Analytical interpretation of data
Analysis and interpretation specializationstarted oct 30enrolldata analysis and interpretation specializationenrollstarted oct 30financial aid is available for learners who cannot afford the fee. Learn more and data science fundamentalsdrive real world impact with a four-course introduction to data this specializationlearn sas or python programming, expand your knowledge of analytical methods and applications, and conduct original research to inform complex data analysis and interpretation specialization takes you from data novice to data expert in just four project-based courses. You will apply basic data science tools, including data management and visualization, modeling, and machine learning using your choice of either sas or python, including pandas and scikit-learn. In the capstone project, you will use real data to address an important issue in society, and report your findings in a professional-quality report. You will have the opportunity to work with our industry partners, drivendata and the connection. Help drivendata solve some of the world's biggest social challenges by joining one of their competitions, or help the connection better understand recidivism risk for people on parole in substance use treatment. This specialization is designed to help you whether you are considering a career in data, work in a context where supervisors are looking to you for data insights, or you just have some burning questions you want to explore. By the end you will have mastered statistical methods to conduct original research to inform complex d by:industry partners:5 coursesfollow the suggested order or choose your tsdesigned to help you practice and apply the skills you icateshighlight your new skills on your resume or sbeginner prior experience 1data management and visualizationcurrent session: oct 30commitment4 weeks of study, 4-5 hours/weeksubtitlesenglishabout the coursewhether being used to customize advertising to millions of website visitors or streamline inventory ordering at a small restaurant, data is becoming more integral to success. Too often, we’re not sure how use data to find answers to the questi... Learn 2data analysis toolscurrent session: oct 30subtitlesenglishabout the coursein this course, you will develop and test hypotheses about your data. You will learn a variety of statistical tests, as well as strategies to know how to apply the appropriate one to your specific data and question. Learn 3regression modeling in practiceupcoming session: nov 3commitment4 weeks, 4 - 5 hours per weeksubtitlesenglishabout the coursethis course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Learn 4machine learning for data analysiscurrent session: oct 30subtitlesenglishabout the courseare you interested in predicting future outcomes using your data?
Learn 5data analysis and interpretation capstoneupcoming session: nov 13subtitlesenglishabout the capstone projectthe capstone project will allow you to continue to apply and refine the data analytic techniques learned from the previous courses in the specialization to address an important issue in society. All rights raaboutleadershipcareerscatalogcertificatesdegreesfor businessfor governmentcommunitypartnersmentorstranslatorsdevelopersbeta testersconnectblogfacebooklinkedintwittergoogle+tech blogm analysis and interpretation specializationstarted oct 30enrolldata analysis and interpretation specializationenrollstarted oct 30financial aid is available for learners who cannot afford the fee. Introductionregistries have the potential to produce databases that are an important source of information regarding health care patterns, decisionmaking, and delivery, as well as the subsequent association of these factors with patient outcomes. The utility and applicability of registry data rely heavily on the quality of the data analysis plan and its users' ability to interpret the results. Analysis and interpretation of registry data begin with a series of core questions:study purpose: were the objectives/hypotheses predefined or post hoc? Registry data present many opportunities for meaningful analysis, there are inherent challenges to making appropriate inferences. A principal concern with registries is that of making inferences without regard to the quality of data, since quality standards have not been previously well established or consistently reported. Chapter explains how analysis plans are constructed for registries, how they differ depending on the registry's purpose, and how registry design and conduct can affect analysis and interpretation. The analytic techniques generally used for registry data are presented, addressing how conclusions may be drawn from the data and what caveats are appropriate. The chapter also describes how timelines for data analysis can be built in at registry inception and how to determine when the registry data are complete enough to begin analysis. These registries play a particularly important role in the study of rare the case of registries where the aim is to study the associations between specific exposures and outcomes, prespecification of the study methodology and presence or absence of a priori hypotheses or research questions may affect the acceptance of results of studies derived from registry data. The investigators have no prior knowledge of analyses in this database that would bias them in the formulation of their study objective. For registries intended to support national coverage determinations with data collection as a condition of coverage, the specific coverage decision question may be specified a priori as the research question in lieu of a formal the other extreme, a study may evolve out of an unexpected observation in a database in the course of doing analyses for another purpose.
Transparency in methods is needed so that readers may know whether these studies are the result of hypotheses developed independently of the study database, or whether the question and analyses evolved from experience with the database and multiple iterations of exploratory analyses. One method of evaluating representativeness is to describe the demographics and other key descriptors of the registry study population and to contrast its composition with patients with similar characteristics who are identified from an external database, such as might be obtained from health insurers, health maintenance organizations, or the u. Therefore, in this example, the lack of geographical representativeness would not affect interpretation of reason for using an intended population rather than the whole accessible population for the study is simply a matter of convenience and practicality. Proportion of baby visits for “well babies”), then that sampling strategy would substantially alter the interpretations from the registry and would be considered a meaningful extent to which the actual population is not fully representative of the intended population is generally a matter of real-world issues that prevent the initial inclusion of study subjects or adequate followup. In assessing representativeness, one must consider the likely underlying factors that caused those subjects not to be included in the analysis of study results and how that might affect the interpretations from the registry. For example, an investigator may request a data set that will be used to analyze a subset of the registry population, such as those who had a specific treatment or condition. Later on, patients with less severe disease may start using the y, patients who are included in the analytic population for a given analysis of registry data may also be subject to selection or inclusion criteria (admissibility criteria), and these may affect interpretation of the resulting analyses. Data quality issuesin addition to a full understanding of study design and methodology, analysis of registry events and outcomes requires an assessment of data quality. One must consider whether most or all important covariates were collected, whether the data were complete, and whether the problem of missing data was handled appropriately, as well as whether the data are accurate. When using an available database for additional purposes, one needs to be sure that all the information necessary to address a specific research question was collected in a manner that is sufficient to answer the example, suppose the research question addresses the comparative effectiveness of two treatments for a given disease using an existing registry. While it is not possible to identify all confounding factors in planning a registry, it is desirable to give serious thought to what will be important and how the necessary data can be collected. While effect modification is not a threat to validity, it is important to consider potential effect modifiers for data collection and analysis in order to evaluate whether an association varies within specific subgroups. Data completenessassuming that a registry has the necessary data elements, the next step is to ensure that the data are complete.
Missing data include situations in which a variable is directly reported as missing or unavailable, a variable is “nonreported” (i. The observation is blank), the reported data may not be interpretable, or the value must be imputed to be missing because of data inconsistency or out-of-range results. And attempts should be made to obtain as much missing data as realistically possible from source documents. A “yes” answer to a question at one point and “no” to the same question at another) and out-of-range data (e. Finally, the degree of data completeness should be summarized for the researcher and eventual consumer of analyses from the registry. Missing data can threaten this goal both by reducing the information yield of the study and, in many cases, by introducing bias. A thorough review of types of missing data with examples can be found in chapter 18. Finally, missing data fall into three classic categories of randomness:7missing completely at random (mcar): instances where there are no differences between subjects with missing data and those with complete data. In such random instances, missing data only reduce study power without introducing g at random (mar): instances where missing data depend on known or observed values but not unmeasured data. In such cases, accounting for these known factors in the analysis will produce unbiased g not at random (mnar): here, missing data depend on events or factors not measured by the researcher and thus potentially introduce gain insight into which of the three categories of missing data are in play, one can compare the distribution of observed variables for patients with specific missing data to the distribution of those variables for patients for whom those same data are pragmatically it is difficult to determine whether data are mcar or mar, there are, nonetheless, several means of managing missing data within an analysis. In observational studies with prospective, structured data collection, missing data are not uncommon, and the complete case strategy is inefficient and not generally used. In order to include patients with missing data, one of several imputation techniques may be used to estimate the missing tion is a common strategy in which average values are substituted for missing data using strategies such as unconditional and conditional mean, multiple hot-deck, and expectation maximum, among others. 8 for data that are captured at multiple time points or repeated measures, investigators often “carry forward” a last observation.
This produces multiple complete data sets for analysis from which a single summary finding is are several issues concerning how prognostic models for decisionmaking can be influenced by data completeness and missing data. Comparison of complete cases with incomplete cases was provided in 10 studies, and the methods used to handle missing data were summarized in 32 studies. The most common techniques used for handling missing data in this review article were (a) complete case analysis (12), (b) dropping variables with high numbers of missing cases from model consideration (6), and (c) using some simple author imputation rule (6). The reviewers concluded that there was room for improvement in the reporting and handling of missing data within registry studiesreaders interested in learning more about methods for handling missing data and the potential for bias are directed to other useful resources by greenland and finkle,11 hernán and colleagues,12 and lash, fox, and fink. Is important to keep in mind that the impact of data completeness will differ, depending on the extent of missing data and the intended use of the registry. For all registries, it is important to have a strategy for how to identify and handle missing data as well as how to explicitly report on data completeness to facilitate interpretation of study results. Food and drug administration and international conference on harmonisation standards of good clinical practice developed for clinical trials, sponsors and contract research organizations that conduct registry studies are responsible for ensuring the accuracy of study data to the extent possible. Detailed plans for site monitoring, quality assurance, and data verification should be developed at the beginning of a study and adhered to throughout its lifespan. Chapter 11 discusses in detail approaches to data collection and quality assurance, including data management, site monitoring, and source data ng the accuracy and validity of data and programming at the analysis stage requires additional consideration. The office of surveillance and epidemiology (ose) of the food and drug administration's center for drug evaluation and research uses the manual standards of data management and analytic process in the office of surveillance and epidemiology for analyses of databases conducted within ose; the manual addresses many of these issues and may be consulted for further elaboration on these topics. Topics addressed that pertain to ensuring the accuracy of data just before and during analysis include developing a clear understanding of the data at the structural level of the database and variable attributes; creating analytic programs with careful documentation and an approach to variable creation and naming conventions that is straightforward and, when possible, consistent with the clinical data interchange standards consortium initiative; and complete or partial verification of programming and analytic data set creation by a second more detail about validation substudies, please see chapter 18. Data analysisthis section provides an overview of practical considerations for analysis of data from a registry. As the name suggests, a descriptive study focuses on describing frequency and patterns of various elements of a patient population, whereas an analytical study focuses on examining associations between patients or treatment characteristics and health outcomes of interest (e.
Lasagna plots are one convenient method to visually assess missing data over time when conducting a longitudinal 13–2 illustrates key points of information that provide a useful description of losses to followup and study 13–2the flow of participants into an analysis. For analytical studies, the association between a risk factor and outcome may be expressed as attributable risk, relative risk, odds ratio, or hazard ratio, depending on the nature of the data collected, the duration of the study, and the frequency of the outcome. 16, 17,18, 19for analytical studies of data derived from observational studies such as registries, it is important to consider the role of confounding. Although those planning a study try to collect as much data as possible to address known confounders, there is always the chance that unknown confounders will affect the interpretation of analyses derived from observational studies. The uptake of these approaches in the medical literature in recent years has been extremely rapid, and their application to analyses of registry data has also been broad. 47it is important to emphasize that cost-effectiveness analyses, much like safety and clinical effectiveness analyses, require collection of specific data elements suited to the purpose. Although cost-effectiveness-type analyses are becoming more important and registries can play a key role in such analyses, registries traditionally have not collected much information on quality of life or resource use that can be linked to cost data. Need for a statistical analysis planit is important to develop a statistical analysis plan (sap) that describes the analytical principles and statistical techniques to be employed in order to address the primary and secondary objectives, as specified in the study protocol or plan. Although the evolving nature of data collection practices in some registries poses challenges for data analysis and interpretation, it is important to keep in mind that the ability to answer questions emerging during the course of the study is one of the advantages (and challenges) of a registry. Supplemental saps can be developed only when enough data become available to analyze a particular research question. At times, the method of statistical analysis may have to be modified to accommodate the amount and quality of data available. To the extent that the research question and sap are formulated before the data analyses are conducted and results are used to answer specific questions or hypotheses, such supplemental analysis retains much of the intent of prespecification rather than being wide-ranging exploratory analyses (sometimes referred to as “fishing expeditions”). The key to success is to provide sufficient details in the sap that, together with the study protocol and the case report forms, describe the overall process of the data analysis and reporting.
Preliminary descriptive analysis to assist sap developmentduring sap development, one particular aspect of a registry that is somewhat different from a randomized controlled study is the necessity to understand the “shape” of the data collected in the study by conducting a simple stratified analysis. The distribution of age, for example, may help to determine if more detailed analyses should be conducted in the “oldest old” age group (80 years and older) to help understand health outcomes in this subgroup that might be different from those of their younger a registry is designed to limit data collection to a fixed number of regimens, the study population may experience many “regimens,” considering the combination of various dose levels, drug names, frequency and timing of medication use (e. In this case, the sap may need to define how these patients will be analyzed (either as a separate group or as part of the overall study population) and how these different approaches might affect the interpretation of the study is a need to evaluate the presence of potential sources of bias and, to the extent feasible, use appropriate statistical measures to address such biases. Timing of analyses during the studyunlike a typical clinical trial, registries, especially those that take several years to complete, may conduct intermediate analyses before all patients have been enrolled and/or all data collection has been completed. Investigators should use sound clinical and epidemiological judgment when planning an intermediate analysis and, whenever possible, use data from previous studies to help to determine the feasibility and utility of such an planning the timing of the analysis, it may be helpful to consider substudies if emerging questions require data not initially collected. Different analytic approaches may be required to address issues of patients enrolling in a registry at different times and/or having different lengths of observation during the study ial for bias: successful analysis of observational studies also depends to a large extent on the ability to measure and analytically address the potential for bias. In addition, consistency in measurement of specific variables and in data collection methods make the comparison more valid. The absence of a good internal comparator, one may have to leverage external comparators to provide critical context to help interpret data revealed by a registry. An external or historical comparison may involve another study or another database that has disease or treatment characteristics similar to those of registry subjects. Such data may be viewed as a context for anticipating the rate of an event. Seer cancer registry data, because seer provides detailed annual incidence rates of cancer stratified by cancer site, age group, gender, and tumor staging at diagnosis. A procedure for formalizing comparisons with external data is known as standardized incidence rate or ratio;15 when used appropriately, it can be interpreted as a proxy measure of risk or relative of an external comparator, however, may present significant challenges. The seer data cover the general population and have no exclusion criteria pertaining to history of smoking or cancer screening, for example.
For these patients, the data are said to be censored, indicating that the observation period of the registry was stopped before all events occurred (e. One method of analyzing censored data to estimate the conditional probability of the event occurring is to use the kaplan-meier method. Summary of analytic considerationsin summary, a meaningful analysis requires careful consideration of study design features and the nature of the data collected. Most typical epidemiological study analytical methods can be applied, and there is no one-size-fits-all approach. Efforts should be made to carefully evaluate the presence of biases and to control for identified potential biases during data analysis. This requires close collaboration among clinicians, epidemiologists, statisticians, study coordinators, and others involved in the design, conduct, and interpretation of the registry. Number of biostatistics and epidemiology textbooks cover in depth the issues raised in this section and the appropriate analytic approaches for addressing them—for example, “time-to-event” or survival analyses59 and issues of recurrent outcomes and repeated measures, with or without missing data,60 in longitudinal cohort studies. Interpretation of registry datainterpretation of registry data is needed so that the lessons from the registry can be applied to the target population and used to change future health care and improve patient outcomes. Proper interpretation of registry data allows users to understand the precision of the observed risk or incidence estimates, to evaluate the hypotheses tested in the current registry, and often also to generate new hypotheses to be examined in future registries or in randomized controlled trials. If the purpose of the registry is explicit, the actual population studied is reasonably representative of the target population, the data quality monitored, and the analyses performed so as to reduce potential biases, then the interpretation of the registry data should allow a realistic picture of the quality of medical care, the natural history of the disease studied, or the safety, effectiveness, or value of a clinical evaluation. Each of these topics needs to be discussed in the interpretation of the registry data, and potential shortcomings should be explored. Assumptions or biases that could have influenced the outcomes of the analyses should be highlighted and separated from those that do not affect the interpretation of the registry results. The use of a comparator of the highest reasonably possible quality is integral to the proper interpretation of the retation of registry results may also be aided by comparisons with external information.
Examples include rates, or prevalence, of the outcomes of interest in other studies and different data sources (taking into account reasons why they may be similar or different). First analysis and interpretation of the registry will demonstrate strengths and limitations of the original registry design and will allow the registry developers to make needed design changes for future versions of the registry. Another group consists of the study's sponsors and related oversight/governance groups, such as the scientific committee and data monitoring committee. Interpretation of the analyses allows the oversight committees to offer recommendations concerning continued use and/or adaptation of the registry and to evaluate patient safety. These are the people for whom the data were collected and who may use the results to choose a treatment or intervention, to determine the need for additional research programs to change clinical practice, to develop clinical practice guidelines, or to determine policy. Ideally, all three user groups work toward the ultimate goal of each registry—improving patient examples for chapter 13case example 26using registry data to evaluate outcomes by practiceview in own windowdescriptionthe epidemiologic study of cystic fibrosis (escf) registry was a multicenter, encounter-based, observational, postmarketing study designed to monitor product safety, define clinical practice patterns, explore risks for pulmonary function decline, and facilitate quality improvement for cystic fibrosis (cf) patients. The registry collected comprehensive data on pulmonary function, microbiology, growth, pulmonary exacerbations, cf-associated medical conditions, and chronic and acute treatments for children and adult cf patients at each visit to the clinical rgenentech, started1993year endedpatient enrollment completed in 2005; followup . To determine whether differences in lung health existed between groups of patients attending different cf care sites, and to determine whether these differences were associated with differences in monitoring and intervention, data on a large number of cf patients from a wide variety of cf sites were a large, observational, prospective registry, escf collected data on a large number of patients from a range of participating sites. At the time of the outcomes study, the registry was estimated to have data on over 80 percent of cf patients in the united states, and it collected data from more than 90 percent of the sites accredited by the u. Because the registry contained a representative population of cf patients, the registry database offered strong potential for analyzing the association between practice patterns and ed solutionin designing the study, the team decided to compare cf sites using lung function (i. Data from 18,411 patients followed in 194 care sites were reviewed, and 8,125 patients from 132 sites (minimum of 50 patients per site) were included. Example 27using registry data to study patterns of use and outcomesview in own windowdescriptionthe palivizumab outcomes registry was designed to characterize the population of infants receiving prophylaxis for respiratory syncytial virus (rsv) disease, to describe the patterns and scope of the use of palivizumab, and to gather data on hospitalization rmedimmune, llcyear started2000year ended2004no. The objectives of the study were to better understand the population receiving the prophylaxis for rsv disease and to study the patterns of use and hospitalization ed solutiona multicenter registry study was created to collect data on infants receiving palivizumab injections.
Data were collected by the primary health care provider in the office or clinic setting. Infants were enrolled at the time of their first injection, and data were obtained on palivizumab injections, demographics, and risk factors, as well as on medical and family up forms were used to collect data on subsequent palivizumab injections, including dates and doses, during the rsv season. Data were also collected for all enrolled infants hospitalized for rsv and were directly reported to an onsite registry coordinator. In postmarketing reports, cases of severe thrombocytopenia (platelet count <50,000/microliter) and injection site reactions were sfrom september 2000 through may 2004, the registry collected data on 19,548 infants. The registry data also showed that the use of palivizumab was mostly consistent with the 2003 guidelines of the american academy of pediatrics for use of palivizumab for prevention of rsv infections. Infants in the registry had a low hospitalization rate, and these data support the effectiveness of this treatment outside of a controlled clinical study. These data supported an analysis of postlicensure effectiveness of rsv prophylaxis, in addition to describing the patient population and usage more s, kohlhase k. 13, analysis, interpretation, and reporting of registry data to evaluate this pageintroductionhypotheses and purposes of the registrypatient populationdata quality issuesdata analysissummary of analytic considerationsinterpretation of registry datacase examples for chapter 13references for chapter 13other titles in these collectionsahrq methods for effective health carehealth services/technology assessment texts. Hstat)related informationpmcpubmed central citationspubmedlinks to pubmedrecent activityclearturn offturn onanalysis, interpretation, and reporting of registry data to evaluate outcomes - ... Interpretation, and reporting of registry data to evaluate outcomes - registries for evaluating patient outcomesyour browsing activity is ty recording is turned recording back onsee more... See our privacy policy and user agreement for tation, analysis and interpretation of this presentation? Related slideshares at tation, analysis and interpretation of ella perez, cielito zamora high hed on jul 24, 2014. Thank u god you sure you want message goes ing & sales tant / ka tamilselvan e the inherent independence of tables and text, include in the body of the report sufficient analytical and summary statements derived from each table to provide the reader a comprehensible and logical interpretation of findings for expedience, place tables as close as possible to the discussion of the facts or data in the text, if this is not possible, mention the table number whenever it is being referred to in the preparation and reproduction of figures are more time-consuming and more expensive than those of tables.
One possible reason is that the instrument used for data collection was not a valid one, thus it was not able to measure what is intended to measure (lacaba-bago, 2011) *theoretical concerns – in general, hypothesis are logically deduced from theories based on certain assumptions. Highly opinionated and sweeping statements should be f – of that or tation, analysis and interpretation of data. Tabular - (a systematic related idea in which classes cal facts or data are given and their subclasses are a column in order to present onships of the sets or or data in a definite, compact and. The table should be so it enables the reader hend the data t referring to the text;. The findings are compared sted with that of retations are made ts from a college career course - linkedin ng to run course - linkedin ing learning course - linkedin r 10-data analysis & mae nalzaro,bsm,bsn,r 4 presentation of chnic university of the escolar analysis analysis, presentation and interpretation of analysis sent successfully.. Rnablast (basic local alignment search tool)blast (stand-alone)e-utilitiesgenbankgenbank: bankitgenbank: sequingenbank: tbl2asngenome workbenchinfluenza virusnucleotide databasepopsetprimer-blastprosplignreference sequence (refseq)refseqgenesequence read archive (sra)spligntrace archiveunigeneall dna & rna resources... Softwareblast (basic local alignment search tool)blast (stand-alone)cn3dconserved domain search service (cd search)e-utilitiesgenbank: bankitgenbank: sequingenbank: tbl2asngenome protmapgenome workbenchprimer-blastprosplignpubchem structure searchsnp submission toolsplignvector alignment search tool (vast)all data & software resources... Structuresbiosystemscn3dconserved domain database (cdd)conserved domain search service (cd search)structure (molecular modeling database)vector alignment search tool (vast)all domains & structures resources... Expressionbiosystemsdatabase of genotypes and phenotypes (dbgap)e-utilitiesgenegene expression omnibus (geo) database gene expression omnibus (geo) datasetsgene expression omnibus (geo) profilesgenome workbenchhomologenemap vieweronline mendelian inheritance in man (omim)refseqgeneunigeneall genes & expression resources... Medicinebookshelfdatabase of genotypes and phenotypes (dbgap)genetic testing registryinfluenza virusmap vieweronline mendelian inheritance in man (omim)pubmedpubmed central (pmc)pubmed clinical queriesrefseqgeneall genetics & medicine resources... Mapsdatabase of genomic structural variation (dbvar)genbank: tbl2asngenomegenome projectgenome protmapgenome workbenchinfluenza virusmap viewernucleotide databasepopsetprosplignsequence read archive (sra)spligntrace archiveall genomes & maps resources... Basic local alignment search tool)blast (stand-alone)blast link (blink)conserved domain database (cdd)conserved domain search service (cd search)genome protmaphomologeneprotein clustersall homology resources... Utilitiesjournals in ncbi databasesmesh databasencbi handbookncbi help manualncbi news & blogpubmedpubmed central (pmc)pubmed clinical queriespubmed healthall literature resources...
Basic local alignment search tool)blast (stand-alone)blast link (blink)conserved domain database (cdd)conserved domain search service (cd search)e-utilitiesprosplignprotein clustersprotein databasereference sequence (refseq)all proteins resources... Of genomic structural variation (dbvar)database of genotypes and phenotypes (dbgap)database of single nucleotide polymorphisms (dbsnp)snp submission toolall variation resources... Toall how tochemicals & bioassaysdna & rnadata & softwaredomains & structuresgenes & expressiongenetics & medicinegenomes & mapshomologyliteratureproteinssequence analysistaxonomytraining & tutorialsvariationabout ncbi accesskeysmy ncbisign in to ncbisign : abstractformatsummarysummary (text)abstractabstract (text)medlinexmlpmid listapplysend tochoose destinationfileclipboardcollectionse-mailordermy bibliographycitation managerformatsummary (text)abstract (text)medlinexmlpmid listcsvcreate file1 selected item: 17286625formatsummarysummary (text)abstractabstract (text)medlinexmlpmid listmesh and other datae-mailsubjectadditional texte-maildidn't get the message? 2007 aug;42(4):ative data analysis for health services research: developing taxonomy, themes, and y eh1, curry la, devers information1department of epidemiology and public health, yale university school of medicine, 60 college street, new haven, ct 06520-8034, ctobjective: to provide practical strategies for conducting and evaluating analyses of qualitative data applicable for health services researchers. Data sources and design: we draw on extant qualitative methodological literature to describe practical approaches to qualitative data analysis. Approaches to data analysis vary by discipline and analytic tradition; however, we focus on qualitative data analysis that has as a goal the generation of taxonomy, themes, and theory germane to health services ple findings: we describe an approach to qualitative data analysis that applies the principles of inductive reasoning while also employing predetermined code types to guide data analysis and interpretation. Intersectional analyses with data coded for participant characteristics and setting codes can facilitate comparative sions: qualitative inquiry can improve the description and explanation of complex, real-world phenomena pertinent to health services research. Greater understanding of the processes of qualitative data analysis can be helpful for health services researchers as they use these methods themselves or collaborate with qualitative researchers from a wide range of : 17286625 pmcid: pmc1955280 doi: 10.