Biomedical subjects

C Shivakumar

Publications and source records attributed to C Shivakumar.

4 recordsLinked to original sources

PubMed1999

Testing differences in response trends across a normalized time domain.

A two-stage mixed model analysis of repeated measurement calculates participant-specific regression slopes relating change in available measurements to associated assessment times, and then the difference between mean regression slopes in two or more treatment groups is tested for significance against the within-groups variability of the participant-specific regression slopes. It is not necessary that all participants have the same schedule or number of repeated measurements. However, when dropouts are included in an "intent to treat" analysis, the shortened treatment exposures for the dropouts substantially increase variability and reduce power of tests for differences in rates of change. Previous work has suggested that normalizing the time scale to unit length for all participants prior to fitting the individual regression equations materially reduces the power attenuation produced by dropouts. This article reports a more detailed evaluation of the enhanced robustness against dropouts that is achieved by rescaling the time dimension. The robust analysis is recognized to be equivalent to weighting ordinary least squares regression on the original time scale by the duration of treatment for each participant. Slope coefficients calculated across a shortened time span for dropouts are less stable, so they are given less weight in defining the (linear) treatment effects.

Bias↗

PubMed1999

Problematic formulations of SAS PROC.MIXED models for repeated measurements.

The work reported in this article was undertaken to evaluate the utility of SAS PROC.MIXED for testing hypotheses concerning GROUP and TIME x GROUP effects in repeated measurements designs with drop-outs. If dropouts are not completely at random, covariate control over informative individual differences on which dropout data patterns depend is widely recognized to be important. However, the inclusion of baseline scores and time-in-study as between-subject covariates in an otherwise well formulated SAS PROC.MIXED model resulted in inadequate control over type I error in simulated data with or without drop-outs present. The inadequate model formulations and resulting deviant test sizes are presented here as a warning for others who might be guided by the same information sources to employ similar model specifications when analyzing data from actual clinical trials. It is important that the complete model specification be provided in detail when reporting applications of the general linear mixed-model procedure. A single random-coefficients model produced appropriate test sizes, but it provided inferior power when informative covariates were added in the attempt to adjust for dropouts. As an alternative, the incorporation of covariate controls in simpler two-stage endpoint or random regression analyses is documented to be effective in dealing with dropouts under specifiable conditions.

Biopharmaceutics↗

PubMed1998

Adjusting sample size for anticipated dropouts in clinical trials.

Statistical models for calculating sample sizes for controlled clinical trials often fail to take into account the negative impact that dropouts have on the power of intent-to-treat analyses. Empirically defined dropout correction coefficients are proposed to adjust sample sizes for endpoint analysis of variance (ANOVA) and analysis of covariance (ANCOVA) that have been initially calculated assuming complete data. The implications of type of analysis (change-score ANOVA or ANCOVA), correlational structure of the repeated measurements (compound symmetry or autoregressive), and percentage of dropouts (20% or 30%) are considered, together with other less influential design and data parameters. We recommend the use of ANCOVA to correct for baseline differences and for time-in-study if there is a nonspecific change across time. Given a realistic autoregressive (order 1) correlational structure for the repeated measurements and a proposed endpoint ANCOVA, the empirical results support the common practice of increasing calculated sample size by the anticipated number of dropouts. The previous rationale has been to retain a requisite number of "completers" on which to base statistical inferences. We believe the present results provide the first documentation of the relevance of that strategy for intent-to-treat analyses in which the incomplete data for dropouts must be included. Based on comparative power analyses, the strategy also seems appropriate for maintaining the power of mixed-model regression analyses, simple regression on a normalized time scale, and analyses of trends fitted to imputed scores for dropouts.

Clinical Trials as Topic↗

PubMed1995

The MDR1 downstream promoter contains sequence-specific binding sites for wild-type p53.

We have examined the interaction of the wild-type p53 protein with the downstream promoter of the human multidrug resistance gene-1 (MDR1). Our findings indicate that wild-type p53 inhibits reporter activity driven by the MDR1 downstream promoter (base pairs -189 to +133 relative to the major transcriptional initiation site) in a dose-dependent manner in cotransfection assays in the BHK and the Saos-2 cell lines. A 123 base-pair segment of DNA (-119 to +4 relative to the major transcriptional initiation site), a 193 base-pair segment (-189 to +4), and a 135 base-pair segment (-2 to +133) have been isolated from the MDR1 downstream promoter which, like the full promoter, are negatively controlled by wild-type p53. In addition, we show sequence-specific binding of wild-type p53 protein to the MDR1 downstream promoter. These in vitro results suggest that the presence of wild-type p53 negatively affects expression of the MDR1 gene product, p-glycoprotein, at the transcriptional level.

ATP Binding Cassette Transporter, Subfamily B, Mem↗