GLORIA

GEOMAR Library Ocean Research Information Access

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    In: Statistics in Medicine, Wiley, Vol. 38, No. 19 ( 2019-08-30), p. 3669-3681
    Abstract: In epidemiological studies of secondary data sources, lack of accurate disease classifications often requires investigators to rely on diagnostic codes generated by physicians or hospital systems to identify case and control groups, resulting in a less‐than‐perfect assessment of the disease under investigation. Moreover, because of differences in coding practices by physicians, it is hard to determine the factors that affect the chance of an incorrectly assigned disease status. What results is a dilemma where assumptions of non‐differential misclassification are questionable but, at the same time, necessary to proceed with statistical analyses. This paper develops an approach to adjust exposure‐disease association estimates for disease misclassification, without the need of simplifying non‐differentiality assumptions, or prior information about a complicated classification mechanism. We propose to leverage rich temporal information on disease‐specific healthcare utilization to estimate each participant's probability of being a true case and to use these estimates as weights in a Bayesian analysis of matched case‐control data. The approach is applied to data from a recent observational study into the early symptoms of multiple sclerosis (MS), where MS cases were identified from Canadian health administrative databases and matched to population controls that are assumed to be correctly classified. A comparison of our results with those from non‐differentially adjusted analyses reveals conflicting inferences and highlights that ill‐suited assumptions of non‐differential misclassification can exacerbate biases in association estimates.
    Type of Medium: Online Resource
    ISSN: 0277-6715 , 1097-0258
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 1491221-1
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 2
    In: Multiple Sclerosis and Related Disorders, Elsevier BV, Vol. 25 ( 2018-10), p. 232-240
    Type of Medium: Online Resource
    ISSN: 2211-0348
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2018
    detail.hit.zdb_id: 2645330-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 3
    In: The Lancet Neurology, Elsevier BV, Vol. 16, No. 6 ( 2017-06), p. 445-451
    Type of Medium: Online Resource
    ISSN: 1474-4422
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2017
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 4
    In: Neuroepidemiology, S. Karger AG, Vol. 54, No. 2 ( 2020), p. 140-147
    Abstract: 〈 b 〉 〈 i 〉 Background: 〈 /i 〉 〈 /b 〉 There is growing evidence of a prodromal period in multiple sclerosis (MS). A better understanding of the prodrome may facilitate prompt recognition and treatment of MS as well as narrowing of the etiologically relevant ­period when searching for MS risk factors. 〈 b 〉 〈 i 〉 Objectives: 〈 /i 〉 〈 /b 〉 To explore and further delineate the MS prodrome, we used statistical learning techniques to examine associations of physician-generated diagnostic codes and prescription medication classes in the 5 years before the first demyelinating-related claim for MS cases and matched population controls. 〈 b 〉 〈 i 〉 Methods: 〈 /i 〉 〈 /b 〉 In this matched cohort study, we accessed data from linked health administrative hospital, physician, and prescription databases from British Columbia, Canada, between 1996 and 2013. We focused on 7 medication classes previously identified as associated with the MS prodrome: urinary anti-spasmodics, glucocorticoids, muscle relaxants, anti-epileptics, dopa-derivatives, benzodiazepine, and antivertigo preparations. Diagnostic codes associated with the use of each medication class were first identified using LASSO logistic regression analyses in two-thirds of the cohort and then validated using multivariate logistic regressions in the remaining cohort. 〈 b 〉 〈 i 〉 Results: 〈 /i 〉 〈 /b 〉 Our analyses included 4,862 MS cases and 22,649 controls. Although the identified diagnostic codes showed fair to good predictive performance in 6 medication classes (C-index = 0.712–0.858), these codes failed to fully explain the higher usage of these medications by the MS cases. Compared to controls of the same age, sex, and diagnostic codes, MS cases had higher odds of filling a prescription for antivertigo preparations (adjusted OR [aOR] 2.48; 95% CI 1.92–3.19), anti-epileptics (aOR 2.34; 1.90–2.90), glucocorticoids (aOR 1.76; 1.52–2.03), urinary anti-spasmodics (aOR 1.72; 1.20–2.46), and muscle relaxants (aOR 1.33; 1.13–1.56). 〈 b 〉 〈 i 〉 Conclusions: 〈 /i 〉 〈 /b 〉 We observed markedly higher use of specific medications in MS cases in the 5 years before the first demyelinating claim. The overrepresentation of specific medications in MS cases, which was not fully explained by the physician diagnoses, may represent a signature of the MS prodrome.
    Type of Medium: Online Resource
    ISSN: 0251-5350 , 1423-0208
    Language: English
    Publisher: S. Karger AG
    Publication Date: 2020
    detail.hit.zdb_id: 1483032-2
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 5
    Online Resource
    Online Resource
    Wiley ; 2017
    In:  Statistics in Medicine Vol. 36, No. 26 ( 2017-11-20), p. 4196-4213
    In: Statistics in Medicine, Wiley, Vol. 36, No. 26 ( 2017-11-20), p. 4196-4213
    Abstract: We examine the impact of nondifferential outcome misclassification on odds ratios estimated from pair‐matched case‐control studies and propose a Bayesian model to adjust these estimates for misclassification bias. The model relies on access to a validation subgroup with confirmed outcome status for all case‐control pairs as well as prior knowledge about the positive and negative predictive value of the classification mechanism. We illustrate the model's performance on simulated data and apply it to a database study examining the presence of ten morbidities in the prodromal phase of multiple sclerosis.
    Type of Medium: Online Resource
    ISSN: 0277-6715 , 1097-0258
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2017
    detail.hit.zdb_id: 1491221-1
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 6
    Online Resource
    Online Resource
    American Society of Clinical Oncology (ASCO) ; 2012
    In:  Journal of Clinical Oncology Vol. 30, No. 27_suppl ( 2012-09-20), p. 7-7
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 30, No. 27_suppl ( 2012-09-20), p. 7-7
    Abstract: 7 Background: The Gail model has been validated in the United States and several European countries, but to our knowledge, it has not been validated in organized breast screening programs in Canada. The Screening Mammography Program of British Columbia (SMPBC) records participant data from a questionnaire based on Gail model parameters (which include family and personal medical history). This study investigates whether the Gail model is a valid tool to predict the breast cancer risk for the population undergoing screening mammography in the province of BC. Methods: Client information of the 223,349 British Columbian women who participated in the year 2000, along with their tumor status from 2000-2004, was extracted from the provincial database. A software program was developed to rapidly calculate the absolute 5-year Gail score from questionnaire data. Participant data was separated into .5% risk intervals and also into quintiles based on increasing Gail scores, and the mean absolute risks were compared to the actual five year rate of cancer as detected by the SMPBC. Results: Overall, goodness of fit between Gail score and SMPBC detection (E/O) across the categories can be rejected (χ2=247.9, df=9, p value 〈 .001). The Gail model significantly underpredicts the cancer detection for risk categories up to 2%, however it provides a sufficient fit for categories 2%-4% as the E/O ratio is not significantly different from 1.0 in these intervals. For the highest risk interval, categorized as greater than 4% risk, the model significantly overpredicts cancer detection. Additionally, when presented in quintiles, the Gail model under-predicts risk in all but the highest quintile (1.77-11.43% risk range). Conclusions: Our results, based on participants of SMPBC, suggest that the Gail model significantly under-predicts cancer detection. Although this model provides a sufficient fit for women with a Gail score between 1.51-4%, it does not predict breast cancer risk accurately for low and high risk women in the Screening Mammography Program of BC.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    RVK:
    RVK:
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2012
    detail.hit.zdb_id: 2005181-5
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 7
    In: American Journal of Epidemiology, Oxford University Press (OUP), Vol. 191, No. 6 ( 2022-05-20), p. 1116-1124
    Abstract: Warfarin’s complex dosing is a significant barrier to measurement of its exposure in observational studies using population databases. Using population-based administrative data (1996–2019) from British Columbia, Canada, we developed a method based on statistical modeling (Random Effects Warfarin Days’ Supply (REWarDS)) that involves fitting a random-effects linear regression model to patients’ cumulative dosage over time for estimation of warfarin exposure. Model parameters included a minimal universally available set of variables from prescription records for estimation of patients’ individualized average daily doses of warfarin. REWarDS estimates were validated against a reference standard (manual calculation of the daily dose using the free-text administration instructions entered by the dispensing pharmacist) and compared with alternative methods (fixed window, fixed tablet, defined daily dose, and reverse wait time distribution) using Pearson’s correlation coefficient (r), the intraclass correlation coefficient, and the root mean squared error. REWarDS-estimated days’ supply showed strong correlation and agreement with the reference standard (r = 0.90 (95% confidence interval (CI): 0.90, 0.90); intraclass correlation coefficient = 0.95 (95% CI: 0.94, 0.95); root mean squared error = 8.24 days) and performed better than all of the alternative methods. REWarDS-estimated days’ supply was valid and more accurate than estimates from all other available methods. REWarDS is expected to confer optimal precision in studies measuring warfarin exposure using administrative data.
    Type of Medium: Online Resource
    ISSN: 0002-9262 , 1476-6256
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2030043-8
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...