Small Sample Confidence Intervals for the Odds Ratio

2004 ◽  
Vol 33 (4) ◽  
pp. 1095-1113 ◽  
Author(s):  
Raef Lawson
Keyword(s):  
Confidence Intervals ◽  
Odds Ratio ◽  
Small Sample

scholarly journals Prevalence and risk factors of apical periodontitis in endodontically treated teeth in a selected population of Brazilian adults

2008 ◽  
Vol 19 (1) ◽  
pp. 34-39 ◽  
Author(s):  
Carlos Estrela ◽  
Cláudio Rodrigues Leles ◽  
Augusto César Braz Hollanda ◽  
Marcelo Sampaio Moura ◽  
Jesus Djalma Pécora
Keyword(s):  
Risk Factors ◽  
Confidence Intervals ◽  
Odds Ratio ◽  
Apical Periodontitis ◽  
Endodontic Treatment ◽  
Technical Quality ◽  
Endodontically Treated Teeth ◽  
Chi Square ◽  
Chi Square Test

The aim of this study was to assess the prevalence and risk factors of apical periodontitis in endodontically treated teeth in a selected population of Brazilian adults. A total of 1,372 periapical radiographs of endodontically treated teeth were analyzed based on the quality of root filling, status of coronal restoration and presence of posts associated with apical periodontitis (AP). Data were analyzed statistically using odds ratio, confidence intervals and chi-square test. The prevalence of AP with adequate endodontic treatment was low (16.5%). This percentage dropped to 12.1% in cases with adequate root filling and adequate coronal restoration. Teeth with adequate endodontic treatment and poor coronal restoration had an AP prevalence of 27.9%. AP increased to 71.7% in teeth with poor endodontic treatment associated with poor coronal restoration. When poor endodontic treatment was combined with adequate coronal restoration, AP prevalence was 61.8%. The prevalence of AP was low when associated with high technical quality of root canal treatment. Poor coronal restoration increased the risk of AP even when endodontic treatment was adequate (OR=2.80; 95%CI=1.87-4.22). The presence of intracanal posts had no influence on AP prevalence.

Using the diagnostic odds ratio to select multivariate sequential patterns in order to build an interpretable pattern-based classifier in a clinical domain (Preprint)

2021 ◽  
Author(s):  
Isidoro J. Casanova ◽  
Manuel Campos ◽  
Jose M. Juarez ◽  
Antonio Gomariz ◽  
Marta Lorente-Ros ◽  
...  
Keyword(s):  
Confidence Intervals ◽  
Odds Ratio ◽  
Diagnostic Odds Ratio ◽  
University Hospital ◽  
Classification Model ◽  
Sequential Patterns ◽  
Pattern Selection ◽  
Essential Property ◽  
Frequency Properties ◽  
Clinical Domain

BACKGROUND It is important to exploit all available data on patients in settings such as Intensive Care Burn Units (ICBUs), where several variables are recorded over time. It is possible to take advantage of the multivariate patterns that model the evolution of patients in order to predict their survival. However, pattern discovery algorithms generate a large number of patterns, of which only some are relevant for classification. The interpretability of the model is, moreover, an essential property in the clinical domain. OBJECTIVE We propose to use the Diagnostic Odds Ratio (DOR) to select the multivariate sequential patterns used in the classification in a clinical domain, rather than employing frequency properties. This makes it possible to employ a terminology closer to the language of clinicians, in which a pattern is considered to be a risk factor or to have a protection factor. METHODS We employ data obtained from the ICBU at the University Hospital of Getafe, where six temporal variables for 465 patients were registered every day during 5 days, and to model the evolution of these clinical variables we use multivariate sequential patterns. We compare four ways in which to employ the DOR for pattern selection: 1) We use it as a threshold in order to select patterns with a minimum DOR; 2) We select patterns whose differential DORs are higher than a threshold as regards their extensions; 3) We select patterns whose DOR confidence intervals do not overlap; and 4) We propose the combination of threshold and non-overlapping confidence intervals in order to select the most discriminative patterns. As a baseline, we compare our proposals with Jumping Emerging Patterns (JEPs), one of the most frequently used techniques for pattern selection that utilize frequency properties. RESULTS We have compared the number and length of the patterns eventually selected, classification performance, and pattern and model interpretability. We show that discretization has a great impact on the accuracy of the classification model, but that a trade off must be found between classification accuracy and the physicians' capacity to interpret the patterns obtained. We have, therefore, opted to use expert discretization without losing too much accuracy. We have also identified that the experiments combining threshold and non-overlapping confidence intervals (Option 4) obtained the fewest number of patterns but also with the smallest size, thus implying the loss of an acceptable accuracy as regards clinician interpretation. CONCLUSIONS A method for the classification of patients’ survival can benefit from the use of sequential patterns, since these patterns consider knowledge about the temporal evolution of the variables in the case of ICBU. We have proved that the DOR can be used in several ways, and that it is a suitable measure with which to select discriminative and interpretable quality patterns.

SMALL SAMPLE SIZE SCIENTIST

PEDIATRICS ◽  
1989 ◽  
Vol 83 (3) ◽  
pp. A72-A72
Author(s):  
Student
Keyword(s):  
Sample Size ◽  
Confidence Intervals ◽  
Causal Explanation ◽  
Small Sample Size ◽  
Small Sample ◽  
Small Samples ◽  
High Expectations ◽  
Sampling Variation ◽  
The Law ◽  
The Stability

The believer in the law of small numbers practices science as follows: 1. He gambles his research hypotheses on small samples without realizing that the odds against him are unreasonably high. He overestimates power. 2. He has undue confidence in early trends (e.g., the data of the first few subjects) and in the stability of observed patterns (e.g., the number and identity of significant results). He overestimates significance. 3. In evaluating replications, his or others', he has unreasonably high expectations about the replicability of significant results. He underestimates the breadth of confidence intervals. 4. He rarely attributes a deviation of results from expectations to sampling variability, because he finds a causal "explanation" for any discrepancy. Thus, he has little opportunity to recognize sampling variation in action. His belief in the law of small numbers, therefore, will forever remain intact.

Confidence intervals for a difference between lognormal means in cluster randomization trials

2014 ◽  
Vol 26 (2) ◽  
pp. 598-614 ◽  
Author(s):  
Julia Poirier ◽  
GY Zou ◽  
John Koval
Keyword(s):  
Confidence Intervals ◽  
Community Acquired Pneumonia ◽  
Small Sample ◽  
Cluster Randomization ◽  
Critical Pathway ◽  
Arithmetic Means ◽  
Multiple Parameters ◽  
The Difference ◽  
Using Data ◽  
Small Sample Sizes

Cluster randomization trials, in which intact social units are randomized to different interventions, have become popular in the last 25 years. Outcomes from these trials in many cases are positively skewed, following approximately lognormal distributions. When inference is focused on the difference between treatment arm arithmetic means, existent confidence interval procedures either make restricting assumptions or are complex to implement. We approach this problem by assuming log-transformed outcomes from each treatment arm follow a one-way random effects model. The treatment arm means are functions of multiple parameters for which separate confidence intervals are readily available, suggesting that the method of variance estimates recovery may be applied to obtain closed-form confidence intervals. A simulation study showed that this simple approach performs well in small sample sizes in terms of empirical coverage, relatively balanced tail errors, and interval widths as compared to existing methods. The methods are illustrated using data arising from a cluster randomization trial investigating a critical pathway for the treatment of community acquired pneumonia.

scholarly journals True and false positive rates for different criteria of evaluating statistical evidence from clinical trials

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Don van Ravenzwaaij ◽  
John P. A. Ioannidis
Keyword(s):  
Clinical Trials ◽  
Confidence Intervals ◽  
False Positive ◽  
Evaluation Criteria ◽  
Small Sample ◽  
Statistical Evidence ◽  
Bayes Factors ◽  
True Positive ◽  
Small Sample Sizes ◽  
Meta Analyses

Abstract Background Until recently a typical rule that has often been used for the endorsement of new medications by the Food and Drug Administration has been the existence of at least two statistically significant clinical trials favoring the new medication. This rule has consequences for the true positive (endorsement of an effective treatment) and false positive rates (endorsement of an ineffective treatment). Methods In this paper, we compare true positive and false positive rates for different evaluation criteria through simulations that rely on (1) conventional p-values; (2) confidence intervals based on meta-analyses assuming fixed or random effects; and (3) Bayes factors. We varied threshold levels for statistical evidence, thresholds for what constitutes a clinically meaningful treatment effect, and number of trials conducted. Results Our results show that Bayes factors, meta-analytic confidence intervals, and p-values often have similar performance. Bayes factors may perform better when the number of trials conducted is high and when trials have small sample sizes and clinically meaningful effects are not small, particularly in fields where the number of non-zero effects is relatively large. Conclusions Thinking about realistic effect sizes in conjunction with desirable levels of statistical evidence, as well as quantifying statistical evidence with Bayes factors may help improve decision-making in some circumstances.

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