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PubMed2004

Biostatistical approaches to reducing the number of animals used in biomedical research.

For ethical reasons, the least number of animals possible should be used in biomedical research, though not so few as to fail to detect biologically important effects or to necessitate the repetition of experiments. We describe biostatistical approaches that can contribute to either reducing the number of animals in single experiments or to increasing the quality of studies so that fewer subsequent studies (and thus animals) will be needed. The described approaches regard different phases of experimentation, specifically: planning the experimental design and calculating the sample size, controlling variability, choosing the response variable, postulating the statistical hypothesis to be tested, choosing the procedure for analysing data, and interpreting and suitably presenting the results.

Animal Experimentation↗
PubMed2005

Prostate cancer outcome: epidemiology and biostatistics.

Substantial gaps exist in our ability to accurately predict prognosis, and these gaps limit our understanding of the complex mechanisms that contribute to the greatest cancer epidemic of our time, prostate cancer. This review addresses contemporary epidemiologic and biostatistical issues in prostate cancer. It covers the science of outcome prediction and biomarker evaluation, recognition of the need to combine biomarkers to improve the accuracy of our outcome estimates and an analysis of current outcome assessment methods, including the TNM staging system and multivariate regression models. The simplicity and intuitive ease of the current TNM staging system must be balanced against its serious limitations in predictive accuracy and its loss of clinical utility. Statistical regression methods are required as we move to the new era of personalized medicine. We must implement statistical approaches that integrate the new molecular biomarkers with existing prognostic biomarkers to accurately predict which patients require treatment and to determine the optimal therapy.

Biomarkers, Tumor↗
PubMedDate not supplied

[DNAStat, version 1.0--a software package for processing a genetic profile database and for biostatistical calculations].

The application of DNA studies to the administration of justice has led to the necessity of developing appropriate computer programs. Such programs must address two critical problems, i.e. the broadly understood data processing and archivization, and biostatistical calculations. This paper discusses DNAStat 1.0, a program that enables its user to 1) create and process an individual database; 2) analyze biological evidence by calculating the unconditional f and conditional p(X/X) profile frequency, with the possibility of taking into account the inbreeding (coancestry) coefficient, as well as setting the minimum allele frequency; 3) analyze paternity cases by calculating the paternity index PI and probability of paternity W for full and motherless trios, with the possibility of taking into account the silent allele frequency and prior probability.

DNA Fingerprinting↗
PubMed1991

Linear modeling of the effect of intravenous histamine infusion on rat gastric acid secretion: a biostatistical evaluation by splitting the time-effect curves.

Lumen perfusion experiments have been widely employed for the evaluation of gastric acid secretion in rats. The advantage of the lumen perfusion models is the simplicity in evaluating the effectiveness of secretagogues and antisecretagogues. There is, however, no efficient methodology for biostatistical modeling of gastric acid secretion in lumen-perfused rats. In order to optimize methodology, we applied a linear regression model after splitting the time-effect curves obtained from histamine-induced gastric acid secretion in rats.

Animals↗
PubMed1990

Uses and abuses of biostatistics in medical research in Pakistan.

Medical research in Pakistan has gained momentum over the past several years. However, the logical conclusions based on information and data are rarely witnessed. This could be due to the fact that medical researchers and doctors are unaware of Biostatistics, its logic, use and inferences to be obtained. Most researches are based on the pattern of works already done elsewhere. Following others blindly generates various snags. In the present study, research articles published during 1986 in the Journal of Pakistan Medical Association (JPMA) and Pakistan Journal of Medical Research (PJMR) are being reviewed with respect to use and abuse of Statistical Methods.

Biometry↗
PubMed1986

Biostatistical aspects of adjuvant therapy assay.

The specific problems of biostatistical evaluation of adjuvant therapy are discussed. These problems arise from the fact that the effect of adjuvant therapy is usually rather small; in particular, for many kinds of cancer it is smaller than the variability in prognosis among patients due to different prognostic factors. Knowledge about prognostic factors for a particular type of cancer is therefore an essential precondition for the correct planning of controlled clinical trials. The importance of prognostic factor studies is stressed and it is shown how results of such studies can be utilized for the design of efficient trials for adjuvant therapy assay. The results of the retrospective DOSAK study on carcinomas of the oral cavity are reported. Based on a sample of 1021 patients in this study, numerous prognostic factors were analysed by both univariate and multivariate statistical techniques. On the basis of these analyses, a prognostic index was developed which should be a valuable aid for the design of clinical trials.

Biometry↗
PubMed1983

Introduction to biostatistics. An annotated bibliography for medical researchers.

Biostatistical methods have become thoroughly integrated into modern biomedical and clinical research. Nevertheless, every observer who has evaluated articles in medical journals has noted that as many as half the reported results were based on questionable statistical analysis. This situation, combined with the fact that most errors involve relatively simple statistical procedures, points to the need for researchers and practitioners to be able to personally judge the quality of the statistical analyses in what they read. Fortunately, there are several excellent papers and texts available for those interested.

Bibliographies as Topic↗
PubMed1996

[Current developments in biostatistics].

Recent developments in biostatistics in three key areas: survival analysis of censured data, generalised linear models for correlated data, Bayesian modelling of complex data, are presented. Particular emphasis is placed upon illustrating common trends in these areas, in particular advances in computer intensive methods, the development of unifying modelling structures and the increasing variety of complex biomedical problems which can be realistically analysed.

Bayes Theorem↗
PubMed1998

Biostatistical modeling using traditional preoperative and pathological prognostic variables in the selection of men at high risk for disease recurrence after radical prostatectomy for prostate cancer.

PURPOSE: Biostatistical models predicting the risk of recurrence after radical prostatectomy for clinically localized prostate cancer are necessary. Identifying these high risk patients shortly after surgery, while tumor burden is minimal, makes them candidates for possible adjuvant therapy and/or investigational phase II clinical trials. This study builds on previously proposed models that predict the likelihood of early recurrence after radical prostatectomy. MATERIALS AND METHODS: In our analysis we evaluate age, race, prostatic acid phosphatase and nuclear grade with the established prognostic variables of pretreatment prostate specific antigen, postoperative Gleason sum and pathological stage. RESULTS: After multivariable Cox regression analysis using only statistically significant variables that predicted recurrence we developed an equation that calculates the relative risk of recurrence (Rr) as: Rr = exp[(0.51 x Race) + (0.12 x PSAST) + (0.25 x Postop Gleason sum) + (0.89 x Organ Conf.). These cases are then categorized into 3 distinct risk groups of relative risk of recurrence of low (< 10.0), intermediate (10.0 to 30.0) and high (> 30.0). Kaplan-Meier survival analysis of these 3 risk groups reveals that each category has significantly different risks of recurrence (p < 0.05). This model is validated with an independent cohort of radical prostatectomy patients treated at a different medical center by multiple primary surgeons. CONCLUSIONS: This model suggests that race, preoperative prostate specific antigen, postoperative Gleason sum and pathological stage are important independent prognosticators of recurrence after radical prostatectomy for clinically localized prostate cancer. Race should be considered in future models that attempt to predict the likelihood of recurrence after surgery.

Aged↗