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Biomedical subjects

Devan V Mehrotra

Publications and source records attributed to Devan V Mehrotra.

10 recordsLinked to original sources
PubMed2007

Analysis of antiretroviral immunotherapy trials with potentially non-normal and incomplete longitudinal data.

For many HIV-infected patients, use of antiretroviral therapy (ART) results in a sustained suppression of plasma viral load to undetectable levels. However, due to lack of antigenic stimulation, this may also result in a gradual loss of cell-mediated immune (CMI) responses that help control HIV infection. In concept, augmenting ART with periodic administrations of an HIV vaccine that boosts CMI responses could enhance control of viral replication. Researchers are designing 'antiretroviral immunotherapy' (ARI) trials to test this hypothesis. In a typical ARI trial, HIV-infected patients with sustained viral suppression will receive inoculations of an experimental HIV vaccine or a placebo, and subsequently stop taking their antiretroviral drugs. The goal is to assess whether plasma viral loads during the ART interruption phase are generally lower in the vaccine group. Assessment of a vaccine effect will be challenging if some subjects resume ART or drop out before the end of the treatment interruption phase. To tackle this 'missing' data problem and potential non-normality of the viral loads in ARI trials, we propose a two-step approach: multiple imputation of the missing values followed by use of the Wei-Lachin method with Wilcoxon scores. We use a numerical example and extensive simulations to illustrate the robustness and power advantages of our proposed method compared with other methods for incomplete longitudinal data, including REML, weighted GEE, last observation carried forward, and 'worst-rank' methods. Our proposed method is general enough for the robust analysis of longitudinal data in other therapeutic areas as well.

AIDS Vaccines↗
PubMed2006

Analysis of incomplete longitudinal binary data using multiple imputation.

We propose a propensity score-based multiple imputation (MI) method to tackle incomplete missing data resulting from drop-outs and/or intermittent skipped visits in longitudinal clinical trials with binary responses. The estimation and inferential properties of the proposed method are contrasted via simulation with those of the commonly used complete-case (CC) and generalized estimating equations (GEE) methods. Three key results are noted. First, if data are missing completely at random, MI can be notably more efficient than the CC and GEE methods. Second, with small samples, GEE often fails due to 'convergence problems', but MI is free of that problem. Finally, if the data are missing at random, while the CC and GEE methods yield results with moderate to large bias, MI generally yields results with negligible bias. A numerical example with real data is provided for illustration.

Clinical Trials as Topic↗
PubMed2006

Adaptive vs. group sequential self-designing trials.

This is a discussion of the paper. 'Repeated Confidence Intervals in Self-Designing Clinical Trials and Switching between Non-Inferiority and Superinferiority' by Joachim Hartung and Guido Knapp, appearing in this special issue on adaptive designs.

Biometry↗
PubMed2006

A comparison of eight methods for the dual-endpoint evaluation of efficacy in a proof-of-concept HIV vaccine trial.

To support the design of the world's first proof-of-concept (POC) efficacy trial of a cell-mediated immunity-based HIV vaccine, we evaluate eight methods for testing the composite null hypothesis of no-vaccine effect on either the incidence of HIV infection or the viral load set point among those infected, relative to placebo. The first two methods use a single test applied to the actual values or ranks of a burden-of-illness (BOI) outcome that combines the infection and viral load endpoints. The other six methods combine separate tests for the two endpoints using unweighted or weighted versions of the two-part z, Simes', and Fisher's methods. Based on extensive simulations that were used to design the landmark POC trial, the BOI methods are shown to have generally low power for rejecting the composite null hypothesis (and hence advancing the vaccine to a subsequent large-scale efficacy trial). The unweighted Simes' and Fisher's combination methods perform best overall. Importantly, this conclusion holds even after the test for the viral load component is adjusted for bias that can be introduced by conditioning on a postrandomization event (HIV infection). The adjustment is derived using a selection bias model based on the principal stratification framework of causal inference.

AIDS Vaccines↗
PubMed2005

Cross-reactivity of anti-HIV-1 T cell immune responses among the major HIV-1 clades in HIV-1-positive individuals from 4 continents.

BACKGROUND: The genetic diversity of human immunodeficiency virus type 1 (HIV-1) raises the question of whether vaccines that include a component to elicit antiviral T cell immunity based on a single viral genetic clade could provide cellular immune protection against divergent HIV-1 clades. Therefore, we quantified the cross-clade reactivity, among unvaccinated individuals, of anti-HIV-1 T cell responses to the infecting HIV-1 clade relative to other major circulating clades. METHODS: Cellular immune responses to HIV-1 clades A, B, and C were compared by standardized interferon- gamma enzyme-linked immunospot assays among 250 unvaccinated individuals, infected with diverse HIV-1 clades, from Brazil, Malawi, South Africa, Thailand, and the United States. Cross-clade reactivity was evaluated by use of the ratio of responses to heterologous versus homologous (infecting) clades of HIV-1. RESULTS: Cellular immune responses were predominantly focused on viral Gag and Nef proteins. Cross-clade reactivity of cellular immune responses to HIV-1 clade A, B, and C proteins was substantial for Nef proteins (ratio, 0.97 [95% confidence interval, 0.89-1.05]) and lower for Gag proteins (ratio, 0.67 [95% confidence interval, 0.62-0.73]). The difference in cross-clade reactivity to Nef and Gag proteins was significant (P<.0001). CONCLUSIONS: Cross-clade reactivity of cellular immune responses can be substantial but varies by viral protein.

Adult↗
PubMed2004

Use of the false discovery rate for evaluating clinical safety data.

Clinical adverse experience (AE) data are routinely evaluated using between group P values for every AE encountered within each of several body systems. If the P values are reported and interpreted without multiplicity considerations, there is a potential for an excess of false positive findings. Procedures based on confidence interval estimates of treatment effects have the same potential for false positive findings as P value methods. Excess false positive findings can needlessly complicate the safety profile of a safe drug or vaccine. Accordingly, we propose a novel method for addressing multiplicity in the evaluation of adverse experience data arising in clinical trial settings. The method involves a two-step application of adjusted P values based on the Benjamini and Hochberg false discovery rate (FDR). Data from three moderate to large vaccine trials are used to illustrate our proposed 'Double FDR' approach, and to reinforce the potential impact of failing to account for multiplicity. This work was in collaboration with the late Professor John W. Tukey who coined the term 'Double FDR'.

Chickenpox Vaccine↗
PubMed2003

Stratified experiments reexamined with emphasis on multicenter trials.

In many stratified experiments the researcher fixes the total sample size but either cannot or does not exert control over the sample size per stratum. A classic example is a randomized, two-treatment, multicenter clinical trial-the total sample sizes per treatment group are fixed by design but the sample sizes per center are allowed to vary. Standard analyses of continuous data from such trials fail to recognize the random nature of the stratum sizes. We show that this can lead to biased inference and estimation for both so-called "type II" (unequal weighting of strata) as well as "type III" (equal weighting of strata) analyses. We propose an alternative method of analysis that explicitly accounts for the randomness of the stratum sizes and illustrate its validity using simulations. A reanalysis of published data from a 29-center clinical trial serves to reinforce the key points.

Data Interpretation, Statistical↗
PubMed2003

A cautionary note on exact unconditional inference for a difference between two independent binomial proportions.

Fisher's exact test for comparing response proportions in a randomized experiment can be overly conservative when the group sizes are small or when the response proportions are close to zero or one. This is primarily because the null distribution of the test statistic becomes too discrete, a partial consequence of the inference being conditional on the total number of responders. Accordingly, exact unconditional procedures have gained in popularity, on the premise that power will increase because the null distribution of the test statistic will presumably be less discrete. However, we caution researchers that a poor choice of test statistic for exact unconditional inference can actually result in a substantially less powerful analysis than Fisher's conditional test. To illustrate, we study a real example and provide exact test size and power results for several competing tests, for both balanced and unbalanced designs. Our results reveal that Fisher's test generally outperforms exact unconditional tests based on using as the test statistic either the observed difference in proportions, or the observed difference divided by its estimated standard error under the alternative hypothesis, the latter for unbalanced designs only. On the other hand, the exact unconditional test based on the observed difference divided by its estimated standard error under the null hypothesis (score statistic) outperforms Fisher's test, and is recommended. Boschloo's test, in which the p-value from Fisher's test is used as the test statistic in an exact unconditional test, is uniformly more powerful than Fisher's test, and is also recommended.

Humans↗