Measurement Error in Epidemiological Studies of Allergenic Pollen Due to Heterogeneity in Flowering Phenology

2018 ◽  
Vol 2018 (1) ◽  
Author(s):  
Daniel SW Katz ◽  
Stuart Batterman
Keyword(s):  
Measurement Error ◽  
Flowering Phenology ◽  
Epidemiological Studies ◽  
Allergenic Pollen

scholarly journals The Effect of Random Error in Exposure Measurement upon the Shape of the Exposure Response

Dose-Response ◽  
2005 ◽  
Vol 3 (4) ◽  
pp. dose-response.0 ◽  
Author(s):  
Kenny S. Crump
Keyword(s):  
Measurement Error ◽  
Measurement Errors ◽  
Epidemiological Data ◽  
Epidemiological Studies ◽  
Random Errors ◽  
Exposure Measurement ◽  
Considerable Uncertainty ◽  
Exposure Response ◽  
Log Normal ◽  
Normally Distributed

Although statistical analyses of epidemiological data usually treat the exposure variable as being known without error, estimated exposures in epidemiological studies often involve considerable uncertainty. This paper investigates the theoretical effect of random errors in exposure measurement upon the observed shape of the exposure response. The model utilized assumes that true exposures are log-normally distributed, and multiplicative measurement errors are also log-normally distributed and independent of the true exposures. Under these conditions it is shown that whenever the true exposure response is proportional to exposure to a power r, the observed exposure response is proportional to exposure to a power K, where K < r. This implies that the observed exposure response exaggerates risk, and by arbitrarily large amounts, at sufficiently small exposures. It also follows that a truly linear exposure response will appear to be supra-linear—i.e., a linear function of exposure raised to the K-th power, where K is less than 1.0. These conclusions hold generally under the stated log-normal assumptions whenever there is any amount of measurement error, including, in particular, when the measurement error is unbiased either in the natural or log scales. Equations are provided that express the observed exposure response in terms of the parameters of the underlying log-normal distribution. A limited investigation suggests that these conclusions do not depend upon the log-normal assumptions, but hold more widely. Because of this problem, in addition to other problems in exposure measurement, shapes of exposure responses derived empirically from epidemiological data should be treated very cautiously. In particular, one should be cautious in concluding that the true exposure response is supra-linear on the basis of an observed supra-linear form.

Evaluating the power of the causal impact method in observational studies of HCV treatment as prevention

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Pantelis Samartsidis ◽  
Natasha N. Martin ◽  
Victor De Gruttola ◽  
Frank De Vocht ◽  
Sharon Hutchinson ◽  
...  
Keyword(s):  
Time Series ◽  
Measurement Error ◽  
Time Series Data ◽  
Temporal Trends ◽  
Epidemiological Studies ◽  
Series Data ◽  
Treatment As Prevention ◽  
Open Problems ◽  
Multiple Characteristics ◽  
Causal Impact

Abstract Objectives The causal impact method (CIM) was recently introduced for evaluation of binary interventions using observational time-series data. The CIM is appealing for practical use as it can adjust for temporal trends and account for the potential of unobserved confounding. However, the method was initially developed for applications involving large datasets and hence its potential in small epidemiological studies is still unclear. Further, the effects that measurement error can have on the performance of the CIM have not been studied yet. The objective of this work is to investigate both of these open problems. Methods Motivated by an existing dataset of HCV surveillance in the UK, we perform simulation experiments to investigate the effect of several characteristics of the data on the performance of the CIM. Further, we quantify the effects of measurement error on the performance of the CIM and extend the method to deal with this problem. Results We identify multiple characteristics of the data that affect the ability of the CIM to detect an intervention effect including the length of time-series, the variability of the outcome and the degree of correlation between the outcome of the treated unit and the outcomes of controls. We show that measurement error can introduce biases in the estimated intervention effects and heavily reduce the power of the CIM. Using an extended CIM, some of these adverse effects can be mitigated. Conclusions The CIM can provide satisfactory power in public health interventions. The method may provide misleading results in the presence of measurement error.

Mixture models in measurement error problems, with reference to epidemiological studies

2002 ◽  
Vol 165 (3) ◽  
pp. 549-566 ◽  
Author(s):  
Sylvia Richardson ◽  
Laurent Leblond ◽  
Isabelle Jaussent ◽  
Peter J. Green
Keyword(s):  
Measurement Error ◽  
Mixture Models ◽  
Epidemiological Studies

Correcting for the bias caused by exposure measurement error in epidemiological studies

Respirology ◽  
2014 ◽  
Vol 19 (7) ◽  
pp. 979-984 ◽  
Author(s):  
Michael T. Fahey ◽  
Andrew B. Forbes ◽  
Alison M. Hodge
Keyword(s):  
Measurement Error ◽  
Epidemiological Studies ◽  
Exposure Measurement ◽  
Exposure Measurement Error

scholarly journals A quantitative analysis of the relationship between habitual energy expenditure, fitness and the metabolic cardiovascular syndrome

1998 ◽  
Vol 80 (3) ◽  
pp. 235-241 ◽  
Author(s):  
Nicholas J. Wareham ◽  
Susie J. Hennings ◽  
Christopher D. Byrne ◽  
C. Nicholas Hales ◽  
Andrew M. Prentice ◽  
...  
Keyword(s):  
Physical Activity ◽  
Measurement Error ◽  
Energy Expenditure ◽  
Total Energy ◽  
Odds Ratio ◽  
Epidemiological Studies ◽  
Total Energy Expenditure ◽  
Activity Level ◽  
The Relationship ◽  
Metabolic Cardiovascular Syndrome

Previous epidemiological studies have suggested an association between low levels of physical activity, fitness and the metabolic cardiovascular syndrome. However, many studies have used subjective non-quantitative questionnaire-based methods for assessing physical activity which do not distinguish between the different dimensions of this complex exposure, and in which measurement error in the exposure has not been estimated. These deficiencies in the measurement of this exposure complicate the interpretation of the results of epidemiological studies, and consequently make it difficult to design appropriate interventions and to estimate the expected benefit which would result from intervention. In particular, it is unclear whether public health advice should be to increase total energy expenditure, or to attempt to raise fitness by recommending periods of vigorous activity. To separate the effects of fitness and total energy expenditure in the aetiology of the metabolic cardiovascular syndrome, we measured the physical activity level (PAL), defined as total energy expenditure: BMR, and fitness (maximum O2consumption (Vo2maxper kg), measured in a sub-maximal test) in a cross-sectional population-based study of 162 adults aged 30–40 years. Heart-rate monitoring with individual calibration was used to measure total energy expenditure using the HRFlex method (Ceesayet al.1989) which has been validated previously against doubly-labelled water and whole-body calorimetry. The relationship between a single measure of PAL,Vo2maxper kg and the usual or habitual level for each exposure was measured in a sub-study of twenty-two subjects who undertook four repeated measures over the course of 1 year. This study design allows the reliability coefficient to be computed, which is used to adjust the observed associations for measurement error in the exposure. Twelve men (16.4%) and sixteen women (18.0%) were defined as having one or more features of the metabolic cardiovascular syndrome. The univariate odds ratio for each increasing quartile for PAL was 0.64 (95 % CI 0.43–0.94) and was 0.49 (95 % CI 0.32–0.74) forVo2maxper kg, suggesting that the association with the metabolic cardiovascular syndrome was stronger for fitness than for PAL. However, after adjustment for obesity and sex, and correction for exposure measurement error, the odds ratio per quartile for PAL was 0.32 (95 % CI 0.13–0.83) and 0.44 (95 % CI 0.24–0.78) forVo2maxper kg. Thus, although univariate analysis would suggest that fitness has a stronger association with the metabolic cardiovascular syndrome than PAL, this conclusion is reversed once confounding and the differences in measurement error are considered. We conclude from the present study that the metabolic cardiovascular syndrome is strongly associated with reduced habitual energy expenditure. The method employed to assess the exposure in the present study demonstrates the utility of assessing a known dimension of physical activity using a physiologically-based and objective measure with repeated estimation to adjust for measurement error. Such quantitative epidemiological data provide the basis for planning and evaluating the expected benefit of population-level interventions.

scholarly journals A Method to adjust for measurement error in multiple exposure variables measured with correlated errors in the absence of an internal validation study

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1486
Author(s):  
Alexander K. Muoka ◽  
George O. Agogo ◽  
Oscar O. Ngesa ◽  
Henry G. Mwambi
Keyword(s):  
Sensitivity Analysis ◽  
Measurement Error ◽  
Validation Study ◽  
Real Data ◽  
Epidemiological Studies ◽  
Correlated Errors ◽  
Internal Validation ◽  
Multivariate Method ◽  
Multiple Exposures ◽  
Naive Method

Difficulty in obtaining the correct measurement for an individual’s longterm exposure is a major challenge in epidemiological studies that investigate the association between exposures and health outcomes. Measurement error in an exposure biases the association between the exposure and a disease outcome. Usually, an internal validation study is required to adjust for exposure measurement error; it is challenging if such a study is not available. We propose a general method for adjusting for measurement error where multiple exposures are measured with correlated errors (a multivariate method) and illustrate the method using real data. We compare the results from the multivariate method with those obtained using a method that ignores measurement error (the naive method) and a method that ignores correlations between the errors and true exposures (the univariate method). It is found that ignoring measurement error leads to bias and underestimates the standard error. A sensitivity analysis shows that the magnitude of adjustment in the multivariate method is sensitive to the magnitude of measurement error, sign, and the correlation between the errors. We conclude that the multivariate method can be used to adjust for bias in the outcome-exposure association in a case where multiple exposures are measured with correlated errors in the absence of an internal validation study. The method is also useful in conducting a sensitivity analysis on the magnitude of measurement error and the sign of the error correlation.

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