scholarly journals Sample size and power calculations in Mendelian randomization with a single instrumental variable and a binary outcome

2014 ◽  
Vol 43 (3) ◽  
pp. 922-929 ◽  
Author(s):  
S. Burgess
Keyword(s):  
Sample Size ◽  
Instrumental Variable ◽  
Mendelian Randomization ◽  
Binary Outcome ◽  
Power Calculations

scholarly journals Minimum sample size for external validation of a clinical prediction model with a binary outcome

2021 ◽  
Author(s):  
Richard D. Riley ◽  
Thomas P. A. Debray ◽  
Gary S. Collins ◽  
Lucinda Archer ◽  
Joie Ensor ◽  
...  
Keyword(s):  
Prediction Model ◽  
Sample Size ◽  
External Validation ◽  
Minimum Sample Size ◽  
Binary Outcome ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Minimum Sample

scholarly journals Identification of a nonseparable model under endogeneity using binary proxies for unobserved heterogeneity

10.3982/qe674 ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 527-563 ◽  
Author(s):  
Benjamin Williams
Keyword(s):  
Sample Size ◽  
Instrumental Variable ◽  
Unobserved Heterogeneity ◽  
Real Data ◽  
Fixed Number ◽  
Nonparametric Estimator

In this paper, I study identification of a nonseparable model with endogeneity arising due to unobserved heterogeneity. Identification relies on the availability of binary proxies that can be used to control for the unobserved heterogeneity. I show that the model is identified in the limit as the number of proxies increases. The argument does not require an instrumental variable that is excluded from the outcome equation nor does it require the support of the unobserved heterogeneity to be finite. I then propose a nonparametric estimator that is consistent as the number of proxies increases with the sample size. I also show that, for a fixed number of proxies, nontrivial bounds on objects of interest can be obtained. Finally, I study two real data applications that illustrate computation of the bounds and estimation with a large number of items.

scholarly journals Outcome assessment by central adjudicators in randomised stroke trials: Simulation of differential and non-differential misclassification

2020 ◽  
Vol 5 (2) ◽  
pp. 174-183 ◽  
Author(s):  
Peter J Godolphin ◽  
Philip M Bath ◽  
Christopher Partlett ◽  
Eivind Berge ◽  
Martin M Brown ◽  
...  
Keyword(s):  
Sample Size ◽  
Outcome Assessment ◽  
Treatment Effect ◽  
Primary Outcome ◽  
Event Rate ◽  
Considerable Proportion ◽  
Binary Outcome ◽  
Primary Trial ◽  
Differential Misclassification ◽  
Larger Sample

Introduction Adjudication of the primary outcome in randomised trials is thought to control misclassification. We investigated the amount of misclassification needed before adjudication changed the primary trial results. Patients (or materials) and methods: We included data from five randomised stroke trials. Differential misclassification was introduced for each primary outcome until the estimated treatment effect was altered. This was simulated 1000 times. We calculated the between-simulation mean proportion of participants that needed to be differentially misclassified to alter the treatment effect. In addition, we simulated hypothetical trials with a binary outcome and varying sample size (1000–10,000), overall event rate (10%–50%) and treatment effect (0.67–0.90). We introduced non-differential misclassification until the treatment effect was non-significant at 5% level. Results For the five trials, the range of unweighted kappa values were reduced from 0.89–0.97 to 0.65–0.85 before the treatment effect was altered. This corresponded to 2.1%–6% of participants misclassified differentially for trials with a binary outcome. For the hypothetical trials, those with a larger sample size, stronger treatment effect and overall event rate closer to 50% needed a higher proportion of events non-differentially misclassified before the treatment effect became non-significant. Discussion: We found that only a small amount of differential misclassification was required before adjudication altered the primary trial results, whereas a considerable proportion of participants needed to be misclassified non-differentially before adjudication changed trial conclusions. Given that differential misclassification should not occur in trials with sufficient blinding, these results suggest that central adjudication is of most use in studies with unblinded outcome assessment. Conclusion: For trials without adequate blinding, central adjudication is vital to control for differential misclassification. However, for large blinded trials, adjudication is of less importance and may not be necessary.

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.

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