11 research outputs found
Robust Variance Estimation for Covariate-Adjusted Unconditional Treatment Effect in Randomized Clinical Trials with Binary Outcomes
To improve precision of estimation and power of testing hypothesis for an
unconditional treatment effect in randomized clinical trials with binary
outcomes, researchers and regulatory agencies recommend using g-computation as
a reliable method of covariate adjustment. However, the practical application
of g-computation is hindered by the lack of an explicit robust variance formula
that can be used for different unconditional treatment effects of interest. To
fill this gap, we provide explicit and robust variance estimators for
g-computation estimators and demonstrate through simulations that the variance
estimators can be reliably applied in practice
A General Form of Covariate Adjustment in Randomized Clinical Trials
In randomized clinical trials, adjusting for baseline covariates has been
advocated as a way to improve credibility and efficiency for demonstrating and
quantifying treatment effects. This article studies the augmented inverse
propensity weighted (AIPW) estimator, which is a general form of covariate
adjustment that includes approaches using linear and generalized linear models
and machine learning models. Under covariate-adaptive randomization, we
establish a general theorem that shows a complete picture about the asymptotic
normality, efficiency gain, and applicability of AIPW estimators. Based on the
general theorem, we provide insights on the conditions for guaranteed
efficiency gain and universal applicability under different randomization
schemes, which also motivate a joint calibration strategy using some
constructed covariates after applying AIPW. We illustrate the application of
the general theorem with two examples, the generalized linear model and the
machine learning model. We provide the first theoretical justification of using
machine learning methods with dependent data under covariate-adaptive
randomization. Our methods are implemented in the R package RobinCar
AutoPrep: An Automatic Preprocessing Framework for In-the-Wild Speech Data
Recently, the utilization of extensive open-sourced text data has
significantly advanced the performance of text-based large language models
(LLMs). However, the use of in-the-wild large-scale speech data in the speech
technology community remains constrained. One reason for this limitation is
that a considerable amount of the publicly available speech data is compromised
by background noise, speech overlapping, lack of speech segmentation
information, missing speaker labels, and incomplete transcriptions, which can
largely hinder their usefulness. On the other hand, human annotation of speech
data is both time-consuming and costly. To address this issue, we introduce an
automatic in-the-wild speech data preprocessing framework (AutoPrep) in this
paper, which is designed to enhance speech quality, generate speaker labels,
and produce transcriptions automatically. The proposed AutoPrep framework
comprises six components: speech enhancement, speech segmentation, speaker
clustering, target speech extraction, quality filtering and automatic speech
recognition. Experiments conducted on the open-sourced WenetSpeech and our
self-collected AutoPrepWild corpora demonstrate that the proposed AutoPrep
framework can generate preprocessed data with similar DNSMOS and PDNSMOS scores
compared to several open-sourced TTS datasets. The corresponding TTS system can
achieve up to 0.68 in-domain speaker similarity
Covariate-Adjusted Log-Rank Test: Guaranteed Efficiency Gain and Universal Applicability
Nonparametric covariate adjustment is considered for log-rank type tests of
treatment effect with right-censored time-to-event data from clinical trials
applying covariate-adaptive randomization. Our proposed covariate-adjusted
log-rank test has a simple explicit formula and a guaranteed efficiency gain
over the unadjusted test. We also show that our proposed test achieves
universal applicability in the sense that the same formula of test can be
universally applied to simple randomization and all commonly used
covariate-adaptive randomization schemes such as the stratified permuted block
and Pocock and Simon's minimization, which is not a property enjoyed by the
unadjusted log-rank test. Our method is supported by novel asymptotic theory
and empirical results for type I error and power of tests
Toward Better Practice of Covariate Adjustment in Analyzing Randomized Clinical Trials
In randomized clinical trials, adjustments for baseline covariates at both
design and analysis stages are highly encouraged by regulatory agencies. A
recent trend is to use a model-assisted approach for covariate adjustment to
gain credibility and efficiency while producing asymptotically valid inference
even when the model is incorrect. In this article we present three
considerations for better practice when model-assisted inference is applied to
adjust for covariates under simple or covariate-adaptive randomized trials: (1)
guaranteed efficiency gain: a model-assisted method should often gain but never
hurt efficiency; (2) wide applicability: a valid procedure should be
applicable, and preferably universally applicable, to all commonly used
randomization schemes; (3) robust standard error: variance estimation should be
robust to model misspecification and heteroscedasticity. To achieve these, we
recommend a model-assisted estimator under an analysis of heterogeneous
covariance working model including all covariates utilized in randomization.
Our conclusions are based on an asymptotic theory that provides a clear picture
of how covariate-adaptive randomization and regression adjustment alter
statistical efficiency. Our theory is more general than the existing ones in
terms of studying arbitrary functions of response means (including linear
contrasts, ratios, and odds ratios), multiple arms, guaranteed efficiency gain,
optimality, and universal applicability.Isaact Newton Trus
Testing for Treatment Effect Twice Using Internal and External Controls in Clinical Trials
Leveraging external controls -- relevant individual patient data under
control from external trials or real-world data -- has the potential to reduce
the cost of randomized controlled trials (RCTs) while increasing the proportion
of trial patients given access to novel treatments. However, due to lack of
randomization, RCT patients and external controls may differ with respect to
covariates that may or may not have been measured. Hence, after controlling for
measured covariates, for instance by matching, testing for treatment effect
using external controls may still be subject to unmeasured biases. In this
paper, we propose a sensitivity analysis approach to quantify the magnitude of
unmeasured bias that would be needed to alter the study conclusion that
presumed no unmeasured biases are introduced by employing external controls.
Whether leveraging external controls increases power or not depends on the
interplay between sample sizes and the magnitude of treatment effect and
unmeasured biases, which may be difficult to anticipate. This motivates a
combined testing procedure that performs two highly correlated analyses, one
with and one without external controls, with a small correction for multiple
testing using the joint distribution of the two test statistics. The combined
test provides a new method of sensitivity analysis designed for data fusion
problems, which anchors at the unbiased analysis based on RCT only and spends a
small proportion of the type I error to also test using the external controls.
In this way, if leveraging external controls increases power, the power gain
compared to the analysis based on RCT only can be substantial; if not, the
power loss is small. The proposed method is evaluated in theory and power
calculations, and applied to a real trial