The Impact of Ignoring Measurement Error when Estimating Sample Size for Epidemiologic Studies

2003 ◽  
Vol 26 (3) ◽  
pp. 315-339 ◽  
Author(s):  
Owen Devine
1991 ◽  
Vol 134 (12) ◽  
pp. 1470-1472 ◽  
Author(s):  
Lawrence H. Kushi ◽  
Daniel Zelterman ◽  
David R. Jacobs, ◽  
John D. Potter

1990 ◽  
Vol 132 (6) ◽  
pp. 1185-1195 ◽  
Author(s):  
LAURENCE S. FREEDMAN ◽  
ARTHUR SCHATZKIN ◽  
YOHANAN WAX

2000 ◽  
Vol 47 (8) ◽  
pp. 762-766 ◽  
Author(s):  
Diana O Perkins ◽  
Richard Jed Wyatt ◽  
John J Bartko

1996 ◽  
Vol 115 (5) ◽  
pp. 422-428
Author(s):  
Roy E. Shore

A number of topics are discussed related to the potential for and pitfalls in undertaking epidemiologic studies of the late effects of nasopharyngeal radium irradiation. The available evidence indicates that linear extrapolation of risk estimates from high-dose studies is a reasonable basis for estimating risk from radium exposure or other situations in which the radiation exposures were fairly low and fractionated. Epidemiologic study of populations given nasopharyngeal radium irradiation is worthwhile scientifically if several criteria can be met. It is very Important that any such study has adequate statistical power, which is a function of the doses to the organs of interest and the radiation risk coefficients for those organs, as wed as the available sample size. If the organ doses are low, a prohibitively large sample size would be required. Other problems with low-dose studies include the likelihood of false-positive results when a number of health end points are evaluated and the impact of dose uncertainties, small biases, and confounding factors that make the interpretation uncertain. Cluster studies or studies of self-selected cohorts of irradiated patients are not recommended because of the potential for severe bias with such study designs. The ability to define subgroups of the population who have heightened genetic susceptibility may become a reality in the next few years as genes conferring susceptibility to brain cancers or other head and neck tumors are identified; this scientific advance would have the potential to alter greatly the prospects and approaches of epidemiologic studies.


Author(s):  
David Aaby ◽  
Juned Siddique

Abstract Background Lifestyle intervention studies often use self-reported measures of diet as an outcome variable to measure changes in dietary intake. The presence of measurement error in self-reported diet due to participant failure to accurately report their diet is well known. Less familiar to researchers is differential measurement error, where the nature of measurement error differs by treatment group and/or time. Differential measurement error is often present in intervention studies and can result in biased estimates of the treatment effect and reduced power to detect treatment effects. Investigators need to be aware of the impact of differential measurement error when designing intervention studies that use self-reported measures. Methods We use simulation to assess the consequences of differential measurement error on the ability to estimate treatment effects in a two-arm randomized trial with two time points. We simulate data under a variety of scenarios, focusing on how different factors affect power to detect a treatment effect, bias of the treatment effect, and coverage of the 95% confidence interval of the treatment effect. Simulations use realistic scenarios based on data from the Trials of Hypertension Prevention Study. Simulated sample sizes ranged from 110-380 per group. Results Realistic differential measurement error seen in lifestyle intervention studies can require an increased sample size to achieve 80% power to detect a treatment effect and may result in a biased estimate of the treatment effect. Conclusions Investigators designing intervention studies that use self-reported measures should take differential measurement error into account by increasing their sample size, incorporating an internal validation study, and/or identifying statistical methods to correct for differential measurement error.


2020 ◽  
Author(s):  
Qing Zhao ◽  
Pei Chen ◽  
Yu Zhang ◽  
Haining Liu ◽  
Xianwen Li

BACKGROUND Mobile health application has become an important tool for healthcare systems. One such tool is the delivery of assisting in people with cognitive impairment and their caregivers. OBJECTIVE This scoping review aims to explore and evaluate the existing evidence and challenges on the use of mHealth applications that assisting in people with cognitive impairment and their caregivers. METHODS Nine databases, including PubMed, EMBASE, Cochrane, PsycARTICLES, CINAHL, Web of Science, Applied Science & Technology Source, IEEE Xplore and the ACM Digital Library were searched from inception through June 2020 for the studies of mHealth applications on people with cognitive impairment and their caregivers. Two reviewers independently extracted, checked synthesized data independently. RESULTS Of the 6101 studies retrieved, 64 studies met the inclusion criteria. Three categories emerged from this scoping review. These categories are ‘application functionality’, ‘evaluation strategies’, ‘barriers and challenges’. All the included studies were categorized into 7 groups based on functionality: (1) cognitive assessment; (2) cognitive training; (3) life support; (4) caregiver support; (5) symptom management; (6) reminiscence therapy; (7) exercise intervention. The included studies were broadly categorized into four types: (1) Usability testing; (2) Pilot and feasibility studies; (3) Validation studies; and (4) Efficacy or Effectiveness design. These studies had many defects in research design such as: (1) small sample size; (2) deficiency in active control group; (3) deficiency in analyzing the effectiveness of intervention components; (4) lack of adverse reactions and economic evaluation; (5) lack of consideration about the education level, electronic health literacy and smartphone proficiency of the participants; (6) deficiency in assessment tool; (7) lack of rating the quality of mHealth application. Some progress should be improved in the design of smartphone application functionality, such as: (1) the design of cognitive measurements and training game need to be differentiated; (2) reduce the impact of the learning effect. Besides this, few studies used health behavior theory and performed with standardized reporting. CONCLUSIONS Preliminary results show that mobile technologies facilitate the assistance in people with cognitive impairment and their caregivers. The majority of mHealth application interventions incorporated usability outcome and health outcomes. However, these studies have many defects in research design that limit the extrapolation of research. The content of mHealth application is urgently improved to adapt to demonstrate the real effect. In addition, further research with strong methodological rigor and adequate sample size are needed to examine the feasibility, effectiveness, and cost-effectiveness of mHealth applications for people with cognitive impairment and their caregivers.


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