scholarly journals Incorporating Missing Outcome Data in The Sample Size Calculation For a Future Trial: A Case Study Using a Single Trial, a Pairwise and Network Meta-Analysis

2021 ◽  
Author(s):  
Loukia Spineli
Keyword(s):  
Sample Size ◽  
Meta Analysis ◽  
Sample Size Calculation ◽  
Outcome Data ◽  
Binary Outcome ◽  
Single Trial ◽  
Future Trial ◽  
The Absolute ◽  
And Control

Abstract Background: To illustrate the advantages of using network meta-analysis (NMA) as compared to a trial or a pairwise meta-analysis to estimate the amount of missing outcome data (MOD) for a target comparison in order to adjust the required sample size for possible participant losses in a future trial.Methods: We introduced the concept of transitive risks to obtain the absolute risks of MOD for all interventions of the network. We used the network of a published systematic review on a binary outcome to apply the proposed concept and to calculate the required sample size in a future trial for a selected target comparison. For that comparison, we also calculated the required sample size using the corresponding trials separately, and after pooling these trials in a random-effects meta-analysis. Results: Ignoring MOD from the sample size calculation led to the smallest sample size. When either trial was considered, the risk of MOD ranged from 1% to 13% in the compared intervention arms, therefore, increasing the sample size from 1% to 12%. Performing a pairwise meta-analysis yielded a risk of MOD equal to 6% and 9% in the active and control arms, respectively, which inflated the sample size by 8%. Using NMA, the corresponding risks of MOD were 10% and 13%, which increased the sample size by 13%. Conclusions: Provided that the transitivity assumption holds, incorporating the absolute risks of MOD in the sample size calculation for a target comparison of the network led to better planning of a future trial.

scholarly journals A Menu-driven Facility for Complex Sample Size Calculation in Randomized Controlled Trials with a Survival or a Binary Outcome

2002 ◽  
Vol 2 (2) ◽  
pp. 151-163 ◽  
Author(s):  
Patrick Royston ◽  
Abdel Babiker
Keyword(s):  
Sample Size ◽  
Sample Size Calculation ◽  
Binary Outcome ◽  
Test Statistic ◽  
Logrank Test ◽  
Loss To Follow Up ◽  
Patient Allocation ◽  
Hazard Ratios ◽  
Event Distribution

We present a menu-driven Stata program for the calculation of sample size or power for complex clinical trials with a survival time or a binary outcome. The features supported include up to six treatment arms, an arbitrary time-to-event distribution, fixed or time-varying hazard ratios, unequal patient allocation, loss to follow-up, staggered patient entry, and crossover of patients from their allocated treatment to an alternative treatment. The computations of sample size and power are based on the logrank test and are done according to the asymptotic distribution of the logrank test statistic, adjusted appropriately for the design features.

scholarly journals Precisely Equal Group Size and Allocation Bias in Nursing Randomized Controlled Trials: A Scientiometric Study

2021 ◽  
Author(s):  
Richard Gray ◽  
Daniel Bressington ◽  
Martin Jones ◽  
David R. Thompson
Keyword(s):  
Randomized Controlled Trials ◽  
Sample Size ◽  
Group Size ◽  
Sample Size Calculation ◽  
Positive Outcome ◽  
Outcome Data ◽  
Controlled Trials ◽  
Nursing Science ◽  
Equal Group ◽  
Randomized Controlled

The manipulation of participant allocation in randomized controlled trials to achieve equal groups sizes may introduce allocation bias potentially leading to larger treatment effect estimates. This study aimed to estimate the proportion of nursing trials that have precisely equal group sizes and examine if there was an association with trial outcome. Data were extracted from a sample of 148 randomized controlled trials published in nursing science journals in 2017. One hundred trials (68%) had precisely equal group sizes. Respectively, a positive outcome was reported in 70% and 58% of trials with equal/unequal groups. Trials from Asia were more likely to have equal group sizes than those from the rest of the world. Most trials reported a sample size calculation (n=105, 71%). In a third of trials (n=36, 34%), the number of participants recruited precisely matched the requirement of the sample size calculation; this was significantly more common in studies with equal group sizes. The high number of nursing trials with equal groups may suggest nurses con-ducting clinical trials are manipulating participant allocation to ensure equal group size increasing the risk of bias.

Effects of Alcohol Policy on Population Variables and Control Measures

2019 ◽  
pp. 179-189
Author(s):  
R. W. Kisusu ◽  
N. Kalimang'asi ◽  
N. Macha ◽  
J. L. Mzungu
Keyword(s):  
Sample Size ◽  
Alcohol Policy ◽  
Control Measures ◽  
Primary Data ◽  
Statistical Tools ◽  
Chi Square ◽  
Municipal Council ◽  
The Masses ◽  
And Control

This case study of Dodoma Municipal Council focuses on the application of statistical tools to establish Population Variables (PVs) affected by alcohol and suggested control measures. The establishment relied on primary data involving a sample size of 156 respondents selected through purposive sampling and analyzed by cross-tabs and Chi-square. The analysis found alcohol policy affects mostly the lower-educated population, small householders and youths, and these were significant at 0.029, 0.002, and 0.006 levels, respectively. The inferences drawn shows within PVs, alcohol reduces students' performances, influences separation of families, and increases poverty in the households, and all were significant at 0.003, 0.028, and 0.003, respectively. The findings conclude that alcohol affects all PVs, which consequently ends up deteriorating welfare. Therefore, to combat alcohol, the chapter recommends usage policy legal measures and educating the masses on the effect of alcohol.

Effects of Alcohol Policy on Population Variables and Control Measures

2014 ◽  
pp. 267-280
Author(s):  
R. W. Kisusu ◽  
N. Kalimangʼasi ◽  
N. Macha ◽  
J. L. Mzungu
Keyword(s):  
Sample Size ◽  
Alcohol Policy ◽  
Control Measures ◽  
Primary Data ◽  
Statistical Tools ◽  
Chi Square ◽  
Municipal Council ◽  
The Masses ◽  
And Control

This case study of Dodoma Municipal Council focuses on the application of statistical tools to establish Population Variables (PVs) affected by alcohol and suggested control measures. The establishment relied on primary data involving a sample size of 156 respondents selected through purposive sampling and analyzed by cross-tabs and Chi-square. The analysis found alcohol policy affects mostly the lower-educated population, small householders and youths, and these were significant at 0.029, 0.002, and 0.006 levels, respectively. The inferences drawn shows within PVs, alcohol reduces students’ performances, influences separation of families, and increases poverty in the households, and all were significant at 0.003, 0.028, and 0.003, respectively. The findings conclude that alcohol affects all PVs, which consequently ends up deteriorating welfare. Therefore, to combat alcohol, the chapter recommends usage policy legal measures and educating the masses on the effect of alcohol.

scholarly journals Synthesis of Binary Outcome Data on the Log-Odds and Absolute Risk Scales – A Case Study in Chronic Hepatitis C

2016 ◽  
Vol 19 (7) ◽  
pp. A361-A362
Author(s):  
L Nagy ◽  
G Kay ◽  
C Parker ◽  
A Padhiar ◽  
JM O'Rourke ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis C ◽  
Chronic Hepatitis C ◽  
Absolute Risk ◽  
Outcome Data ◽  
Binary Outcome ◽  
Log Odds
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