A proportional risk model for time-to-event analysis in randomized controlled trials

2020 ◽  
pp. 096228022095359
Author(s):  
Oliver Kuss ◽  
Annika Hoyer
Keyword(s):  
Proportional Hazards Model ◽  
Failure Time ◽  
Risk Model ◽  
Likelihood Function ◽  
Controlled Trial ◽  
Parametric Method ◽  
Parametric Models ◽  
Time To Event ◽  
Technical Problems ◽  
Randomized Controlled

Regression models for continuous, binary, nominal, and ordinal outcomes almost completely rely on parametric models, whereas time-to-event outcomes are mainly analyzed by Cox’s Proportional Hazards model, an essentially non-parametric method. This is done despite a long list of disadvantages that have been reported for the hazard ratio, and also for the odds ratio, another effect measure sometimes used for time-to-event modelling. In this paper, we propose a parametric proportional risk model for time-to-event outcomes in a two-group situation. Modelling explicitly a risk instead of a hazard or an odds solves the current interpretational and technical problems of the latter two effect measures. The model further allows for computing absolute effect measures like risk differences or numbers needed to treat. As an additional benefit, results from the model can also be communicated on the original time scale, as an accelerated or a prolongated failure time thus facilitating interpretation for a non-technical audience. Parameter estimation by maximum likelihood, while properly accounting for censoring, is straightforward and can be implemented in each statistical package that allows coding and maximizing a univariate likelihood function. We illustrate the model with an example from a randomized controlled trial on efficacy of a new glucose-lowering drug for the treatment of type 2 diabetes mellitus and give the results of a small simulation study.

scholarly journals Effectiveness of Two Web-Based Interventions for Chronic Cancer-Related Fatigue Compared to an Active Control Condition: Results of the �Fitter na kanker� Randomized Controlled Trial (Preprint)

2016 ◽  
Author(s):  
Fieke Z. Bruggeman-Everts ◽  
Marije D. J. Wolvers ◽  
Rens van de Schoot ◽  
Miriam M. R. Vollenbroek-Hutten ◽  
Marije L. Van der Lee
Keyword(s):  
Randomized Controlled Trial ◽  
Active Control ◽  
Controlled Trial ◽  
Depression Scale ◽  
Secondary Outcome ◽  
Web Based ◽  
Technical Problems ◽  
Randomized Controlled ◽  
Cancer Related Fatigue ◽  
Fatigue Severity

BACKGROUND Approximately one third of all patients who have been successfully treated for cancer suffer from chronic cancer-related fatigue (CCRF). Effective and easily accessible interventions are needed for these patients. OBJECTIVE The current paper reports on the results of a 3-armed randomized controlled trial investigating the clinical effectiveness of two different guided Web-based interventions for reducing CCRF compared to an active control condition. METHODS Severely fatigued cancer survivors were recruited via online and offline channels, and self-registered on an open-access website. After eligibility checks, 167 participants were randomized via an embedded automated randomization function into: (1) physiotherapist-guided Ambulant Activity Feedback (AAF) therapy encompassing the use of an accelerometer (n=62); (2) psychologist-guided Web-based mindfulness-based cognitive therapy (eMBCT; n=55); or (3) an unguided active control condition receiving psycho-educational emails (n=50). All interventions lasted nine weeks. Fatigue severity was self-assessed using the Checklist Individual Strength - Fatigue Severity subscale (primary outcome) six times from baseline (T0b) to six months (T2). Mental health was self-assessed three times using the Hospital Anxiety and Depression Scale and Positive and Negative Affect Schedule (secondary outcome). Treatment dropout was investigated. RESULTS Multiple group latent growth curve analysis, corrected for individual time between assessments, showed that fatigue severity decreased significantly more in the AAF and eMBCT groups compared to the psycho-educational group. The analyses were checked by a researcher who was blind to allocation. Clinically relevant changes in fatigue severity were observed in 66% (41/62) of patients in AAF, 49% (27/55) of patients in eMBCT, and 12% (6/50) of patients in psycho-education. Dropout was 18% (11/62) in AAF, mainly due to technical problems and poor usability of the accelerometer, and 38% (21/55) in eMBCT, mainly due to the perceived high intensity of the program. CONCLUSIONS Both the AAF and eMBCT interventions are effective for managing fatigue severity compared to receiving psycho-educational emails. CLINICALTRIAL Trialregister.nl NTR3483; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=3483 (Archived by WebCite at http://www.webcitation.org/6NWZqon3o)

scholarly journals An Updated Algorithm for Moderate Censoring in Time-to-Event Data Using Rank-based Regression

2019 ◽  
pp. 1-18
Author(s):  
Milind A. Phadnis
Keyword(s):  
Survival Data ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Real Life ◽  
Dispersion Function ◽  
Event Data ◽  
Time To Event ◽  
Karnofsky Score ◽  
Time To Event Data

Aim: To propose an updated algorithm with an extra step added to the Newton-type algorithm used in robust rank based non-parametric regression for minimizing the dispersion function associated with Wilcoxon scores in order to account for the effect of covariates. Methodology: The proposed accelerated failure time approach is aimed at incorporating right random censoring in survival data sets for low to moderate levels of censoring. The existing Newton algorithm is modified to account for the effect of one or more covariates. This is done by first applying Mantel scores to residuals obtained from a regression model, and second by minimizing the dispersion function of these scored residuals. Diagnostic check of the model fit is performed by observing the distribution of the residuals and suitable Bent scores are considered in the case of skewed residuals. To demonstrate the efficacy of this method, a simulation study is conducted to compare the power of this method under three different scenarios: non-proportional hazard, proportional and constant hazard, and proportional but non-constant hazard. Results: In most situations, this method yielded reasonable estimates of power for detecting an association of the covariate with the response as compared to popular parametric and semi-parametric approaches. The estimates of the regression coefficient obtained from this method were evaluated and were found to have low bias, low mean square error, and adequate coverage. In a real-life example pertaining to pancreatic cancer study, the proposed method performed admirably well and provided a more realistic interpretation about the effect of covariates (age and Karnofsky score) compared to a standard parametric (lognormal) model. Conclusion: In situations where there is no clear best parametric fit for time-to-event data with moderate level of censoring, the proposed method provides a robust alternative to obtain regression coefficients (both adjusted and unadjusted) with a performance comparable to that of a proportional hazards model.

scholarly journals Does Dynamic Tailoring of a Narrative-Driven Exergame Result in Higher User Engagement among Adolescents? Results from a Cluster-Randomized Controlled Trial

2021 ◽  
Vol 18 (14) ◽  
pp. 7444
Author(s):  
Ayla Schwarz ◽  
Greet Cardon ◽  
Sebastien Chastin ◽  
Jeroen Stragier ◽  
Lieven De Marez ◽  
...  
Keyword(s):  
Physical Activity ◽  
Randomized Controlled Trial ◽  
Physical Activity Intervention ◽  
Controlled Trial ◽  
Cluster Randomized Controlled Trial ◽  
Future Research ◽  
User Engagement ◽  
Technical Problems ◽  
Randomized Controlled ◽  
Cluster Randomized

Physical activity interventions for youth are direly needed given low adherence to physical activity guidelines, but many interventions suffer from low user engagement. Exergames that require bodily movement while played may provide an engaging form of physical activity intervention but are not perceived as engaging to all. This study aimed to evaluate whether dynamic tailoring in a narrative-driven mobile exergame for adolescents played in leisure settings, can create higher user engagement compared to a non-tailored exergame. A cluster-randomized controlled trial assessed differences in user engagement between a dynamically tailored (based on an accelerometer sensor integrated in a T-shirt) and non-tailored condition. In total, 94 participants (M age = 14.61 ± 0.1.93; 35% female) participated and were assigned to one of the two conditions. User engagement was measured via a survey and game metric data. User engagement was low in both conditions. Narrative sensation was higher in the dynamically tailored condition, but the non-tailored condition showed longer play-time. User suggestions to create a more appealing game included simple and more colorful graphics, avoiding technical problems, more variety and shorter missions and multiplayer options. Less cumbersome or more attractive sensing options than the smart T-shirt may offer a more engaging solution, to be tested in future research.

scholarly journals A family of partial-linear single-index models for analyzing complex environmental exposures with continuous, categorical, time-to-event, and longitudinal health outcomes

2020 ◽  
Author(s):  
Yuyan Wang ◽  
Yinxiang Wu ◽  
Melanie H. Jacobson ◽  
Myeonggyun Lee ◽  
Peng Jin ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Nonlinear Effects ◽  
Parametric Models ◽  
Time To Event ◽  
Joint Effects ◽  
Single Index ◽  
Hazards Model ◽  
Partial Linear

Abstract Background: Statistical methods to study the joint effects of environmental factors are of great importance to understand the impact of correlated exposures that may act synergistically or antagonistically on health outcomes. This study proposes a family of statistical models under a unified partial-linear single-index (PLSI) modeling framework, to assess the joint effects of environmental factors for continuous, categorical, time-to-event, and longitudinal outcomes. All PLSI models consist of a linear combination of exposures into a single index for practical interpretability of relative direction and importance, and a nonparametric link function for modeling flexibility. Methods: We presented PLSI linear regression and PLSI quantile regression for continuous outcome, PLSI generalized linear regression for categorical outcome, PLSI proportional hazards model for time-to-event outcome, and PLSI mixed-effects model for longitudinal outcome. These models were demonstrated using a dataset of 800 subjects from NHANES 2003-2004 survey including 8 environmental factors. Serum triglyceride concentration was analyzed as a continuous outcome and then dichotomized as a binary outcome. Simulations were conducted to demonstrate the PLSI proportional hazards model and PLSI mixed-effects model. The performance of PLSI models was compared with their counterpart parametric models. Results: PLSI linear, quantile, and logistic regressions showed similar results that the 8 environmental factors had both positive and negative associations with triglycerides, with a-Tocopherol having the most positive and trans-b-carotene the most negative association. For the time-to-event and longitudinal settings, simulations showed that PLSI models could correctly identify directions and relative importance for the 8 environmental factors. Compared with parametric models, PLSI models got similar results when the link function was close to linear, but clearly outperformed in simulations with nonlinear effects. Conclusions: We presented a unified family of PLSI models to assess the joint effects of exposures on four commonly-used types of outcomes in environmental research, and demonstrated their modeling flexibility and effectiveness, especially for studying environmental factors with mixed directional effects and/or nonlinear effects. Our study has expanded the analytical toolbox for investigating the complex effects of environmental factors. A practical contribution also included a coherent algorithm for all proposed PLSI models with R codes available.

scholarly journals A family of partial-linear single-index models for analyzing complex environmental exposures with continuous, categorical, time-to-event, and longitudinal health outcomes

2020 ◽  
Author(s):  
Yuyan Wang ◽  
Yinxiang Wu ◽  
Melanie Jacobson ◽  
Myeonggyun Lee ◽  
Peng Jin ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Nonlinear Effects ◽  
Parametric Models ◽  
Time To Event ◽  
Joint Effects ◽  
Single Index ◽  
Hazards Model ◽  
Partial Linear

Abstract Background: Statistical methods to study the joint effects of environmental factors are of great importance to understand the impact of correlated exposures that may act synergistically or antagonistically on health outcomes. This study proposes a family of statistical models under a unified partial-linear single-index (PLSI) modeling framework, to assess the joint effects of environmental factors for continuous, categorical, time-to-event, and longitudinal outcomes. All PLSI models consist of a linear combination of exposure factors into a single index for practical interpretability of relative direction and importance, and a nonparametric link function for modeling flexibility. Methods: We presented PLSI linear regression and PLSI quantile regression for continuous outcome, PLSI generalized linear regression for categorical outcome, PLSI proportional hazards model for time-to-event outcome, and PLSI mixed-effects model for longitudinal outcome. These models were demonstrated using a dataset of 800 subjects from NHANES 2003-2004 survey including 8 environmental factors. Serum triglyceride concentration was analyzed as a continuous outcome and then dichotomized as a binary outcome. Simulations were conducted to demonstrate the PLSI proportional hazards model and PLSI mixed-effects model. The performance of PLSI models was compared with their counterpart parametric models. Results: PLSI linear, quantile, and logistic regressions showed similar results that the 8 environmental factors had both positive and negative associations with triglycerides, with a-Tocopherol having the most positive and trans-b-carotene the most negative association. For the time-to-event and longitudinal settings, simulations showed that PLSI models could correctly identify directions and relative importance for the 8 environmental factors. Compared with parametric models, PLSI models got similar results when the link function was close to linear, but clearly outperformed in simulations with nonlinear effects. Conclusions: We presented a unified family of PLSI models to assess the joint effects of exposures on four commonly-used types of outcomes in environmental research, and demonstrated their modeling flexibility and effectiveness, especially for studying environmental factors with mixed directional effects and/or nonlinear effects. Our study has expanded the analytical toolbox for investigating the complex effects of environmental factors. A practical contribution also included a coherent algorithm for all proposed PLSI models with R codes available.

scholarly journals Health Care Professionals� Attitudes Toward, and Experiences of Using, a Culture-Sensitive Smartphone App for Women with Gestational Diabetes Mellitus: Qualitative Study (Preprint)

2017 ◽  
Author(s):  
Lisa Garnweidner-Holme ◽  
Therese Hoel Andersen ◽  
Mari Wastvedt Sando ◽  
Josef Noll ◽  
Mirjam Lukasse
Keyword(s):  
Diabetes Mellitus ◽  
Health Care ◽  
Randomized Controlled Trial ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Blood Sugar ◽  
Health Care Professionals ◽  
Controlled Trial ◽  
Technical Problems ◽  
Randomized Controlled

BACKGROUND The increasing prevalence of gestational diabetes mellitus (GDM) among women of different ethnic backgrounds provides new challenges for health care professionals, who often find it difficult to provide information about the management of this disease to such individuals. Mobile health (mHealth) may act as a useful tool for blood sugar control and care process enhancement. However, little is known about health care professionals’ experiences and attitudes toward the use of mHealth for women with GDM. OBJECTIVE The aim of this study was to explore how health care professionals perceived the provision of care to pregnant women who managed their GDM using the culture-sensitive Pregnant+ app in a randomized controlled trial. METHODS Individual interviews with 9 health care professionals providing care for women with GDM were conducted. Braun and Clark’s method of thematic content analysis inspired the analysis. This study included health care professionals who were primarily responsible for providing care to participants with GDM in the Pregnant+ randomized controlled trial at 5 diabetes outpatient clinics in Oslo, Norway. RESULTS Health care professionals perceived mHealth, particularly the Pregnant+ app, as an appropriate tool for the care of women with GDM, who were described as individuals comprising a heterogeneous, motivated group that could be easily approached with health-related information. Some participants reported challenges with respect to provision of advice to women with different food cultures. The advantages of the Pregnant+ app included provision of information that women could access at home, the information provided being perceived as trustworthy by health care professionals, the culture sensitivity of the app, and the convenience for women to register blood sugar levels. Technical problems, particularly those associated with the automatic transfer of blood glucose measurements, were identified as the main barrier to the use of the Pregnant+ app. Strict inclusion criteria and the inclusion of participants who could not speak Norwegian were the main challenges in the recruitment process for the randomized controlled trial. CONCLUSIONS The findings of this study suggest that mHealth is a useful tool to enhance the care provided by health care professionals to women with GDM. Future mobile apps for the management of GDM should be developed by a trustworthy source and in cooperation with health care professionals. They should also be culture sensitive and should not exhibit technical problems.

scholarly journals Oxygen therapy in patients with ST elevation myocardial infarction based on the culprit vessel: results from the randomized controlled SOCCER trial

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Arash Mokhtari ◽  
Mahin Akbarzadeh ◽  
David Sparv ◽  
Pallonji Bhiladvala ◽  
Håkan Arheden ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Oxygen Therapy ◽  
Troponin T ◽  
Controlled Trial ◽  
St Elevation Myocardial Infarction ◽  
University Hospital ◽  
Myocardial Salvage ◽  
Technical Problems ◽  
Randomized Controlled ◽  
St Elevation

Abstract Background Oxygen (O2) treatment has been a cornerstone in the treatment of patients with myocardial infarction. Recent studies, however, state that supplemental O2 therapy may have no effect or harmful effects in these patients. The aim of this study was thus to evaluate the effect of O2 therapy in patients with ST Elevation Myocardial Infarction (STEMI) based on the culprit vessel; Left Anterior Descending Artery (LAD) or Non-LAD. Methods This was a two-center, investigator-initiated, single-blind, parallel-group, randomized controlled trial at the Skåne university hospital, Sweden. A simple computer-generated randomization was used. Patients were either randomized to standard care with O2 therapy (10 l/min) or air until the end of the primary percutaneous coronary intervention. The patients underwent a Cardiac Magnetic Resonance Imaging (CMRI) days 2–6. The main outcome measures were Myocardium at Risk (MaR), Infarct Size (IS) and Myocardial Salvage Index (MSI) as measured by CMRI, and median high-sensitive troponin T (hs-cTnT). Results A total of 229 patients were assessed for eligibility, and 160 of them were randomized to the oxygen or air arm. Because of primarily technical problems with the CMRI, 95 patients were included in the final analyses; 46 in the oxygen arm and 49 in the air arm. There were no significant differences between patients with LAD and Non-LAD as culprit vessel with regard to their allocation (oxygen or air) with regards to MSI, MaR, IS and hs-cTnT. Conclusion The results indicate that the location of the culprit vessel has probably no effect on the role of supplemental oxygen therapy in STEMI patients. Trial registration Swedish Medical Products Agency (EudraCT No. 2011–001452-11) and ClinicalTrials.gov Identifier (NCT01423929).

scholarly journals A family of partial-linear single-index models for analyzing complex environmental exposures with continuous, categorical, time-to-event, and longitudinal health outcomes

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Yuyan Wang ◽  
Yinxiang Wu ◽  
Melanie H. Jacobson ◽  
Myeonggyun Lee ◽  
Peng Jin ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Nonlinear Effects ◽  
Parametric Models ◽  
Time To Event ◽  
Joint Effects ◽  
Single Index ◽  
Hazards Model ◽  
Partial Linear

Abstract Background Statistical methods to study the joint effects of environmental factors are of great importance to understand the impact of correlated exposures that may act synergistically or antagonistically on health outcomes. This study proposes a family of statistical models under a unified partial-linear single-index (PLSI) modeling framework, to assess the joint effects of environmental factors for continuous, categorical, time-to-event, and longitudinal outcomes. All PLSI models consist of a linear combination of exposures into a single index for practical interpretability of relative direction and importance, and a nonparametric link function for modeling flexibility. Methods We presented PLSI linear regression and PLSI quantile regression for continuous outcome, PLSI generalized linear regression for categorical outcome, PLSI proportional hazards model for time-to-event outcome, and PLSI mixed-effects model for longitudinal outcome. These models were demonstrated using a dataset of 800 subjects from NHANES 2003–2004 survey including 8 environmental factors. Serum triglyceride concentration was analyzed as a continuous outcome and then dichotomized as a binary outcome. Simulations were conducted to demonstrate the PLSI proportional hazards model and PLSI mixed-effects model. The performance of PLSI models was compared with their counterpart parametric models. Results PLSI linear, quantile, and logistic regressions showed similar results that the 8 environmental factors had both positive and negative associations with triglycerides, with a-Tocopherol having the most positive and trans-b-carotene having the most negative association. For the time-to-event and longitudinal settings, simulations showed that PLSI models could correctly identify directions and relative importance for the 8 environmental factors. Compared with parametric models, PLSI models got similar results when the link function was close to linear, but clearly outperformed in simulations with nonlinear effects. Conclusions We presented a unified family of PLSI models to assess the joint effects of exposures on four commonly-used types of outcomes in environmental research, and demonstrated their modeling flexibility and effectiveness, especially for studying environmental factors with mixed directional effects and/or nonlinear effects. Our study has expanded the analytical toolbox for investigating the complex effects of environmental factors. A practical contribution also included a coherent algorithm for all proposed PLSI models with R codes available.

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