Like many long-standing SAS users and Data Step programmers, I avoided learning Proc SQL for many... more Like many long-standing SAS users and Data Step programmers, I avoided learning Proc SQL for many years. After making the decision to take the plunge and add Proc SQL to my toolkit of techniques, I went overboard and began using Proc SQL for everything. This paper describes my approach to choosing between Proc SQL and the Data Step for particular tasks. This paper is appropriate for beginning SAS users who have a basic understanding of the Data Step and Proc SQL.
Importing Microsoft Excel files into SAS can often be a challenge. Perfectly formatted Excel file... more Importing Microsoft Excel files into SAS can often be a challenge. Perfectly formatted Excel files with labels in the first row and idiosyncrasy-free, clean data is not usually the norm. We will show how to overcome many of the obstacles associated with creating SAS data sets from Excel workbooks by using various combinations of SAS Enterprise Guide 4.3's features. The import wizard, generated code, code suggestion mechanism, options, and the ability to preview the first section of a CSV file will all be shown as mechanisms for creating analytic data sets from Excel input.
There is considerable uncertainty regarding treatment of Lyme disease patients who do not respond... more There is considerable uncertainty regarding treatment of Lyme disease patients who do not respond fully to initial short-term antibiotic therapy. Choosing the best treatment approach and duration remains challenging because treatment response among these patients varies: some patients improve with treatment while others do not. A previous study examined treatment response variation in a sample of over 3500 patients enrolled in the MyLymeData patient registry developed by LymeDisease.org (San Ramon, CA, USA). That study used a validated Global Rating of Change (GROC) scale to identify three treatment response subgroups among Lyme disease patients who remained ill: nonresponders, low responders, and high responders. The present study first characterizes the health status, symptom severity, and percentage of treatment response across these three patient subgroups together with a fourth subgroup, patients who identify as well. We then employed machine learning techniques across these subgroups to determine features most closely associated with improved patient outcomes, and we used traditional statistical techniques to examine how these features relate to treatment response of the four groups. High treatment response was most closely associated with (1) the use of antibiotics or a combination of antibiotics and alternative treatments, (2) longer duration of treatment, and (3) oversight by a clinician whose practice focused on the treatment of tick-borne diseases.
Lyme disease is caused by the bacteria borrelia burgdorferi and is spread primarily through the b... more Lyme disease is caused by the bacteria borrelia burgdorferi and is spread primarily through the bite of a tick. There is considerable uncertainty in the medical community regarding the best approach to treating patients with Lyme disease who do not respond fully to short-term antibiotic therapy. These patients have persistent Lyme disease symptoms resulting from lack of treatment, under-treatment, or lack of response to their antibiotic treatment protocol. In the past, treatment trials have used small restrictive samples and relied on average treatment effects as their measure of success and produced conflicting results. To provide individualized care, clinicians need information that reflects their patient population. Today, we have the ability to analyze large data bases, including patient registries, that reflect the broader range of patients more typically seen in clinical practice. This allows us to examine treatment variation within the sample and identify groups of patients that are most responsive to treatment. Using patient-reported outcome data from the MyLymeData online patient registry, we show that subgroup analysis techniques can unmask valuable information that is hidden if averages alone are used. In our analysis, this approach revealed treatment effectiveness for up to a third of patients with Lyme disease. This study is important because it can help open the door to more individualized patient care using patient-centered outcomes and real-world evidence.
SAS ® Enterprise Guide ® empowers organizations exploiting the power of SAS by offering programme... more SAS ® Enterprise Guide ® empowers organizations exploiting the power of SAS by offering programmers, business analysts, statisticians and end-users with powerful built-in wizards to perform a multitude of reporting and analytical tasks, access multiplatform enterprise data sources, deliver data and results to a variety of mediums and outlets, perform important data manipulations without the need to learn complex coding constructs, and support data management and documentation requirements quickly and easily. Attendees learn how to use the graphical user interface (GUI) to access tab-delimited and Excel input files; subset, group, and summarize data; join two or more tables together; flexibly export results to HTML, PDF and Excel; and visually manage projects using flowcharts and diagrams.
Biological sex should be included as an important variable in clinical research studies to identi... more Biological sex should be included as an important variable in clinical research studies to identify outcome differences between men and women. Very few Lyme disease studies were designed to consider sex-based differences or gender bias as an important component of the research design. Methods: To assess sex-based differences in Lyme disease patients who were clinically diagnosed and reported remaining ill for six or more months after receiving antibiotic treatment, we analyzed self-reported clinical data from 2170 patients in the MyLymeData patient registry. We also reviewed previous Lyme disease studies for distribution of patients by biological sex according to stage of illness, data source, and definition of disease used as enrollment criteria. Results: In MyLymeData, women reported more tick-borne coinfections, worse symptoms, longer diagnostic delays, more misdiagnoses, and worse functional impairment than men. No differences were reported in antibiotic treatment response or side effects. In our review, of clinical research trials and data sources, we identified a smaller percentage of women in studies of acute Lyme disease and a larger percentage of women in studies of persistent illness. Samples and data sources that were more reflective of patients seen in clinical practice had a higher percentage of women than randomized controlled trials and post-treatment Lyme disease studies. Conclusion: Our results indicate that biological sex should be integrated into Lyme disease research as a distinct variable. Future Lyme disease studies should include sex-based disaggregated data to illuminate differences that may exist between men and women with persistent illness. Keywords: post-treatment lyme disease, chronic lyme disease, MyLymeData, Borrelia burgdorferi, tick-borne disease, gender bias, sex-based differences-Ixodes scapularis and Ixodes pacificus. The Centers for Disease Control and Prevention (CDC) estimates that 476,000 new Lyme disease cases occur annually in the USA. 4,5 If diagnosed and treated promptly, most patients recover without complications. 6 However, even patients who are diagnosed early can remain ill after antibiotic treatment. 6 In a recent study, 43% of patients reported persisting symptoms six months or more after early treatment with 14% reporting functional impairment, and only 57% of patients were classified as having recovered from their illness. 6 Many patients are not diagnosed and treated early for Lyme disease, which further increases their risk of remaining ill after initial antibiotic treatment. 7,8 Clinical studies of patients with persisting Lyme disease symptoms following antibiotic treatment have used different research definitions to enroll patients. A number of NIH-funded studies have used the randomized controlled trial (RCT)
Lyme disease is the most common vector-borne disease in the United States. Diagnostic errors are ... more Lyme disease is the most common vector-borne disease in the United States. Diagnostic errors are believed to be common. Errors stemming from delayed or inaccurate diagnosis are generally the number one cause of serious harms among medical errors. The Agency for Healthcare Research and Quality has made reduction of diagnostic errors one of its three strategic priorities. The National Academy of Medicine (NAM) has developed a schematic to identify points in the medical care system where diagnostic errors occur. It defines diagnostic medical error as "the failure to (a) establish an accurate and timely explanation of the patient's health problem(s) or (b) communicate that explanation to the patient." NAM looks at errors that occur when the patient first encounters the healthcare system, when clinical history is taken, during physical exam, and when diagnostic testing is being conducted. They then look at the consequences and costs of the diagnostic errors. This study anal...
SAS ® Enterprise Guide ® empowers organizations exploiting the power of SAS by offering programme... more SAS ® Enterprise Guide ® empowers organizations exploiting the power of SAS by offering programmers, business analysts, statisticians and end-users with powerful built-in wizards to perform a multitude of reporting and analytical tasks, access multi-platform enterprise data sources, deliver data and results to a variety of mediums and outlets, perform important data manipulations without the need to learn complex coding constructs, and support data management and documentation requirements quickly and easily. Attendees learn how to use the graphical user interface (GUI) to access tab-delimited and Excel input files; subset, group, and summarize data; join two or more tables together; flexibly export results to HTML, PDF and Excel; and visually manage projects using flowcharts and diagrams.
Like many long-standing SAS users and Data Step programmers, I avoided learning Proc SQL for many... more Like many long-standing SAS users and Data Step programmers, I avoided learning Proc SQL for many years. After making the decision to take the plunge and add Proc SQL to my toolkit of techniques, I went overboard and began using Proc SQL for everything. This paper describes my approach to choosing between Proc SQL and the Data Step for particular tasks. This paper is appropriate for beginning SAS users who have a basic understanding of the Data Step and Proc SQL. In this example we have two data sets for a case control study, one with information about our cases and another with information about our controls. We want to find all the potential healthy controls for our cases. We have been told that we must match the controls to the cases by sex and year of birth. We have also been told that there may be more than one control per case and that is desirable since that will give us an opportunity to randomly select one control per case. Furthermore, many of the variables in both of the ...
Importing Microsoft Excel files into SAS can often be a challenge. Perfectly formatted Excel file... more Importing Microsoft Excel files into SAS can often be a challenge. Perfectly formatted Excel files with labels in the first row and idiosyncrasy-free, clean data is not usually the norm. We will show how to overcome many of the obstacles associated with creating SAS data sets from Excel workbooks by using various combinations of SAS Enterprise Guide 4.3's features. The import wizard, generated code, code suggestion mechanism, options, and the ability to preview the first section of a CSV file will all be shown as mechanisms for creating analytic data sets from Excel input.
Longtime SAS users can benefit by adding JMP to their repertoire. JMP provides an easy-to-use and... more Longtime SAS users can benefit by adding JMP to their repertoire. JMP provides an easy-to-use and robust environment for data exploration, graphics and analytics without the need for programming expertise. This paper will provide an introduction to JMP 9 with an emphasis on features that SAS users will find useful. During this presentation, users will learn how to read their SAS data, import Excel spreadsheets, transform their data, explore distributions, create reports and create sophisticated graphics all in the JMP environment. Users will be introduced to the tools within the JMP 9 environment that provide a pathway to quickly learn how to use the product and some of its unique features.
Like many long-standing SAS users and Data Step programmers, I avoided learning Proc SQL for many... more Like many long-standing SAS users and Data Step programmers, I avoided learning Proc SQL for many years. After making the decision to take the plunge and add Proc SQL to my toolkit of techniques, I went overboard and began using Proc SQL for everything. This paper describes my approach to choosing between Proc SQL and the Data Step for particular tasks. This paper is appropriate for beginning SAS users who have a basic understanding of the Data Step and Proc SQL.
Importing Microsoft Excel files into SAS can often be a challenge. Perfectly formatted Excel file... more Importing Microsoft Excel files into SAS can often be a challenge. Perfectly formatted Excel files with labels in the first row and idiosyncrasy-free, clean data is not usually the norm. We will show how to overcome many of the obstacles associated with creating SAS data sets from Excel workbooks by using various combinations of SAS Enterprise Guide 4.3's features. The import wizard, generated code, code suggestion mechanism, options, and the ability to preview the first section of a CSV file will all be shown as mechanisms for creating analytic data sets from Excel input.
There is considerable uncertainty regarding treatment of Lyme disease patients who do not respond... more There is considerable uncertainty regarding treatment of Lyme disease patients who do not respond fully to initial short-term antibiotic therapy. Choosing the best treatment approach and duration remains challenging because treatment response among these patients varies: some patients improve with treatment while others do not. A previous study examined treatment response variation in a sample of over 3500 patients enrolled in the MyLymeData patient registry developed by LymeDisease.org (San Ramon, CA, USA). That study used a validated Global Rating of Change (GROC) scale to identify three treatment response subgroups among Lyme disease patients who remained ill: nonresponders, low responders, and high responders. The present study first characterizes the health status, symptom severity, and percentage of treatment response across these three patient subgroups together with a fourth subgroup, patients who identify as well. We then employed machine learning techniques across these subgroups to determine features most closely associated with improved patient outcomes, and we used traditional statistical techniques to examine how these features relate to treatment response of the four groups. High treatment response was most closely associated with (1) the use of antibiotics or a combination of antibiotics and alternative treatments, (2) longer duration of treatment, and (3) oversight by a clinician whose practice focused on the treatment of tick-borne diseases.
Lyme disease is caused by the bacteria borrelia burgdorferi and is spread primarily through the b... more Lyme disease is caused by the bacteria borrelia burgdorferi and is spread primarily through the bite of a tick. There is considerable uncertainty in the medical community regarding the best approach to treating patients with Lyme disease who do not respond fully to short-term antibiotic therapy. These patients have persistent Lyme disease symptoms resulting from lack of treatment, under-treatment, or lack of response to their antibiotic treatment protocol. In the past, treatment trials have used small restrictive samples and relied on average treatment effects as their measure of success and produced conflicting results. To provide individualized care, clinicians need information that reflects their patient population. Today, we have the ability to analyze large data bases, including patient registries, that reflect the broader range of patients more typically seen in clinical practice. This allows us to examine treatment variation within the sample and identify groups of patients that are most responsive to treatment. Using patient-reported outcome data from the MyLymeData online patient registry, we show that subgroup analysis techniques can unmask valuable information that is hidden if averages alone are used. In our analysis, this approach revealed treatment effectiveness for up to a third of patients with Lyme disease. This study is important because it can help open the door to more individualized patient care using patient-centered outcomes and real-world evidence.
SAS ® Enterprise Guide ® empowers organizations exploiting the power of SAS by offering programme... more SAS ® Enterprise Guide ® empowers organizations exploiting the power of SAS by offering programmers, business analysts, statisticians and end-users with powerful built-in wizards to perform a multitude of reporting and analytical tasks, access multiplatform enterprise data sources, deliver data and results to a variety of mediums and outlets, perform important data manipulations without the need to learn complex coding constructs, and support data management and documentation requirements quickly and easily. Attendees learn how to use the graphical user interface (GUI) to access tab-delimited and Excel input files; subset, group, and summarize data; join two or more tables together; flexibly export results to HTML, PDF and Excel; and visually manage projects using flowcharts and diagrams.
Biological sex should be included as an important variable in clinical research studies to identi... more Biological sex should be included as an important variable in clinical research studies to identify outcome differences between men and women. Very few Lyme disease studies were designed to consider sex-based differences or gender bias as an important component of the research design. Methods: To assess sex-based differences in Lyme disease patients who were clinically diagnosed and reported remaining ill for six or more months after receiving antibiotic treatment, we analyzed self-reported clinical data from 2170 patients in the MyLymeData patient registry. We also reviewed previous Lyme disease studies for distribution of patients by biological sex according to stage of illness, data source, and definition of disease used as enrollment criteria. Results: In MyLymeData, women reported more tick-borne coinfections, worse symptoms, longer diagnostic delays, more misdiagnoses, and worse functional impairment than men. No differences were reported in antibiotic treatment response or side effects. In our review, of clinical research trials and data sources, we identified a smaller percentage of women in studies of acute Lyme disease and a larger percentage of women in studies of persistent illness. Samples and data sources that were more reflective of patients seen in clinical practice had a higher percentage of women than randomized controlled trials and post-treatment Lyme disease studies. Conclusion: Our results indicate that biological sex should be integrated into Lyme disease research as a distinct variable. Future Lyme disease studies should include sex-based disaggregated data to illuminate differences that may exist between men and women with persistent illness. Keywords: post-treatment lyme disease, chronic lyme disease, MyLymeData, Borrelia burgdorferi, tick-borne disease, gender bias, sex-based differences-Ixodes scapularis and Ixodes pacificus. The Centers for Disease Control and Prevention (CDC) estimates that 476,000 new Lyme disease cases occur annually in the USA. 4,5 If diagnosed and treated promptly, most patients recover without complications. 6 However, even patients who are diagnosed early can remain ill after antibiotic treatment. 6 In a recent study, 43% of patients reported persisting symptoms six months or more after early treatment with 14% reporting functional impairment, and only 57% of patients were classified as having recovered from their illness. 6 Many patients are not diagnosed and treated early for Lyme disease, which further increases their risk of remaining ill after initial antibiotic treatment. 7,8 Clinical studies of patients with persisting Lyme disease symptoms following antibiotic treatment have used different research definitions to enroll patients. A number of NIH-funded studies have used the randomized controlled trial (RCT)
Lyme disease is the most common vector-borne disease in the United States. Diagnostic errors are ... more Lyme disease is the most common vector-borne disease in the United States. Diagnostic errors are believed to be common. Errors stemming from delayed or inaccurate diagnosis are generally the number one cause of serious harms among medical errors. The Agency for Healthcare Research and Quality has made reduction of diagnostic errors one of its three strategic priorities. The National Academy of Medicine (NAM) has developed a schematic to identify points in the medical care system where diagnostic errors occur. It defines diagnostic medical error as "the failure to (a) establish an accurate and timely explanation of the patient's health problem(s) or (b) communicate that explanation to the patient." NAM looks at errors that occur when the patient first encounters the healthcare system, when clinical history is taken, during physical exam, and when diagnostic testing is being conducted. They then look at the consequences and costs of the diagnostic errors. This study anal...
SAS ® Enterprise Guide ® empowers organizations exploiting the power of SAS by offering programme... more SAS ® Enterprise Guide ® empowers organizations exploiting the power of SAS by offering programmers, business analysts, statisticians and end-users with powerful built-in wizards to perform a multitude of reporting and analytical tasks, access multi-platform enterprise data sources, deliver data and results to a variety of mediums and outlets, perform important data manipulations without the need to learn complex coding constructs, and support data management and documentation requirements quickly and easily. Attendees learn how to use the graphical user interface (GUI) to access tab-delimited and Excel input files; subset, group, and summarize data; join two or more tables together; flexibly export results to HTML, PDF and Excel; and visually manage projects using flowcharts and diagrams.
Like many long-standing SAS users and Data Step programmers, I avoided learning Proc SQL for many... more Like many long-standing SAS users and Data Step programmers, I avoided learning Proc SQL for many years. After making the decision to take the plunge and add Proc SQL to my toolkit of techniques, I went overboard and began using Proc SQL for everything. This paper describes my approach to choosing between Proc SQL and the Data Step for particular tasks. This paper is appropriate for beginning SAS users who have a basic understanding of the Data Step and Proc SQL. In this example we have two data sets for a case control study, one with information about our cases and another with information about our controls. We want to find all the potential healthy controls for our cases. We have been told that we must match the controls to the cases by sex and year of birth. We have also been told that there may be more than one control per case and that is desirable since that will give us an opportunity to randomly select one control per case. Furthermore, many of the variables in both of the ...
Importing Microsoft Excel files into SAS can often be a challenge. Perfectly formatted Excel file... more Importing Microsoft Excel files into SAS can often be a challenge. Perfectly formatted Excel files with labels in the first row and idiosyncrasy-free, clean data is not usually the norm. We will show how to overcome many of the obstacles associated with creating SAS data sets from Excel workbooks by using various combinations of SAS Enterprise Guide 4.3's features. The import wizard, generated code, code suggestion mechanism, options, and the ability to preview the first section of a CSV file will all be shown as mechanisms for creating analytic data sets from Excel input.
Longtime SAS users can benefit by adding JMP to their repertoire. JMP provides an easy-to-use and... more Longtime SAS users can benefit by adding JMP to their repertoire. JMP provides an easy-to-use and robust environment for data exploration, graphics and analytics without the need for programming expertise. This paper will provide an introduction to JMP 9 with an emphasis on features that SAS users will find useful. During this presentation, users will learn how to read their SAS data, import Excel spreadsheets, transform their data, explore distributions, create reports and create sophisticated graphics all in the JMP environment. Users will be introduced to the tools within the JMP 9 environment that provide a pathway to quickly learn how to use the product and some of its unique features.
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Papers by Mira Shapiro