Statistical analysis of two arm randomized pre-post design with one post-treatment measurement

Randomized pre-post designs, with outcomes measured at baseline and follow-ups, have been commonly used to compare the clinical effectiveness of two competing treatments. There are vast, but often conflicting, amount of information in current literature about the best analytic methods for pre-post design. It is challenging for applied researchers to make an informed choice. We discuss six methods commonly used in literature: one way analysis of variance (ANOVA), analysis of covariance main effect and interaction models on post-treatment measurement (ANCOVA I and II), ANOVA on change score between baseline and post-treatment measurements, repeated measures and constrained repeated measures models (cRM) on baseline and post-treatment measurements as joint outcomes. We review a number of study endpoints in pre-post designs and identify the difference in post-treatment measurement as the common treatment effect that all six methods target. We delineate the underlying differences and links between these competing methods in homogeneous and heterogeneous study population. We demonstrate that ANCOVA and cRM outperform other alternatives because their treatment effect estimators have the smallest variances. cRM has comparable performance to ANCOVA I main effect model in homogeneous scenario and to ANCOVA II interaction model in heterogeneous scenario. In spite of that, ANCOVA has several advantages over cRM, including treating baseline measurement as covariate because it is not an outcome by definition, the convenience of incorporating other baseline variables and handling complex heteroscedasticity patterns in a linear regression framework.Comment: 38 pages, 2 figures, 3 table

Instrumental Variable and Propensity Score Methods for Bias Adjustment in Non-Linear Models

Unmeasured confounding is a common concern when clinical and health services researchers attempt to estimate a treatment effect using observational data or randomized studies with non-perfect compliance. To address this concern, instrumental variable (IV) methods, such as two-stage predictor substitution (2SPS) and two-stage residual inclusion (2SRI), have been widely adopted. In many clinical studies of binary and survival outcomes, 2SRI has been accepted as the method of choice over 2SPS but a compelling theoretical rationale has not been postulated. First, We directly compare the bias in the causal hazard ratio estimated by these two IV methods. Under the potential outcome and principal stratification framework, we derive closed form solutions for asymptotic bias in estimating the causal hazard ratio among compliers for both the 2SPS and 2SRI methods by assuming survival time follows the Weibull distribution with random censoring. When there is no unmeasured confounding and no always takers, our analytic results show that 2SRI is generally asymptotically unbiased but 2SPS is not. However, when there is substantial unmeasured confounding, 2SPS performs better than 2SRI with respect to bias under certain scenarios. We use extensive simulation studies to confirm the analytic results from our closed-form solutions. We apply these two methods to prostate cancer treatment data from SEER-Medicare and compare these 2SRI and 2SPS estimates to results from two published randomized trials. Next, we propose a novel two-stage structural modeling framework to understanding the bias in estimating the conditional treatment effect for 2SPS and 2SRI when the outcome is binary, count or time to event. Under this framework, we demonstrate that the bias in 2SPS and 2SRI estimators can be reframed to mirror the problem of omitted variables in non-linear models. We demonstrate that only when the influence of the unmeasured covariates on the treatment is proportional to their effect on the outcome that 2SRI estimates are generally unbiased for logit and Cox models. We also propose a novel dissimilarity metric to quantify the difference in these effects and demonstrate that with increasing dissimilarity, the bias of 2SRI increases in magnitude. We investigate these methods using simulation studies and data from an observational study of perinatal care for premature infants. Last, we extend Heller and Venkatraman\u27s covariate adjusted conditional log rank test by using the propensity score method. We introduce the propensity score to balance the distribution of covariates among treatment groups and reduce the dimensionality of covariates to fit the conditional log rank test. We perform the simulation to assess the performance of this new method and covariates adjusted Cox model and score test

Statistical analysis of two arm randomized pre-post designs with one post-treatment measurement

BACKGROUND: Randomized pre-post designs, with outcomes measured at baseline and after treatment, have been commonly used to compare the clinical effectiveness of two competing treatments. There are vast, but often conflicting, amount of information in current literature about the best analytic methods for pre-post designs. It is challenging for applied researchers to make an informed choice. METHODS: We discuss six methods commonly used in literature: one way analysis of variance ( ANOVA ), analysis of covariance main effect and interaction models on the post-treatment score ( ANCOVAI and ANCOVAII ), ANOVA on the change score between the baseline and post-treatment scores ( ANOVA-Change ), repeated measures ( RM ) and constrained repeated measures ( cRM ) models on the baseline and post-treatment scores as joint outcomes. We review a number of study endpoints in randomized pre-post designs and identify the mean difference in the post-treatment score as the common treatment effect that all six methods target. We delineate the underlying differences and connections between these competing methods in homogeneous and heterogeneous study populations. RESULTS: ANCOVA and cRM outperform other alternative methods because their treatment effect estimators have the smallest variances. cRM has comparable performance to ANCOVAI in the homogeneous scenario and to ANCOVAII in the heterogeneous scenario. In spite of that, ANCOVA has several advantages over cRM: i) the baseline score is adjusted as covariate because it is not an outcome by definition; ii) it is very convenient to incorporate other baseline variables and easy to handle complex heteroscedasticity patterns in a linear regression framework. CONCLUSIONS: ANCOVA is a simple and the most efficient approach for analyzing pre-post randomized designs

BaFe2Se2O as an Iron-Based Mott Insulator with Antiferromagnetic Order

A new compound with a quasi-two-dimensional array of FeSe3O tetrahedra and an orthorombic structure, namely BaFe2Se2O, has been successfully fabricated. Experimental results show that this compound is an insulator and has an antiferromagnetic (AF) transition at 240 K. Band structure calculation reveals the narrowing of Fe 3d bands near the Fermi energy, which leads to the localization of magnetism and the Mott insulating behavior. The large distances between the Fe atoms perhaps are responsible for the characters. Linear response calculation further indicates a strong in-plane AF exchange $J$, this can account for the enhanced magnetic susceptibility (which has a maximum at about 450 K) above the Neel temperature.Comment: submitted to PRL on 2 May 2012, resubmitted to PRB on 31 May 2012, and accepted by PRB on 5 July 201

Theory of high energy features in angle-resolved photo-emission spectra of hole-doped cuprates

The recent angle-resolved photoemission measurements performed up to binding energies of the order of 1eV reveals a very robust feature: the nodal quasi-particle dispersion breaks up around 0.3-0.4eV and reappears around 0.6-0.8eV. The intensity map in the energy-momentum space shows a waterfall like feature between these two energy scales. We argue and numerically demonstrate that these experimental features follow naturally from the strong correlation effects built in the familiar t-J model, and reflect the connection between the fermi level and the lower Hubbard band. The results were obtained by a mean field theory that effectively projects electrons by quantum interference between two bands of fermions instead of binding slave particles.Comment: 5 pages 2 fig