Biomedical subjects

Stan Pounds

Publications and source records attributed to Stan Pounds.

4 recordsLinked to original sources

PubMed2005

Severe cardiopulmonary complications consistent with systemic inflammatory response syndrome caused by leukemia cell lysis in childhood acute myelomonocytic or monocytic leukemia.

BACKGROUND: Life-threatening pulmonary complications that coincide with cell lysis during early chemotherapy and that mimic systemic inflammatory response syndrome (SIRS) have been reported in patients with acute myeloid leukemia (AML). METHODS: We reviewed the records of patients with de novo AML, excluding M3 and Down syndrome, treated at our institution between 1991 and 2002 to determine the prevalence of severe SIRS with grade 3/4 pulmonary complications and to identify AML subtypes associated with severe SIRS. To examine the role of cell lysis, we compared leukocyte reduction in AML subtypes affected by severe SIRS with that in unaffected subtypes. RESULTS: Of 155 patients, 5 (3 with M4eo and 2 with M5) experienced severe pulmonary complications attributed to tumor lysis, met the criteria for severe SIRS, and showed no clear evidence of infection. Four required pressor support for severe hypotension. Severe SIRS was significantly more common in myelomonocytic or monocytic AML (M4/M4eo/M5) than in other subtypes (P = 0.010) and significantly more common in M4eo than in M4/M5 (P = 0.008). Among 112 cases for which information was available, leukocyte reduction was significantly greater in patients with M4/M4eo/M5 than among others during the first 4 days of chemotherapy (P = 0.015). Leukocyte reduction was significantly more rapid among patients who had severe SIRS than among others (P = 0.008). CONCLUSIONS: Patients with M4/M4eo/M5 AML, especially M4eo, experience life-threatening cardiopulmonary complications of tumor lysis that meet the criteria for severe SIRS. This observation may reflect more rapid cell reduction and the unique biology of this subtype.

Adolescent↗

PubMed2005

Statistical development and evaluation of microarray gene expression data filters.

Filtering is a common practice used to simplify the analysis of microarray data by removing from subsequent consideration probe sets believed to be unexpressed. The m/n filter, which is widely used in the analysis of Affymetrix data, removes all probe sets having fewer than m present calls among a set of n chips. The m/n filter has been widely used without considering its statistical properties. The level and power of the m/n filter are derived. Two alternative filters, the pooled p-value filter and the error-minimizing pooled p-value filter are proposed. The pooled p-value filter combines information from the present-absent p-values into a single summary p-value which is subsequently compared to a selected significance threshold. We show that pooled p-value filter is the uniformly most powerful statistical test under a reasonable beta model and that it exhibits greater power than the m/n filter in all scenarios considered in a simulation study. The error-minimizing pooled p-value filter compares the summary p-value with a threshold determined to minimize a total-error criterion based on a partition of the distribution of all probes' summary p-values. The pooled p-value and error-minimizing pooled p-value filters clearly perform better than the m/n filter in a case-study analysis. The case-study analysis also demonstrates a proposed method for estimating the number of differentially expressed probe sets excluded by filtering and subsequent impact on the final analysis. The filter impact analysis shows that the use of even the best filter may hinder, rather than enhance, the ability to discover interesting probe sets or genes. S-plus and R routines to implement the pooled p-value and error-minimizing pooled p-value filters have been developed and are available from www.stjuderesearch.org/depts/biostats/index.html.

Computational Biology↗

PubMed2004

Improving false discovery rate estimation.

MOTIVATION: Recent attempts to account for multiple testing in the analysis of microarray data have focused on controlling the false discovery rate (FDR). However, rigorous control of the FDR at a preselected level is often impractical. Consequently, it has been suggested to use the q-value as an estimate of the proportion of false discoveries among a set of significant findings. However, such an interpretation of the q-value may be unwarranted considering that the q-value is based on an unstable estimator of the positive FDR (pFDR). Another method proposes estimating the FDR by modeling p-values as arising from a beta-uniform mixture (BUM) distribution. Unfortunately, the BUM approach is reliable only in settings where the assumed model accurately represents the actual distribution of p-values. METHODS: A method called the spacings LOESS histogram (SPLOSH) is proposed for estimating the conditional FDR (cFDR), the expected proportion of false positives conditioned on having k 'significant' findings. SPLOSH is designed to be more stable than the q-value and applicable in a wider variety of settings than BUM. RESULTS: In a simulation study and data analysis example, SPLOSH exhibits the desired characteristics relative to the q-value and BUM. AVAILABILITY: The Web site www.stjuderesearch.org/statistics/splosh.html has links to freely available S-plus code to implement the proposed procedure.

Algorithms↗

PubMed2003

Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-values.

MOTIVATION: The occurrence of false positives and false negatives in a microarray analysis could be easily estimated if the distribution of p-values were approximated and then expressed as a mixture of null and alternative densities. Essentially any distribution of p-values can be expressed as such a mixture by extracting a uniform density from it. RESULTS: The occurrence of false positives and false negatives in a microarray analysis could be easily estimated if the distribution of p-values were approximated and then expressed as a mixture of null and alternative densities. Essentially any distribution of p-values can be expressed as such a mixture by extracting a uniform density from it. AVAILABILITY: An S-plus function library is available from http://www.stjuderesearch.org/statistics.

Adaptor Proteins, Signal Transducing↗