Using cumulative sums of martingale residuals for model checking in nested case-control studies

Biometrics ◽  
2015 ◽  
Vol 71 (3) ◽  
pp. 696-703 ◽  
Author(s):  
Ørnulf Borgan ◽  
Ying Zhang
Keyword(s):  
Model Checking ◽  
Case Control ◽  
Case Control Studies ◽  
Martingale Residuals ◽  
Nested Case Control ◽  
Cumulative Sums

scholarly journals A systematic assessment of the association between frequently prescribed medicines and the risk of common cancers: a series of nested case-control studies

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
R. D. McDowell ◽  
C. Hughes ◽  
P. Murchie ◽  
C. Cardwell
Keyword(s):  
Cancer Risk ◽  
Conditional Logistic Regression ◽  
Exposure Dose ◽  
Case Control ◽  
Case Control Studies ◽  
Systematic Assessment ◽  
Exposure Response ◽  
Prescribed Medicines ◽  
Nested Case Control ◽  
Novel Associations

Abstract Background Studies systematically screening medications have successfully identified prescription medicines associated with cancer risk. However, adjustment for confounding factors in these studies has been limited. We therefore investigated the association between frequently prescribed medicines and the risk of common cancers adjusting for a range of confounders. Methods A series of nested case-control studies were undertaken using the Primary Care Clinical Informatics Unit Research (PCCIUR) database containing general practice (GP) records from Scotland. Cancer cases at 22 cancer sites, diagnosed between 1999 and 2011, were identified from GP records and matched with up to five controls (based on age, gender, GP practice and date of registration). Odds ratios (OR) and 95% confidence intervals (CI) comparing any versus no prescriptions for each of the most commonly prescribed medicines, identified from prescription records, were calculated using conditional logistic regression, adjusting for comorbidities. Additional analyses adjusted for smoking use. An association was considered a signal based upon the magnitude of its adjusted OR, p-value and evidence of an exposure-response relationship. Supplementary analyses were undertaken comparing 6 or more prescriptions versus less than 6 for each medicine. Results Overall, 62,109 cases and 276,580 controls were included in the analyses and a total of 5622 medication-cancer associations were studied across the 22 cancer sites. After adjusting for comorbidities 2060 medicine-cancer associations for any prescription had adjusted ORs greater than 1.25 (or less than 0.8), 214 had a corresponding p-value less than or equal to 0.01 and 118 had evidence of an exposure-dose relationship hence meeting the criteria for a signal. Seventy-seven signals were identified after additionally adjusting for smoking. Based upon an exposure of 6 or more prescriptions, there were 118 signals after adjusting for comorbidities and 82 after additionally adjusting for smoking. Conclusions In this study a number of novel associations between medicine and cancer were identified which require further clinical and epidemiological investigation. The majority of medicines were not associated with an altered cancer risk and many identified signals reflected known associations between medicine and cancer.

scholarly journals Nested Case-Control Studies With Multiple Measurements: Estimating Marginal Relationships

2021 ◽  
Author(s):  
Joshua N. Sampson ◽  
Paul S. Albert ◽  
Mark P. Purdue
Keyword(s):  
Conditional Logistic Regression ◽  
Case Control ◽  
Case Control Studies ◽  
Robust Variance ◽  
Non Hodgkin Lymphoma ◽  
Multiple Measurements ◽  
Matched Case ◽  
Nested Case Control ◽  
Standard Software ◽  
Single Time Point

Abstract Background: We consider the analysis of nested, matched, case-control studies that have multiple biomarker measurements per individual. We propose a simple approach for estimating the marginal relationship between a biomarker measured at a single time point and the risk of an event. We know of no other standard software package that can perform such analyses while explicitly accounting for the matching. Results: We propose an application of conditional logistic regression (CLR) that can include all measurements and uses a robust variance estimator. We compare our approach to other methods such as performing CLR with only the first measurement, CLR with an average of all measurements, and Generalized Estimating Equations. In simulations, our approach is significantly more powerful than CLR with one measurement or an average of all measurements, and has similar to power to GEE but correctly accounts for the matching. We then apply our approach to the CLUE cohort to show that an increased level of the immune marker sCD27 is associated with non‐Hodgkin lymphoma (NHL) and, by evaluating the strength of the association as a function of time until diagnosis, that the an increased level is likely an effect of the disease as opposed to a cause of the disease. The approach can be implemented by the R function clogitRV available at https://github.com/sampsonj74/clogitRV.Conclusion: We offered an approach and software for analyzing matched case-control studies with multiple measurements. We demonstrated that these methods are accurate, precise, and statistically powerful.

scholarly journals Risk of osteoporosis and fragility fractures in asthma due to oral and inhaled corticosteroids: two population-based nested case-control studies

Thorax ◽  
2020 ◽  
Vol 76 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Christos V Chalitsios ◽  
Dominick E Shaw ◽  
Tricia M McKeever
Keyword(s):  
Cumulative Dose ◽  
Inhaled Corticosteroids ◽  
Conditional Logistic Regression ◽  
Fragility Fractures ◽  
Case Control ◽  
Practice Research ◽  
Clinical Practice Research Datalink ◽  
Case Control Studies ◽  
Increased Risk ◽  
Nested Case Control

BackgroundInhaled (ICS) and oral (OCS) corticosteroids are used widely in asthma; however, the risk of osteoporosis and fragility fracture (FF) due to corticosteroids in asthma is not well-established.MethodsWe conducted two nested case-control studies using linked data from the Clinical Practice Research Datalink (CPRD) and Hospital Episode Statistics (HES) databases. Using an asthma cohort, we separately identified patients with osteoporosis or FF and gender-, age- and practice-matched controls. Conditional logistic regression was used to determine the association between ICS and OCS exposure, and the risk of osteoporosis or FF. The prevalence of patients receiving at least one bisphosphonate was also calculated.ResultsThere was a dose–response relationship between both cumulative dose and number of OCS/ICS prescriptions within the previous year, and risk of osteoporosis or FF. After adjusting for confounders, people receiving more OCS prescriptions (≥9 vs 0) had a 4.50 (95% CI 3.21 to 6.11) and 2.16 (95% CI 1.56 to 3.32) increased risk of osteoporosis and FF, respectively. For ICS (≥11 vs 0) the ORs were 1.60 (95% CI 1.22 to 2.10) and 1.31 (95% CI 1.02 to 1.68). The cumulative dose had a similar impact, with those receiving more OCS or ICS being at greater risk. The prevalence of patients taking ≥9 OCS and at least one bisphosphonate prescription was just 50.6% and 48.4% for osteoporosis and FF, respectively.ConclusionsThe findings suggest that exposure to OCS or ICS is an independent risk factors for bone health in patients with asthma. Steroid administration at the lowest possible level to maintain asthma control is recommended.

Reuse of Controls in Nested Case-Control Studies

Epidemiology ◽  
2014 ◽  
Vol 25 (2) ◽  
pp. 315-317 ◽  
Author(s):  
Nathalie C. Støer ◽  
Haakon E. Meyer ◽  
Sven Ove Samuelsen
Keyword(s):  
Case Control ◽  
Case Control Studies ◽  
Nested Case Control

scholarly journals Viremia preceding multiple sclerosis: Two nested case-control studies

Virology ◽  
2018 ◽  
Vol 520 ◽  
pp. 21-29
Author(s):  
Emilie Hultin ◽  
Laila Sara Arroyo Mühr ◽  
Zurab Bzhalava ◽  
Maria Hortlund ◽  
Camilla Lagheden ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Case Control ◽  
Case Control Studies ◽  
Nested Case Control
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