Abstract:
Systems and methods are disclosed for identifying and/or compensating for relationships between influencers and outcomes. First, a metadata model of information that characterizes relationships between influencers and outcomes is built. A query is accepted from a user, and an outcome of interest is determined based on the query. The model is subsequently relied on to flag influencers that might have an impact on an outcome of interest. The impact of these influencers on the outcome of interest is then analyzed with respect to specific conditions. When the impact is sufficiently large, the user is notified or an adjustment is made to the user&#39;s query.

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to the field of data analysis, and more specifically to identifying and/or compensating for influencers that can have an impact on a statistical outcome. 
     2. Background 
     Databases are often created as a by-product of normal operations such as health care, retail sales, and loan processing, and it is often possible to extract highly useful information by properly analyzing these databases. For example, medical researchers may obtain valuable insights into disease progression, adverse side effects of medications, or typical patient characteristics from hospital databases; buyers can identify important purchasing patterns from inventory or point-of-sale data; and bank analysts can develop fair and accurate criteria for screening loan applicants by examining the payment histories of previous borrowers. 
     Conventional techniques for analyzing such databases, however, are susceptible to errors that may be introduced by “confounders.” Confounders are factors whose significance has been overlooked by the data analyst, but nevertheless influence the outcome of interest. An excellent example of a confounder&#39;s impact on an outcome can be found in the following data, taken from “The Effectiveness of Adjustment by Subclassification in Removing Bias in Observational Studies for Causal Effects” by W. G. Cochran, Biometrics, v. 24, pp. 295–313 (1968). 
     
       
         
               
               
               
               
             
           
               
                   
                   
               
               
                   
                   
                 Non- 
                 Cigarette 
               
               
                   
                 Smoking status 
                 smoker 
                 smoker 
               
               
                   
                   
               
             
             
               
                   
                 Mortality rates 
                 20.2 
                 20.5 
               
               
                   
                 per 1000 person-years 
               
               
                   
                   
               
             
          
         
       
     
     Taking this data at face value could lead one to the incorrect conclusion that cigarette smoking is not harmful. A more in-depth analysis reveals, however, that the above results were confounded by age—it turns out that the nonsmokers represented in the database were significantly older than the cigarette smokers, with average ages of 54.9 years and 50.5 years, respectively. When the above mortality rates are adjusted for age, the results are as follows: 
     
       
         
               
               
               
               
             
           
               
                   
                   
               
               
                   
                   
                 Non- 
                 Cigarette 
               
               
                   
                 Smoking status 
                 smoker 
                 smoker 
               
               
                   
                   
               
             
             
               
                   
                 Age-adjusted mortality rates 
                 20.2 
                 29.5 
               
               
                   
                 per 1000 person-years 
               
               
                   
                   
               
             
          
         
       
     
     Analysts with an in-depth understanding of a particular subject matter may be able to recognize the impact of a confounder, and eventually track down the source of error. But analysts who do not recognize the presence of a confounder may reach an incorrect conclusion. 
     One prior art approach for avoiding the effects of confounders is to carefully design an experiment or scientific trial using a control group. A particular factor (e.g., receiving a particular drug) is then randomly varied among the participants, and the results are observed. This approach is commonly used in medical and scientific research to verify a hypothesis. Unfortunately, this approach is very expensive to implement, because it requires performing new experiments and data analysis to test each and every hypothesis, or risking that a flawed hypothesis will be accepted and perhaps acted on. 
     Another prior art approach for avoiding the effects of confounders is by using preexisting data (e.g., from an existing database), and obtaining the participation of an expert in the relevant domain (e.g., a medical doctor) and a statistician to compensate for confounders for each hypothesis proposed by a data analyst. This approach can provide high quality results, but makes inefficient use of the domain expert&#39;s time and the statistician&#39;s time, who may be asked similar questions by multiple data analysts. And due to the heavy involvement of the domain expert and statistician, this prior art approach is also expensive to implement. 
     The inventors have recognized the need to improve the existing situation, and to enable researchers to form and verify their hypotheses more easily, without relying so heavily on freshly obtained experimental data to verify each hypothesis, and without relying on close cooperation with domain experts and statisticians. 
     SUMMARY OF THE INVENTION 
     One aspect of the invention relates to a method of identifying relationships between influencers and outcomes under a particular set of conditions. In this method, a model of information that characterizes relationships under many different conditions between influencers and outcomes is built. A query that specifies a set of conditions is inputted, and a relationship between the set of conditions specified in the query and a particular outcome that is represented in the model is determined. Based on the model, at least one potential influencer of the particular outcome is identified. 
     Another aspect of the invention relates to a method of identifying relationships between influencers and outcomes under a particular set of conditions. In this method, a model of information that characterizes relationships under many different conditions between influencers and outcomes is built. A query that specifies a set of conditions is inputted, and a relationship between the set of conditions specified in the query and a particular influencer that is represented in the model is determined. A relationship between the query and a particular outcome that is represented in the model is also determined. Based on the model, at least one potential influencer of the particular outcome is identified. This potential influencer is unaccounted for by the query. 
     Another aspect of the invention relates to a method of identifying relationships between influencers and outcomes under a particular set of conditions. In this method, a model of information that characterizes relationships under many different conditions between a plurality of nodes is built. In this model, at least some of the nodes represent influencers and at least some of the nodes represent outcomes. A query that relates to at least one of the nodes is inputted, and a relationship is determined between the query and the nodes of the model. Based on the model, a potential influencer of an outcome associated with the query is identified. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a functional block diagram of a preferred embodiment of the present invention. 
         FIG. 2A  is a pictorial representation of a first metadata model depicting relationships between influencers and outcomes. 
         FIG. 2B  is a pictorial representation of a second metadata model depicting relationships between influencers and outcomes. 
         FIG. 3  is flowchart of a method for identifying potential confounders and reporting those confounders to a user. 
         FIG. 4  is flowchart of a method for identifying potential confounders and adjusting for the impact of those confounders. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
       FIG. 1  is a functional block diagram of a preferred embodiment of the present invention. The illustrated functions are preferably implemented using conventional computer hardware and peripherals. A user  10  provides inputs to the system via input interface  12 , and receives outputs from the system via output interface  11 . A query processor  13  accepts the user&#39;s input from the input interface  12 . The input interface  12  may be implemented using any of a variety of conventional techniques, such as graphical, command-line, or natural language interfaces. Inputted queries are submitted to the query processor  13 , which parses each query and submits the parsed query to the confounder compensation/identification process  14  (hereinafter “CCIP”). Parsed queries may be represented in a variety of ways, such as SQL select statements or natural language expressions. The CCIP  14  accepts the parsed query, implements a process to determine a result, and sends the result to the user  10  via the output interface  11 . The result may be presented using any suitable user interface technique, such as text, tabular or graphical displays, or combinations thereof. 
     Examples of the processes implemented in the CCIP  14  are described below. Preferably, the CCIP  14  has access to metadata  15  and to a raw data database  18 . The metadata  15  and the raw data database  18  may be, for example, relational database systems, computer file systems, or other systems that are capable of maintaining the required data. 
     Compensation for confounders relies heavily on metadata  15 . The metadata  15  may be generated by human experts in a particular field of study (aided by statisticians when necessary), by a computerized process that recognizes relationships within a body of raw data (using, for example, statistical techniques or artificial intelligence methods), or by a combination of human and computerized processes. 
     Preferably, the metadata  15  specifies whether a relationship exists between a plurality of influencers and outcomes. Optionally, the metadata  15  may also describe a characteristic of that relationship. The metadata  15  is preferably established so as to encode both expert domain information and statistical opinion information. Because this information is encoded into the metadata  15 , it can be subsequently extracted from the metadata  15  by an automated process (including, e.g., the processes described below). The metadata  15  can serve as a repository for the experts&#39; knowledge concerning the various influencers and outcomes. Consulting this repository enables data analysts to obtain the benefit of this knowledge without actually consulting a live expert. 
     Once the metadata  15  has been established, it is stored in a suitable media (e.g., RAM, hard disks, etc.) for access by the processes described below. The mechanics of storing and accessing the metadata  15  may be implemented in any conventional manner, using any of a variety of conventional database programs running on conventional computer hardware (not shown). 
       FIG. 2A  is an example (in the context of the health field) of a directed graph of relationships that may be used as the metadata  15  in the processes described herein. In the illustrated directed graph, nodes represent influencers and outcomes, and directed edges represent relationships. Although other representations are feasible, a directed graph representation is convenient for visual presentation during the development of the metadata model by domain and statistical experts because it assists visualization of the relationships that exist between the various influencers and outcomes. It can also be easily translated into a format that can be accessed by the CCIP  14  (shown in  FIG. 1 ). 
     In the example shown in  FIG. 2A , nodes  20 – 26  appear on the left side of the figure, and nodes  27 – 29  appears on the right side of the page. When a causal relationship exists between two nodes, a directed edge is shown as an arrow from the influencer to the outcome. When no such relationship exists between two nodes, no directed edge is shown between those nodes. In the illustrated example, nodes  20 – 26  are all influencers of one or more of the nodes  27 – 29  (for example, smoking status  23  increases the probability of lung cancer  27 ). Nodes  23 ,  24 , and  25  are also outcomes, because they are affected by the state of the nodes  27  and  29  (e.g., lung cancer status  27  may cause a person to quit smoking, and move to a zip code with cleaner air). Nodes  27  and  29  are both influencers and outcomes, and node  28  is an outcome only. Of course, in the context of health, the relationships illustrated in  FIG. 2A  represent only a small subset selected from the potential universe of entries that could impact the various illustrated outcomes. Preferably, all factors known to have a significant impact on each of the outcomes should be included in the metadata  15 . 
       FIG. 2B  is another example of a directed graph of relationships that that may be used as the metadata  15  in the processes described herein.  FIG. 2B  is similar to  FIG. 2A , except that additional information about the strength of each relationship is included. In  FIG. 2B , strong relationships are indicated by solid lines, medium relationships by dashed lines, and weak relationships by dotted lines. 
       FIG. 3  illustrates a “diagnostic” mode of operation, where the metadata is used to identify potential confounders and to report those confounders to the user. In step  32 , the metadata model is formed to define a set of relationships between the influencers and the outcomes. Examples of forming the metadata model are described above in connection with  FIGS. 2A and 2B . This step is preferably implemented before the remaining steps (e.g., days or even months in advance). 
     In step  33 , the system accepts a query from the user (via the input interface  12 , shown in  FIG. 1 ), and the influencers, outcomes, and relationships contained in that query are captured by the system. Queries from the user may take any number of forms. Some forms will identify only an influencer (e.g., “what are the bad effects of smoking?”) or only an outcome (e.g., “tell me the factors that influence lung cancer status”). Other queries may include both influencers and outcomes (e.g., “is there a relationship between rate of death and a subject&#39;s zip code?”). Still other queries may include the nature of the relationship between the influencers and the outcomes in an imprecise manner, either implicitly (e.g., “show me rates of death for smokers and non-smokers”) or explicitly (e.g., “does smoking increase the risk of lung cancer?”). Other queries may include the nature of the relationship between the influencers and the outcomes in a more precise manner (e.g., does smoking two packs of low-tar cigarettes a day increase the risk of lung cancer by a factor of four?”). In some cases, a query may involve more than one influencer or outcome (e.g., “what is the death rate of diabetics that also have skin cancer, as compared to the general population?”). 
     Queries may be accepted in step  33  in any desired format, including, for example, statements, questions, and lists of terms, as long as the query processor  13  (shown in  FIG. 1 ) is programmed to handle the desired input format. Any suitable input interface  12  (shown in  FIG. 1 ) may be used for inputting the query. One example is to provide a set of text-input fields on a computer-generated display, and to display an appropriate message (e.g., “to investigate the relationship between an influencer and an outcome, type one or more influencers in field A, an outcome in field B, then click “go”). Another example of a suitable user interface would be to select an influencer, an outcome, and a relationship from drop-down menus that are populated based on the contents of the metadata  15 . Similar arrangements can be readily envisioned for various different query types, including those described above. Queries may also be accepted from the user in plain English (or any other language) using natural-language recognition software. In this case, a user would be able to type (or speak) a complete statement of their query using any conventional user interface. Alternative user interfaces can be readily envisioned. 
     The end result of the query accepting step is a set of influencers, outcomes, and relationships. Depending on the particular query that was inputted by the user, this set may contain only influencers, only outcomes, or both influencers and outcomes. If relationships were also inputted by the user, these relationships would also be included in the set. 
     In step  34 , the terminology that was used in the inputted query is analyzed to determine which entries in the metadata are implicated by the query. In cases when the query uses the exact same terminology as the metadata, this step may be implemented by looking up the query terms in a table that contains all the metadata terms. When the identical terminology is not used, the query terminology must be analyzed to map the query terms onto the metadata terms before subsequent processing can occur. For example, if the metadata contains a relationship indicating links skin cancer and death, but the query used the terminology “how long do people with melanomas usually live?” The query term “melanoma” must be associated with “skin cancer” in the metadata, and the query terms “how long” and “live” must be associated with “death” in the metadata. This correspondence may be implemented, for example, using a thesaurus. More sophisticated language-recognition algorithms may also be used. In certain cases, an appropriate computation may be required to match the query terminology with the metadata. For example, if the metadata includes “age,” but the query relates to date of birth, subtraction would be appropriate. Similar conversions may also be required when the raw data is accessed. The end result of this step is the set of metadata concepts that were recognized in the query, referred to hereinafter as “recognized concepts.” 
     In step  35  the metadata is analyzed, preferably for each of the recognized concepts, to determine which things in the metadata influence the recognized concepts, and/or which things in the metadata are influenced by the recognized concepts. In the examples of  FIGS. 2A and 2B , this may be accomplished by noting which nodes are linked to each of the recognized concepts by a directed edge. Any node in the metadata that has an influence on one of the recognized concepts (and has not already been accounted for in the query) is identified as a potential confounder. For example, if a query asks for the relationship between outdoor leisure and skin cancer for a particular age group, examination of the  FIG. 2A  metadata model would reveal that zip code and heredity are potential confounders of the “skin cancer” outcome. Optionally, when the strength of the relationships are encoded in the metadata (as shown in  FIG. 2B ), the user may be provided with an option to set a threshold that would exclude relationships classified as “weak” or “medium” from being identified. 
     In step  36 , the impact of each of the identified potential confounders on the query is analyzed to determine whether, under the circumstances presented by the query, the potential confounder is likely to have an impact on the outcome. This step is preferably implemented by having the CCIP  14  access the raw data database  18  (both shown in  FIG. 1 ) to determine whether the confounder is implicated by the subpopulation delineation called for by the query. The database may be accessed using conventional database accessing techniques such as SQL queries in conjunction with conventional statistical techniques. 
     The concept of subpopulation delineation can be understood in the context of the following example, where the query is “do females have a higher incidence of skin cancer than males”? In this example, age would be identified as a potential confounder for the outcome of “skin cancer” based on the metadata. Based on the query, the universe is divided into two subpopulations—males and females. If the age distribution in these two subpopulations is different, age is likely to have an impact on the outcome, and is accepted as a probable confounder. But if the age distribution in the two subpopulations is similar, then age is unlikely to have an impact on the outcome, and would not be a probable confounder (despite the fact that the metadata indicates a causal relationship may exist between smoking and death). 
     Note that step  36  is optional, and may be omitted. In this case, the potential confounders would be reported below in step  37  (instead of the probable confounders). 
     In step  37 , a response to the original query is reported to the user. Preferably, this response is based on an analysis of the raw data  18  by the CCIP  14  data (both shown in  FIG. 1 ), and is responsive to the presented query. For example, for the query “how do the mortality rates for smokers and nonsmokers compare?”, the reported result could be something like “the mortality for smokers and nonsmokers are as follows . . . ”. In addition to the response to the original query, any probable confounders that were identified in step  36  are reported to the user (or, if step  36  was omitted, the potential confounders). For example, in cases where age impacts the mortality rate, a suitable message (e.g., “WARNING: you did not indicate an age range for the subjects, which would have a significant impact on the reported results”) would be presented to the user. Both the response to the query and the reporting of confounders are preferably presented to the user via output interface  11  (shown in  FIG. 1 ) using any user interface technique appropriate for the data being displayed (e.g., pop-up display windows paragraphs of text, tabular displays, graphic displays, etc.). 
     Optionally, an additional level of detail may be provided when reporting a confounder. For example, if the inputted query was “show me the mortality rates for smokers and non-smokers,” and step  36  identified age as a probable confounder, the system could generate a message such as “WARNING: the results provided above do not account for age—the average age of the smokers in the examined database is 45, but the average age of non-smokers is 57.” The user could subsequently use this information to refine their query. 
       FIG. 4  illustrates a “statistical-analytic” mode of operation, where the metadata is used to actually adjust or correct a query. Steps  42 – 46  of this embodiment are similar to steps  32 – 36  of the embodiment described above in connection with  FIG. 3 . 
     After steps  42 – 46  have been implemented (i.e., after all the probable confounders have been identified), processing proceeds to step  48  where, the actual impact of the probable confounders on the query is computed for the particular situation in question. This step is preferably implemented by having the CCIP  14  access the raw data database  18  (both shown in  FIG. 1 ), using conventional database accessing techniques such as SQL queries in conjunction with conventional statistical techniques. For example, if the inputted query was “show me the mortality rates for smokers and non-smokers,” and age was identified as an probable confounder in step  46 , the CCIP  14  could interrogate the database using appropriate SQL queries for the age group in question to determine the actual impact of heredity on skin cancer. If the impact is sufficiently large, the probable confounder “heredity” would be recognized as an actual confounder under the particular set of circumstances delineated by the query. 
     Statistical techniques for determining the effects of probable confounders may be chosen on the basis of many factors, including computational and statistical resources available in the query processor  13 , the CCIP  14 , types of entities (e.g., continuous vs. discrete), relationships between a confounder and outcome, and the ability of an end user to understand the technique and its output. For many queries, the appropriate analysis involves comparing the distribution of probable confounders in two groups, such as “smokers” vs. “non-smokers.” For individual confounders, applicable techniques may include descriptive statistics, graphical summaries, or Student&#39;s t statistic comparing the means for each group. It may be necessary to consider groups of confounders, in which case multi-way analyses of variance may be appropriate. Propensity scores may also be used to analyze the relevant data, either alone or in conjunction with other statistical analysis techniques. 
     One example of the application of statistical techniques to the database analysis is age-normalization. For example, to determine whether smoking contributes to mortality for a particular subpopulation, the system can divide the subpopulation into a number of age groups (e.g., into five age groups), and compute the mortality rates for both the smokers and nonsmokers in each age group. The average of the five smoking numbers are then compared to the average of the five non-smoking numbers, and an age-adjusted mortality rate is determined. If the age-adjusted mortality rate differs from the non-adjusted mortality rate, this would indicate that age is an actual confounder for the particular circumstance presented by the query. 
     Once the actual impact of the confounder has been determined, the query is adjusted accordingly. For example, if the inputted query was “show me the mortality rates for smokers and non-smokers,” and age was identified as an probable confounder in step  46 , the system would first determine the impact of the confounder on the query (i.e., the impact of age), and then adjust the query to take the confounder into account. In this example, the query adjustment can be implemented by changing “mortality rate” to “age-adjusted mortality rate.” The specifics of adjusting the query to compensate for the confounders will depend on the form of the query, and may be implemented, for example, using a suitable language processor. 
     Once the appropriate adjustment to the query has been determined, processing proceeds to step  49  where the adjusted query is presented to the user together with the results for the adjusted query (via the output interface  11  shown in  FIG. 1 ). In the current example, one appropriate presentation would be “the AGE-ADJUSTED mortality rates for smokers and non-smokers are as follows . . . ”. Preferably, any modifications that were made to the query are flagged to the user in a suitable manner (e.g., by highlighting them, using italics, or generating up a suitable explanatory message in a pop-up window). 
     The above-described embodiments can advantageously be used to either flag probable confounders, so that a data explorers can re-formulate their hypothesis, or to compensate for the effects of the confounders. It can also be used to reduce reliance on the expensive and time consuming process of obtaining fresh experimental data to validate each hypothesis, thereby making the data-explorer&#39;s efforts more productive. It can also help amortize the cost of domain and statistical knowledge across many data-explorers working in the same general field, because the metadata and processing steps allow reuse of the domain and statistical knowledge numerous times once it has been captured. 
     It should be noted that while the above-described embodiments have been explained in the context of health care, they are equally applicable in other fields, including, but not limited to, finance, retail sales, agriculture, etc. Moreover, while the present invention has been explained in the context of the preferred embodiments described above, it is to be understood that various changes may be made to those embodiments, and various equivalents may be substituted, without departing from the spirit or scope of the invention, as will be apparent to persons skilled in the relevant arts.