Sample size determination for disease prevalence studies with partially validated data

Qiu, Shi-Fang; Poon, Wai‐Yin; Tang, Man‐Lai

doi:10.1177/0962280212439576

Cited by 11 publications

(14 citation statements)

References 26 publications

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“…Partially validated datasets are recommended in cases where the outcome variable is difficult or costly to measure [15], as in the case of sleepwalking. This involves all data points being classified by fallible tests and some of the data points being validated by also being classified by an accurate gold-standard test.…”

Section: Introductionmentioning

confidence: 99%

Prevalence of Sleepwalking: A Systematic Review and Meta-Analysis

Stallman

Köhler

2016

PLoS ONE

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Sleepwalking is thought to be a common arousal disorder; however, the epidemiology of this disorder has not yet been systematically examined. A systematic search of MEDLINE, CINAHL, EMBASE, PsycINFO, PubMed, and ScienceDirect was conducted for ‘sleepwalking’ OR ‘somnambulism’ in any field, to identify studies that reported the epidemiology of sleepwalking or sleepwalking disorders. Fifty-one studies assessed the prevalence rates of sleepwalking in a total sample of 100 490. The meta-analysis showed the estimated lifetime prevalence of sleepwalking was 6.9% (95% CI 4.6%–10.3%). The current prevalence rate of sleepwalking—within the last 12 months—was significantly higher in children 5.0% (95% CI 3.8%–6.5%) than adults 1.5% (95% CI 1.0%–2.3%). There was no evidence of developmental trends in sleepwalking across childhood. The significant risk of bias across all studies suggests these results should be used cautiously. Further epidemiological research that addresses methodological problems found in studies of sleepwalking to date is needed.

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Section: Introductionmentioning

confidence: 99%

Prevalence of Sleepwalking: A Systematic Review and Meta-Analysis

Stallman

Köhler

2016

PLoS ONE

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“…When the misclassification probabilities are not known, we can use partially validated data to seek information related to the misclassification . Two devices are used to obtain partially validated data.…”

Section: Model and Estimationmentioning

confidence: 99%

“…When the misclassification probabilities are not known, we can use partially validated data to seek information related to the misclassification. 18,28,29 Two devices are used to obtain partially validated data. One device is called the true classifier, which classifies participants accurately but cannot be applied to all participants for various reasons, such as high cost or difficulty of access.…”

Section: Model With Unknown Misclassification Probabilitiesmentioning

confidence: 99%

Noninferiority testing for matched‐pair ordinal data with misclassification

Han

Poon

2019

Statistics in Medicine

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New treatments that are noninferior or equivalent to—but not necessarily superior to—the reference treatment may still be beneficial to patients because they have fewer side effects, are more convenient, take less time, or cost less. The noninferiority test is widely used in medical research to provide guidance in such situation. In addition, categorical variables are frequently encountered in medical research, such as in studies involving patient‐reported outcomes. In this paper, we develop a noninferiority testing procedure for correlated ordinal categorical variables based on a paired design with a latent normal distribution approach. Misclassification is frequently encountered in the collection of ordinal categorical data; therefore, we further extend the procedure to account for misclassification using information in the partially validated data. Simulation studies are conducted to investigate the accuracy of the estimates, the type I error rates, and the power of the proposed procedure. Finally, we analyze one substantive example to demonstrate the utility of the proposed approach.

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“…sample size calculations are well known, the accurate computation of sample sizes for complex designs is still an active area of research. Several authors have recently considered sample size computations for specific — more complex — experimental designs [ 10 , 17 , 23 , 29 ]. Furthermore, researchers have recently focussed on Bayesian methods for computing sample sizes [ 4 ], and have considered the embedding of the trial within its larger context [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

A practical approach to sample size calculation for fixed populations

Kaptein

2019

Contemporary Clinical Trials Communications

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Researchers routinely compute desired sample sizes of clinical trials to control type-i and type-ii errors. While for many experimental designs sample size calculations are well-known, it remains an active area of research. Work in this area focusses predominantly on controlling properties of the trial. In this paper we provide ready-to-use methods to compute sample sizes using an alternative objective, namely that of maximizing the outcome for a whole population. Considering the expected outcome of both the trial, and the resulting guideline, we formulate and numerically analyze the expected value of the entire allocation procedure. Our approach strongly relates to theoretical work presented in the 60's which demonstrated the effectiveness of allocation procedures that incorporate population sizes when planning experiments over designs that focus solely on error rates within the trial. We add to this work by a) extending to alternative designs (mean comparisons not assuming equal variances and comparisons of proportions), b) providing easy-to-use software to compute sample sizes for multiple experimental designs, and c) presenting numerical analysis that demonstrate the efficiency of the suggested approach.

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Sample size determination for disease prevalence studies with partially validated data

Cited by 11 publications

References 26 publications

Prevalence of Sleepwalking: A Systematic Review and Meta-Analysis

Prevalence of Sleepwalking: A Systematic Review and Meta-Analysis

Noninferiority testing for matched‐pair ordinal data with misclassification

A practical approach to sample size calculation for fixed populations

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