BACKGROUND: 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 num-ber of patients using a sampling design instead of analyzing the full cohort.
BACKGROUND: 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 num-ber of patients using a sampling design instead of analyzing the full cohort.
Authors: Maja von Cube; Derek Hazard; James Balmford; Paulina Staus; Sam Doerken; Ksenia Ershova; Martin Wolkewitz Journal: Clin Epidemiol Date: 2022-09-14 Impact factor: 5.814
Authors: Rupal Mehta; Xuan Cai; Jungwha Lee; Dawei Xie; Xue Wang; Julia Scialla; Amanda H Anderson; Jon Taliercio; Mirela Dobre; Jing Chen; Michael Fischer; Mary Leonard; James Lash; Chi-Yuan Hsu; Ian H de Boer; Harold I Feldman; Myles Wolf; Tamara Isakova Journal: Am J Kidney Dis Date: 2019-12-19 Impact factor: 8.860
Authors: Sara Denicolò; Verena Vogi; Felix Keller; Stefanie Thöni; Susanne Eder; Hiddo J L Heerspink; László Rosivall; Andrzej Wiecek; Patrick B Mark; Paul Perco; Johannes Leierer; Andreas Kronbichler; Marion Steger; Simon Schwendinger; Johannes Zschocke; Gert Mayer; Emina Jukic Journal: Kidney Int Rep Date: 2022-02-03