scholarly journals Comparing the cohort design and the nested case–control design in the presence of both time‐invariant and time‐dependent treatment and competing risks: bias and precision

2012 ◽  
Vol 21 (7) ◽  
pp. 714-724 ◽  
Author(s):  
Peter C. Austin ◽  
Geoffrey M. Anderson ◽  
Candemir Cigsar ◽  
Andrea Gruneir
Keyword(s):  
Competing Risks ◽  
Control Design ◽  
Case Control ◽  
Time Dependent ◽  
Cohort Design ◽  
Nested Case Control ◽  
Time Invariant

Analysis of Clinical Cohort Data Using Nested Case-control and Case-cohort Sampling Designs

2015 ◽  
Vol 54 (06) ◽  
pp. 505-514 ◽  
Author(s):  
M. Wolkewitz ◽  
J. Beyersmann ◽  
M. Palomar-Martinez ◽  
P. Olaechea-Astigarraga ◽  
F. Alvarez-Lerma ◽  
...  
Keyword(s):  
Cohort Studies ◽  
Clinical Studies ◽  
Control Design ◽  
Case Control ◽  
Parameter Estimates ◽  
Full Cohort ◽  
Cohort Design ◽  
Number Of Patients ◽  
First Choice ◽  
Nested Case Control

SummaryBackground: Sampling from a large cohort in order to derive a subsample that would be sufficient for statistical analysis is a frequently used method for handling large data sets in epidemiological studies with limited resources for exposure measurement. For clinical studies however, when interest is in the influence of a potential risk factor, cohort studies are often the first choice with all individuals entering the analysis.Objectives: Our aim is to close the gap between epidemiological and clinical studies with respect to design and power considerations. Schoenfeld’s formula for the number of events required for a Cox’ proportional hazards model is fundamental. Our objective is to compare the power of analyzing the full cohort and the power of a nested case- control and a case-cohort design.Methods: We compare formulas for power for sampling designs and cohort studies. In our data example we simultaneously apply a nested case-control design with a varying number of controls matched to each case, a case cohort design with varying subcohort size, a random subsample and a full cohort analysis. For each design we calculate the standard error for estimated regression coefficients and the mean number of distinct persons, for whom covariate information is required.Results: The formula for the power of a nested case-control design and the power of a case-cohort design is directly connected to the power of a cohort study using the well known Schoenfeld formula. The loss in precision of parameter estimates is relatively small compared to the saving in resources.Conclusions: Nested case-control and case-cohort studies, but not random subsamples yield an attractive alternative for analyzing clinical studies in the situation of a low event rate. Power calculations can be conducted straightforwardly to quantify the loss of power compared to the savings in the number of patients using a sampling design instead of analyzing the full cohort.

scholarly journals Application of the matched nested case-control design to the secondary analysis of trial data

2020 ◽  
Author(s):  
Christopher Partlett ◽  
Nigel J Hall ◽  
Alison Leaf ◽  
Edmund Juszczak ◽  
Louise Linsell
Keyword(s):  
Clinical Trial ◽  
Case Control Study ◽  
Secondary Analysis ◽  
Control Design ◽  
Case Control ◽  
Necrotising Enterocolitis ◽  
Trial Data ◽  
Standard Case ◽  
Nested Case Control ◽  
Control Study

Abstract Background A nested case-control study is an efficient design that can be embedded within an existing cohort study or randomised trial. It has a number of advantages compared to the conventional case-control design, and has the potential to answer important research questions using untapped prospectively collected data. Methods We demonstrate the utility of the matched nested case-control design by applying it to a secondary analysis of the Abnormal Doppler Enteral Prescription Trial. We investigated the role of milk feed type and changes in milk feed type in the development of necrotising enterocolitis in a group of 398 high risk growth-restricted preterm infants. Results Using matching, we were able to generate a comparable sample of controls selected from the same population as the cases. In contrast to the standard case-control design, exposure status was ascertained prior to the outcome event occurring and the comparison between the cases and matched controls could be made at the point at which the event occurred. This enabled us to reliably investigate the temporal relationship between feed type and necrotising enterocolitis. Conclusions A matched nested case-control study can be used to identify credible associations in a secondary analysis of clinical trial data where the exposure of interest was not randomised, and has several advantages over a standard case-control design. This method offers the potential to make reliable inferences in scenarios where it would be unethical or impractical to perform a randomised clinical trial.

scholarly journals Estimation of Relative and Absolute Risks in a Competing-Risks Setting Using a Nested Case-Control Study Design: Example From the ProMort Study

2019 ◽  
Vol 188 (6) ◽  
pp. 1165-1173 ◽  
Author(s):  
Renata Zelic ◽  
Daniela Zugna ◽  
Matteo Bottai ◽  
Ove Andrén ◽  
Jonna Fridfeldt ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Competing Risks ◽  
Case Control Study ◽  
Case Control ◽  
Likelihood Method ◽  
Cancer Death ◽  
Hazard Ratios ◽  
Prostate Cancer Death ◽  
Nested Case Control ◽  
Control Study

Abstract In this paper, we describe the Prognostic Factors for Mortality in Prostate Cancer (ProMort) study and use it to demonstrate how the weighted likelihood method can be used in nested case-control studies to estimate both relative and absolute risks in the competing-risks setting. ProMort is a case-control study nested within the National Prostate Cancer Register (NPCR) of Sweden, comprising 1,710 men diagnosed with low- or intermediate-risk prostate cancer between 1998 and 2011 who died from prostate cancer (cases) and 1,710 matched controls. Cause-specific hazard ratios and cumulative incidence functions (CIFs) for prostate cancer death were estimated in ProMort using weighted flexible parametric models and compared with the corresponding estimates from the NPCR cohort. We further drew 1,500 random nested case-control subsamples of the NPCR cohort and quantified the bias in the hazard ratio and CIF estimates. Finally, we compared the ProMort estimates with those obtained by augmenting competing-risks cases and by augmenting both competing-risks cases and controls. The hazard ratios for prostate cancer death estimated in ProMort were comparable to those in the NPCR. The hazard ratios for dying from other causes were biased, which introduced bias in the CIFs estimated in the competing-risks setting. When augmenting both competing-risks cases and controls, the bias was reduced.

scholarly journals Food consumption and advanced β cell autoimmunity in young children with HLA-conferred susceptibility to type 1 diabetes: a nested case-control design

2012 ◽  
Vol 95 (2) ◽  
pp. 471-478 ◽  
Author(s):  
Suvi M Virtanen ◽  
Jaakko Nevalainen ◽  
Carina Kronberg-Kippilä ◽  
Suvi Ahonen ◽  
Heli Tapanainen ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Young Children ◽  
Food Consumption ◽  
Control Design ◽  
Case Control ◽  
Β Cell ◽  
Nested Case Control

scholarly journals Application of the matched nested case-control design to the secondary analysis of trial data

2019 ◽  
Author(s):  
Christopher Partlett ◽  
Nigel J Hall ◽  
Alison Leaf ◽  
Edmund Juszczak ◽  
Louise Linsell
Keyword(s):  
Clinical Trial ◽  
Case Control Study ◽  
Randomised Trial ◽  
Secondary Analysis ◽  
Control Design ◽  
Case Control ◽  
Trial Data ◽  
Standard Case ◽  
Nested Case Control ◽  
Control Study

Abstract Background: A nested case-control study is an efficient design that can be embedded within an existing cohort study or randomised trial. It has a number of advantages compared to the conventional case-control design, and has the potential to answer important research questions using untapped prospectively collected data.Methods: We demonstrate the utility of the matched nested case-control design by applying it to a secondary analysis of the Abnormal Doppler Enteral Prescription Trial. We investigated the role of milk feed type and changes in milk feed type in the development of necrotising enterocolitis in a group of 398 high risk growth-restricted preterm infants. Results: Using matching, we were able to generate a comparable sample of controls selected from the same population as the cases. In contrast to the standard case-control design, exposure status was ascertained prior to the outcome event occurring and the comparison between the cases and matched controls could be made at the point at which the event occurred. This enabled us to reliably investigate the temporal relationship between feed type and NEC. Conclusions: A matched nested case-control study can be used to identify credible associations in a secondary analysis of clinical trial data where the exposure of interest was not randomised, and has several advantages over a standard case-control design. This method offers the potential to make reliable inferences in scenarios where it would be unethical or impractical to perform a randomised clinical trial.

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