Formulae for Sample Size, Power and Minimum Detectable Relative Risk in Medical Studies

1992 ◽  
Vol 41 (2) ◽  
pp. 185 ◽  
Author(s):  
Mark Woodward
Keyword(s):  
Relative Risk ◽  
Sample Size ◽  
Medical Studies

Estimation of Bivariate Survival Function from Random Censored Data with Poisson Sample Size

2020 ◽  
Vol 72 (2) ◽  
pp. 111-121
Author(s):  
Abdurakhim Akhmedovich Abdushukurov ◽  
Rustamjon Sobitkhonovich Muradov
Keyword(s):  
Relative Risk ◽  
Sample Size ◽  
Censored Data ◽  
Survival Function ◽  
Survival Functions ◽  
Power Structures ◽  
Bivariate Survival ◽  
Empirical Estimates ◽  
Bivariate Survival Function ◽  
Product Limit

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

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

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.

scholarly journals Sample Size Calculation in Medical Studies; a Brief Report

2019 ◽  
Vol 5 (1) ◽  
pp. 49-53
Author(s):  
Maryam Taghdir ◽  
Mojtaba Sepandi ◽  
◽  
Keyword(s):  
Sample Size ◽  
Sample Size Calculation ◽  
Medical Studies

Epidemiology and evidence-based medicine

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.

scholarly journals Sample Size Considerations in Clinical Trials When Comparing Two Interventions Using Multiple Co-Primary Binary Relative Risk Contrasts

2015 ◽  
Vol 7 (2) ◽  
pp. 81-94 ◽  
Author(s):  
Yuki Ando ◽  
Toshimitsu Hamasaki ◽  
Scott R. Evans ◽  
Koko Asakura ◽  
Tomoyuki Sugimoto ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Relative Risk ◽  
Sample Size

scholarly journals Formulas, Algorithms and Examples for Binomial Distributed Data Confidence Interval Calculation: Excess Risk, Relative Risk and Odds Ratio

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2506
Author(s):  
Lorentz Jäntschi
Keyword(s):  
Relative Risk ◽  
Confidence Interval ◽  
Odds Ratio ◽  
Risk Difference ◽  
Excess Risk ◽  
Distributed Data ◽  
Medical Studies ◽  
Measures Of Association ◽  
Discrete Population ◽  
Current Guidelines

Medical studies often involve a comparison between two outcomes, each collected from a sample. The probability associated with, and confidence in the result of the study is of most importance, since one may argue that having been wrong with a percent could be what killed a patient. Sampling is usually done from a finite and discrete population and it follows a Bernoulli trial, leading to a contingency of two binomially distributed samples (better known as 2×2 contingency table). Current guidelines recommend reporting relative measures of association (such as the relative risk and odds ratio) in conjunction with absolute measures of association (which include risk difference or excess risk). Because the distribution is discrete, the evaluation of the exact confidence interval for either of those measures of association is a mathematical challenge. Some alternate scenarios were analyzed (continuous vs. discrete; hypergeometric vs. binomial), and in the main case—bivariate binomial experiment—a strategy for providing exact p-values and confidence intervals is proposed. Algorithms implementing the strategy are given.

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