Philip M. Westgate

Fibre type‐specific satellite cell response to aerobic training in sedentary adults

Satellite cell activation and fusion accompany resistance exercise training. Aerobic exercise training is capable of inducing subtle muscle fibre hypertrophy; however, the role of satellite cell activation during aerobic exercise‐induced muscle adaptation is unknown. Twelve weeks of aerobic training in sedentary subjects yielded an increase in myosin heavy chain type I and type II muscle fibre cross‐sectional area. Satellite cell activation and myonuclear addition occurred only in myosin heavy chain type I fibres, with no change in myosin heavy chain type II fibres. These results help us better understand the role of satellite cells in muscle fibre adaptation to aerobic exercise, and suggest differential fibre type regulation of the myonuclear domain.

A bias correction for covariance estimators to improve inference with generalized estimating equations that use an unstructured correlation matrix.

Generalized estimating equations (GEEs) are routinely used for the marginal analysis of correlated data. The efficiency of GEE depends on how closely the working covariance structure resembles the true structure, and therefore accurate modeling of the working correlation of the data is important. A popular approach is the use of an unstructured working correlation matrix, as it is not as restrictive as simpler structures such as exchangeable and AR-1 and thus can theoretically improve efficiency. However, because of the potential for having to estimate a large number of correlation parameters, variances of regression parameter estimates can be larger than theoretically expected when utilizing the unstructured working correlation matrix. Therefore, standard error estimates can be negatively biased. To account for this additional finite-sample variability, we derive a bias correction that can be applied to typical estimators of the covariance matrix of parameter estimates. Via simulation and in application to a longitudinal study, we show that our proposed correction improves standard error estimation and statistical inference.

Improving the correlation structure selection approach for generalized estimating equations and balanced longitudinal data

Generalized estimating equations are commonly used to analyze correlated data. Choosing an appropriate working correlation structure for the data is important, as the efficiency of generalized estimating equations depends on how closely this structure approximates the true structure. Therefore, most studies have proposed multiple criteria to select the working correlation structure, although some of these criteria have neither been compared nor extensively studied. To ease the correlation selection process, we propose a criterion that utilizes the trace of the empirical covariance matrix. Furthermore, use of the unstructured working correlation can potentially improve estimation precision and therefore should be considered when data arise from a balanced longitudinal study. However, most previous studies have not allowed the unstructured working correlation to be selected as it estimates more nuisance correlation parameters than other structures such as AR-1 or exchangeable. Therefore, we propose appropriate penalties for the selection criteria that can be imposed upon the unstructured working correlation. Via simulation in multiple scenarios and in application to a longitudinal study, we show that the trace of the empirical covariance matrix works very well relative to existing criteria. We further show that allowing criteria to select the unstructured working correlation when utilizing the penalties can substantially improve parameter estimation.

Criterion for the simultaneous selection of a working correlation structure and either generalized estimating equations or the quadratic inference function approach.

Generalized estimating equations (GEE) are commonly used for the marginal analysis of correlated data, although the quadratic inference function (QIF) approach is an alternative that is increasing in popularity. This method optimally combines distinct sets of unbiased estimating equations that are based upon a working correlation structure, therefore asymptotically increasing or maintaining estimation efficiency relative to GEE. However, in finite samples, additional estimation variability arises when combining these sets of estimating equations, and therefore the QIF approach is not guaranteed to work as well as GEE. Furthermore, estimation efficiency can be improved for both analysis methods by accurate modeling of the correlation structure. Our goal is to improve parameter estimation, relative to existing methods, by simultaneously selecting a working correlation structure and choosing between GEE and two versions of the QIF approach. To do this, we propose the use of a criterion based upon the trace of the empirical covariance matrix (TECM). To make GEE and both QIF versions directly comparable for any given working correlation structure, the proposed TECM utilizes a penalty to account for the finite-sample variance inflation that can occur with either version of the QIF approach. Via a simulation study and in application to a longitudinal study, we show that penalizing the variance inflation that occurs with the QIF approach is necessary and that the proposed criterion works very well.

The effect of cluster size imbalance and covariates on the estimation performance of quadratic inference functions

Generalized estimating equations (GEE) are commonly used for the analysis of correlated data. However, use of quadratic inference functions (QIFs) is becoming popular because it increases efficiency relative to GEE when the working covariance structure is misspecified. Although shown to be advantageous in the literature, the impacts of covariates and imbalanced cluster sizes on the estimation performance of the QIF method in finite samples have not been studied. This cluster size variation causes QIFs estimating equations and GEE to be in separate classes when an exchangeable correlation structure is implemented, causing QIF and GEE to be incomparable in terms of efficiency. When utilizing this structure and the number of clusters is not large, we discuss how covariates and cluster size imbalance can cause QIF, rather than GEE, to produce estimates with the larger variability. This occurrence is mainly due to the empirical nature of weighting QIF employs, rather than differences in estimating equations classes. We demonstrate QIFs lost estimation precision through simulation studies covering a variety of general cluster randomized trial scenarios and compare QIF and GEE in the analysis of data from a cluster randomized trial.

A bias‐corrected covariance estimate for improved inference with quadratic inference functions

The method of quadratic inference functions (QIF) is an increasingly popular method for the analysis of correlated data because of its multiple advantages over generalized estimating equations (GEE). One advantage is that it is more efficient for parameter estimation when the working covariance structure for the data is misspecified. In the QIF literature, the asymptotic covariance formula is used to obtain standard errors. We show that in small to moderately sized samples, these standard error estimates can be severely biased downward, therefore inflating test size and decreasing coverage probability. We propose adjustments to the asymptotic covariance formula that eliminate finite-sample biases and, as shown via simulation, lead to substantial improvements in standard error estimates, inference, and coverage. The proposed method is illustrated in application to a cluster randomized trial and a longitudinal study. Furthermore, QIF and GEE are contrasted via simulation and these applications.

Non-invasive brain stimulation and robot-assisted gait training after incomplete spinal cord injury: A randomized pilot study

BACKGROUND Locomotor training with a robot-assisted gait orthosis (LT-RGO) and transcranial direct current stimulation (tDCS) are interventions that can significantly enhance motor performance after spinal cord injury (SCI). No studies have investigated whether combining these interventions enhances lower extremity motor function following SCI. OBJECTIVE Determine whether active tDCS paired with LT-RGO improves lower extremity motor function more than a sham condition, in subjects with motor incomplete SCI. METHODS Fifteen adults with SCI received 36 sessions of either active (n = 9) or sham (n = 6) tDCS (20 minutes) preceding LT-RGO (1 hour). Outcome measures included manual muscle testing (MMT; primary outcome measure); 6-Minute Walk Test (6MinWT); 10-Meter Walk Test (10MWT); Timed Up and Go Test (TUG); Berg Balance Scale (BBS); and Spinal Cord Independence Measure-III (SCIM-III). RESULTS MMT showed significant improvements after active tDCS, with the most pronounced improvement in the right lower extremity. 10MWT, 6MinWT, and BBS showed improvement for both groups. TUG and SCIM-III showed improvement only for the sham tDCS group. CONCLUSION Pairing tDCS with LT-RGO can improve lower extremity motor function more than LT-RGO alone. Future research with a larger sample size is recommended to determine longer-term effects on motor function and activities of daily living.

A covariance correction that accounts for correlation estimation to improve finite-sample inference with generalized estimating equations: a study on its applicability with structured correlation matrices

ABSTRACT When generalized estimating equations (GEEs) incorporate an unstructured working correlation matrix, the variances of regression parameter estimates can inflate due to the estimation of the correlation parameters. In previous work, an approximation for this inflation that results in a corrected version of the sandwich formula for the covariance matrix of regression parameter estimates was derived. Use of this correction for correlation structure selection also reduces the over-selection of the unstructured working correlation matrix. In this manuscript, we conduct a simulation study to demonstrate that an increase in variances of regression parameter estimates can occur when GEE incorporates structured working correlation matrices as well. Correspondingly, we show the ability of the corrected version of the sandwich formula to improve the validity of inference and correlation structure selection. We also study the relative influences of two popular corrections to a different source of bias in the empirical sandwich covariance estimator.

Sex differences in the subjective effects of oral Δ9-THC in cannabis users.

ABSTRACT Previous studies suggest that there are sex differences in endocannabinoid function and the response to exogenous cannabinoids, though data from clinical studies comparing acute cannabinoid effects in men and women under controlled laboratory conditions are limited. To further explore these potential differences, data from 30 cannabis users (N = 18 M, 12 F) who completed previous &Dgr;9‐tetrahydrocannabinol (&Dgr;9‐THC) discrimination studies were combined for this retrospective analysis. In each study, subjects learned to discriminate between oral &Dgr;9‐THC and placebo and then received a range of &Dgr;9‐THC doses (0, 5, 15 and a “high” dose of either 25 or 30 mg). Responses on a drug‐discrimination task, subjective effects questionnaire, psychomotor performance tasks, and physiological measures were assessed. &Dgr;9‐THC dose‐dependently increased drug‐appropriate responding, ratings on “positive” Visual Analog Scale (VAS) items (e.g., good effects, like drug, take again), and items related to intoxication (e.g., high, stoned). &Dgr;9‐THC also dose‐dependently impaired performance on psychomotor tasks and elevated heart rate. Sex differences on VAS items emerged as a function of dose. Women exhibited significantly greater subjective responses to oral drug administration than men at the 5 mg &Dgr;9‐THC dose, whereas men were more sensitive to the subjective effects of the 15 mg dose of &Dgr;9‐THC than women. These results demonstrate dose‐dependent separation in the subjective response to oral &Dgr;9‐THC administration by sex, which might contribute to the differential development of problematic cannabis use. HighlightsRetrospective analysis of drug discrimination data was conducted to examine sex differences in acute effects of &Dgr;9‐THC.Subjects received a range of &Dgr;9‐THC doses (0, 5, 15 and a “high” dose of either 25 or 30 mg).Drug effects were assessed using drug discrimination, subjective effects, psychomotor performance, and physiological measures.Dose‐dependent sex differences were found.Observed sex differences might contribute to the differential development of dependence in men and women.

On small‐sample inference in group randomized trials with binary outcomes and cluster‐level covariates

Group randomized trials (GRTs) randomize groups, or clusters, of people to intervention or control arms. To test for the effectiveness of the intervention when subject-level outcomes are binary, and while fitting a marginal model that adjusts for cluster-level covariates and utilizes a logistic link, we develop a pseudo-Wald statistic to improve inference. Alternative Wald statistics could employ bias-corrected empirical sandwich standard error estimates, which have received limited attention in the GRT literature despite their broad utility and applicability in our settings of interest. The test could also be carried out using popular approaches based upon cluster-level summary outcomes. A simulation study covering a variety of realistic GRT settings is used to compare the accuracy of these methods in terms of producing nominal test sizes. Tests based upon the pseudo-Wald statistic and a cluster-level summary approach utilizing the natural log of observed cluster-level odds worked best. Due to weighting, some popular cluster-level summary approaches were found to lead to invalid inference in many settings. Finally, although use of bias-corrected empirical sandwich standard error estimates did not consistently result in nominal sizes, they did work well, thus supporting the applicability of marginal models in GRT settings.