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Biostatistical analysis of mortality data for cohorts of cancer patients.

The Hardin Jones principle states that for a homogeneous cohort of cancer patients the logarithm of the fraction surviving at time t has a constant slope. With use of this principle, the survival times of the members of a heterogeneous cohort can be analyzed to divide the cohort into subcohorts with different mortality rate constants. Probable values of the additional survival time can be estimated for members surviving at the closing date of a clinical trial, permitting them to be included in the biostatistical analysis of the results of the trial in a more significant way than through Kaplan-Meier renormalization.

Humans↗

A biostatistical approach to ayurveda: quantifying the tridosha.

OBJECTIVE: To compute quantitative estimates of the tridosha--the qualitative characterization that constitutes the core of diagnosis and treatment in Ayurveda--to provide a basis for biostatistical analysis of this ancient Indian science, which is a promising field of alternative medicine. SUBJECTS: The data sources were 280 persons from among the residents and visitors/training students at the Brahmvarchas Research Centre and Shantikuj, Hardwar, India. DESIGN/METHODOLOGY: A quantitative measure of the tridosha level (for vata, pitta, and kapha) is obtained by applying an algorithmic heuristic approach to the exhaustive list of qualitative features/factors that are commonly used by Ayurvedic doctors. A knowledge-based concept of worth coefficients and fuzzy multiattribute decision functions are used here for regression modeling. VALIDATION AND APPLICATIONS: Statistical validation on a large sample shows the accuracy of this study's estimates with statistical confidence level above 90%. The estimates are also suited for diagnostic and prognostic applications and systematic drug-response analysis of Ayurvedic (herbal and rasayanam) medicines. An application with regard to the former is elucidated, extensions of which might also be of use in investigating the role of nadis in Ayurvedic healing vis-a-vis acupuncture and acupressure techniques. The importance and scope of this novel approach are discussed. CONCLUSIONS: This pioneering study shows that the concept of tridosha has a sound empirical basis that could be used for the scientific establishment of Ayurveda in a new light.

Algorithms↗

Experience with an independent study programme in epidemiology and biostatistics.

An independent study programme (ISP) was devised to permit self-paced learning in a combined epidemiology and biostatistics course for second-year medical students. The ISP was offered as an alternative to lecture instruction and the enrolment was restricted to 40 students out of a class of 176. The students who selected the ISP were superior to the remainder of the class with regard to MCAT scores and performance in first-year subjects. The ISP group scored higher on the examinations given in the course, including a set of 35 final examination questions which were identical for all students. This difference persisted even when the scores were adjusted for ability by use of MCAT scores. The time taken to complete the ISP varied considerably and there was no correlation between completion time and performance. Almost all the ISP students indicated satisfaction with the course and expressed a desire to have a similar option in subsequent courses.

Education, Medical, Undergraduate↗

The health agency training program: continuing education courses in biostatistics and epidemiology.

The authors describe the development and evaluation of a continuing education program in biostatistics and epidemiology. Short courses were presented to public health and mental health professionals using teaching strategies that included lecture, discussion, practice-oriented examples, and interactive problem-solving. A total of 1723 health professionals attended one or more of the 120 courses presented from 1992 to 1996 in seven US states. Most course participants were female: the highest education level for 40% was a bachelor's degree, while 42% had advanced degrees. Approximately 66% of participants signed up for continuing education credits. The program represents a successful partnership between an academic institution and health agencies in a seven-state region.

Adult↗

An introduction to the biostatistical technique of resampling.

This article will introduce resampling method to burn injury researchers and care givers. With the use of a data set taken from a published burn injury research project, this technique is extensively described and fully illustrated in a stepwise fashion by tables and figures. The recent exponential growth in the use of this method has produced a concurrent need to understand its fundamentals. Researchers, reviewers, and care givers will need to be familiar with resampling to evaluate the published results of various studies. Additionally, as burn research continues to focus on the molecular and cellular aspects of injury and healing, data analysis by resampling might prove to be a useful adjunct to standard biostatistical methods. Many useful links on the Internet (starting at www.statistics.com) can be followed for further readings, software, and tutorials.

Burns↗

A survey of biostatistics in clinical journals.

A survey of selected journals revealed that use of biostatistics procedures is a common practice in clinical writing. Most of the procedures used could be explained in an introductory coverage of statistics, yet, clinicians rarely show an interest in this subject. Mysterious, misused, and by no means the major matter in clinically pertinent research, statistics is often required but seldom admired.

Medicine↗

An introduction to biostatistics.

This introduction to biostatistics and measurement is the first in a series of articles designed to provide Radiology readers with a basic understanding of statistical concepts. Although most readers of the radiology literature know that application of study results to their practice requires an understanding of statistical issues, many may not be fully conversant with how to interpret statistics. The goal of this series is to enhance the ability of radiologists to evaluate the literature competently and critically, not make them into statisticians.

Biometry↗

Analysis of the 'epidemic' of multiple sclerosis in the Faroe Islands. II. Biostatistical aspects.

A series of biostatistical tests were applied to the data upon which the Faroe epidemic hypothesis was constructed. Temporal, cluster analysis using three commonly applied methods were unsuitable because of the very small size of the sample (32 cases of MS). Methods to detect non-random clusters of disease identified a cluster of 16 cases with date of onset between 1941-1950 (p less than 0.05). However, when 1 questionable case of MS was excluded, the resulting cluster did not support the epidemic hypothesis. The overlapping 95% confidence limits of 5- and 10-year based incidence rates of MS, by date of onset (Poisson distribution) argues against the presence of an epidemic. Analysis of the exposure data, i.e. contacts between the Faroese and the British troops, yielded only borderline statistical significance but changing a single case of MS from the exposed to the unexposed category failed to support the exposure theory. All calculations were also carried out using the probable date of acquisition of the disease, between the ages of 5 and 14 years; these also failed to substantiate the hypotheses of epidemic and transmissibility. Since the validity of statistical analysis to test for the presence of epidemics with small sample sizes is not well established, the lack of concordance between the results of the various approaches leads to the conclusion that there was no epidemic of MS in the Faroes.

Adolescent↗

In vitro phototoxicity testing: development and validation of a new concentration response analysis software and biostatistical analyses related to the use of various prediction models.

As demonstrated in several validation studies, the dermal phototoxic potential of chemicals in humans can be effectively assessed by in vitro methods. The core of these methods is to monitor dose-response curves of a chemical in the absence and presence of light, to quantify the difference between these two curves by appropriate measures (either the photo-irritancy factor [PIF], or the mean photo effect [MPE]), and to use these measures as predictors of in vivo phototoxicity. We present new concentration-response analysis software for in vitro phototoxicity testing, which runs on current personal computers, and takes into account all the limitations identified when using a former program. We also demonstrate the validity and robustness of this new software by applying it retrospectively to all data available from two phases of the EU/COLIPA validation trial for the 3T3 neutral red update in vitro phototoxicity test. Some frequently raised questions pertaining to the use of prediction models in phototoxicity testing are addressed, including: the necessity of using prediction models based on a cut-off; whether it is justifiable to use sharp prediction cut-off values; whether there is a biostatistical justification for the highest concentration of the test chemical; and whether repeated testing of a chemical is required.

3T3 Cells↗

Selected biostatistical aspects of the validation of in vitro toxicological assays.

An overview is presented on selected biostatistical aspects of the validation of in vitro toxicological assays. Primarily, the statistical analysis of single assays is discussed. Several approaches are compared for the possible non-monotonic dose-response relationship with a priori unknown shapes. The use of confidence intervals instead of p values for toxicologically appropriate decision making is explained. New methods are discussed for demonstrating interlaboratory similarity for dose-response designs are discussed. For validation, the inappropriateness of the concordance coefficient is shown, and sensitive and specificity as well as predictive values are proposed as alternatives. The problem of the missing gold standard is highlighted.

Animal Testing Alternatives↗

Biostatistical methods for the validation of alternative methods for in vitro toxicity testing.

Statistical methods for the validation of toxicological in vitro test assays are developed and applied. Validation is performed either in comparison with in vivo assays or in comparison with other in vitro assays of established validity. Biostatistical methods are presented which are of potential use and benefit for the validation of alternative methods for the risk assessment of chemicals, providing at least an equivalent level of protection through in vitro toxicity testing to that obtained through the use of current in vivo methods. Characteristic indices are developed and determined. Qualitative outcomes are characterised by the rates of false-positive and false-negative predictions, sensitivity and specificity, and predictive values. Quantitative outcomes are characterised by regression coefficients derived from predictive models. The receiver operating characteristics (ROC) technique, applicable when a continuum of cut-off values is considered, is discussed in detail, in relation to its use for statistical modelling and statistical inference. The methods presented are examined for their use for the proof of safety and for toxicity detection and testing. We emphasise that the final validation of toxicity testing is human toxicity, and that the in vivo test itself is only a predictor with an inherent uncertainty. Therefore, the validation of the in vitro test has to account for the vagueness and uncertainty of the "gold standard" in vivo test. We address model selection and model validation, and a four-step scheme is proposed for the conduct of validation studies. Gaps and research needs are formulated to improve the validation of alternative methods for in vitro toxicity testing.

Animal Testing Alternatives↗

The next 10 years of biostatistics.

I return to an article on the future of statistics written by Sir Maurice Kendall some 30 years ago. I draw some parallels between the state of general statistics at that time and the state of biostatistics today, and try to learn from the successes and omissions in Sir Maurice's predictions to make my own limited projections for the next 10 years.

Biometry↗

Markov chain Monte Carlo methods in biostatistics.

Appropriate models in biostatistics are often quite complicated. Such models are typically most easily fit using Bayesian methods, which can often be implemented using simulation techniques. Markov chain Monte Carlo (MCMC) methods are an important set of tools for such simulations. We give an overview and references of this rapidly emerging technology along with a relatively simple example. MCMC techniques can be viewed as extensions of iterative maximization techniques, but with random jumps rather than maximizations at each step. Special care is needed when implementing iterative maximization procedures rather than closed-form methods, and even more care is needed with iterative simulation procedures: it is substantially more difficult to monitor convergence to a distribution than to a point. The most reliable implementations of MCMC build upon results from simpler models fit using combinations of maximization algorithms and noniterative simulations, so that the user has a rough idea of the location and scale of the posterior distribution of the quantities of interest under the more complicated model. These concerns with implementation, however, should not deter the biostatistician from using MCMC methods, but rather help to ensure wise use of these powerful techniques.

Algorithms↗

[The role of biostatistics in pharmacological studies (randomization and statistical evaluation)].

Biostatistics has an important role in both designing a pharmaceutical experiment and evaluating its result. Randomization techniques are essentially important in designing an experiment. The goal of randomization is transforming systematic errors into random errors and confirming comparability among experimental groups. Randomization also provides a rationale for applying statistical tests. Combining randomization techniques with blinding and local control enables us to construct a scientifically reliable and effective experiment. An appropriate statistical analysis absolutely depends on the method of randomization. In order for a pharmacological study to be successful, it is very important to consider statistical aspects in the designing stage.

Biometry↗

Biostatistical issues in the design and analysis of multiple or repeated genotoxicity assays.

Tests for genotoxic or mutagenic effects of chemicals have prompted efficient biostatistical methods for the quantification of dose-response data, especially from the Ames Salmonella/microsome assay. A decision about the genotoxicity of a compound is, however, always based on several assays, and results from multiple or repeated genotoxicity assays have to be combined either qualitatively or, even better, quantitatively. The latter problem is considered here, and issues for design and analysis are addressed. General recommendations for designing genotoxicity assays are given. A long-known methodology for combining quantitative parameters from different experiments is updated and other statistical methods suitable for the combined analyses of multiple assays are presented. Some aspects of design and analysis are elucidated on count data from unscheduled DNA synthesis assays.

Biometry↗

The medical undergraduate programme at McMaster University: learning epidemiology and biostatistics in an integrated curriculum.

The medical undergraduate programme at McMaster University is a three-year integrated course in which major emphasis is placed upon self-directed, problem-based learning. Group tutorials are the major meeting place for students, and there are very few formal lectures. In this paper we describe the curriculum in broad outline and the way in which epidemiology and biostatistics have been incorporated into it in the past seven years.

Curriculum↗

[Methodological aspects of biostatistics and epidemiology in a clinical trial].

Various methodological issues in biostatistics and epidemiology which are relevant for the development of a clinical trial are discussed. A brief set of definitions regarding clinical trials and methods for the assignment of treatments is presented. In addition, methods to reduce the possibility of bias that may be introduced into a clinical trial are summarized. The different elements that must be included in the informed consent form are defined. General considerations for data analysis and for sample size required to obtain an adequate statistical power are illustrated. Finally, critical questions that can be used for the review of the literature on clinical trials are suggested.

Aged↗