scholarly journals A stochastic mixed effects model to assess treatment effects and fluctuations in home‐measured peak expiratory flow and the association with exacerbation risk in asthma

2021 ◽  
Author(s):  
Jacob Leander ◽  
Mats Jirstrand ◽  
Ulf G Eriksson ◽  
Robert Palmér
Keyword(s):  
Peak Expiratory Flow ◽  
Treatment Effects ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Exacerbation Risk ◽  
Expiratory Flow

Modeling response curves and testing treatment effects in repeated measures experiments: a multilevel nonlinear mixed-effects model approach

2005 ◽  
Vol 35 (1) ◽  
pp. 122-132 ◽  
Author(s):  
Dehai Zhao ◽  
Machelle Wilson ◽  
Bruce E Borders
Keyword(s):  
Repeated Measures ◽  
Growth Response ◽  
Treatment Effects ◽  
Model Building ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
General Strategy ◽  
Nonlinear Mixed Effects ◽  
Nonlinear Mixed Effects Model ◽  
Vegetation Control

A multilevel nonlinear mixed-effects modeling approach is used to model loblolly pine (Pinus taeda L.) stand volume growth in conjunction with four silvicultural treatments. Comparisons of treatment effects over time are integrated with the model-building process. Three-level random effects are introduced into a modified Richards growth model. Within-plot heterogeneity and correlation still occur, which are described by the exponential variance function and a first-order autoregressive model. The combination of complete vegetation control with fertilization results in the largest growth response; annual fertilization has the next largest growth response, with the exception that at very early stages the response is lower than that of vegetation control only; the control has the lowest growth response. The advantages of the multilevel nonlinear mixed effects model include its ability to handle unbalanced and incomplete repeated measures data, its flexibility to model multiple sources of heterogeneity and complex patterns of correlation, and its higher power to make treatment comparisons. We address in detail a general strategy of multilevel nonlinear mixed effects model building.

Hierarchical Bayes approach for subgroup analysis

2017 ◽  
Vol 28 (1) ◽  
pp. 275-288 ◽  
Author(s):  
Yu-Yi Hsu ◽  
Jyoti Zalkikar ◽  
Ram C Tiwari
Keyword(s):  
Treatment Effect ◽  
Subgroup Analysis ◽  
Treatment Effects ◽  
Decision Procedures ◽  
Mixed Effects ◽  
Hierarchical Bayes ◽  
Mixed Effects Model ◽  
Linear Mixed Effects Model ◽  
Linear Mixed Effects ◽  
Bayes Approach

In clinical data analysis, both treatment effect estimation and consistency assessment are important for a better understanding of the drug efficacy for the benefit of subjects in individual subgroups. The linear mixed-effects model has been used for subgroup analysis to describe treatment differences among subgroups with great flexibility. The hierarchical Bayes approach has been applied to linear mixed-effects model to derive the posterior distributions of overall and subgroup treatment effects. In this article, we discuss the prior selection for variance components in hierarchical Bayes, estimation and decision making of the overall treatment effect, as well as consistency assessment of the treatment effects across the subgroups based on the posterior predictive p-value. Decision procedures are suggested using either the posterior probability or the Bayes factor. These decision procedures and their properties are illustrated using a simulated example with normally distributed response and repeated measurements.

EFFECT OF BODY MASS INDEX ON PEAK EXPIRATORY FLOW RATE

2019 ◽  
Vol 3 (9) ◽  
Author(s):  
K. Subramanyam ◽  
Dr. P. Subhash Babu
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Peak Expiratory Flow ◽  
Flow Rate ◽  
Peak Expiratory Flow Rate ◽  
Normal Weight ◽  
The Body ◽  
Health Care Centre ◽  
Expiratory Flow ◽  
Group B

Obesity has become one of the major health issues in India. WHO defines obesity as “A condition with excessive fat accumulation in the body to the extent that the health and wellbeing are adversely affected”. Obesity results from a complex interaction of genetic, behavioral, environmental and socioeconomic factors causing an imbalance in energy production and expenditure. Peak expiratory flow rate is the maximum rate of airflow that can be generated during forced expiratory manoeuvre starting from total lung capacity. The simplicity of the method is its main advantage. It is measured by using a standard Wright Peak Flow Meter or mini Wright Meter. The aim of the study is to see the effect of body mass index on Peak Expiratory Flow Rate values in young adults. The place of a study was done tertiary health care centre, in India for the period of 6 months. Study was performed on 80 subjects age group 20 -30 years, categorised as normal weight BMI =18.5 -24.99 kg/m2 and overweight BMI =25-29.99 kg/m2. There were 40 normal weight BMI (Group A) and 40 over weight BMI (Group B). BMI affects PEFR. Increase in BMI decreases PEFR. Early identification of risk individuals prior to the onset of disease is imperative in our developing country. Keywords: BMI, PEFR.

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