The Reliability of Sensor-Assisted Soft Tissue Measurements in Primary Total Knee Arthroplasty.

BACKGROUND: In pursuit to improve soft tissue balancing in total knee arthroplasties (TKAs), a wireless device was introduced to assess femorotibial pressures. The aim of this study was to evaluate the reliability of this device. METHODS: After 33 TKAs were balanced by conventional techniques, contact pressures were measured using a wireless sensor 3 times in a row; twice while the examiner was blinded for the result (n = 29); and once while the examiner was able to see the result as visual feedback (n = 32). Femorotibial pressures were measured in the medial and lateral compartments with the knee in 10°, 45°, and 90° of flexion (6 measurements per TKA). Furthermore, both the combined pressure and the difference in pressure between the compartments was calculated throughout the 3 positions (together another 6 measurements per TKA). RESULTS: The intraclass correlation coefficient between the blind measurements was poor in 2 of the 12 (17%), moderate in 4 of 12 (33%), and good in 6 of 12 (50%) measurements. The intraclass correlation coefficient between the blind and observing measurement was poor in 2 of the 12 (17%), moderate in 6 of 12 (50%), and good in 4 of 12 (33%) measurements. Especially measurements in 10° of flexion are associated with poorer reliability. CONCLUSION: The wireless sensor has a moderate to good reliability in 83% of the measurements.

A finite mixture method for outlier detection and robustness in meta-analysis.

When performing a meta-analysis unexplained variation above that predicted by within study variation is usually modeled by a random effect. However, in some cases, this is not sufficient to explain all the variation because of outlier or unusual studies. A previously described method is to define an outlier as a study requiring a higher random effects variance and testing each study sequentially. An extension is described where the studies are considered to be a finite mixture of outliers and non-outliers, allowing any number of outlier studies and the use of standard mixture model techniques. The bootstrap likelihood ratio test is used to determine if there are any outliers present by comparing models with and without outliers, and the outlier studies are identified using posterior predicted probabilities. The estimation of the overall treatment effect is then determined including all observations but with the outliers down-weighted. This has the advantage that studies that are marginal outliers are still included in the meta-analysis but with an appropriate weighting. The method is applied to examples from meta-analysis and meta-regression.

Multilevel latent variable models for global health-related quality of life assessment.

Quality of life (QOL) assessment is a key component of many clinical studies and frequently requires the use of single global summary measures that capture the overall balance of findings from a potentially wide-ranging assessment of QOL issues. We propose and evaluate an irregular multilevel latent variable model suitable for use as a global summary tool for health-related QOL assessments. The proposed model is a multiple indicator and multiple cause style of model with a two-level latent variable structure. We approach the modeling from a general multilevel modeling perspective, using a combination of random and nonrandom cluster types to accommodate the mixture of issues commonly evaluated in health-related QOL assessments--overall perceptions of QOL and health, along with specific psychological, physical, social, and functional issues. Using clinical trial data, we evaluate the merits and application of this approach in detail, both for mean global QOL and for change from baseline. We show that the proposed model generally performs well in comparing global patterns of treatment effect and provides more precise and reliable estimates than several common alternatives such as selecting from or averaging observed global item measures. A variety of computational methods could be used for estimation. We derived a closed-form expression for the marginal likelihood that can be used to obtain maximum likelihood parameter estimates when normality assumptions are reasonable. Our approach is useful for QOL evaluations aimed at pharmacoeconomic or individual clinical decision making and in obtaining summary QOL measures for use in quality-adjusted survival analyses.

Infant growth modelling using a shape invariant model with random effects.

Models for infant growth have usually been based on parametric forms, commonly an exponential or similar model, which have been shown to fit poorly especially during the first year of life. An alternative approach is to use a non-parametric model, based on a shape invariant model (SIM), where a single function is transformed by shifting and scaling to fit each subject. In the model a regression spline is used as the function, with log transformation of the data and a simplification of the SIM, obtained from the relationship with the exponential model. All subjects are fitted as a nonlinear mixed effects model, allowing the variation in the parameters between subjects to be determined. Methods for the inclusion of covariates in growth models based on SIM are developed, with parameters for time independent covariates included in the model by varying either the shape, the size parameter or the growth parameter and time-dependent co-variates included by transforming the time axis, to either increase or decrease the growth rate dependent on the co-variate, similar to methods used for accelerated failure-time models. The model is used to fit weight data for 602 infants, measured from 0 to 2 years as part of the Childhood Asthma Prevention Study (CAPS) trial, and to determine the effect of breastfeeding on infant weight.