin-dependent enzyme. Evidence that carbon-cobalt bond cleavage is driven in part by conformational alterations of the corrin ring.

Previous work has shown that the interaction between ethanolamine ammonia-lyase (ethanolamine ammonia-lyase, EC 4.3.1.7) and adenosylcobalamin weakens the C-Co bond of the cofactor with respect to homolytic cleavage. To obtain information concerning the mechanism by which this is accomplished, a study was conducted in which optical and circular dichroism spectroscopy were used to explore the interaction between ethanoloamine ammonia-lyase and a series of adenosylcobalamin analogs composed of an adenyl residue attached to the cobalt atom of cobalamin by a methylene chain whose length varies from 2 to 6 carbons. These studies indicated that the binding of a cobalamin to the active site activates forces which tend to alter the conformation of the enzyme, and with it that of the corrin ring, but that these conformational changes are blocked by bulky Co-beta substituents which restrict corrin ring flexibility. We postulate that at least one element of the force which weakens the C-Co bond of the enzyme-bound cofactor is the relief of conformational strain which occurs when C-Co bond cleavage, by releasing the interfering adenosyl group, permits the enzyme and the corrin ring to assume the energetically favored conformation.

Ammonia-Lyases↗
PubMed1978

The mechanism of action of ethanolamine ammonia-lyase, an adenosylcobalamin-dependent enzyme. Reaction of the enzyme.cofactor complex with 2-aminoacetaldehyde.

Ethanolamine ammonia-lyase (EC 4.3.1.7) catalyzes the adenosylcobalamin-dependent deamination of ethanolamine and 2-aminopropanol. Incubation of the enzyme.cofactor complex with 2-aminoacetaldehyde leads to rapid cleavage of the carbon--cobalt bond accompanied by the destruction of the corrinoid portion of the cofactor. During this reaction the adenosyl portion of the cofactor is oxidized to 4',5'-anhydroadenosine, and the aminoacetaldehyde is converted to acetic acid, which remains associated with the enzyme as a noncovalent complex which survives gel filtration. There is no evidence for the alkylation of the corrin metal by the substrate analog. The enzyme.AdoCbl complex is thus able to eliminate an amino group from a substrate analog without the formation of a new alkyl cobalamin in which the analog is a ligand. These observations do not support the participation of what might be termed "substratylcobalamin" as an intermediate in the ammonia migration occurring in reactions catalyzed by ethanolamine ammonia-lyase.

Ammonia-Lyases↗