scholarly journals On Stratified Adjusted Tests by Binomial Trials

2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Asanao Shimokawa ◽  
Etsuo Miyaoka
Keyword(s):  
Treatment Effect ◽  
Randomized Clinical Trials ◽  
Type I Error ◽  
Traditional Approach ◽  
Control Group ◽  
Type I ◽  
Test Statistics ◽  
Number Of Patients ◽  
The Difference ◽  
Conservative Result

AbstractTo estimate or test the treatment effect in randomized clinical trials, it is important to adjust for the potential influence of covariates that are likely to affect the association between the treatment or control group and the response. If these covariates are known at the start of the trial, random assignment of the treatment within each stratum would be considered. On the other hand, if these covariates are not clear at the start of the trial, or if it is difficult to allocate the treatment within each stratum, completely randomized assignment of the treatment would be performed. In both sampling structures, the use of a stratified adjusted test is a useful way to evaluate the significance of the overall treatment effect by reducing the variance and/or bias of the result. If the trial has a binary endpoint, the Cochran and Mantel-Haenszel tests are generally used. These tests are constructed based on the assumption that the number of patients within a stratum is fixed. However, in practice, the stratum sizes are not fixed at the start of the trial in many situations, and are instead allowed to vary. Therefore, there is a risk that using these tests under such situations would result in an error in the estimated variation of the test statistics. To handle the problem, we propose new test statistics under both sampling structures based on multinomial distributions. Our proposed approach is based on the Cochran test, and the difference between the two tests tends to have similar values in the case of a large number of patients. When the total number of patients is small, our approach yields a more conservative result. Through simulation studies, we show that the new approach could correctly maintain the type I error better than the traditional approach.

scholarly journals Estimating and reporting treatment effects in clinical trials for weight management: using estimands to interpret effects of intercurrent events and missing data

2021 ◽  
Author(s):  
Sean Wharton ◽  
Arne Astrup ◽  
Lars Endahl ◽  
Michael E. J. Lean ◽  
Altynai Satylganova ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Weight Management ◽  
Treatment Effect ◽  
Repeated Measures ◽  
Treatment Effects ◽  
Randomized Clinical Trials ◽  
Weight Losses ◽  
The Difference ◽  
Treatment Of Obesity

AbstractIn the approval process for new weight management therapies, regulators typically require estimates of effect size. Usually, as with other drug evaluations, the placebo-adjusted treatment effect (i.e., the difference between weight losses with pharmacotherapy and placebo, when given as an adjunct to lifestyle intervention) is provided from data in randomized clinical trials (RCTs). At first glance, this may seem appropriate and straightforward. However, weight loss is not a simple direct drug effect, but is also mediated by other factors such as changes in diet and physical activity. Interpreting observed differences between treatment arms in weight management RCTs can be challenging; intercurrent events that occur after treatment initiation may affect the interpretation of results at the end of treatment. Utilizing estimands helps to address these uncertainties and improve transparency in clinical trial reporting by better matching the treatment-effect estimates to the scientific and/or clinical questions of interest. Estimands aim to provide an indication of trial outcomes that might be expected in the same patients under different conditions. This article reviews how intercurrent events during weight management trials can influence placebo-adjusted treatment effects, depending on how they are accounted for and how missing data are handled. The most appropriate method for statistical analysis is also discussed, including assessment of the last observation carried forward approach, and more recent methods, such as multiple imputation and mixed models for repeated measures. The use of each of these approaches, and that of estimands, is discussed in the context of the SCALE phase 3a and 3b RCTs evaluating the effect of liraglutide 3.0 mg for the treatment of obesity.

scholarly journals Testing for treatment effect in covariate-adaptive randomized trials with generalized linear models and omitted covariates

2021 ◽  
pp. 096228022110082
Author(s):  
Yang Li ◽  
Wei Ma ◽  
Yichen Qin ◽  
Feifang Hu
Keyword(s):  
Generalized Linear Models ◽  
Treatment Effect ◽  
Linear Models ◽  
Type I Error ◽  
Type I ◽  
Test Statistic ◽  
Asymptotic Results ◽  
Adaptive Randomization ◽  
Adjustment Method ◽  
Inflated Type

Concerns have been expressed over the validity of statistical inference under covariate-adaptive randomization despite the extensive use in clinical trials. In the literature, the inferential properties under covariate-adaptive randomization have been mainly studied for continuous responses; in particular, it is well known that the usual two-sample t-test for treatment effect is typically conservative. This phenomenon of invalid tests has also been found for generalized linear models without adjusting for the covariates and are sometimes more worrisome due to inflated Type I error. The purpose of this study is to examine the unadjusted test for treatment effect under generalized linear models and covariate-adaptive randomization. For a large class of covariate-adaptive randomization methods, we obtain the asymptotic distribution of the test statistic under the null hypothesis and derive the conditions under which the test is conservative, valid, or anti-conservative. Several commonly used generalized linear models, such as logistic regression and Poisson regression, are discussed in detail. An adjustment method is also proposed to achieve a valid size based on the asymptotic results. Numerical studies confirm the theoretical findings and demonstrate the effectiveness of the proposed adjustment method.

scholarly journals Efficient Noninferiority Testing Procedures for Simultaneously Assessing Sensitivity and Specificity of Two Diagnostic Tests

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Guogen Shan ◽  
Amei Amei ◽  
Daniel Young
Keyword(s):  
Sensitivity And Specificity ◽  
Diagnostic Tests ◽  
Error Control ◽  
Type I Error ◽  
Binary Outcomes ◽  
Type I ◽  
Test Statistics ◽  
Asymptotic Approach ◽  
Testing Procedures ◽  
Assessing Sensitivity

Sensitivity and specificity are often used to assess the performance of a diagnostic test with binary outcomes. Wald-type test statistics have been proposed for testing sensitivity and specificity individually. In the presence of a gold standard, simultaneous comparison between two diagnostic tests for noninferiority of sensitivity and specificity based on an asymptotic approach has been studied by Chen et al. (2003). However, the asymptotic approach may suffer from unsatisfactory type I error control as observed from many studies, especially in small to medium sample settings. In this paper, we compare three unconditional approaches for simultaneously testing sensitivity and specificity. They are approaches based on estimation, maximization, and a combination of estimation and maximization. Although the estimation approach does not guarantee type I error, it has satisfactory performance with regard to type I error control. The other two unconditional approaches are exact. The approach based on estimation and maximization is generally more powerful than the approach based on maximization.

scholarly journals Implementation of the Omega (ω) Index to detect large-scale systematic cheating

2019 ◽  
Author(s):  
Alvin Vista
Keyword(s):  
Standardized Testing ◽  
Large Scale ◽  
Type I Error ◽  
R Package ◽  
Statistical Testing ◽  
System Level ◽  
Control Group ◽  
Type I ◽  
Data Contamination ◽  
Cheating Detection

Cheating detection is an important issue in standardized testing, especially in large-scale settings. Statistical approaches are often computationally intensive and require specialised software to conduct. We present a two-stage approach that quickly filters suspected groups using statistical testing on an IRT-based answer-copying index. We also present an approach to mitigate data contamination and improve the performance of the index. The computation of the index was implemented through a modified version of an open source R package, thus enabling wider access to the method. Using data from PIRLS 2011 (N=64,232) we conduct a simulation to demonstrate our approach. Type I error was well-controlled and no control group was falsely flagged for cheating, while 16 (combined n=12,569) of the 18 (combined n=14,149) simulated groups were detected. Implications for system-level cheating detection and further improvements of the approach were discussed.

Approximate confidence intervals for the difference in proportions for partially observed binary data

2021 ◽  
pp. 096228022110605
Author(s):  
Ujjwal Das ◽  
Ranojoy Basu
Keyword(s):  
Binary Data ◽  
Type I Error ◽  
Missing At Random ◽  
Matched Pair ◽  
Data Sets ◽  
Type I ◽  
Real World Data ◽  
Partially Observed ◽  
The Difference ◽  
Interval Estimator

We consider partially observed binary matched-pair data. We assume that the incomplete subjects are missing at random. Within this missing framework, we propose an EM-algorithm based approach to construct an interval estimator of the proportion difference incorporating all the subjects. In conjunction with our proposed method, we also present two improvements to the interval estimator through some correction factors. The performances of the three competing methods are then evaluated through extensive simulation. Recommendation for the method is given based on the ability to preserve type-I error for various sample sizes. Finally, the methods are illustrated in two real-world data sets. An R-function is developed to implement the three proposed methods.

scholarly journals A novel approach to sample size determination of clinical trials for rare diseases assuming symmetry

2019 ◽  
Author(s):  
Emma Wang ◽  
Bernard North ◽  
Peter Sasieni
Keyword(s):  
Clinical Trials ◽  
Hypothesis Testing ◽  
Rare Diseases ◽  
Statistical Power ◽  
Type I Error ◽  
Standard Of Care ◽  
Testing Procedure ◽  
Sample Size Determination ◽  
Type I ◽  
Number Of Patients

Abstract Abstract Background Rare and uncommon diseases are difficult to study in clinical trials due to limited recruitment. If the incidence of the disease is very low, international collaboration can only solve the problem to a certain extent. A consequence is a disproportionately high number of deaths from rare diseases, due to unclear knowledge of the best way to treat patients suffering from these diseases. Hypothesis testing using the conventional Type I error in conjunction with the number of patients who can realistically be enrolled for a rare disease, would cause the trial to be severely underpowered. Methods Our proposed method recognises these pragmatic limitations and suggests a new testing procedure, wherein conclusion of efficacy of one arm is grounded in robust evidence of non-inferiority in the endpoint of interest, and reasonable evidence of superiority, over the other arm. Results Simulations were conducted to illustrate the gains in statistical power compared with conventional hypothesis testing in several statistical settings as well as the example of clinical trials for Merkel cell carcinoma, a rare skin tumour. Conclusions Our proposed analysis method enables conducting clinical trials for rare diseases, potentially leading to better standard of care for patients suffering from rare diseases

scholarly journals Вплив поліморфізму генів PTHR1 та COL1A1 на розвиток захворювань тканин пародонта у людей молодого віку

2017 ◽  
Author(s):  
M. A. Luchynskyi ◽  
Y. V. Boliuk ◽  
V. M. Luchynskyi
Keyword(s):  
Young People ◽  
Periodontal Diseases ◽  
Main Group ◽  
Control Group ◽  
Type I ◽  
Periodontal Tissue ◽  
Endogenous Factors ◽  
Stage Of Development ◽  
The Difference ◽  
Collagen Chain

At the present stage of development of dentistry, the leading Ukrainian and foreign scientists devote a considerable part of the research to a deeper study of the etiology and pathogenetic mechanisms of periodontal tissue diseases and the influence of various exogenous and endogenous factors on their course.The aim of the study – to learn the ability and methods of forecasting and early diagnosis of the periodontal tissue lesions in young people. Materials and Methods. During our research we examined 24 young people with periodontal tissue diseases, who were included to the main group, and 15 healthy people, who formed the control group. The complex clinical examination was performed in each research group. It was studied the distribution of polymorphous variants of the type I parathormone receptor and the α1-chain of collagen gene with a help of polymerase chain reaction by restrictase cleavage of DNA fragments and electrophoresis in polyacrylamide gel (AA/BA 29:1). Results and Discussion. The distribution of genotypes by PTHR1 gene in control group was similar to those in main group (p>0.05). Also we didn’t find the difference between frequencies of the separate alleles in people with periodontal tissue pathology and without it (p>0.05). Yes, the repetitions of the allele 5 encoding normal type I parathormone receptor were found more often, comparing with the allele 6 that is responsible for the formation of unfunctional PTHR1 (р<0.001) in both main and control groups. The dominance of the genotype TT, which corresponds to the pathology, was found in young people with the periodontal tissue lesions – (38.46 ± 4.79) %, while among the control group the genotype of norm GG was met the most often – (68.24±5.08) %. Also, the frequency of repetitions of the allele T encoding the imperfect collagen chain was (57.60±3.79) % in young people with periodontal diseases, and in the control group this figure was (13.27±2.81) %, p<0.001.  Conclusions. According to our results the presence of allele T and genotype TT that correspond the imperfect collagen chain may be one of the causes of periodontal tissue pathology.

scholarly journals Efektivitas Pemberian Edukasi Berbasis Audiovisual dan Tutorial Tentang Antiretroviral (ARV) Terhadap Kepatuhan Pengobatan pada Pasien HIV/AIDS di Klinik Teratai Rumah Sakit Hasan Sadikin Bandung Tahun 2016

2017 ◽  
Vol 3 (2) ◽  
pp. 38
Author(s):  
Sinta Fresia
Keyword(s):  
Quality Of Life ◽  
Control Group ◽  
Research Use ◽  
Social And Emotional ◽  
Number Of Patients ◽  
Significant Difference ◽  
Quasi Experimental ◽  
The Difference ◽  
Hiv Aids

Abstrak Latar Belakang : Terjadinya peningkatan jumlah pasien HIV/AIDS dan rendahnya kualitas hidup pasien HIV/AIDS menimbulkan masalah yang cukup luas pada individu yang terinfeksi yakni masalah fisik, social dan emosional.Untuk meningkatkan kualitas dan harapan hidup pasien HIV/AIDS harus mendapatkan terapi Antiretrovirus (ARV) seumur hidup dan dibutuhkan pengawasan terhadap kepatuhan minum obat.Oleh karena itu pasien HIV/AIDS membutuhkan edukasi untuk meningkatkan kepatuhan minum obat dengan metode terbaru yaitu tutorial dan audiovisual.Tujuan penelitian ini untuk menganalisa perbedaan efektivitas pemberian edukasi berbasis audiovisual dan tutorial tentang ARV terhadap kepatuhan pengobatan pasien HIV/ AIDS. Metode : Penelitian ini menggunakan desain Quasi eksperimental dengan rancangan pretest-posttes design without control group.Jumlah sampel 27 responden dibagi 3 kelompok dengan 3 perlakuan berbeda.Masing-masing 9 responden diberikan edukasi dengan metode audiovisual, tutorial, audiovisual dan tutorial.Penelitian dilakukan di Klinik Teratai Rumah Sakit Hasan Sadikin Bandung pada bulan Mei-Juni 2016. Hasil : Ada perbedaan rata-rata mean kepatuhan edukasi dengan audiovisual 2,444, (Pvalue=0,003, 95% CI=1,107-3,782), edukasi dengan metode tutorial perbedaan mean 1,556 (Pvalue=0,023, 95% CI=1,274-2,837), edukasi dengan audiovisual dan tutorial didapatkan perbedaan mean 3,667 (Pvalue=0,003, 95% CI=1,670-5,664). Kesimpulan : Terdapat perbedaan yang significant rata-rata kepatuhan pada masing-masing kelompok intervensi edukasi.Kombinasi edukasi berbasis audiovisual dan tutorial memberikan hasil yang paling baik. Abstract Background : An increasing number of patients with HIV/AIDS and low quality of life of patients with HIV/AIDS cause considerable problems in individuals infected area.There are physical, social and emotional problems.To improve the quality of life of receive antiretroviral (ARV) therapy for life.This requires adherence and supervision taking medication. There fore urgently needed education to improve adherence with the latest audiovisual and tutorial methods. The purpose of this research is to analyze the difference effectiveness of education based audiovisual and tutorial method on ARV treatment adherence with HIV/AIDS patients.Methods : This research use quasi experimental design with pretest and posttest without control group. The numbers of sample in this research is 27 sample. Responden group divided into three different education methode. 9 responden in audiovisual methode,9 responden in tutorial methode and 9 responden in audiovisual and tutorial methode. The study was conducted at the Clinic Teratai Hasan Sadikin Hospital in May-June, 2016. Results : There is a diference in average adherence. In audiovisual methode mean 2,444 (Pvalue=0,003, 95% CI=1,107-3,782), tutorial methode 1,556(Pvalue=0,023, 95% CI=1,274-2,837), audiovisual and tutorial methode mean 3,667 (Pvalue =0,003, 95% CI=1,670-5,664).Conclusion : There is a significant difference in the average adherence in difference methode.Especially in audiovisual and tutorial methode. The combination of audiovisual and tutorial-based education gives the best results

scholarly journals Statistical Issues and Lessons Learned From COVID-19 Clinical Trials With Lopinavir-Ritonavir and Remdesivir (Preprint)

2020 ◽  
Author(s):  
Guosheng Yin ◽  
Chenyang Zhang ◽  
Huaqing Jin
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Error Rate ◽  
Recovery Time ◽  
Type I Error ◽  
Lessons Learned ◽  
Type I ◽  
Statistical Issues ◽  
The Difference ◽  
Mean Time

BACKGROUND Recently, three randomized clinical trials on coronavirus disease (COVID-19) treatments were completed: one for lopinavir-ritonavir and two for remdesivir. One trial reported that remdesivir was superior to placebo in shortening the time to recovery, while the other two showed no benefit of the treatment under investigation. OBJECTIVE The aim of this paper is to, from a statistical perspective, identify several key issues in the design and analysis of three COVID-19 trials and reanalyze the data from the cumulative incidence curves in the three trials using more appropriate statistical methods. METHODS The lopinavir-ritonavir trial enrolled 39 additional patients due to insignificant results after the sample size reached the planned number, which led to inflation of the type I error rate. The remdesivir trial of Wang et al failed to reach the planned sample size due to a lack of eligible patients, and the bootstrap method was used to predict the quantity of clinical interest conditionally and unconditionally if the trial had continued to reach the originally planned sample size. Moreover, we used a terminal (or cure) rate model and a model-free metric known as the restricted mean survival time or the restricted mean time to improvement (RMTI) to analyze the reconstructed data. The remdesivir trial of Beigel et al reported the median recovery time of the remdesivir and placebo groups, and the rate ratio for recovery, while both quantities depend on a particular time point representing local information. We use the restricted mean time to recovery (RMTR) as a global and robust measure for efficacy. RESULTS For the lopinavir-ritonavir trial, with the increase of sample size from 160 to 199, the type I error rate was inflated from 0.05 to 0.071. The difference of RMTIs between the two groups evaluated at day 28 was –1.67 days (95% CI –3.62 to 0.28; <i>P</i>=.09) in favor of lopinavir-ritonavir but not statistically significant. For the remdesivir trial of Wang et al, the difference of RMTIs at day 28 was –0.89 days (95% CI –2.84 to 1.06; <i>P</i>=.37). The planned sample size was 453, yet only 236 patients were enrolled. The conditional prediction shows that the hazard ratio estimates would reach statistical significance if the target sample size had been maintained. For the remdesivir trial of Beigel et al, the difference of RMTRs between the remdesivir and placebo groups at day 30 was –2.7 days (95% CI –4.0 to –1.2; <i>P</i>&lt;.001), confirming the superiority of remdesivir. The difference in the recovery time at the 25th percentile (95% CI –3 to 0; <i>P</i>=.65) was insignificant, while the differences became more statistically significant at larger percentiles. CONCLUSIONS Based on the statistical issues and lessons learned from the recent three clinical trials on COVID-19 treatments, we suggest more appropriate approaches for the design and analysis of ongoing and future COVID-19 trials.

scholarly journals Evaluation of the Impact of Calibration of Patient-Reported Outcomes Measures on Clinical Trial Results: A Simulation Study based on Rasch Measurement Theory

2021 ◽  
Author(s):  
Angély Loubert ◽  
Antoine Regnault ◽  
Véronique Sébille ◽  
Jean-Benoit Hardouin
Keyword(s):  
Clinical Trial ◽  
Treatment Effect ◽  
Type I Error ◽  
Measurement Theory ◽  
Item Parameter ◽  
Parameter Estimates ◽  
Type I ◽  
Item Parameters ◽  
Patient Reported ◽  
The Impact

Abstract BackgroundIn the analysis of clinical trial endpoints, calibration of patient-reported outcomes (PRO) instruments ensures that resulting “scores” represent the same quantity of the measured concept between applications. Rasch measurement theory (RMT) is a psychometric approach that guarantees algebraic separation of person and item parameter estimates, allowing formal calibration of PRO instruments. In the RMT framework, calibration is performed using the item parameter estimates obtained from a previous “calibration” study. But if calibration is based on poorly estimated item parameters (e.g., because the sample size of the calibration sample was low), this may hamper the ability to detect a treatment effect, and direct estimation of item parameters from the trial data (non-calibration) may then be preferred. The objective of this simulation study was to assess the impact of calibration on the comparison of PRO results between treatment groups, using different analysis methods.MethodsPRO results were simulated following a polytomous Rasch model, for a calibration and a trial sample. Scenarios included varying sample sizes, with instrument of varying number of items and modalities, and varying item parameters distributions. Different treatment effect sizes and distributions of the two patient samples were also explored. Comparison of treatment groups was performed using different methods based on a random effect Rasch model. Calibrated and non-calibrated approaches were compared based on type-I error, power, bias, and variance of the estimates for the difference between groups.Results There was no impact of the calibration approach on type-I error, power, bias, and dispersion of the estimates. Among other findings, mistargeting between the PRO instrument and patients from the trial sample (regarding the level of measured concept) resulted in a lower power and higher position bias than appropriate targeting. ConclusionsCalibration of PROs in clinical trials does not compromise the ability to accurately assess a treatment effect and is essential to properly interpret PRO results. Given its important added value, calibration should thus always be performed when a PRO instrument is used as an endpoint in a clinical trial, in the RMT framework.

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