USE OF FIXED EFFECT MODELS AND MIXED MODELS TO ESTIMATE HEIGHT IN AN ESTUARINE FLOODPLAIN FOREST, AMAZON, BRAZIL

Unmeasured confounding almost always exists in observational studies and can bias estimates of exposure effects. Instrumental variable methods are popular choices in combating unmeasured confounding to obtain less biased effect estimates. However, we demonstrate that alternative methods may give less biased estimates depending on the nature of unmeasured confounding. Treatment preferences of clusters (e.g. physician practices) are the most frequently used instruments in instrumental variable analyses. These preference-based instrumental variable analyses are usually conducted on data clustered by region, hospital/facility, or physician, where unmeasured confounding often occurs within or between clusters. We aim to quantify the impact of unmeasured confounding on the bias of effect estimators in instrumental variable analysis, as well as several common alternative methods including ordinary least squares regression, linear mixed models, and fixed-effect models to study the effect of a continuous exposure (e.g. treatment dose) on a continuous outcome. We derive closed-form expressions of asymptotic bias of estimators from these four methods in the presence of unmeasured within- and/or between-cluster confounders. Simulations demonstrate that the asymptotic bias formulae well approximate bias in finite samples for all methods. The bias formulae show that instrumental variable analyses can provide consistent estimates when unmeasured within-cluster confounding exists, but not when between-cluster confounding exists. On the other hand, fixed-effect models and linear mixed models can provide consistent estimates when unmeasured between-cluster confounding exits, but not for within-cluster confounding. Whether instrumental variable analyses are advantageous in reducing bias over fixed-effect models and linear mixed models depends on the extent of unmeasured within-cluster confounding relative to between-cluster confounding. Furthermore, the impact of unmeasured between-cluster confounding on instrumental variable analysis estimates is larger than the impact of unmeasured within-cluster confounding on fixed-effect model and linear mixed model estimates. We illustrate the use of these methods in estimating the effect of erythropoiesis stimulating agents on hemoglobin levels. Our findings provide guidance for choosing appropriate methods to combat the dominant types of unmeasured confounders and help interpret statistical results in the context of unmeasured confounding.

Download Full-text

IMPACT OF VARIANCE COMPONENT ESTIMATES ON FIXED EFFECT INFERENCE IN UNBALANCED LINEAR MIXED MODELS

Conference on Applied Statistics in Agriculture ◽

10.4148/2475-7772.1198 ◽

2002 ◽

Cited By ~ 5

Author(s):

Walt Stroup ◽

Ramon Littell

Keyword(s):

Mixed Models ◽

Variance Component ◽

Linear Mixed Models ◽

Fixed Effect ◽

Variance Component Estimates

Download Full-text

Risk Adjustment for Lumbar Dysfunction: Comparison of Linear Mixed Models With and Without Inclusion of Between-Clinic Variation as a Random Effect

Physical Therapy ◽

10.2522/ptj.20140444 ◽

2015 ◽

Vol 95 (12) ◽

pp. 1692-1702 ◽

Cited By ~ 4

Author(s):

Sheng-Che Yen ◽

Marie B. Corkery ◽

Kevin K. Chui ◽

Justin Manjourides ◽

Ying-Chih Wang ◽

...

Keyword(s):

Physical Therapy ◽

Patient Outcomes ◽

Risk Adjustment ◽

Mixed Models ◽

Linear Mixed Models ◽

Random Effect ◽

Fixed Effect Model ◽

The United States ◽

Fixed Effect ◽

Effect Model

BackgroundValid comparison of patient outcomes of physical therapy care requires risk adjustment for patient characteristics using statistical models. Because patients are clustered within clinics, results of risk adjustment models are likely to be biased by random, unobserved between-clinic differences. Such bias could lead to inaccurate prediction and interpretation of outcomes.PurposeThe purpose of this study was to determine if including between-clinic variation as a random effect would improve the performance of a risk adjustment model for patient outcomes following physical therapy for low back dysfunction.DesignThis was a secondary analysis of data from a longitudinal cohort of 147,623 patients with lumbar dysfunction receiving physical therapy in 1,470 clinics in 48 states of the United States.MethodsThree linear mixed models predicting patients' functional status (FS) at discharge, controlling for FS at intake, age, sex, number of comorbidities, surgical history, and health care payer, were developed. Models were: (1) a fixed-effect model, (2) a random-intercept model that allowed clinics to have different intercepts, and (3) a random-slope model that allowed different intercepts and slopes for each clinic. Goodness of fit, residual error, and coefficient estimates were compared across the models.ResultsThe random-effect model fit the data better and explained an additional 11% to 12% of the between-patient differences compared with the fixed-effect model. Effects of payer, acuity, and number of comorbidities were confounded by random clinic effects.LimitationsModels may not have included some variables associated with FS at discharge. The clinics studied may not be representative of all US physical therapy clinics.ConclusionsRisk adjustment models for functional outcome of patients with lumbar dysfunction that control for between-clinic variation performed better than a model that does not.

Download Full-text