The clinical burden of prostate cancer in Canada: forecasts from the Montreal Prostate Cancer Model.
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OBJECTIVES: The incidence of prostate cancer is increasing, as is the number of diagnostic and therapeutic interventions to manage this disease. We developed a Markov state-transition model--the Montreal Prostate Cancer Model--for improved forecasting of the health care requirements and outcomes associated with prostate cancer. We then validated the model by comparing its forecasted outcomes with published observations for various cohorts of men. METHODS: We combined aggregate data on the age-specific incidence of prostate cancer, the distribution of diagnosed tumours according to patient age, clinical stage and tumour grade, initial treatment, treatment complications, and progression rates to metastatic disease and death. Five treatments were considered: prostatectomy, radiation therapy, hormonal therapies, combination therapies and watchful waiting. The resulting model was used to calculate age-, stage-, grade- and treatment-specific clinical outcomes such as expected age at prostate cancer diagnosis and death, and metastasis-free, disease-specific and overall survival. RESULTS: We compared the model's forecasts with available cohort data from the Surveillance, Epidemiology and End Results (SEER) Program, based on over 59,000 cases of localized prostate cancer. Among the SEER cases, the 10-year disease-specific survival rates following prostatectomy for tumour grades 1, 2 and 3 were 98%, 91% and 76% respectively, as compared with the model's estimates of 96%, 92% and 84%. We also compared the model's forecasts with the grade-specific survival among patients from the Connecticut Tumor Registry (CTR). The 10-year disease-specific survival among the CTR cases for grades 1, 2 and 3 were 91%, 76% and 54%, as compared with the model's estimates of 91%, 73% and 37%. INTERPRETATION: The Montreal Prostate Cancer Model can be used to support health policy decision-making for the management of prostate cancer. The model can also be used to forecast clinical outcomes for individual men who have prostate cancer or are at risk of the disease. (+info)
Using database matches with for HMMGene for automated gene detection in Drosophila.
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The application of the gene finder HMMGene to the Adh region of the Drosophila melanogaster is described, and the prediction results are analyzed. HMMGene is based on a probabilistic model called a hidden Markov model, and the probabilistic framework facilitates the inclusion of database matches of varying degrees of certainty. It is shown that database matches clearly improve the performance of the gene finder. For instance, the sensitivity for coding exons predicted with both ends correct grows from 62% to 70% on a high-quality test set, when matches to proteins, cDNAs, repeats, and transposons are included. The specificity drops more than the sensitivity increases when ESTs are used. This is due to the high noise level in EST matches, and it is discussed in more detail why this is and how it might be improved. (+info)
Genie--gene finding in Drosophila melanogaster.
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A hidden Markov model-based gene-finding system called Genie was applied to the genomic Adh region in Drosophila melanogaster as a part of the Genome Annotation Assessment Project (GASP). Predictions from three versions of the Genie gene-finding system were submitted, one based on statistical properties of coding genes, a second included EST alignment information, and a third that integrated protein sequence homology information. All three programs were trained on the provided Drosophila training data. In addition, promoter assignments from an integrated neural network were submitted. The gene assignments overlapped >90% of the 222 annotated genes and 26 possibly novel genes were predicted, of which some might be overpredictions. The system correctly identified the exon boundaries of 70% of the exons in cDNA-confirmed genes and 77% of the exons with the addition of EST sequence alignments. The best of the three Genie submissions predicted 19 of the annotated 43 gene structures entirely correct (44%). In the promoter category, only 30% of the transcription start sites could be detected, but by integrating this program as a sensor into Genie the false-positive rate could be dropped to 1/16,786 (0.006%). The results of the experiment on the long contiguous genomic sequence revealed some problems concerning gene assembly in Genie. The results were used to improve the system. We show that Genie is a robust hidden Markov model system that allows for a generalized integration of information from different sources such as signal sensors (splice sites, start codon, etc.), content sensors (exons, introns, intergenic) and alignments of mRNA, EST, and peptide sequences. The assessment showed that Genie could effectively be used for the annotation of complete genomes from higher organisms. (+info)
The variation of the probability of a son within and across couples.
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It is suggested that there is a flaw in the currently accepted account of the variation of P, the probability of a boy, within and across couples. It was previously suggested that P has a mean (for Caucasian couples) of approximately 0.514 with an SD of approximately 0.05 across couples: and that the variation within couples is rather less. Grounds are offered here for suspecting that this formulation underestimates both SDs by a factor of as much as 4. It is suggested that in estimating these sources of variation, earlier workers did not consider the possibility that within-couple variation might be random and substantial. In view of the established epidemiology of human sex ratios, it now seems likely that such variation exists, and that there is a substantial measure of counterbalancing across-couple variation. (+info)
Cost-effectiveness modeling of Dermagraft for the treatment of diabetic foot ulcers in the french context.
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To assess the cost-effectiveness of Dermagraft(R) (human dermal replacement) in the treatment of the diabetic foot ulcer, compared to standard treatment. A Markov model was developed, to simulate, over a 52-week period, the health status of a cohort of 100 patients with a diabetic foot ulcer treated either with conventional therapy or with Dermagraft(R). The considered health states were: healed, same site recurrence, unhealed not infected, cellulitis, osteomyelitis, amputation and death. Each week, the patient may progress among states according to a set of transition probabilities directly derived from the original clinical trial conducted in the USA. The cost of each health state was estimated by a Delphi panel of French diabetologists (direct costs only, valuated from a societal perspective). A sensitivity analysis was performed. The total number of healed ulcers included first ulcers healed (76.38% for Dermagraft(R) vs. 69.35% for standard treatment; median time to heal is 14-15 weeks for Dermagraft(R) compared with 28-29 weeks for standard treatment) plus recurrences which are subsequently healed within the 52-week period (14.29 for Dermagraft(R) vs. 25.09 for standard treatment; median time to heal is 3-4 weeks for Dermagraft(R) compared with 5-6 weeks for standard treatment). The average expected cost per treated patient (C/E) using standard treatment for the considered 52-week period is 47,418 FF vs. 54,384 FF for Dermagraft(R) (including 18,200 FF for Dermagraft(R) acquisition and 36,184 FF for standard treatment). Because Dermagraft(R) heals more ulcers within 52 weeks, the average cost per healed ulcer is lower (53,522 FF vs. 56,687 FF for standard treatment). The incremental cost-effectiveness ratio of Dermagraft(R) (DeltaC/DeltaE) equals 38,784 FF, indicating the extra investment that the decision-maker has to accept for an additional ulcer healed with Dermagraft(R) compared with conventional treatment. (+info)
Cost-effectiveness analysis of therapy for symptomatic carotid occlusion: PET screening before selective extracranial-to-intracranial bypass versus medical treatment.
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The St. Louis Carotid Occlusion Study (STLCOS) demonstrated that increased cerebral oxygen extraction fraction (OEF) detected by PET scanning predicted stroke in patients with symptomatic carotid occlusion. Consequently, a trial of extracranial-to-intracranial (EC/IC) arterial bypass for these patients was proposed. The purpose of this study was to examine the cost-effectiveness of using PET in identifying candidates for EC/IC bypass. METHODS: A Markov model was created to estimate the cost-effectiveness of PET screening and treating a cohort of 45 symptomatic patients with carotid occlusion. The primary outcome was incremental cost for PET screening and EC/IC bypass (if OEF was elevated) per incremental quality-adjusted life year (QALY) saved. Rates of stroke and death with surgical and medical treatment were obtained from EC/IC Bypass Trial and STLCOS data. Costs were estimated from the literature. Sensitivity analyses were performed for all assumed variables, including the PET OEF threshold used to select patients for surgery. RESULTS: In the base case, PET screening of the cohort followed by EC/IC bypass on 36 of the 45 patients yielded 23.2 additional QALYs at a cost of $20,000 per QALY, compared with medical therapy alone. A more specific PET threshold, which identified 18 surgical candidates, gained 22.6 QALYs at less cost than medical therapy alone. The results were sensitive to the perioperative stroke rate and the stroke risk reduction conferred by EC/IC bypass surgery. CONCLUSION: If postoperative stroke rates are similar to stroke rates observed in the EC/IC Bypass Trial, EC/IC bypass will be cost-effective in patients with symptomatic carotid occlusion who have increased OEF. A clinical trial of medical therapy versus PET followed by EC/IC bypass (if OEF is elevated) is warranted. (+info)
Aedes aegypti in Tahiti and Moorea (French Polynesia): isoenzyme differentiation in the mosquito population according to human population density.
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Genetic differences at five polymorphic isoenzyme loci were analyzed by starch gel electrophoresis for 28 Aedes aegypti samples. Considerable (i.e., high Fst values) and significant (i.e., P values >10(-4)) geographic differences were found. Differences in Ae. aegypti genetic structure were related to human population densities and to particularities in mosquito ecotopes in both Tahiti and Moorea islands. In highly urbanized areas (i.e., the Papeete agglomeration), mosquitoes were highly structured. Recurrent extinction events consecutive to insecticidal treatments during dengue outbreaks tend to differentiate mosquito populations. In less populated zones (i.e., the east coast of Moorea and Tahiti), differences in ecotope characteristics could explain the lack of differentiation among mosquitoes from rural environments such as the east coast of Tahiti where natural breeding sites predominate. When the lowest populated zones such as Tahiti Iti and the west coast of Moorea are compared, mosquito are less differentiated in Moorea. These results will be discussed in relation to the recent findings of variation in mosquito infection rates for dengue-2 virus. (+info)
Likelihood analysis of phylogenetic networks using directed graphical models.
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A method for computing the likelihood of a set of sequences assuming a phylogenetic network as an evolutionary hypothesis is presented. The approach applies directed graphical models to sequence evolution on networks and is a natural generalization of earlier work by Felsenstein on evolutionary trees, including it as a special case. The likelihood computation involves several steps. First, the phylogenetic network is rooted to form a directed acyclic graph (DAG). Then, applying standard models for nucleotide/amino acid substitution, the DAG is converted into a Bayesian network from which the joint probability distribution involving all nodes of the network can be directly read. The joint probability is explicitly dependent on branch lengths and on recombination parameters (prior probability of a parent sequence). The likelihood of the data assuming no knowledge of hidden nodes is obtained by marginalization, i.e., by summing over all combinations of unknown states. As the number of terms increases exponentially with the number of hidden nodes, a Markov chain Monte Carlo procedure (Gibbs sampling) is used to accurately approximate the likelihood by summing over the most important states only. Investigating a human T-cell lymphotropic virus (HTLV) data set and optimizing both branch lengths and recombination parameters, we find that the likelihood of a corresponding phylogenetic network outperforms a set of competing evolutionary trees. In general, except for the case of a tree, the likelihood of a network will be dependent on the choice of the root, even if a reversible model of substitution is applied. Thus, the method also provides a way in which to root a phylogenetic network by choosing a node that produces a most likely network. (+info)