On the analysis of composite measures of quality in medical research

2014 ◽  
Vol 26 (2) ◽  
pp. 633-660
Author(s):  
Rahim Moineddin ◽  
Christopher Meaney ◽  
Eva Grunfeld
Keyword(s):  
Regression Model ◽  
Logistic Regression Model ◽  
Error Rates ◽  
Type 1 Error ◽  
Common Effect ◽  
The Common ◽  
Composite Endpoints ◽  
Over Dispersion ◽  
Binary Composite

Composite endpoints are commonplace in biomedical research. The complex nature of many health conditions and medical interventions demand that composite endpoints be employed. Different approaches exist for the analysis of composite endpoints. A Monte Carlo simulation study was employed to assess the statistical properties of various regression methods for analyzing binary composite endpoints. We also applied these methods to data from the BETTER trial which employed a binary composite endpoint. We demonstrated that type 1 error rates are poor for the Negative Binomial regression model and the logistic generalized linear mixed model (GLMM). Bias was minimal and power was highest in the binomial logistic regression model, the linear regression model, the Poisson (corrected for over-dispersion) regression model and the common effect logistic generalized estimating equation (GEE) model. Convergence was poor in the distinct effect GEE models, the logistic GLMM and some of the zero-one inflated beta regression models. Considering the BETTER trial data, the distinct effect GEE model struggled with convergence and the collapsed composite method estimated an effect, which was greatly attenuated compared to other models. All remaining models suggested an intervention effect of similar magnitude. In our simulation study, the binomial logistic regression model (corrected for possible over/under-dispersion), the linear regression model, the Poisson regression model (corrected for over-dispersion) and the common effect logistic GEE model appeared to be unbiased, with good type 1 error rates, power and convergence properties.

Statistical Power in Psychiatric Research

1986 ◽  
Vol 20 (2) ◽  
pp. 189-200 ◽  
Author(s):  
Kevin D. Bird ◽  
Wayne Hall
Keyword(s):  
Sample Size ◽  
Error Rate ◽  
Statistical Power ◽  
Error Rates ◽  
Psychiatric Research ◽  
Type 1 Error ◽  
Type 2 Error ◽  
Power Analyses

Statistical power is neglected in much psychiatric research, with the consequence that many studies do not provide a reasonable chance of detecting differences between groups if they exist in the population. This paper attempts to improve current practice by providing an introduction to the essential quantities required for performing a power analysis (sample size, effect size, type 1 and type 2 error rates). We provide simplified tables for estimating the sample size required to detect a specified size of effect with a type 1 error rate of α and a type 2 error rate of β, and for estimating the power provided by a given sample size for detecting a specified size of effect with a type 1 error rate of α. We show how to modify these tables to perform power analyses for multiple comparisons in univariate and some multivariate designs. Power analyses for each of these types of design are illustrated by examples.

scholarly journals Analytic and bootstrap confidence intervals for the common-language effect size estimate

Methodology ◽  
2021 ◽  
Vol 17 (1) ◽  
pp. 1-21
Author(s):  
Johnson Ching-Hong Li ◽  
Virginia Man Chung Tze
Keyword(s):  
Confidence Intervals ◽  
Effect Size ◽  
Sexual Compulsivity ◽  
Common Language ◽  
Bootstrap Confidence Intervals ◽  
Effect Size Estimate ◽  
Type 1 Error ◽  
Size Estimate ◽  
The Common

Evaluating how an effect-size estimate performs between two continuous variables based on the common-language effect size (CLES) has received increasing attention. While Blomqvist (1950; https://doi.org/10.1214/aoms/1177729754) developed a parametric estimator (q') for the CLES, there has been limited progress in further refining CLES. This study: a) extends Blomqvist’s work by providing a mathematical foundation for Bp (a non-parametric version of CLES) and an analytic approach for estimating its standard error; and b) evaluates the performance of the analytic and bootstrap confidence intervals (CIs) for Bp. The simulation shows that the bootstrap bias-corrected-and-accelerated interval (BCaI) has the best protected Type 1 error rate with a slight compromise in Power, whereas the analytic-t CI has the highest overall Power but with a Type 1 error slightly larger than the nominal value. This study also uses a real-world data-set to demonstrate the applicability of the CLES in measuring the relationship between age and sexual compulsivity.

scholarly journals Magnitude Based Inference in Relation to One-sided Hypotheses Testing Procedures

2020 ◽  
Author(s):  
Janet Aisbett ◽  
Daniel Lakens ◽  
Kristin Sainani
Keyword(s):  
Error Control ◽  
Hypothesis Test ◽  
A Priori ◽  
Error Rates ◽  
Equivalence Testing ◽  
High Type ◽  
Testing Procedures ◽  
Type 1 Error ◽  
Sample Size Calculations

Magnitude based inference (MBI) was widely adopted by sport science researchers as an alternative to null hypothesis significance tests. It has been criticized for lacking a theoretical framework, mixing Bayesian and frequentist thinking, and encouraging researchers to run small studies with high Type 1 error rates. MBI terminology describes the position of confidence intervals in relation to smallest meaningful effect sizes. We show these positions correspond to combinations of one-sided tests of hypotheses about the presence or absence of meaningful effects, and formally describe MBI as a multiple decision procedure. MBI terminology operates as if tests are conducted at multiple alpha levels. We illustrate how error rates can be controlled by limiting each one-sided hypothesis test to a single alpha level. To provide transparent error control in a Neyman-Pearson framework and encourage the use of standard statistical software, we recommend replacing MBI with one-sided tests against smallest meaningful effects, or pairs of such tests as in equivalence testing. Researchers should pre-specify their hypotheses and alpha levels, perform a priori sample size calculations, and justify all assumptions. Our recommendations show researchers what tests to use and how to design and report their statistical analyses to accord with standard frequentist practice.

scholarly journals On the Statistical Testing Methods for Single Laboratory Validation of Qualitative Microbiological Assays with a Paired Design

2020 ◽  
Vol 103 (6) ◽  
pp. 1667-1679
Author(s):  
Shizhen S Wang
Keyword(s):  
Average Power ◽  
Mixed Effects ◽  
Error Rates ◽  
T Test ◽  
Type 1 Error ◽  
Minimum Detectable Difference ◽  
Microbiological Assays ◽  
Paired Design ◽  
Paired T Test

Abstract Background There are several statistical methods for detecting a difference of detection rates between alternative and reference qualitative microbiological assays in a single laboratory validation study with a paired design. Objective We compared performance of eight methods including McNemar’s test, sign test, Wilcoxon signed-rank test, paired t-test, and the regression methods based on conditional logistic (CLOGIT), mixed effects complementary log-log (MCLOGLOG), mixed effects logistic (MLOGIT) models, and a linear mixed effects model (LMM). Methods We first compared the minimum detectable difference in the proportion of detections between the alternative and reference detection methods among these statistical methods for a varied number of test portions. We then compared power and type 1 error rates of these methods using simulated data. Results The MCLOGLOG and MLOGIT models had the lowest minimum detectable difference, followed by the LMM and paired t-test. The MCLOGLOG and MLOGIT models had the highest average power but were anticonservative when correlation between the pairs of outcome values of the alternative and reference methods was high. The LMM and paired t-test had mostly the highest average power when the correlation was low and the second highest average power when the correlation was high. Type 1 error rates of these last two methods approached the nominal value of significance level when the number of test portions was moderately large (n > 20). Highlights The LMM and paired t-test are better choices than other competing methods, and we provide an example using real data.

scholarly journals Type 1 Error Rates of the Parsimony Permutation Tail Probability Test

2002 ◽  
Vol 51 (3) ◽  
pp. 524-527 ◽  
Author(s):  
Mark Wilkinson ◽  
Pedro R. Peres-Neto ◽  
Peter G. Foster ◽  
Clive B. Moncrieff
Keyword(s):  
Tail Probability ◽  
Error Rates ◽  
Type 1 Error

scholarly journals Additional file to "Why Psychologists Should by Default Use Welch's t-test Instead of Student's t-test." (in press for the International Review of Social Psychology)

2017 ◽  
Author(s):  
Marie Delacre ◽  
Daniel Lakens ◽  
Christophe Leys
Keyword(s):  
Psychological Research ◽  
Error Rates ◽  
T Test ◽  
International Review ◽  
Type 1 Error ◽  
Statistical Inferences ◽  
Homogeneity Of Variance ◽  
Student’S T ◽  
Student’S T Test

When comparing two independent groups, researchers in Psychology commonly use Student’s t-test. Assumptions of normality and of homogeneity of variance underlie this test. More often than not, when these conditions are not met, Student’s t-test can be severely biased, and leads to invalid statistical inferences. Moreover, we argue that the assumption of equal variances will seldom hold in psychological research and that choosing between Student’s t-test or Welch’s t-test based on the outcomes of a test of the equality of variances often fails to provide an appropriate answer. We show that the Welch’s t-test provides a better control of Type 1 error rates when the assumption of homogeneity of variance is not met, and loses little robustness compared to Student’s t-test when the assumptions are met. We argue that Welch’s t-test should be used as a default strategy.

A three-gene-based Type 1 diabetes diagnostic signature

2020 ◽  
Vol 26 ◽  
Author(s):  
Rongrong Wang ◽  
Yanan Zhou ◽  
Yan Zhang ◽  
Shaoqing Li ◽  
Runzhou Pan ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Bioinformatic Analysis ◽  
Diagnostic Methods ◽  
Sample Type ◽  
Ppi Network ◽  
Hub Genes

Background:: Type 1 diabetes is a chronic autoimmune disease featured by insulin deprivation caused by pancreatic β-cell loss, followed by hyperglycaemia. Objective: Currently, there is no cure for this disease in clinical treatment, and patients have to accept a lifelong injection of insulin. The exploration of potential diagnosis biomarkers through analysis of mass data by bioinformatic tools and machine learning is important for Type 1 diabetes. Methods: We collected two mRNA expression datasets of Type 1 diabetes peripheral blood samples from GEO, screened out differentially expressed genes (DEGs) by R software, conducted GO and KEGG pathway enrichment using the DEGs. And the STRING database and Cytoscape were used to build PPI network and predict hub genes. We constructed a Logistic regression model by using the hub genes to assess sample type. Results: Bioinformatic analysis of GEO dataset revealed 92 and 75 DEGs in GSE50098 and GSE9006 datasets, separately, and 10 overlapping DEGs. PPI network of these 10 DEGs showed 7 hub genes, namely EGR1, LTF, CXCL1, TNFAIP6, PGLYRP1, CHI3L1 and CAMP. We built a Logistic regression basing on these hub genes and optimized the model to 3 genes (LTF, CAMP and PGLYRP1) based Logistic model. The values of area under curve (AUC) of training set GSE50098 and testing set GSE9006 were 0.8452 and 0.8083, indicating the efficacy of this model. Conclusion: Integrated bioinformatic analysis of gene expression in Type 1 diabetes and the effective Logistic regression model built in our study may provide promising diagnostic methods for Type 1 diabetes.

scholarly journals Knowledge and Attitude on Insulin Self-Administration among Type 1 Diabetic Patients at Metu Karl Referral Hospital, Ethiopia

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Tewodros Yosef
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Referral Hospital ◽  
Favorable Attitude ◽  
Diabetic Patients ◽  
Self Administration ◽  
Knowledge And Attitude ◽  
Type 1 Diabetic Patients

Background. Diabetes mellitus (DM) is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. It is a public health problem as the disease is epidemic in both developed and developing counties. Knowledge and attitude of patients regarding insulin self-administration could lead to better management of diabetes and eventually a good quality of life. Despite this, the evidence that showed the knowledge and attitude on insulin self-administration is a substantial deficiency in Ethiopia. Objective. To assess the level of knowledge, attitude, and associated factors on insulin self-administration among type 1 diabetic patients at Metu Karl Referral Hospital, Ethiopia, in 2019. Methods. An institutional-based cross-sectional study was conducted among systematically selected 245 type 1 diabetic patients at Metu Karl Referral Hospital, Ethiopia, in January 2019. The data were collected through a face-to-face interview. The collected data were entered using EpiData version 4.2.0.0, cleaned, and analyzed using SPSS version 20. A binary logistic regression model was used. Independent variables with a P value of less than 0.05 in the multivariable logistic regression model were considered significant. Results. Out of 242 type 1 diabetic patients interviewed, 93 (38.4%, 95% CI (32.3%-44.5%)) had good knowledge and 50 (20.7%, 95% CI (15.6%-25.8%)) had favorable attitude on insulin self-administration. The study also found that being unmarried (AOR=3.59, 95% CI (1.15-11.3), P=0.028), increased educational level (AOR=3.02, 95% CI (1.36-6.74), P=0.007), and more years of treatment (AOR=3.70, 95% CI (1.16-11.8), P=0.027) were factors associated with good knowledge on insulin self-administration, whereas being a member of DM association (AOR=3.57, 95% CI (1.66-7.69), P=0.001) was the only factor associated with favorable attitude on insulin self-administration. Conclusion. The knowledge and attitude on insulin self-administration among type 1 diabetic patients were substantially low. Diabetes and insulin self-administration education should be imparted by health professionals at each follow-up visit. Besides, strengthening of information, education, and communication (IEC) on the issue of diabetes and insulin self-administration using mass media (television/radio) plays paramount importance.

Sign in / Sign up

Export Citation Format

Share Document