Methods for Classifying Patient Histories in Secondary Healthcare Data

In clinical safety and effectiveness research using secondary health databases, patient medical histories are typically assessed using fixed look-back approaches. Conventional applications of these approaches exclude patients who are not continuously enrolled in the database for the entire look-back period (e.g. one year), and data occurring outside this period is ignored. An alternate approach has been suggested which assesses all of the available data history, though concerns exist that results may be biased by systematic variation in the amount of available database across important study groups. We used applied analyses as well as plasmode simulation methods to explore the application of short (1-year) and long (3-year) fixed look-backs and all-available data approaches in analyses of Medicare fee-for-service (FFS) claims data. We assessed the bias and efficiency of effect estimates when we used the different look-backs to 1) assess cohort eligibility and to 2) identify and adjust for confounders. In the applied analysis, we evaluated the effect of statin initiation (vs. non-use) on incidence of 1) cancer within six months (a negative control outcome we expected a priori to be null) and 2) all-cause mortality within two years. In the plasmode simulation, exposures (conceptually: statin initiation vs. non-initiation) and outcomes (conceptually: inpatient hospitalization) were simulated as a function of self-reported interview data obtained from the Medicare Current Beneficiary Survey (MCBS, which represented the true underlying confounder of exposure-outcome associations. We evaluated estimates after applying different look-back approaches in the linked claims data. Compared to short fixed look-back approaches, all-available approaches selected cohorts with superior classification and produced less biased estimates. Compared to long fixed look-back approaches, all-available approaches selected more inclusive cohorts and produced more precise estimates. Though these studies were conducted in a fairly narrow (applied) setting, our findings provide real-world evidence that using all-available look-backs to classify patient histories is superior to fixed look-back approaches. Our findings provide context to investigators seeking to understand the mechanisms through which the different look-backs may produce different estimates.Doctor of Philosoph

Exploration of structural and statistical biases in the application of propensity score matching to pharmacoepidemiologic data

Certain pitfalls associated with propensity score matching have come to light, recently. The extent to which these pitfalls might threaten validity and precision in pharmacoepidemiologic research, for which propensity score matching often is used, is uncertain. We evaluated the “propensity score matching paradox” – the tendency for covariate imbalance to increase in a propensity score-matched dataset upon continuous pruning of matched sets – as well as the utility of coarsened exact matching, a technique that has been posed as a preferable alternative to propensity score matching, especially in light of the “propensity score matching paradox”. We show that the “propensity score matching paradox” may not threaten causal inference that is based on propensity score matching in typical pharmacoepidemiologic settings to the extent predicted by previous research. Moreover, even though coarsened exact matching substantially improves covariate balance, it may not be optimal in typical pharmacoepidemiologic settings due to the extreme loss of study size (and resulting increase in bias and variance) that may be required to build the matched dataset. Finally, we explain variability in 1:1 propensity score matching without replacement as well as methods that were developed to account for this variability, with application of these methods to an example claims-based study.2021-06-03T00:00:00

Impact of Intra-Articular Injection Use on Patient-Reported Outcomes Among Patients with Knee Osteoarthritis

Background: Knee osteoarthritis (OA) is the most common type of OA and is a major cause of pain and thus results in disability for daily activities among persons living in the community. OA currently has no cure. In addition to the conflicting recommendations from clinical guidelines, evidence about the extent to which long-term use of intra-articular injections improves patient outcomes is also lacking. Methods: Using data from the Osteoarthritis Initiative (OAI), marginal structural models (MSMs) applying inverse probability treatment weights (IPTW) were used to examine the effectiveness of intra-articular injections and changes in symptoms over time. The specific aims of this dissertation were to: 1) evaluate longitudinal use of intra-articular injections after treatment initiation among persons with radiographic knee OA; 2) quantify the extent to which intra-articular injection relieves symptoms among persons with radiographic knee OA; and 3) evaluate the performance of missing data techniques under the setting of MSMs. Results: Of those initiating injections, ~19% switched, ~21% continued injection type, and ~60% did not report any additional injections. For participants initiating corticosteroid (CO) injections, greater symptoms post-initial injection rather than changes in symptoms over time were associated with continued use compared to one-time use. Among participants with radiographic evidence of knee OA, initiating treatments with either CO or hyaluronic acid (HA) injections was not associated with reduced symptoms compared to non-users over two years. Compared to inverse probability weighting (IPW), missing data techniques such as multiple imputation (MI) produced less biased marginal causal effects (IPW: -2.33% to 15.74%; -1.88% to 4.24%). For most scenarios, estimates using MI had smaller mean square error (range: 0.013 to 0.024) than IPW (range: 0.027 to 0.22). Conclusions: Among participants with radiographic evidence of knee OA living in the community, the proportion of those switching injection use and one-time users was substantial after treatment initiation. In addition, initiating injection use was not associated with reduced symptoms over time. With respect to issues of missing data, using MI may confer an advantage over IPW in MSMs applications. The results of this work highlight the importance of using comparative effectiveness research with non-experimental data to study these commonly used injections and may help to understand the usefulness of these treatments for patients with knee OA

Extent, impact, and mitigation of batch effects in tumor biomarker studies using tissue microarrays

Tissue microarrays (TMAs) have been used in thousands of cancer biomarker studies. To what extent batch effects, measurement error in biomarker levels between slides, affects TMA-based studies has not been assessed systematically. We evaluated 20 protein biomarkers on 14 TMAs with prospectively collected tumor tissue from 1,448 primary prostate cancers. In half of the biomarkers, more than 10% of biomarker variance was attributable to between-TMA differences (range, 1–48%). We implemented different methods to mitigate batch effects (R package batchtma), tested in plasmode simulation. Biomarker levels were more similar between mitigation approaches compared to uncorrected values. For some biomarkers, associations with clinical features changed substantially after addressing batch effects. Batch effects and resulting bias are not an error of an individual study but an inherent feature of TMA-based protein biomarker studies. They always need to be considered during study design and addressed analytically in studies using more than one TMA

TINJAUAN PUSTAKA EVALUASI FARMAKOVIGILANS OBAT ANTIDIABETES ORAL PADA PASIEN RAWAT JALAN DIABETES MELITUS TIPE II

Diabetes Mellitus can be treat with pharmacological therapy in the form of oral drug administration. This step has side effects in the form of adverse drug reactions. ADR adapts various factors that can influence the patient to adverse reactions.The purpose of this study was to determine the percentage of incidence of ADR in outpatients with type II diabetes mellitus. This research method is a literature study method searched through PubMed, Google Scholar, and Research Gate using the keywords pharmacovigilance, diabetes mellitus, and adverse drug reaction (ADR). The criteria for articles used were publish in the last 10 years, namely 2012 to 2022, the journal has a title and content that is in accordance with the research objectives. The occurrence of ADR was influence by the gender and type of drug taken. This is in accordance with the research. Oral hypoglycemic drugs that were suspect to be the cause of the onset of ADRs are metformin causing nausea effects and acarbose causing flatulence effects. The use of the Naranjo algorithm is indispensable for assessing the severity of the ADR. Keywords: Farmakovigilans., Diabetes Melitus, AD

Combining the Power of Artificial Intelligence with the Richness of Healthcare Claims Data: Opportunities and Challenges

Combinations of healthcare claims data with additional datasets provide large and rich sources of information. The dimensionality and complexity of these combined datasets can be challenging to handle with standard statistical analyses. However, recent developments in artificial intelligence (AI) have led to algorithms and systems that are able to learn and extract complex patterns from such data. AI has already been applied successfully to such combined datasets, with applications such as improving the insurance claim processing pipeline and reducing estimation biases in retrospective studies. Nevertheless, there is still the potential to do much more. The identification of complex patterns within high dimensional datasets may find new predictors for early onset of diseases or lead to a more proactive offering of personalized preventive services. While there are potential risks and challenges associated with the use of AI, these are not insurmountable. As with the introduction of any innovation, it will be necessary to be thoughtful and responsible as we increasingly apply AI methods in healthcare

Safety surveillance and the estimation of risk in select populations: Flexible methods to control for confounding while targeting marginal comparisons via standardization

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152565/1/sim8410_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152565/2/sim8410.pd