Biomedical subjects

Lurdes Y T Inoue

Publications and source records attributed to Lurdes Y T Inoue.

6 recordsLinked to original sources

PubMed2006

Cluster-based network model for time-course gene expression data.

We propose a model-based approach to unify clustering and network modeling using time-course gene expression data. Specifically, our approach uses a mixture model to cluster genes. Genes within the same cluster share a similar expression profile. The network is built over cluster-specific expression profiles using state-space models. We discuss the application of our model to simulated data as well as to time-course gene expression data arising from animal models on prostate cancer progression. The latter application shows that with a combined statistical/bioinformatics analyses, we are able to extract gene-to-gene relationships supported by the literature as well as new plausible relationships.

Animals↗

PubMed2006

Continuous Bayesian adaptive randomization based on event times with covariates.

In comparative clinical trials, the randomization probabilities may be unbalanced adaptively by utilizing the interim data available at each patient's entry time to favour the treatment or treatments having comparatively superior outcomes. This is ethically appealing because, on average, more patients are assigned to the more successful treatments. Consequently, physicians are more likely to enroll patients onto trials where the randomization is outcome-adaptive rather than balanced in the conventional manner. Outcome-adaptive methods based on a binary variable may be applied by reducing an event time to the indicator of the event's occurrence within a predetermined time interval. This results in a loss of information, however, since it ignores the censoring times of patients who have not experienced the event but whose evaluation interval is not complete. This paper proposes and compares exact and approximate Bayesian outcome-adaptive randomization procedures based on time-to-event outcomes. The procedures account for baseline prognostic covariates, and they may be applied continuously over the course of the trial. We illustrate these methods by application to a phase II selection trial in acute leukaemia. A simulation study in the context of this trial is presented.

Antineoplastic Agents↗

PubMed2005

Role of detection method in predicting breast cancer survival: analysis of randomized screening trials.

BACKGROUND: Screening mammography detects breast cancers earlier than those detected symptomatically, and so mammographically detected breast cancers tend to have better prognoses. The so-called stage shift that results from screen detection is subject to lead-time and length biases, and so earlier detection may not translate into longer survival. We used data from three large breast cancer screening trials--Health Insurance Plan (HIP) of New York and two Canadian National Breast Cancer Screening Studies (CNBSS)--to investigate survival benefits of breast cancer screening beyond stage shift. We also address whether method of detection is an independent prognostic factor in breast cancer. METHODS: The HIP trial randomly assigned approximately 62,000 women to screening and control groups. The two CNBSS trial cohorts CNBSS-1 and CNBSS-2 included a total of 44,970 women in the screening group and 44,961 in the control group. After adjusting for stage and other tumor characteristics in a Cox proportional hazards model, survival distributions were compared by method of breast cancer detection with both univariate and multivariable analyses. All P values are two-sided. RESULTS: Breast cancers detected by screening mammography had a shift in stage distribution to earlier stages (for HIP, P < .001; for CNBSS-1, P = .03; and for CNBSS-2, P < .001). After adjusting for tumor size, lymph node status, and disease stage in a Cox proportional hazards model, method of detection was a statistically significant independent predictor of disease-specific survival. Patients with interval cancers had a 53% (95% confidence interval [CI] = 17% to 100%) greater hazard of death from breast cancer than patients with screen-detected cancers, and patients with cancer in the control groups had a 36% (95% CI = 10% to 68%) greater hazard of death than patients with screen-detected cancer. CONCLUSION: There was an apparent survival benefit beyond stage shift for patients with screen-detected breast cancers compared with patients with breast cancers detected otherwise. Method of detection appears to be an important prognostic factor, even after adjusting for known tumor characteristics. This finding suggests that clinical trialists should routinely collect information about method of detection.

Breast Neoplasms↗

PubMed2004

Combining longitudinal studies of PSA.

Prostate-specific antigen (PSA) is a biomarker commonly used to screen for prostate cancer. Several studies have examined PSA growth rates prior to prostate cancer diagnosis. However, the resulting estimates are highly variable. In this article we propose a non-linear Bayesian hierarchical model to combine longitudinal data on PSA growth from three different studies. Our model enables novel investigations into patterns of PSA growth that were previously impossible due to sample size limitations. The goals of our analysis are twofold: first, to characterize growth rates of PSA accounting for differences when combining data from different studies; second, to investigate the impact of clinical covariates such as advanced disease and unfavorable histology on PSA growth rates.

Adult↗

PubMed2002

Adaptive decision making in a lymphocyte infusion trial.

We describe an adaptive Bayesian design for a clinical trial of an experimental treatment for patients with hematologic malignancies who initially received an allogeneic bone marrow transplant but subsequently suffered a disease recurrence. Treatment consists of up to two courses of targeted immunotherapy followed by allogeneic donor lymphocyte infusion. The immunotherapy is a necessary precursor to the lymphocyte infusion, but it may cause severe liver toxicity and is certain to cause a low white blood cell count and low platelets. The primary scientific goal is to determine the infusion time that has the highest probability of treatment success, defined as the event that the patient does not suffer severe toxicity and is alive with recovered white blood cell count 50 days from the start of therapy. The method is based on a parametric model accounting for toxicity, time to white blood cell recovery, and survival time. The design includes an algorithm for between-patient immunotherapy dose de-escalation based on the toxicity data and an adaptive randomization among five possible infusion times according to their most recent posterior success probabilities. A simulation study shows that the design reliably selects the best infusion time while randomizing greater proportions of patients to superior infusion times.

Algorithms↗

PubMed2002

Seamlessly expanding a randomized phase II trial to phase III.

A sequential Bayesian phase II/III design is proposed for comparative clinical trials. The design is based on both survival time and discrete early events that may be related to survival and assumes a parametric mixture model. Phase II involves a small number of centers. Patients are randomized between treatments throughout, and sequential decisions are based on predictive probabilities of concluding superiority of the experimental treatment. Whether to stop early, continue, or shift into phase III is assessed repeatedly in phase II. Phase III begins when additional institutions are incorporated into the ongoing phase II trial. Simulation studies in the context of a non-small-cell lung cancer trial indicate that the proposed method maintains overall size and power while usually requiring substantially smaller sample size and shorter trial duration when compared with conventional group-sequential phase III designs.

Adenoviridae↗