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PubMed1997

[Statistical errors].

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Clinical Trials as Topic↗
PubMed2003

Analysis and reporting of factorial trials: a systematic review.

CONTEXT: Although factorial trials have become common, standards for the analysis and reporting of such trials have not been established and, despite concerns about the possibility of unrecognized interactions between therapies in factorial trials, the magnitude of this potential problem is unknown. OBJECTIVE: To examine the rationale, methods, and analysis of randomized factorial trials. DATA SOURCES AND STUDY SELECTION: We searched MEDLINE, EMBASE, and the Cochrane Controlled Trials Register using the terms factorial, interaction, 2 x 2, 2 by 2, and incremental to identify factorial randomized trials published from January 2000 to July 2002. To identify trials missed by the electronic search, we performed a hand search of English-language trials in a defined topic area (using the term myocardial ischemia [exp]) listed in MEDLINE (1966-2002), EMBASE (1980-2002), and the Cochrane Controlled Trials Register, as well as all trials in any topic area published in December 2000, excluding trials reporting only continuous surrogate end points. The final set of 33 eligible publications described 29 unique trials. DATA EXTRACTION: Two investigators independently identified factorial trials, generated a list of items affecting validity of results, and abstracted these items from each trial. DATA SYNTHESIS: The sensitivity of electronic searching for identifying factorial trials was 76%. Our 3-pronged search strategy identified 44 factorial trials with clinically important binary outcomes: 36 (82%) were done for reasons of efficiency (testing 2 interventions in the same patient population), and 8 (18%) were done to assess the incremental benefits of combining the 2 treatments. All but 1 of the trials reported treatment effects by comparing all patients who received treatment A (ie, those receiving either A alone or both A and B) vs all those not receiving treatment A (ie, those receiving either B alone or neither A nor B). Twenty-nine of the 44 trials (66%) reported the data from each of the treatment groups separately; 26 trials (59%) reported testing for interactions between the treatments. Only 2 of 31 (6%) comparisons demonstrated a statistically significant interaction between the 2 treatments. CONCLUSIONS: Accurate interpretation of factorial trials depends on the transparent reporting of data for each treatment cell. Despite concerns about unrecognized interactions, our findings suggest that investigators are appropriately restricting their use of the factorial design to those situations in which 2 (or more) treatments do not have the potential for substantive interaction.

Data Interpretation, Statistical↗
PubMed2005

Fishing for biomarkers: analyzing mass spectrometry data with the new ClinProTools software.

Recently, applications of mass spectrometry in the field of clinical proteomics have gained tremendous visibility in the scientific and clinical community. One major objective is the search for potential biomarkers in complex body fluids like serum, plasma, urine, saliva, or cerebral spinal fluid. For this purpose, efficient visualization of large data sets derived from patient cohorts is crucial to provide clinical experts an interactive impression of the data quality. Additionally, it is necessary to apply statistical analysis and pattern matching algorithms to attain validated signal patterns that may allow for later applications in sample classification. We introduce the new ClinProTools bioinformatics software, which performs all major steps of profiling, screening, and monitoring applications in clinical proteomics. ClinProTools is the data interpretation software of the mass spectrometry-based ClinProt solutions for biomarker analysis. ClinProTools performs data pretreatment, visualization, statistics, pattern determination, pattern evaluation, and classification of spectra. This article will focus on ClinProTool's powerful and intuitive visualization options for clinical proteomics applications.

Algorithms↗