A PROGRAM TO CALCULATE SAMPLE SIZE, POWER, AND LEAST DETECTABLE RELATIVE RISK USING A PROGRAMMABLE CALCULATOR

At the present time there are several approaches to estimation of survival functions of vectors of lifetimes. However, some of these estimators either are inconsistent or not fully defined in range of joint survival functions and therefore not applicable in practice. In this article, we consider three types of estimates of exponential-hazard, product-limit, and relative-risk power structures for the bivariate survival function, when replacing the number of summands in empirical estimates with a sequence of Poisson random variables. It is shown that these estimates are asymptotically equivalent. AMS 2000 subject classification: 62N01

Download Full-text

Sample size estimation for modified Poisson analysis of cluster randomized trials with a binary outcome

Statistical Methods in Medical Research ◽

10.1177/0962280221990415 ◽

2021 ◽

pp. 096228022199041

Author(s):

Fan Li ◽

Guangyu Tong

Keyword(s):

Relative Risk ◽

Sample Size ◽

Poisson Regression ◽

Cluster Size ◽

Randomized Trials ◽

Cluster Randomized Trials ◽

Binomial Regression ◽

Size Variability ◽

Working Correlation ◽

Cluster Randomized

The modified Poisson regression coupled with a robust sandwich variance has become a viable alternative to log-binomial regression for estimating the marginal relative risk in cluster randomized trials. However, a corresponding sample size formula for relative risk regression via the modified Poisson model is currently not available for cluster randomized trials. Through analytical derivations, we show that there is no loss of asymptotic efficiency for estimating the marginal relative risk via the modified Poisson regression relative to the log-binomial regression. This finding holds both under the independence working correlation and under the exchangeable working correlation provided a simple modification is used to obtain the consistent intraclass correlation coefficient estimate. Therefore, the sample size formulas developed for log-binomial regression naturally apply to the modified Poisson regression in cluster randomized trials. We further extend the sample size formulas to accommodate variable cluster sizes. An extensive Monte Carlo simulation study is carried out to validate the proposed formulas. We find that the proposed formulas have satisfactory performance across a range of cluster size variability, as long as suitable finite-sample corrections are applied to the sandwich variance estimator and the number of clusters is at least 10. Our findings also suggest that the sample size estimate under the exchangeable working correlation is more robust to cluster size variability, and recommend the use of an exchangeable working correlation over an independence working correlation for both design and analysis. The proposed sample size formulas are illustrated using the Stop Colorectal Cancer (STOP CRC) trial.

Download Full-text

A SIMPLE ESTIMATOR OF MINIMUM DETECTABLE RELATIVE RISK, SAMPLE SIZE, OR POWER IN COHORT STUDIES

American Journal of Epidemiology ◽

10.1093/aje/126.2.356 ◽

1987 ◽

Vol 126 (2) ◽

pp. 356-358 ◽

Cited By ~ 27

Author(s):

B. ARMSTRONG

Keyword(s):

Relative Risk ◽

Cohort Studies ◽

Sample Size

Download Full-text

How to Calculate Sample Size and Why

Clinics in Orthopedic Surgery ◽

10.4055/cios.2013.5.3.235 ◽

2013 ◽

Vol 5 (3) ◽

pp. 235 ◽

Cited By ~ 33

Author(s):

Jeehyoung Kim ◽

Bong Soo Seo

Keyword(s):

Sample Size ◽

Calculate Sample Size

Download Full-text

Epidemiology and evidence-based medicine

10.1093/med/9780199230457.003.0020 ◽

2016 ◽

Keyword(s):

Survival Analysis ◽

Relative Risk ◽

Sample Size ◽

Evidence Based Medicine ◽

Diagnostic Tests ◽

Meta Analysis ◽

Descriptive Statistics ◽

Number Needed To Treat ◽

Evidence Based ◽

Based Medicine

Chapter 20 focuses on epidemiology and evidence-based medicine. It covers study design, types of data and descriptive statistics, from samples to populations, relationships, relative risk, odds ratios, and 'number needed to treat', survival analysis, sample size, diagnostic tests, meta-analysis, before concluding with advice on how to read a paper.

Download Full-text