The Impact of Missing/Incomplete Data in Real-World Data Studies

Yang, Dejie; Miccio, Joseph A.; Jairam, Vikram; Chang, Eric; Yu, J.B.; Park, H.S.M.; Aneja, Sanjay

doi:10.1016/j.ijrobp.2020.07.2432

Cited by 3 publications

(2 citation statements)

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“…Our analysis was limited to the content and quality of available data, which commonly impacts analyses of RWD ( 48 ). The power of our study was determined by available data, and may have impacted our ability to detect the association of factors additional to treatment modality with overall survival.…”

Section: Discussionmentioning

confidence: 99%

‘CROSS’-ing into the ‘Real World’: a retrospective cohort study of patients receiving trimodality and bimodality therapy for esophageal cancer

Higgins¹,

Lester-Coll²,

Ades³

et al. 2023

J Gastrointest Oncol

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Background: A standard of care for nonmetastatic esophageal cancer is trimodality therapy consisting of neoadjuvant chemoradiation and esophagectomy, with evidence for improved overall survival versus surgery alone in the ChemoRadiotherapy for Oesophageal cancer followed by Surgery Study (CROSS) trial.Patients who receive treatment with curative intent but are poor candidates for or decline surgery receive definitive bimodality therapy. Literature characterizing patients who receive bimodality therapy compared to trimodality therapy, and their relative outcomes, is sparse, especially among patients who are too old or too frail to qualify for clinical trials. In this study, we assess a single-institution real-world dataset of patients receiving bimodality and trimodality management.Methods: Patients treated for clinically resectable, nonmetastatic esophageal cancer between 2009 and 2019 who received bimodality or trimodality therapy were reviewed, generating a dataset of 95 patients. Clinical variables and patient characteristics were assessed for association with modality on multivariable logistic regression. Overall, relapse-free, and disease-free survival were assessed with Kaplan-Meier analyses and Cox proportional modeling. For patients nonadherent to planned esophagectomy, reasons for nonadherence were recorded.Results: Bimodality therapy was associated with greater age-adjusted comorbidity index, worse performance status, higher N-stage, presenting symptom other than dysphagia, and held chemotherapy cycles on multivariable analysis. Compared to bimodality therapy, trimodality therapy was associated with higher overall (3-year: 62% vs. 18%, P<0.001), relapse-free (3-year: 71% vs. 18%, P<0.001), and diseasefree (3-year: 58% vs. 12%, P<0.001) survival. Similar results were observed among patients who did not meet CROSS trial qualifying criteria. Only treatment modality was associated with overall survival after adjusting for covariates (HR 0.37, P<0.001, reference group: bimodality). Patient choice accounted for 40% of surgery nonadherence in our population.Conclusions: Patients receiving trimodality therapy were observed to have superior overall survival compared to bimodality therapy.

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Section: Discussionmentioning

confidence: 99%

‘CROSS’-ing into the ‘Real World’: a retrospective cohort study of patients receiving trimodality and bimodality therapy for esophageal cancer

Higgins¹,

Lester-Coll²,

Ades³

et al. 2023

J Gastrointest Oncol

View full text Add to dashboard Cite

show abstract

“…1,2 Central to this challenge are data quality and standardization, 1 which are often compromised by a high proportion of missing or incomplete data. 3,4 Frequently, data elements are not captured in structured tables, or patient care may span multiple different medical record systems making it difficult to capture complete patient information with high accuracy. Even within the same health care system, structured tables across various medical specialties may differ slightly, although it lives on the same electronic medical record (EMR).…”

Section: Introductionmentioning

confidence: 99%

Extraction of Treatment Information From Electronic Health Records and Evaluation of Testosterone Recovery in Patients With Prostate Cancer

Guin

Jun

Patel

et al. 2022

JCO Clinical Cancer Informatics

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PURPOSE Data quality and standardization remain a challenge when analyzing real-world clinical data. We built a clinical research database, using machine learning and natural learning processing, and investigated factors influencing testosterone recovery (T-recovery) in patients with localized prostate cancer (LPC) after initial androgen deprivation therapy (ADT). METHODS Medication and treatment-associated dates missing in structured tables were extracted from patient notes using ConceptMapper, an automated data extraction tool, standardized and curated in Sema4 clinical research database. ADT usage duration was evaluated, and T-recovery in patients with LPC was analyzed by the Kaplan-Meier method and multivariable Cox proportional hazards models. We assessed the prognostic value of post-ADT T-recovery with prostate-specific antigen progression-free survival and failure-free survival. RESULTS In total, 4,125 of 30,832 (13.4%) patients with prostate cancer had medication exclusively from notes with high precision and recall, F1 score ≥ 0.95. Association of dates with medication usage had a F1 score of 0.76. ADT duration estimation had higher accuracy combining information from notes to tables from electronic medical record (70% v 45%). Baseline testosterone was the strongest predictor of T-recovery in these patients. Patients with a baseline testosterone ≥ 300 ng/dL recovered in 9.79 versus 38 months for patients with baseline testosterone < 300 ng/dL ( P < .0001). Shorter prostate-specific antigen progression-free interval was observed for patients with T-recovery (≥ 300 ng/dL) at 6 months after ADT cessation compared with patients without T-recovery (< 300 ng/dL; 13.7 v 25.1 months; P = .055). CONCLUSION We augmented structured electronic medical record data with data extracted from notes and improved the accuracy of medication information for patients. ADT exposure and T-recovery in patients with LPC produced results consistent with the literature and clinical experience and illustrates the power of applying machine learning methods to enhance the quality of real-world evidence in answering clinically relevant questions.

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Characteristics and Outcomes of Patients With Relapsed/Refractory Multiple Myeloma After Exposure to Lenalidomide in First Line of Therapy: A Single Center Database Review in Greece

Kastritis,

Ntanasis-Stathopoulos,

Theodorakakou

et al. 2024

Clinical Lymphoma Myeloma and Leukemia

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The Impact of Missing/Incomplete Data in Real-World Data Studies

Cited by 3 publications

References 0 publications

‘CROSS’-ing into the ‘Real World’: a retrospective cohort study of patients receiving trimodality and bimodality therapy for esophageal cancer

‘CROSS’-ing into the ‘Real World’: a retrospective cohort study of patients receiving trimodality and bimodality therapy for esophageal cancer

Extraction of Treatment Information From Electronic Health Records and Evaluation of Testosterone Recovery in Patients With Prostate Cancer

Characteristics and Outcomes of Patients With Relapsed/Refractory Multiple Myeloma After Exposure to Lenalidomide in First Line of Therapy: A Single Center Database Review in Greece

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