Estimation of age-specific reference ranges via smoother AVAS

Linear time-dependent reference intervals where there is measurement error in the time variable-a parametric approach

This article re-examines parametric methods for the calculation of time specific reference intervals where there is measurement error present in the time covariate. Previous published work has commonly been based on the standard ordinary least squares approach, weighted where appropriate. In fact, this is an incorrect method when there are measurement errors present, and in this article, we show that the use of this approach may, in certain cases, lead to referral patterns that may vary with different values of the covariate. Thus, it would not be the case that all patients are treated equally; some subjects would be more likely to be referred than others, hence violating the principle of equal treatment required by the International Federation for Clinical Chemistry. We show, by using measurement error models, that reference intervals are produced that satisfy the requirement for equal treatment for all subjects.

Partitioning Reference Intervals by Use of Genetic Information

BACKGROUND

Reference intervals that incorporate genetic information could reduce the misidentification of unusual test results caused by non–disease-associated genetic variation and increase the detection of results indicating underlying pathology. Subdividing reference groups by genetic effects, however, may lead to increased uncertainty around reference interval endpoints (because of the smaller subgroup sample sizes), thus offsetting any benefits.

METHODS

We evaluated CLSI guidelines to develop a method appropriate for partitioning reference intervals on the basis of genetic variants with dominant or recessive effects. This method uses information available before reference samples are recruited, thus allowing a preliminary decision regarding partitioning to be made before sampling. We used this method to evaluate the example of Gilbert syndrome.

RESULTS

The decision point for partitioning occurs when the percentage of total variance attributable to a dominant or recessive genetic polymorphism exceeds 4%. Similarly, partitioning decision curves are presented based on difference in means between 2 subgroups, sample SD, and subgroup or allele frequency. Laboratory-specific partitioned reference intervals for Gilbert syndrome appear to be statistically warranted for white and African-American populations, but not for Asian populations.

CONCLUSIONS

We present a simple method to evaluate whether partitioning based on dominant or recessive genetic effects is statistically justified. Important limitations remain that, in many situations, will preclude integration of genetic, laboratory, and clinical information. As society moves toward personalized medicine, additional research is needed on how to evaluate patient normality while accounting for additive genetic, multigenic, and other multifactorial effects.

Implementation and evaluation of a statistical framework for nerve conduction study reference range calculation

Nerve conduction studies (NCS) play a central role in the clinical evaluation of neuropathies. Their clinical utilization depends on reference ranges that define the expected parameter values in disease-free individuals. In this paper, a statistical framework is proposed and described in detail for deriving NCS parameter reference ranges. The bootstrap technique is used to identify demographic and physiologic covariates that influence the NCS measurements. Multi-variate linear regression is used to improve the accuracy and effectiveness of NCS interpretation by reducing parameter variance. Non-linear mappings are used to transform parameters into a Gaussian distribution in order to minimize the influence of outliers. Modeling of heteroscedasticity observed in this and other studies leads to more sensible normal limits for several parameters. The proposed reference range method is automated using the MATLAB programming language. Data from a large sample of healthy subjects are used to establish reference ranges for 24 commonly measured NCS parameters. All but three parameters follow Gaussian distributions in their respective transformed domains. Excluding the distal motor latency difference between median and ulnar nerves, the reduction of the parameter variance as a result of regression in the transform domain is greater than 50% for all F-wave latency parameters and at least 10% for all other NCS parameters. Subject age is found to influence normal limits of all but one parameter and height has a statistically significant impact on all but three parameters. These reference range specifications provide clinicians with an alternative to developing their own reference ranges as long as their NCS techniques are consistent with those described in this paper. The proposed method should also be applicable to reference range development for other NCS techniques and physiological measurements.

Whether western normative laboratory values used for clinical diagnosis are applicable to Indian population? An overview on reference interval

Reference Intervals denote normative values related to laboratory parameters/analytes used by diagnostic centers for clinical diagnosis. International guidelines recommend that every country must establish reference intervals for healthy individuals belonging to a group of homogeneous population. Considering enormous racial and ethnic diversity of Indian population, it is mandatory to establish reference intervals specific to Indian population. The overview on reference interval describes why the national organizations in India need to initiate nationwide efforts to establish its own laboratory standards for apparently healthy reference individuals belonging to our polygenetic, polyethnic, polyracial, multilinguistic and multicultural predominantly rural and appreciable urban Indian population with varied dietary habits.

Construction of the World Health Organization child growth standards: selection of methods for attained growth curves

The World Health Organization (WHO), in collaboration with a number of research institutions worldwide, is developing new child growth standards. As part of a broad consultative process for selecting the best statistical methods, WHO convened a group of statisticians and child growth experts to review available methods, develop a strategy for assessing their strengths and weaknesses, and discuss methodological issues likely to be faced in the process of constructing the new growth curves. To select the method(s) to be used, the group proposed a two-stage decision-making process. First, to select a few relevant methods based on a list of set criteria and, second, to compare the methods using available tests or other established procedures. The group reviewed 30 methods for attained growth curves. Using the pre-defined criteria, a few were selected combining five distributions and two smoothing techniques. Because the number of selected methods was considered too large to be fully tested, a preliminary study was recommended to evaluate goodness of fit of the five distributions. Methods based on distributions with poor performance will be eliminated and the remaining methods fully tested and compared.

Individual reference intervals of hematological and serum biochemical parameters in cynomolgus monkeys

Cynomolgus monkeys, one of a number of primates phylogenetically close to humans, are commonly used in animal studies. The purpose of this study was to assess biological variations in hematological and serum biochemical parameters in cynomolgus monkeys. Summary statistics and reference intervals were calculated using data from 95 male and 95 female Chinese-bred cynomolgus monkeys aged 3 to 7 years showing no abnormalities during the breeding period. Within- and between-animal variations were estimated using a random-effect analysis of variance (ANOVA), then, a simple method that applies prior information was proposed to estimate individual reference intervals. Parameters including MCV, MCH, PT, ALP, total cholesterol, and creatinine appeared to show a large between-animal variation; thus, it is considered that individual reference intervals for these parameters would be relatively small in comparison with overall reference intervals.

Centile Charts II: Alternative Nonparametric Approach for Establishing Time-Specific Reference Centiles and Assessment of the Sample Size Required

Background: Reference intervals, and more generally centile estimates, are used to characterize a reference population for the purposes of interpreting an individual patient’s clinical measurement. We describe methods of calculating reference intervals where these centiles vary with a covariate, usually age or time.

Methods: The US Food and Drug Administration and the IFCC have made recommendations on two approaches: the parametric approach, which models the structural characteristics of the data set with a theoretical distribution, and the nonparametric approach, which makes no particular assumption about this structure. In this report we propose a nonparametric procedure that relies on the principles of regression and show how sample size determination can be assessed. We also show how the sample size calculation is influenced by the distribution of the times measured.

Results: We illustrated our method on three data sets and compared the results for our proposed nonparametric method with parametric estimates. We showed that the bias is reduced and that the nonparametric method is less likely to produce fluctuating profiles.

Conclusions: To achieve adequate precision the sample size needs to be larger than 120, as has often been recommended. If there is doubt about the parametric model, then threshold sample sizes may need to be as high as 500.

Reference intervals: an update

Reference intervals serve as the basis of laboratory testing and aid the physician in differentiating between the healthy and diseased patient. Standard methods for determining the reference interval are to define and obtain a healthy population of at least 120 individuals and use nonparametric estimates of the 95% reference interval. This method is less accurate if the group size is significantly less and does not allow for exclusion of outliers. In order to overcome these limitations many authors in the current literature report reference intervals after arbitrary truncation of the data or use inappropriate parametric calculations. We argue that the use of outlier removal and robust estimators, with or without transformation to normality, address the shortcomings of the standard method and eliminate the need for employing less valid methods.To test these methods of analysis well-defined test groups are required. In a few studies physician-determined health status is provided for each subject along with commonly measured analytes. The NHANES and Fernald studies provide such groups. With such data it is possible to show the range of effects on the reference interval width by including a known non-healthy subgroup. With the NHANES data the effect ranged from negligible to a 30% increase in reference interval width. We found that use of outlier detection with the robust estimator yielded reference intervals that were closer to those of the true healthy group.Another issue is one of demographics. That is, whether or not one should derive separate reference intervals for different demographic groups, e.g., males and females. The standard mathematical test for deriving separate reference intervals is due to Harris and Boyd. Using the NHANES data we examined 33 analytes for each of three ethnic groups (separated by genders). We used the Harris and Boyd procedure and observed that it was necessary to derive separate reference intervals for approximately 30% of the comparisons. The most notable analytes were glucose and gamma GT.The methods used by most laboratories have similar precision, identical units, are linearly related (often on a 1:1 basis) and correlate well with each other. As a result the only difference is the method bias. By using the reference interval width, this bias is eliminated. We argue that the log ratio of the reference interval widths is a good estimate of the variability between groups.

Covariate-adjusted reference intervals for diagnostic data

The analysis of extreme diagnostic measurements in clinical trials relies on reference intervals that help drug developers quickly determine whether a particular value is typical or atypical. The distribution of diagnostic variables is often greatly influenced by various covariates and it is important to properly account for this influence in the analysis of extreme measurements. This paper discusses three approaches to constructing covariate-adjusted reference intervals for quantitative diagnostic data: global quantile smoothing, local quantile smoothing, and stepwise quantile approximations based on recursive partitioning. A detailed review of methods for optimizing the quantile estimation procedures is provided. The paper presents algorithms for selecting the degree of a polynomial approximation in global smoothing, bandwidth parameter in local smoothing, and number of strata in recursive partitioning. The described methods for computing covariate-adjusted reference intervals are applied to the analysis of electrocardiographic data.

Reference curves based on non-parametric quantile regression

Reference curves which take time into account, such as those for age, are often required in medicine, but simple systematic and efficient statistical methods for constructing them are lacking. Classical methods are based on parametric fitting (polynomial curves). Semi-parametric methods are also widely used especially in Europe. Here, we propose a new methodology for the estimation of reference intervals for data sets, based on non-parametric estimation of conditional quantiles. The derived methods should be applicable to all clinical (or more generally biological) variables that are measured on a continuous quantitative scale. As an example, we analyse a data set collected to establish reference curves for biophysical properties of the skin of healthy French women. The results are compared to those obtained with Royston's polynomial parametric method and the semi-parametric LMS approach.

Estimating age-related trends in cross-sectional studies using S-distributions

Growth trends in children are often based on cross-sectional studies, in which a sample of the population is investigated at one given point in time. Estimating age-related percentiles in such studies involves fitting data distributions, each of which is specific for one age group, and a subsequent smoothing of the percentile curves. The first requirement for this process is the selection of a distributional form that is expected to be consistent with the observed data. If a goodness-of-fit test reveals significant discrepancies between the data and the best-fitting member of this distributional form, an alternative distribution must be found. In practice, there is seldom an objective argument for selecting any particular distribution. Also, different distributions can yield very similar fits, so that any selection is somewhat arbitrary. Finally, the shapes of the observed distributions may change throughout the age range so drastically that no single traditional distribution can fit them all in a satisfactory manner. To overcome these difficulties in population studies, non-parametric smoothing techniques and normalizing transformations have been used to derive percentile curves. In this paper we present an alternative strategy in the form of a flexible parametric family of statistical distributions: the S-distribution. We suggest a method that guides the search for well-fitting S-distributions for groups of observed distributions. The method is first tested with simulated data sets and subsequently applied to actual weight distributions of girls of different ages. As far as the results can be tested, they are consistent with observations and with results from other methods.