the ability to compare the relative effectiveness of different technologies and treatment modalities is now imperative in order to meet the growing demand for more rigorous outcomes research . today
, there are many data available to conduct comparative effectiveness research ( cer ) : 1 ) data from surveys and 2 ) data from secondary databases ; however , many lack important information . both of these types of data sources have their own particular limitations .
while prospectively collected observational cohorts lend themselves to longitudinal epidemiologic data for research , they are traditionally limited in external validity , specificity of measurement on key exposures and outcomes , and historially dated beyond application to current therapies .
secondary databases , including administrative claims data collected by the infrastructure of the healthcare system for the purposes of payment for services , also have limitations , as they are typically restricted to billing claims and provide limited clinical detail to confirm disease .
additionally , they often contain few patient characteristics ( such as race and ethnicity ) and have a low proportion of indigent and self - pay populations .
these types of databases also lack in details on patients perception of disease or the impact of disease or treatment on humanistic outcomes .
electronic medical record ( emr ) systems , newly launched to replace paper - based chart systems in tracking clinical symptoms and disease , are still in the early stages of development .
moreover , they are frequently positioned in silos of healthcare practice  traditionally physician practices with limited linkage to inpatient facilities , other specialists or other physician services , emergency and acute services , and pharmacy services .
while each of these datasets provides unique information , they lack a significant amount of information about the patients themselves and their associated treatments . given these challenges and data limitations , there is a need for new , advanced techniques to overcome the limitations of existing data sources , thereby enhancing their utility for cer .
in the absence of the ideal data source , there is an alternative : a method whereby researchers can maximize the benefits of each individual type of data source , utilizing data linkage methods to fill in the gaps of one database with the strengths of another .
blanchette et al . presented this approach at a workshop during the 13 annual european ispor congress in prague in 2010 , and discussed preliminary results demonstrating the application linking the two administrative databases , at a workshop during the 16 annual international ispor meeting in baltimore , in 2011 .
deterministic linkage techniques can be applied when both datasets provide patient - identifying information that can be cross - matched between the two datasets .
this is the preferred form of data linkage because the focus is on matching the same patient from both datasets . in the current climate of regulatory and legal restraints on the use of personal health information ( phi )
, the utility of this approach is limited . in the absence of phi , a probabilistic method may be applied .
however , the focus is not on identifying the same patient but rather on two matching patients with similar behavior and characteristics .
the goal of both of these efforts is to identify the best method for pair - matching between datasets .
probabilistic linking methods incorporate varying strengths of identifiers , depending on information provided by the identifier .
for example , matching on gender ( male or female ) is less likely to produce a matching of equivalent records than matching a record on the combination of birth month and year .
assigned weights are related to frequencies of occurrences of values in linked and unlinked pairs and also to the designated level of agreement .
the determination of agreement can be further defined into subcategories : agreement ( yes / no ) or varying levels of partial agreement ( e.g. , matching of a date within 2 days before / after or matching within a week before / after ) .
using probabilistic linking methods generally increases the sensitivity and specificity of models over a deterministic method in linking records from disparate datasets .
propensity score methods are increasingly used to match similar individuals in observational studies , along with other approaches to control for channeling bias .
score matching , well - matched pairs are closely matched on the predicted probability of experiencing the dependent variable using relevant covariates .
matching techniques then employ a variety of approaches based on the predicted probability score ( ranging from 0 to 1 ) , including nearest neighbor matching heuristics , and often employing sampling without replacement .
to demonstrate the utility and subsequent validity of probabilistic data linkage we employed a historical retrospective cohort design .
patients aged 40 and older with a principal or secondary diagnosis of copd ( international classification of disease , 9 revision codes : 491.xx , 492.xx , and 496 ) and at least 3 years of continuous enrollment in a us health plan contributing to the database between january 1 , 2004 and april 30 , 2009 were selected from two administrative claims databases representing commercially insured us health plan enrollees .
the index date was designated as the date of the first claim ( defined by a 12-month wash - out pre - index period ) for either study drug , treatment 1 or treatment 2 .
the effectiveness measure was the incidence of copd - related exacerbation ( hospitalization , emergency department visit or oral corticosteroid prescription ) observed in the 12-month post - index period , with baseline characteristics being identified in the 12-month pre - index period ( figure 1 ) .
data linkage was performed by restricting the samples in each database to those with similar pre - index and post - index time periods ( as described above ) .
we then pooled data and developed a logistic regression model predicting the propensity to be in database a using overlapping pre - index variables including age , geographic region , comorbidities , and pre - index utilization of health services and respiratory medications ( table 1 ) .
we then directly imputed the outcomes of each matched patient from database a to the matched patient from database b. finally , we conducted the comparative effectiveness study in each cohort ( a , b , and a with imputed outcomes ) ( figure 2 ) .
of 12,926 patients in database a and 21,334 patients in database b , we matched 11,040 patients in each database on the propensity score using a nearest neighbor matching algorithm which matches like pairs of patients from each database without replacement .
post - matching groups were balanced on all pre - index characteristics with the exception of older age ( 6574 and > 75 years ) as well as representation in the southern region .
the event rate of exacerbations in each cohort was nearly identical ( treatment 1 [ database a : 39.3% , database b : 39.7% , and database a with imputed outcomes : 40.1% ] and treatment 2 [ database a : 46.3% , database b : 47.1% , and database a with imputed outcomes : 46.8% ] ) as were the odds ratio and corresponding confidence intervals from the adjusted logistic regression models ( or  database a : 0.72 , database b : 0.74 , database a with imputed outcomes : 0.72 ) ( table 2 ) .
this is not entirely surprising since the probability of being in each cohort was determined by the overlapping similarity in characteristics between the two cohorts , just as if the propensity score match was used in a traditional cohort analysis .
the probabilistic linkage demonstrated that patients from different databases matched on similar pre - index characteristics may demonstrate similar outcomes in the post - index period .
the implication of these findings and this approach is significant as it provides a case example for linkage of simulated cohorts from varying databases ( e.g. , emr and administrative datasets , administrative datasets and surveys , emr and surveys , etc . ) .
the creation of simulated cohorts could help explain a more complete picture of patient outcomes and allow researchers to explore all aspects of a patient experience 
for example , the use of administrative claims databases for comparative effectiveness research is often lacking key variables of interest such as income , race , lab results , clinical detail identified through notes , etc . through this linking technique , one could impute these characteristics into a similar matched - patient cohort and assess the associations with the outcome .
similarly , a survey of patients could be augmented with key administrative and clinical detail .
even a clinical trial sample could be matched to administrative or electronic health record data to populate long - term outcomes .
this technique has the ability to provide a view into the world of more complete data with further validation of the methods and use in diverse data sources .
the divergence of the measures between the databases may also cause a divergence in  like  patients thereby introducing error in the outcomes assessment .
care should be taken to ensure that similar measures are used to build the logistic regression model to create the propensity score ; the assumption is that probabilistic matching produces two individuals with similar attributes and not the same individual .
however , in the absence of complete data , the approach offers a good start and paves the way for further examples of this type of linkage method to be explored with various databases .
these data represent us - based administrative claims submitted from healthcare providers to health insurance plans and therefore there is a potential for miscoded claims .
it is assumed that these patients fully utilize their health insurance benefits to avoid the full cost of the health service ; however unlikely , there is a potential that the patient may be receiving health benefits from a provider who does not submit a claim for reimbursement to the patient s health insurance provider .
the probabilistic linkage method for simulated cohorts demonstrated that patients from different us - based health plan administrative claims databases matched on similar pre - index characteristics may demonstrate similar outcomes in the post - index period .
this is the first application of this method for comparative effectiveness research and further validation in diverse data - sets is required .
substantial contributions to conception and design , acquisition of data , or analysis and interpretation of data ( cmb , mr ) . drafting the article or revising it critically for important intellectual content ( cmb , md , apd , mr ) . final approval of the version to be published ( cmb , md , apd , mr ) .
international committee of medical journal editors ( icmje ) potential conflicts of interests forms for each of the authors are summarized below .
original forms for each of the authors are available for download at : http://drugsincontext.com/download/dic.212258_coi.pdf cmb : payment or receipt of services for any aspect of the submitted work : astrazeneca funded the original study but did not fund the methodological process on which this paper focuses ; financial activities outside the submitted work : consultancy for centecor ( not related ) ; employee of otsuka america pharmaceutical inc ; grants / grants pending from ethicon ( not related ) , bristol myers squib ( not related ) , auxilium ( not related ) , eli lilly ( not related ) , glaxosmithkline ( copd ) ; stock / stock options from celgene , cubist , gilead ; no other relationships / conditions / circumstances that present a potential conflict of interest .
mk : no payment or receipt of services for any aspect of the submitted work ; financial activities outside the submitted work : employee of ims health , alexandria , va , usa ; no other relationships / conditions / circumstances that present a potential conflict of interest .
apd : no payment or receipt of services for any aspect of the submitted work ; financial activities outside the submitted work : employee of ims health , alexandria , va , usa ; no other relationships / conditions / circumstances that present a potential conflict of interest . mr : no payment or receipt of services for any aspect of the submitted work ; financial activities outside the submitted work : grants / grants pending from astra zeneca , glaxosmithkline , boehringer ingelheim , pfizer ; lovelace clinic foundation , albuquerque , nm , usa has received funding for respiratory related healthcare research studies ; no other relationships / conditions / circumstances that present a potential conflict of interest .
 background : in this era of comparative effectiveness research , new , advanced techniques are being investigated by the research community to overcome the limitations of existing data sources . 
 we describe the approach of probabilistic data linkage as a means to address this critical issue.methods:we employed a historical retrospective cohort design . 
 patients aged 40 and older with a principal or secondary diagnosis of copd ( icd-9-cm codes 491.xx , 492.xx , and 496 ) and at least 3 years of continuous enrollment between january 1 , 2004 and april 30 , 2009 were selected from two us - based commercial administrative claims databases . 
 the index date was designated as the date of the first claim ( defined by a 12-month wash - out pre - index period ) for the study drugs , for illustration purposes referred to as treatment 1 or treatment 2 . 
 the primary effectiveness measure was risk of any copd - related exacerbation observed in the 12-month post - index period , with baseline characteristics being identified in the 12-month pre - index period.results:the percentage of the study sample receiving treatment 1 at index who had an exacerbation was 39.3% for database a and 39.7% for database b ; for treatment 2 , the percentages were 46.3% and 47.1% , respectively . 
 the event rate of hospitalizations in each database sample was nearly identical as were the odds ratio and corresponding confidence intervals from the adjusted logistic regression models ( or  database a : 0.72 , database b : 0.74 , database a with imputed outcomes : 0.72).conclusions : the probabilistic linkage demonstrated that patients from different databases matched on similar pre - index characteristics may demonstrate similar outcomes in the post - index period . 