Analysis of Case-Cohort Designs

Barlow, William E.; Ichikawa, Laura; Rosner, Dan; Izumi, Shizue

doi:10.1016/s0895-4356(99)00102-x

Cited by 602 publications

(627 citation statements)

References 20 publications

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“…Standard errors were estimated using the robust Huber -White sandwich estimator to account for additional variance introduced by sampling from the cohort. This method is equivalent to the variance -covariance estimator as presented by Barlow et al (1999). The proportional hazards assumption was tested using the scaled Schoenfeld residuals (Schoenfeld, 1982).…”

Section: Resultsmentioning

confidence: 99%

Meat consumption and K-ras mutations in sporadic colon and rectal cancer in The Netherlands Cohort Study

et al. 2005

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Case -cohort analyses were performed on meat and fish consumption in relation to K-ras mutations in 448 colon and 160 rectal cancers that occurred during 7.3 years of follow-up, excluding the first 2.3 years, and 2948 subcohort members of The Netherlands Cohort Study on diet and cancer. Adjusted incidence rate ratios and 95% confidence intervals were computed for colon and rectal cancer and for K-ras mutation status subgroups. Total fresh meat, most types of fresh meat and fish were not associated with colon or rectal cancer, neither overall nor with K-ras mutation status. However, several weak associations were observed for tumours with a wild-type K-ras, including beef and colon tumours, and an inverse association for pork with colon and rectal tumours; for meat products, an increased association was observed with wild-type K-ras tumours in the colon and possibly with G4A transitions in rectal tumours.

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Section: Resultsmentioning

confidence: 99%

Meat consumption and K-ras mutations in sporadic colon and rectal cancer in The Netherlands Cohort Study

et al. 2005

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“…Barlow [7] proposed weighting each complete observation by the inverse of its probability of being included (1 for the cases). Other authors have proposed variable weights, as a function of time, to slightly improve e ciency [6].…”

Section: Weighted Analysis Of Case Cohort Studiesmentioning

confidence: 99%

Multiple imputation analysis of case–cohort studies

Martí

Chavance

2011

Statistics in Medicine

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The usual methods for analyzing case–cohort studies rely on sometimes not fully efficient weighted estimators. Multiple imputation might be a good alternative because it uses all the data available and approximates the maximum partial likelihood estimator. This method is based on the generation of several plausible complete data sets, taking into account uncertainty about missing values. When the imputation model is correctly defined, the multiple imputation estimator is asymptotically unbiased and its variance is correctly estimated. We show that a correct imputation model must be estimated from the fully observed data (cases and controls), using the case status among the explanatory variable. To validate the approach, we analyzed case–cohort studies first with completely simulated data and then with case–cohort data sampled from two real cohorts. The analyses of simulated data showed that, when the imputation model was correct, the multiple imputation estimator was unbiased and efficient. The observed gain in precision ranged from 8 to 37 per cent for phase‐1 variables and from 5 to 19 per cent for the phase‐2 variable. When the imputation model was misspecified, the multiple imputation estimator was still more efficient than the weighted estimators but it was also slightly biased. The analyses of case–cohort data sampled from complete cohorts showed that even when no strong predictor of the phase‐2 variable was available, the multiple imputation was unbiased, as precised as the weighted estimator for the phase‐2 variable and slightly more precise than the weighted estimators for the phase‐1 variables. However, the multiple imputation estimator was found to be biased when, because of interaction terms, some coefficients of the imputation model had to be estimated from small samples. Multiple imputation is an efficient technique for analyzing case–cohort data. Practically, we suggest building the analysis model using only the case–cohort data and weighted estimators. Multiple imputation can eventually be used to reanalyze the data using the selected model in order to improve the precision of the results. Copyright © 2011 John Wiley & Sons, Ltd.

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“…The analysis of this study follows a case‐cohort design 24. The MrOS study team took the 3572 participants with hip QCT scans and removed any with previous hip fracture or hip replacement, leaving 3515.…”

Section: Methodsmentioning

confidence: 99%

Predicting Hip Fracture Type With Cortical Bone Mapping (CBM) in the Osteoporotic Fractures in Men (MrOS) Study

Treece

Gee

Tonkín³

et al. 2015

Journal of Bone and Mineral Research

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Hip fracture risk is known to be related to material properties of the proximal femur, but fracture prediction studies adding richer quantitative computed tomography (QCT) measures to dual‐energy X‐ray (DXA)‐based methods have shown limited improvement. Fracture types have distinct relationships to predictors, but few studies have subdivided fracture into types, because this necessitates regional measurements and more fracture cases. This work makes use of cortical bone mapping (CBM) to accurately assess, with no prior anatomical presumptions, the distribution of properties related to fracture type. CBM uses QCT data to measure the cortical and trabecular properties, accurate even for thin cortices below the imaging resolution. The Osteoporotic Fractures in Men (MrOS) study is a predictive case‐cohort study of men over 65 years old: we analyze 99 fracture cases (44 trochanteric and 55 femoral neck) compared to a cohort of 308, randomly selected from 5994. To our knowledge, this is the largest QCT‐based predictive hip fracture study to date, and the first to incorporate CBM analysis into fracture prediction. We show that both cortical mass surface density and endocortical trabecular BMD are significantly different in fracture cases versus cohort, in regions appropriate to fracture type. We incorporate these regions into predictive models using Cox proportional hazards regression to estimate hazard ratios, and logistic regression to estimate area under the receiver operating characteristic curve (AUC). Adding CBM to DXA‐based BMD leads to a small but significant (p < 0.005) improvement in model prediction for any fracture, with AUC increasing from 0.78 to 0.79, assessed using leave‐one‐out cross‐validation. For specific fracture types, the improvement is more significant (p < 0.0001), with AUC increasing from 0.71 to 0.77 for trochanteric fractures and 0.76 to 0.82 for femoral neck fractures. In contrast, adding DXA‐based BMD to a CBM‐based predictive model does not result in any significant improvement. © 2015 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals, Inc. on behalf of the American Society for Bone and Mineral Research.

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Analysis of Case-Cohort Designs

Cited by 602 publications

References 20 publications

Meat consumption and K-ras mutations in sporadic colon and rectal cancer in The Netherlands Cohort Study

Meat consumption and K-ras mutations in sporadic colon and rectal cancer in The Netherlands Cohort Study

Multiple imputation analysis of case–cohort studies

Predicting Hip Fracture Type With Cortical Bone Mapping (CBM) in the Osteoporotic Fractures in Men (MrOS) Study

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