Data processing: measuring – calibrating – or testing – Measurement system – Statistical measurement
Reexamination Certificate
2001-10-10
2003-02-04
Hoff, Marc S. (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system
Statistical measurement
C703S011000
Reexamination Certificate
active
06516288
ABSTRACT:
BACKGROUND OF THE INVENTION
1.1. Technical Field
This invention is a method or system to construct at least one profile representing how the actions of an object of investigation are coordinated, the profile(s) being based on computed measures of longitudinal association or temporal contingency that quantify patterns of interaction in repeated measures or time series data that include two or more variables for one individual.
1.2. Description of Related Art
Scientific knowledge often is represented in the form of mathematical models. Prior art related to this invention will be described in the context of computational methods and systems to create, verify, and refine models that represent objects of investigation.
The statistical method is a primary computational method to inform the process of model building. This invention addresses certain fundamental problems that derive from limitations often encountered when the statistical method is a primary means to inform the process of creating, verifying and refining mathematical models. By addressing these problems, this invention facilitates many scientific investigations and practical arts that may benefit from scientific knowledge.
The Appendix is an outline that helps reveal the logical structure of this application.
1.2.1. The Need to Measure Interactions that are Temporal Contingencies
Model builders generally identify an object to model and abstract variables that may be relevant to its functioning. Then modelers determine how the variables interact in order to inform the process of model construction. To a large extent, mathematical models are verified by the extent to which they accurately represent interactions of their objects in the real world.
The statistical method is an important tool for constructing many mathematical models. The statistical method includes various measures and procedures for revealing interactions that can be modeled.
A primary problem addressed by this invention derives from the fact that the statistical method is best suited to address objects of investigation that are collective entities. Groups, samples, and populations are collective entities.
Section 1.2 of parent patent application Ser. No. 09/470,956 describes many limitations and problems of the prior art. Many of these problems, limitations, and solutions are illustrated in the context of clinical trials. This invention also addresses these problems and limitations but generally in the broader context of complex systems. Section 1.2.2 of this document emphasizes problems more specifically addressed by this invention. Section 2.4 of this document describes how this and the parent invention address the following problems.
1.2.2. Specific Problems Involved in the Prior Art
The statistical method and mathematical models often are used to investigate complex systems. For example, mathematical models based on statistical analyses of group data have been used to model the apparent effects of cholesterol and other lipid fractions on mortality and major cardiovascular health events. Such models serve important functions. For example, mathematical models have used laboratory data to predict the long-term health effects of new cholesterol lowering drugs. Nevertheless, statistical analyses have important limitations for investigations of complex systems.
Conventional applications of the statistical method are best suited for analyses of cross-sectional data for collective entities and for predicting events such as death that are not recurrent for individuals. Statistical analyses are not as well suited to measure longitudinal associations or temporal contingencies between and among variables within individuals—interactions that become evident in longitudinal, repeated measures, or time series data.
The statistical method does have some functionality for analyzing repeated measures data, especially for groups. For example, the statistical method often is used to analyze change scores such as pre-post differences in clinical trials. However, this functionality becomes limited as the number of repeated measurements increases. This limitation is due to the fact that the number of differences between any two measurements increases rapidly with the number of repeated measurements. In addition, it is not meaningful, appropriate or useful to conduct statistical tests on all differences that are possible when there are more than a few repeated measurements.
The statistical method also includes techniques such as repeated measures analysis of variance. However, the usefulness of such techniques tends to be limited when the levels of one or more independent variables differ across many repeated measurements for each individual. For example, generally it is not feasible with conventional analyses to substitute blood levels of drug for planned doses before rerunning analyses of the effects of treatment on health.
Conventional data analysis procedures are of limited value for supporting detailed yet comprehensive investigations of complex individual systems whose variables may interact in a nonlinear manner. Here is additional information about five problem areas that are mentioned in the preceding statement—individuality, complexity, nonlinearity, comprehensiveness, and detail—together with a statement about the need to address all these problem areas as a set in particular investigations.
1.2.2.1. Problems Involving Individuality
The statistical method is best suited for analyses of cross-sectional data for collective entities such as groups. Many statistical descriptions and inferences are about measures of central tendency for groups. Statistical analyses often are based on assignments of individuals to groups such as treated or not treated, responder or non-responder. The results of such analyses apply most directly to collective entities.
The fundamental limitation of the statistical method that involves individuality will be viewed from two perspectives: (1) the application and (2) the discovery of scientific knowledge. Both perspectives will be illustrated by example.
The statistical method often is applied to describe groups and to use sample data to make inferences about populations. Statistical inferences are used to draw generalized conclusions and make predictions. The extent to which generalized conclusions and predictions about collective entities apply to individuals generally is limited. This can be illustrated in the context of group clinical trials. Individual patients are not apt to experience the same safety and efficacy as the average patient in a clinical trial.
The extent to which generalized conclusions and predictions about populations apply to individuals depends on the extent to which individuals are typical of groups. It also depends on the extent to which samples represent populations—at least with respect to all considerations relevant to particular investigations—as well as how members of samples are assigned to treatment groups.
Science is accounting for more and more factors that affect the responses of patients to medical treatments. For example, advances in genetics are identifying many ways in which individuals differ in manners that are relevant to disease and response to treatment. People need better ways to individualize treatment.
The fundamental limitation of the statistical method with respect to individuality also has profound implications for scientific discovery. This will be illustrated in the context of functional genomics and proteomics as it involves health disorders and medical treatments.
Now that genomes are being mapped, some high priority tasks are to identify how the products of gene expression function together and to identify how genetic differences that distinguish individuals, such as single nucleotide polymorhpisms, affect biological functions and responses to treatments. Such tasks currently are hampered by a lack of methods that can be applied to individuals to measure how proteins interact to control biological functions, of how treatments affect protein interactions, and of how treatments interact
Brooks & Kushman P.C.
Hoff Marc S.
Raymond Edward
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