scholarly journals Nothing Wrong About Change: The Adequate Choice of the Dependent Variable and Design in Prediction of Intervention Success

2020 ◽  
Author(s):  
André Mattes ◽  
Mandy Roheger
Keyword(s):  
Regression Model ◽  
Test Score ◽  
Statistical Power ◽  
Individualized Medicine ◽  
Intervention Group ◽  
Change Score ◽  
Individual Characteristics ◽  
Control Group ◽  
Absolute Change ◽  
True Model

Abstract Background Investigating predictors of intervention success is a common approach in medical research. In the light of an individualized medicine, it is important not only to investigate the effects of certain pharmacological and nonpharmacological interventions, but also to examine specific individual characteristics of participants who do or do not benefit from these interventions. However, results on specific predictors of intervention success in the overall field are mixed and inconsistent due to different and sometimes inappropriate statistical methods used. Therefore, the present paper gives a guidance on the appropriate use of multiple regression analyses to identify predictors of pharmacological and nonpharmacological interventions.Methods We simulated data based on a predefined true model and ran a series of different analyses to evaluate their performance in retrieving the true model coefficients. The true model consisted of a 2 (between: experimental vs. control group) x 2 (within: pre- vs. post-treatment) design with two continuous predictors, one of which predicted the success in the intervention group and the other did not. In analyzing the data, we considered four commonly used dependent variables (post-test score, absolute change score, relative change score, residual score), five regression models, eight sample sizes, and four levels of reliability.Results Our results indicated that a regression model including the investigated predictor, Group (experimental vs. control), pre-test score, and the interaction between the investigated predictor and the Group as predictors, and the absolute change score as the dependent variable seemed most convenient for the given experimental design. Although the pre-test score should be included as a predictor in the regression model for reasons of statistical power, its coefficient should not be interpreted because even if there is no true relationship, a negative and statistically significant regression coefficient commonly emerges.Conclusion Employing simulation methods, theoretical reasoning, and mathematical derivations, we were able to derive recommendations regarding the analysis of data in one of the most prevalent experimental designs in research on pharmacological and nonpharmacological interventions and external predictors of intervention success. These insights can contribute to the application of considered data analyses in future studies and facilitate cumulative knowledge gain.

scholarly journals Nothing wrong about change: the adequate choice of the dependent variable and design in prediction of cognitive training success

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
André Mattes ◽  
Mandy Roheger
Keyword(s):  
Regression Model ◽  
Cognitive Training ◽  
Test Score ◽  
Statistical Power ◽  
Individualized Medicine ◽  
Intervention Group ◽  
Change Score ◽  
Control Group ◽  
Absolute Change ◽  
True Model

Abstract Background Even though investigating predictors of intervention success (e.g Cognitive Training, CT) is gaining more and more interest in the light of an individualized medicine, results on specific predictors of intervention success in the overall field are mixed and inconsistent due to different and sometimes inappropriate statistical methods used. Therefore, the present paper gives a guidance on the appropriate use of multiple regression analyses to identify predictors of CT and similar non-pharmacological interventions. Methods We simulated data based on a predefined true model and ran a series of different analyses to evaluate their performance in retrieving the true model coefficients. The true model consisted of a 2 (between: experimental vs. control group) × 2 (within: pre- vs. post-treatment) design with two continuous predictors, one of which predicted the success in the intervention group and the other did not. In analyzing the data, we considered four commonly used dependent variables (post-test score, absolute change score, relative change score, residual score), five regression models, eight sample sizes, and four levels of reliability. Results Our results indicated that a regression model including the investigated predictor, Group (experimental vs. control), pre-test score, and the interaction between the investigated predictor and the Group as predictors, and the absolute change score as the dependent variable seemed most convenient for the given experimental design. Although the pre-test score should be included as a predictor in the regression model for reasons of statistical power, its coefficient should not be interpreted because even if there is no true relationship, a negative and statistically significant regression coefficient commonly emerges. Conclusion Employing simulation methods, theoretical reasoning, and mathematical derivations, we were able to derive recommendations regarding the analysis of data in one of the most prevalent experimental designs in research on CT and external predictors of CT success. These insights can contribute to the application of considered data analyses in future studies and facilitate cumulative knowledge gain.

scholarly journals Nothing wrong about change: The adequate choice of the dependent variable and design in prediction of cognitive training success

2020 ◽  
Author(s):  
André Mattes ◽  
Mandy Roheger
Keyword(s):  
Regression Model ◽  
Cognitive Training ◽  
Test Score ◽  
Statistical Power ◽  
Individualized Medicine ◽  
Intervention Group ◽  
Change Score ◽  
Control Group ◽  
Absolute Change ◽  
True Model

Abstract Background Even though investigating predictors of intervention success (e.g Cognitive Training, CT) is gaining more and more interest in the light of an individualized medicine, results on specific predictors of intervention success in the overall field are mixed and inconsistent due to different and sometimes inappropriate statistical methods used. Therefore, the present paper gives a guidance on the appropriate use of multiple regression analyses to identify predictors of CT and similar non-pharmacological interventions.Methods We simulated data based on a predefined true model and ran a series of different analyses to evaluate their performance in retrieving the true model coefficients. The true model consisted of a 2 (between: experimental vs. control group) x 2 (within: pre- vs. post-treatment) design with two continuous predictors, one of which predicted the success in the intervention group and the other did not. In analyzing the data, we considered four commonly used dependent variables (post-test score, absolute change score, relative change score, residual score), five regression models, eight sample sizes, and four levels of reliability.Results Our results indicated that a regression model including the investigated predictor, Group (experimental vs. control), pre-test score, and the interaction between the investigated predictor and the Group as predictors, and the absolute change score as the dependent variable seemed most convenient for the given experimental design. Although the pre-test score should be included as a predictor in the regression model for reasons of statistical power, its coefficient should not be interpreted because even if there is no true relationship, a negative and statistically significant regression coefficient commonly emerges.Conclusion Employing simulation methods, theoretical reasoning, and mathematical derivations, we were able to derive recommendations regarding the analysis of data in one of the most prevalent experimental designs in research on CT and external predictors of CT success. These insights can contribute to the application of considered data analyses in future studies and facilitate cumulative knowledge gain.

scholarly journals Nothing wrong about change: The adequate choice of the dependent variable and design in prediction of cognitive training success 

2020 ◽  
Author(s):  
André Mattes ◽  
Mandy Roheger
Keyword(s):  
Regression Model ◽  
Cognitive Training ◽  
Test Score ◽  
Statistical Power ◽  
Individualized Medicine ◽  
Intervention Group ◽  
Change Score ◽  
Control Group ◽  
Absolute Change ◽  
True Model

Abstract BackgroundEven though investigating predictors of intervention success (e.g Cognitive Training, CT) is gaining more and more interest in the light of an individualized medicine, results on specific predictors of intervention success in the overall field are mixed and inconsistent due to different and sometimes inappropriate statistical methods used. Therefore, the present paper gives a guidance on the appropriate use of multiple regression analyses to identify predictors of CT and similar non-pharmacological interventions.MethodsWe simulated data based on a predefined true model and ran a series of different analyses to evaluate their performance in retrieving the true model coefficients. The true model consisted of a 2 (between: experimental vs. control group) x 2 (within: pre- vs. post-treatment) design with two continuous predictors, one of which predicted the success in the intervention group and the other did not. In analyzing the data, we considered four commonly used dependent variables (post-test score, absolute change score, relative change score, residual score), five regression models, eight sample sizes, and four levels of reliability.ResultsOur results indicated that a regression model including the investigated predictor, Group (experimental vs. control), pre-test score, and the interaction between the investigated predictor and the Group as predictors, and the absolute change score as the dependent variable seemed most convenient for the given experimental design. Although the pre-test score should be included as a predictor in the regression model for reasons of statistical power, its coefficient should not be interpreted because even if there is no true relationship, a negative and statistically significant regression coefficient commonly emerges.ConclusionEmploying simulation methods, theoretical reasoning, and mathematical derivations, we were able to derive recommendations regarding the analysis of data in one of the most prevalent experimental designs in research on CT and external predictors of CT success. These insights can contribute to the application of considered data analyses in future studies and facilitate cumulative knowledge gain.

scholarly journals Nothing wrong about change: The adequate choice of the dependent variable and design in prediction of cognitive training success

2020 ◽  
Author(s):  
André Mattes ◽  
Mandy Roheger
Keyword(s):  
Regression Model ◽  
Cognitive Training ◽  
Test Score ◽  
Statistical Power ◽  
Individualized Medicine ◽  
Intervention Group ◽  
Change Score ◽  
Control Group ◽  
Absolute Change ◽  
True Model

Abstract Background Even though investigating predictors of intervention success (e.g Cognitive Training, CT) is gaining more and more interest in the light of an individualized medicine, results on specific predictors of intervention success in the overall field are mixed and inconsistent due to different and sometimes inappropriate statistical methods used. Therefore, the present paper gives a guidance on the appropriate use of multiple regression analyses to identify predictors of CT and similar non-pharmacological interventions.Methods We simulated data based on a predefined true model and ran a series of different analyses to evaluate their performance in retrieving the true model coefficients. The true model consisted of a 2 (between: experimental vs. control group) x 2 (within: pre- vs. post-treatment) design with two continuous predictors, one of which predicted the success in the intervention group and the other did not. In analyzing the data, we considered four commonly used dependent variables (post-test score, absolute change score, relative change score, residual score), five regression models, eight sample sizes, and four levels of reliability.Results Our results indicated that a regression model including the investigated predictor, Group (experimental vs. control), pre-test score, and the interaction between the investigated predictor and the Group as predictors, and the absolute change score as the dependent variable seemed most convenient for the given experimental design. Although the pre-test score should be included as a predictor in the regression model for reasons of statistical power, its coefficient should not be interpreted because even if there is no true relationship, a negative and statistically significant regression coefficient commonly emerges.Conclusion Employing simulation methods, theoretical reasoning, and mathematical derivations, we were able to derive recommendations regarding the analysis of data in one of the most prevalent experimental designs in research on CT and external predictors of CT success. These insights can contribute to the application of considered data analyses in future studies and facilitate cumulative knowledge gain.

scholarly journals 813. The Reduction of the acquisition rate of carbapenem resistant Acinetobacter baumannii after room privatization in the intensive care unit

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S448-S449
Author(s):  
Jongtak Jung ◽  
Pyoeng Gyun Choe ◽  
Chang Kyung Kang ◽  
Kyung Ho Song ◽  
Wan Beom Park ◽  
...  
Keyword(s):  
Regression Model ◽  
Acinetobacter Baumannii ◽  
Cox Regression ◽  
Univariate Analysis ◽  
Intervention Group ◽  
Feeding Tube ◽  
Control Group ◽  
Cox Regression Model ◽  
Carbapenem Resistant ◽  
Hospital Acquired

Abstract Background Acinetobacter baumannii is one of the major pathogens of hospital-acquired infection recently and hospital outbreaks have been reported worldwide. On September 2017, New intensive care unit(ICU) with only single rooms, remodeling from old ICU with multibed bay rooms, was opened in an acute-care tertiary hospital in Seoul, Korea. We investigated the effect of room privatization in the ICU on the acquisition of carbapenem-resistant Acinetobacter baumannii(CRAB). Methods We retrospectively reviewed medical records of patients who admitted to the medical ICU in a tertiary care university-affiliated 1,800-bed hospital from 1 January 2015 to 1 January 2019. Patients admitted to the medical ICU before the remodeling of the ICU were designated as the control group, and those who admitted to the medical ICU after the remodeling were designated as the intervention group. Then we compared the acquisition rate of CRAB between the control and intervention groups. Patients colonized with CRAB or patients with CRAB identified in screening tests were excluded from the study population. The multivariable Cox regression model was performed using variables with p-values of less than 0.1 in the univariate analysis. Results A total of 1,105 cases admitted to the ICU during the study period were analyzed. CRAB was isolated from 110 cases in the control group(n=687), and 16 cases in the intervention group(n=418). In univariate analysis, room privatization, prior exposure to antibiotics (carbapenem, vancomycin, fluoroquinolone), mechanical ventilation, central venous catheter, tracheostomy, the presence of feeding tube(Levin tube or percutaneous gastrostomy) and the length of ICU stay were significant risk factors for the acquisition of CRAB (p< 0.05). In the multivariable Cox regression model, the presence of feeding tube(Hazard ratio(HR) 4.815, 95% Confidence interval(CI) 1.94-11.96, p=0.001) and room privatization(HR 0.024, 95% CI 0.127-0.396, p=0.000) were independent risk factors. Table 1. Univariate analysis of Carbapenem-resistant Acinetobacter baumannii Table 2. Multivariable Cox regression model of the acquisition of Carbapenem-resistant Acinetobacter baumannii Conclusion In the present study, room privatization of the ICU was correlated with the reduction of CRAB acquisition independently. Remodeling of the ICU to the single room would be an efficient strategy for preventing the spreading of multidrug-resistant organisms and hospital-acquired infection. Disclosures All Authors: No reported disclosures

scholarly journals Does evidence support the high expectations placed in precision medicine? A bibliographic review

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 30 ◽  
Author(s):  
Jordi Cortés ◽  
José Antonio González ◽  
María Nuncia Medina ◽  
Markus Vogler ◽  
Marta Vilaró ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Randomized Trials ◽  
Intervention Group ◽  
Individual Characteristics ◽  
Point Estimate ◽  
Control Group ◽  
Eligibility Criteria ◽  
High Expectations ◽  
The Mean ◽  
And Control

Background: Precision medicine is the Holy Grail of interventions that are tailored to a patient’s individual characteristics. However, the conventional design of randomized trials assumes that each individual benefits by the same amount. Methods: We reviewed parallel trials with quantitative outcomes published in 2004, 2007, 2010 and 2013. We collected baseline and final standard deviations of the main outcome. We assessed homoscedasticity by comparing the outcome variability between treated and control arms. Results: The review provided 208 articles with enough information to conduct the analysis. At the end of the study, 113 (54%, 95% CI 47 to 61%) papers find less variability in the treated arm. The adjusted point estimate of the mean ratio (treated to control group) of the outcome variances is 0.89 (95% CI 0.81 to 0.97). Conclusions: Some variance inflation was observed in just 1 out of 6 interventions, suggesting the need for further eligibility criteria to tailor precision medicine. Surprisingly, the variance was more often smaller in the intervention group, suggesting, if anything, a reduced role for precision medicine.  Homoscedasticity is a useful tool for assessing whether or not the premise of constant effect is reasonable.

Effectiveness of a Student Pharmacist–Led Telephone Follow-Up Intervention to Improve Hemoglobin A1Cin Diabetic Patients

2019 ◽  
Vol 33 (6) ◽  
pp. 832-837 ◽  
Author(s):  
Samuel K. Peasah ◽  
Kathryn Granitz ◽  
Michelle Vu ◽  
Bobby Jacob
Keyword(s):  
Regression Model ◽  
Clinical Care ◽  
Intervention Group ◽  
Standard Of Care ◽  
Multiple Regression Model ◽  
Control Group ◽  
P Value ◽  
Diabetic Patients ◽  
Phone Calls

Objective:To evaluate the effectiveness of a student pharmacist–led telephone follow-up intervention to improve hemoglobin A1c(HbA1c) in diabetic patients.Methods:This was a prospective, randomized, pilot study to implement a telephone follow-up intervention for diabetic patients with HbA1c≥7%. Patients were recruited and randomized into intervention and control groups. All patients received standard of care. Patients in the intervention group additionally received weekly phone calls from a student pharmacist for 12 weeks to encourage medication adherence. HbA1cat baseline and end of study were measured and the data were analyzed using SAS version 9.4. Analysis included descriptive statistics and a multiple regression model to assess the association between the end of study and baseline HbA1cwhile controlling for demographics.Results:Seventy-eight patients participated and the average age was 62 (±11) years. Baseline HbA1cwas 8.2% (±1.4%) in the intervention group and 7.9% (±1.3%) in the control group. HbA1cdecreased by 0.35% in the intervention group ( P = .027) and increased by 0.338% in the control group ( P = .013). The end of study HbA1cwere higher in the control group even after controlling for baseline HbA1cs (0.5547, P value .002) in the regression model.Conclusion:Incorporating student pharmacists in physician offices to provide clinical care services could lead to improved patient outcomes and students’ clinical and research skills.

Statistics for Small Groups: The Power of the Pretest

1988 ◽  
Vol 13 (3) ◽  
pp. 142-146 ◽  
Author(s):  
David A. Cole
Keyword(s):  
Statistical Power ◽  
Change Score ◽  
Small Sample ◽  
Analysis Of Covariance ◽  
Small Samples ◽  
Group Differences ◽  
Control Group ◽  
Small Sample Sizes ◽  
Almost All ◽  
Treatment Control Group

In the area of severe-profound retardation, researchers are faced with small sample sizes. The question of statistical power is critical. In this article, three commonly used tests for treatment-control group differences are compared with respect to their relative power: the posttest-only approach, the change-score approach, and an analysis of covariance (ANCOVA) approach. In almost all cases, the ANCOVA approach is the more powerful than the other two, even when very small samples are involved. Finally, a fourth approach involving ANCOVA plus alternate rank assignments is examined and found to be superior even to the ANCOVA approach, especially in small sample cases. Use of slightly more sophisticated statistics in small sample research is recommended.

Predicting Working Memory Training Responsiveness in Parkinson’s Disease: Both “System Hardware” and Room for Improvement Are Needed

2021 ◽  
Vol 35 (2) ◽  
pp. 117-130
Author(s):  
Anja Ophey ◽  
Sarah Rehberg ◽  
Kathrin Giehl ◽  
Carsten Eggers ◽  
Paul Reker ◽  
...  
Keyword(s):  
Working Memory ◽  
Change Score ◽  
Individual Characteristics ◽  
Working Memory Training ◽  
Memory Training ◽  
Control Group ◽  
Cognitive Dysfunctions ◽  
Lower Baseline ◽  
Younger Age ◽  
Latent Change Score

Background. Patients with Parkinson’s disease (PD) are highly vulnerable to develop cognitive dysfunctions, and the mitigating potential of early cognitive training (CT) is increasingly recognized. Predictors of CT responsiveness, which could help to tailor interventions individually, have rarely been studied in PD. This study aimed to examine individual characteristics of patients with PD associated with responsiveness to targeted working memory training (WMT). Methods. Data of 75 patients with PD (age: 63.99 ± 9.74 years, 93% Hoehn & Yahr stage 2) without cognitive dysfunctions from a randomized controlled trial were analyzed using structural equation modeling. Latent change score models with and without covariates were estimated and compared between the WMT group ( n = 37), who participated in a 5-week adaptive WMT, and a waiting list control group ( n = 38). Results. Latent change score models yielded adequate model fit (χ2-test p > .05, SRMR ≤ .08, CFI ≥ .95). For the near-transfer working memory composite, lower baseline performance, younger age, higher education, and higher fluid intelligence were found to significantly predict higher latent change scores in the WMT group, but not in the control group. For the far-transfer executive function composite, higher self-efficacy expectancy tended to significantly predict larger latent change scores. Conclusions. The identified associations between individual characteristics and WMT responsiveness indicate that there has to be room for improvement (e.g., lower baseline performance) and also sufficient “hardware” (e.g., younger age, higher intelligence) to benefit in training-related cognitive plasticity. Our findings are discussed within the compensation versus magnification account. They need to be replicated by methodological high-quality research applying advanced statistical methods with larger samples.

scholarly journals Meditasi dengan suara alam dapat menurunkan stres pada mahasiswa keperawatan

2019 ◽  
Vol 7 (2) ◽  
pp. 145
Author(s):  
Wahyu Rochdiat Murdhiono ◽  
Santi Damayanti ◽  
Ni Luh Komang Sri Ayunia
Keyword(s):  
Nursing Students ◽  
Test Score ◽  
Intervention Group ◽  
Control Group ◽  
P Value ◽  
Consecutive Sampling ◽  
Post Test ◽  
Stress Score ◽  
The Difference ◽  
Quasi Experiment

Mahasiswa keperawatan memiliki risiko yang lebih tinggi untuk mengalami stres dibandingkan  mahasiswa kesehatan lainnya. Belum pernah ada peneltian yang menggabungkan terapi meditasi dengan terapi musik suara alam untuk menurunkan stres pada mahasiswa keperawatan di Yogyakarta. Tujuan penelitian ntuk mengetahui pengaruh meditasi dengan suara alam pada mahasiswa keperawatan. Penelitian ini merupakan penelitian quasi-experiment dengan pendekatan pre dan post-test nonequivalent control group. Sampel dipilih menggunakan teknik consecutive sampling dan dibagi menjadi dua kelompok, masing-masing berjumlah 30 orang. Instrumen penelitian menggunakan DASS-42. Median skor stres pada kelompok perlakuan sebesar 11,00 pada pre-test sedangkan post-test sebesar 7,00. Di kelompok kontrol, median skor stres pre-test sebesar 10,00 dan median skor stres post-test sebesar 9,50. Uji Wilcoxon untuk menganalisis perbedaan skor stres pre dan post-test menghasilkan nilai p 0,000 di kelompok perlakuan dan pada kelompok kontrol menunjukkan nilai p 0,137. Meditasi menggunakan musik suara alam dapat menurunkan stres dan dapat menjadi terapi komplementer alternatif yang dapat dilakukan perawat. Kata kunci: meditasi, musik suara alam, stres, mahasiswa keperawatan MEDITATION WITH SOUND OF NATURE CAN REDUCE STRESS IN NURSING STUDENTSABSTRACTNursing students have a higher risk to experience stress than other medical students. Previously, there has never been any research regarding meditation using the sound of nature to reduce stress in nursing students in Yogyakarta.Research objective to determine the influence of meditation with the sound of nature to reduce stress in nursing students. This is quasi-experiment research with a pre and post-test nonequivalent control group design. The samples were selected using consecutive sampling and divided into two groups, each was 30 respondents. The research instrument used was DASS 42. The pre-test median stress score in the intervention group was 11.00, and the post-test score was 7.00. In the control group, the pre-test median score was 10.00, and the post-test score of 9.50. Wilcoxon test used to analyze the difference of stress score in the intervention group (p-value = 0.000), and the difference in stress score in the control group (p-value = 0.137). Meditation using the sound of nature can reduce stress in nursing students and can be an alternative complementary therapy for nurses. Keywords: meditation, the sound of nature, stress, nursing students

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