Estimation of a generalized linear mixed model for response-adaptive designs in multi-centre clinical trials

2017 ◽  
Vol 45 (3) ◽  
pp. 310-325 ◽  
Author(s):  
Selvakkadunko Selvaratnam ◽  
Alwell J. Oyet ◽  
Yanqing Yi ◽  
Veeresh Gadag
Keyword(s):  
Clinical Trials ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Adaptive Designs ◽  
Response Adaptive Designs

scholarly journals Is OR “portable” in meta-analysis? Time to consider bivariate generalized linear mixed model

2020 ◽  
Author(s):  
Mengli Xiao ◽  
Yong Chen ◽  
Stephen Cole ◽  
Richard MacLehose ◽  
David Richardson ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Meta Analysis ◽  
Generalized Linear Mixed Model ◽  
Baseline Risk ◽  
Marginal Effect ◽  
List Type ◽  
Effect Measure ◽  
Meta Analyses

AbstractObjectivesA recent paper by Doi et al. advocated completely replacing the relative risk (RR) with the odds ratio (OR) as the effect measure used to report the association between a treatment and a binary outcome in clinical trials and meta-analyses. Besides some practical advantages of RR over OR and the well-known issue of the OR being non-collapsible, Doi et al.’s key assumption that the OR is “portable” in the meta-analysis, i.e., study-specific ORs are likely not correlated with baseline risks, was not well justified.Study designs and settingsWe summarized the Spearman’s rank correlation coefficient between study-specific OR and the baseline risk in 40,243 meta-analyses from the Cochrane Database of Systematic Reviews (CDSR).ResultsStudy-specific ORs are negatively correlated with baseline risk of disease (i.e., higher ORs tend to be observed in studies with lower baseline risks of disease) for most meta-analyses in CDSR. Using a meta-analysis comparing the effect of oral sumatriptan (100 mg) versus placebo on mitigating the acute headache at 2 hours after drug administration, we demonstrate that there is a strong negative correlation between OR (RR or RD) with the baseline risk and the conditional effects notably vary with baseline risks.ConclusionsReplacing RR or RD with OR is currently unadvisable in clinical trials and meta-analyses. It is possible that no effect measure is “portable” in a meta-analysis. In cases where portability of the effect measure is challenging to satisfy, we suggest presenting the conditional effect based on the baseline risk using a bivariate generalized linear mixed model. The bivariate generalized linear mixed model can be used to account for correlation between the effect measure and baseline disease risk. Furthermore, in addition to the overall (or marginal) effect, we recommend that investigators also report the effects conditioning on the baseline risk.What is New?Key findingsIn most meta-analyses in Cochrane Database of Systematic Reviews, there is notable negative correlation between ORs and baseline risks.When such a correlation is not negligible, the OR is not “portable” across studies with different baseline risks.When an effect measure is not “portable”, one may derive the effects conditioning on the baseline risk from a bivariate generalized linear mixed model.What this study adds to what was knownThe recommendation to replace the RR with the OR in clinical trials and meta-analyses is misguided.The OR is not a better effect summary than RR and RD in a single study or in meta-analyses; the noncollapsibility of OR can lead to misleading results in a single study and the OR is generally not portable in the meta-analysis.In addition to reporting effect measures such as the OR, RR or RD, it is also important to present the baseline risk.What is the implication and what should change now?When none of the effects are “portable” in a meta-analysis, in addition to report the overall (or marginal) effect, one should also report the effects conditioning on the baseline risk, regardless of the measure of choice.

Analyzing discontinuities in longitudinal count data: A multilevel generalized linear mixed model.

2020 ◽  
Author(s):  
James L. Peugh ◽  
Sarah J. Beal ◽  
Meghan E. McGrady ◽  
Michael D. Toland ◽  
Constance Mara
Keyword(s):  
Count Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Longitudinal Count Data

scholarly journals Reducing Aggression by Developing Emotional and Inhibitory Control

2021 ◽  
Vol 18 (10) ◽  
pp. 5263
Author(s):  
Miriam Romero-López ◽  
María Carmen Pichardo ◽  
Ana Justicia-Arráez ◽  
Judit Bembibre-Serrano
Keyword(s):  
Psychosocial Development ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Emotional Control ◽  
Control Group ◽  
Post Intervention ◽  
Mixed Model Analysis ◽  
Intervention Measures ◽  
Experimental Group

The objective of this study is to measure the effectiveness of a program on improving inhibitory and emotional control among children. In addition, it is assessed whether the improvement of these skills has an effect on the reduction of aggressive behavior in pre-school children. The participants were 100 children, 50 belonging to the control group and 50 to the experimental group, aged between 5 and 6 years. Pre-intervention and post-intervention measures of inhibitory and emotional control (BRIEF-P) and aggression (BASC) were taken. A Generalized Linear Mixed Model analysis (GLMM) was performed and found that children in the experimental group scored higher on inhibitory and emotional control compared to their peers in the control group. In addition, these improvements have an effect on the decrease in aggressiveness. In conclusion, preventive research should have among its priorities the design of such program given their implications for psychosocial development.

scholarly journals Changes in Dairy Cow Behavior with and without Assistance at Calving

Agriculture ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 722
Author(s):  
Bethan Cavendish ◽  
John McDonagh ◽  
Georgios Tzimiropoulos ◽  
Kimberley R. Slinger ◽  
Zoë J. Huggett ◽  
...  
Keyword(s):  
Dairy Cows ◽  
Video Surveillance ◽  
Dairy Cow ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Behavioral Data ◽  
Behavioral Patterns ◽  
Holstein Friesian ◽  
Time Periods

The aim of this study was to characterize calving behavior of dairy cows and to compare the duration and frequency of behaviors for assisted and unassisted dairy cows at calving. Behavioral data from nine hours prior to calving were collected for 35 Holstein-Friesian dairy cows. Cows were continuously monitored under 24 h video surveillance. The behaviors of standing, lying, walking, shuffle, eating, drinking and contractions were recorded for each cow until birth. A generalized linear mixed model was used to assess differences in the duration and frequency of behaviors prior to calving for assisted and unassisted cows. The nine hours prior to calving was assessed in three-hour time periods. The study found that the cows spent a large proportion of their time either lying (0.49) or standing (0.35), with a higher frequency of standing (0.36) and shuffle (0.26) bouts than other behaviors during the study. There were no differences in behavior between assisted and unassisted cows. During the three-hours prior to calving, the duration and bouts of lying, including contractions, were higher than during other time periods. While changes in behavior failed to identify an association with calving assistance, the monitoring of behavioral patterns could be used as an alert to the progress of parturition.

Response-Adaptive Designs in Phase III Clinical Trials

2008 ◽  
pp. 33-53
Author(s):  
Atanu Biswas ◽  
Uttam Bandyopadhyay ◽  
Rahul Bhattacharya
Keyword(s):  
Clinical Trials ◽  
Adaptive Designs ◽  
Phase Iii ◽  
Phase Iii Clinical Trials ◽  
Response Adaptive Designs

A generalized linear mixed model association tool for biobank-scale data

2021 ◽  
Author(s):  
Longda Jiang ◽  
Zhili Zheng ◽  
Hailing Fang ◽  
Jian Yang
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Scale Data

scholarly journals Analyzing Child Malnutrition in Bangladesh: Generalized Linear Mixed Model Approach

2016 ◽  
Vol 64 (2) ◽  
pp. 163-167
Author(s):  
Tahmidul Islam ◽  
Md Golam Rabbani ◽  
Wasimul Bari
Keyword(s):  
Mixed Model ◽  
Sampling Design ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Child Malnutrition ◽  
Demographic Health Survey ◽  
Factors Affecting ◽  
Mixed Model Approach ◽  
Potential Factors ◽  
Model Approach

Child malnutrition is a serious issue for overall child health and future development. Stunting is a key anthropometric indicator of child malnutrition. Because of the nature of sampling design used in Bangladesh Demographic Health Survey, 2011, responses obtained from children under same family might be correlated. Again, children residing in same cluster may also be correlated. To tackle this problem, generalized linear mixed model (GLMM), instead of usual fixed effect logistic regression model, has been utilized in this paper to find out potential factors affecting child malnutrition. Model performances have also been compared. Dhaka Univ. J. Sci. 64(2): 163-167, 2016 (July)

Analysis of aggregation, a worked example: numbers of ticks on red grouse chicks

Parasitology ◽  
2001 ◽  
Vol 122 (5) ◽  
pp. 563-569 ◽  
Author(s):  
D. A. ELSTON ◽  
R. MOSS ◽  
T. BOULINIER ◽  
C. ARROWSMITH ◽  
X. LAMBIN
Keyword(s):  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Negative Binomial ◽  
Generalized Linear Mixed Model ◽  
Red Grouse ◽  
Worked Example ◽  
Fixed And Random Effects ◽  
Individual Effects ◽  
Lagopus Lagopus Scoticus

The statistical aggregation of parasites among hosts is often described empirically by the negative binomial (Poisson-gamma) distribution. Alternatively, the Poisson-lognormal model can be used. This has the advantage that it can be fitted as a generalized linear mixed model, thereby quantifying the sources of aggregation in terms of both fixed and random effects. We give a worked example, assigning aggregation in the distribution of sheep ticksIxodes ricinuson red grouseLagopus lagopus scoticuschicks to temporal (year), spatial (altitude and location), brood and individual effects. Apparent aggregation among random individuals in random broods fell 8-fold when spatial and temporal effects had been accounted for.

Sign in / Sign up

Export Citation Format

Share Document