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PubMed1994

The use of predicted confidence intervals when planning experiments and the misuse of power when interpreting results.

Although there is a growing understanding of the importance of statistical power considerations when designing studies and of the value of confidence intervals when interpreting data, confusion exists about the reverse arrangement: the role of confidence intervals in study design and of power in interpretation. Confidence intervals should play an important role when setting sample size, and power should play no role once the data have been collected, but exactly the opposite procedure is widely practiced. In this commentary, we present the reasons why the calculation of power after a study is over is inappropriate and how confidence intervals can be used during both study design and study interpretation.

Bayes Theorem↗

PubMed2006

Cluster-crossover design: a method for limiting clusters level effect in community-intervention studies.

The cluster-crossover design can be used for clinical trials comparing two or more interventions in a naturally formed study population, i.e. a cluster. This design differs from that of a crossover study, in that the treatment sequence is allocated at the cluster level. A cluster-crossover study can thus be considered as a cluster-randomized controlled study with additional periodic cluster-randomization(s) or treatment permutation(s) during the study. The data must be analyzed with hierarchical models with random effects in order to allow for different outcome probabilities in each period, cluster and cluster-period. Original data from two published field studies of hospital infection control based on this design are used here to illustrate the impact of different statistical models on the interpretation of the results.

Clinical Trials as Topic↗

PubMed1999

Analysis of quantitative research data: Part 2.

Statistical and clinical significance are different. An observed difference that does not reach statistical significance may still be clinically relevant. Results presented within research reports should always be interpreted with care.

Chi-Square Distribution↗

PubMed1995

Questions from practice: a basis for research.

Occupational health nurses in clinical practice are in an excellent position to identify unanswered questions that affect the health and well being of employees. Once these questions have been asked, the occupational health nurses may proceed with structured research to find answers. The research begins with a thorough review of existing literature to learn the background of the issue and clearly define a research question. This question is then framed conceptually to guide the study. The theoretical framework may be supported or refuted by the research project. The study method is dictated by the research question and the theoretical framework. Two basic research methods are intervention and descriptive studies. Intervention studies, with treatment and control groups, attempt to show differences between groups when one receives a certain treatment and the other group does not. Descriptive studies generally survey attitudes or activities, then test for associations. Data analysis is determined by the type of study and type of data collected. Descriptive statistics generate frequencies and correlations; inferential statistics yield stronger information about associations of the variables. Interpretation of research findings must include consideration of threats to validity. Internal validity allows the investigator to state with confidence that the intervention was responsible for the difference between the groups. External validity allows for generalizability of the findings to other populations. The purpose of nursing research is to advance the discipline of nursing. Research refines nursing theories and guides practice. It is through research that occupational health nurses gain confidence to alter procedures and provide interventions that have been shown to be effective.

Clinical Nursing Research↗

PubMed1992

Importance of trends in the interpretation of an overall odds ratio in the meta-analysis of clinical trials.

This paper contains a proposition related to the publication of meta-analyses of clinical trials. We consider the situation where the results of a number of trials are summarized by a common or typical odds ratio. We show that stating such an odds ratio as the summary of evidence from a number of trials can be misleading if certain systematic differences between trials exist. In such cases the author should state not just one odds ratio but also its dependence on the relevant characteristics of the trials. In particular, we propose that those reporting a meta-analysis state in advance a (limited) number of variables to be considered for potential interaction with the exposure (risk factor or treatment) of interest. The list might include centre size and the odds in the placebo or control group if such an effect is a priori clinically plausible. The trials should be ordered according to each of these variables and a trend test for the odds ratio should be computed. Apart from a 'genuine' effect, an appreciable interaction could also be indicative of the (multiplicative) odds ratio being an inappropriate measure for the particular meta-analysis. Without any consideration as to the possibility of interaction, the meta-analysis should be considered incomplete. If such an interaction exists, the odds ratio should be stated as a function of the interacting variable, either as a formula or (preferably) in a table stating the odds ratio for a number of different values of the interacting variable, and not as a single summary statistic.

Clinical Trials as Topic↗

PubMed2006

Statistical inference of chromosomal homology based on gene colinearity and applications to Arabidopsis and rice.

BACKGROUND: The identification of chromosomal homology will shed light on such mysteries of genome evolution as DNA duplication, rearrangement and loss. Several approaches have been developed to detect chromosomal homology based on gene synteny or colinearity. However, the previously reported implementations lack statistical inferences which are essential to reveal actual homologies. RESULTS: In this study, we present a statistical approach to detect homologous chromosomal segments based on gene colinearity. We implement this approach in a software package ColinearScan to detect putative colinear regions using a dynamic programming algorithm. Statistical models are proposed to estimate proper parameter values and evaluate the significance of putative homologous regions. Statistical inference, high computational efficiency and flexibility of input data type are three key features of our approach. CONCLUSION: We apply ColinearScan to the Arabidopsis and rice genomes to detect duplicated regions within each species and homologous fragments between these two species. We find many more homologous chromosomal segments in the rice genome than previously reported. We also find many small colinear segments between rice and Arabidopsis genomes.

Algorithms↗

PubMedDate not supplied

Examination of two methods for statistical analysis of data with magnitude and direction emphasizing vestibular research applications.

When the dependent (or response) variable response variable in an experiment has direction and magnitude, one approach that has been used for statistical analysis involves splitting magnitude and direction and applying univariate statistical techniques to the components. However, such treatment of quantities with direction and magnitude is not justifiable mathematically and can lead to incorrect conclusions about relationships among variables and, as a result, to flawed interpretations. This note discusses a problem with that practice and recommends mathematically correct procedures to be used with dependent variables that have direction and magnitude for 1) computation of mean values, 2) statistical contrasts of and confidence intervals for means, and 3) correlation methods.

Animals↗

PubMedDate not supplied

[P value and confidence intervals: reporting and interpreting the result of a clinical study].

The main purpose of statistics in the analysis of clinical and epidemiological studies is to summarize data and information, as well as assess variability, trying to distinguish between chance findings and results that may be replicated upon repetition. Statistical analyses only convey the effect of chance element in data (random error). Statistics cannot control non-sampling errors concerning study design, conduct and methods adopted. At the end of the study, a result is defined statistically significant if the observed difference in the outcome variable is too large to be attributed to chance. A small P value provides evidence against the null hypothesis (of no effect), since data have been observed that would be unlikely if the null hypothesis was true. However, confidence intervals estimate separate the two data dimensions (strength of the relation between exposure and disease, and precision with which the relation is measured), and add to the hypothesis testing useful information for finding interpretation and further research.

Biomedical Research↗

PubMed1993