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Borgan, Ørnulf; Langholz, Bryan; Samuelsen, Sven Ove; Goldstein, Larry; Pogoda, Janice M.

doi:10.1023/a:1009661900674

Cited by 250 publications

(150 citation statements)

References 4 publications

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“…The multiple imputation estimator was compared to the standard weighted estimator [6], calibrated weights estimator [3] and re-estimated weights estimator [3]. The mean of the 1,000 log relative risk estimates, corresponding to the 1,000 subcohorts, are given in Table 6.…”

Section: Resultsmentioning

confidence: 99%

“…However, when a phase-1 variable is strongly predictive of the phase-2 variable, strati ed sampling of the subcohort can improve e ciency as compared to simple random selection [6]. Weighted estimators of the log-relative risks maximize a weighted pseudolikelihood ( L(β)):…”

Section: Weighted Analysis Of Case Cohort Studiesmentioning

confidence: 99%

“…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%

“…Using weighted analysis, the variance estimator II proposed by Borgan et al [6] was used. One thousand cohorts were simulated.…”

Section: Completely Simulated Datamentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Weighted Analysis Of Case Cohort Studiesmentioning

confidence: 99%

Section: Weighted Analysis Of Case Cohort Studiesmentioning

confidence: 99%

“…Using weighted analysis, the variance estimator II proposed by Borgan et al [6] was used. One thousand cohorts were simulated.…”

Section: Completely Simulated Datamentioning

confidence: 99%

See 2 more Smart Citations

Multiple imputation analysis of case–cohort studies

Martí

Chavance

2011

Statistics in Medicine

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“…The idea behind this paper is to apply existing methodology on case-cohort methods for univariate survival data, where all individuals are assumed to be independent, to family data, where families are assumed to be independent, but individuals within a family are dependent. We specifically use methodology from the papers of Kalbfleisch and Lawless [14] and Borgan et al [15]. The main difference is that we sample families instead of individuals.…”

Section: Introductionmentioning

confidence: 99%

Case–cohort methods for survival data on families from routine registers

Moger

Pawitan

Borgan

2007

Statistics in Medicine

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In the Nordic countries, there exist several registers containing information on diseases and risk factors for millions of individuals. This information can be linked to families by the use of personal identification numbers, and represents a great opportunity for studying diseases that show familial aggregation. Due to the size of the registers, it is difficult to analyze the data by using traditional methods for multivariate survival analysis, such as frailty or copula models. Since the size of the cohort is known, case–cohort methods based on pseudo‐likelihoods are suitable for analyzing the data. We present methods for sampling control families both with and without replacement, and with or without stratification. The data are stratified according to family size and covariate values. Depending on the sampling method, results from simulations indicate that one only needs to sample 1–5 per cent of the control families in order to obtain good efficiency compared with a traditional cohort analysis. We also provide an application to survival data from the Medical Birth Registry of Norway. Copyright © 2007 John Wiley & Sons, Ltd.

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