scholarly journals Evaluation and validation of social and psychological markers in randomised trials of complex interventions in mental health: a methodological research programme

2015 ◽  
Vol 19 (93) ◽  
pp. 1-116 ◽  
Author(s):  
Graham Dunn ◽  
Richard Emsley ◽  
Hanhua Liu ◽  
Sabine Landau ◽  
Jonathan Green ◽  
...  
Keyword(s):  
Clinical Outcomes ◽  
Treatment Effect ◽  
Repeated Measures ◽  
Causal Effect ◽  
Complex Interventions ◽  
Research Programme ◽  
Measurement Properties ◽  
Stratified Medicine ◽  
Model Identifiability

BackgroundThe development of the capability and capacity to evaluate the outcomes of trials of complex interventions is a key priority of the National Institute for Health Research (NIHR) and the Medical Research Council (MRC). The evaluation of complex treatment programmes for mental illness (e.g. cognitive–behavioural therapy for depression or psychosis) not only is a vital component of this research in its own right but also provides a well-established model for the evaluation of complex interventions in other clinical areas. In the context of efficacy and mechanism evaluation (EME) there is a particular need for robust methods for making valid causal inference in explanatory analyses of the mechanisms of treatment-induced change in clinical outcomes in randomised clinical trials.ObjectivesThe key objective was to produce statistical methods to enable trial investigators to make valid causal inferences about the mechanisms of treatment-induced change in these clinical outcomes. The primary objective of this report is to disseminate this methodology, aiming specifically at trial practitioners.MethodsThe three components of the research were (1) the extension of instrumental variable (IV) methods to latent growth curve models and growth mixture models for repeated-measures data; (2) the development of designs and regression methods for parallel trials; and (3) the evaluation of the sensitivity/robustness of findings to the assumptions necessary for model identifiability. We illustrate our methods with applications from psychological and psychosocial intervention trials, keeping the technical details to a minimum, leaving the reporting of the more theoretical and mathematically demanding results for publication in appropriate specialist journals.ResultsWe show how to estimate treatment effects and introduce methods for EME. We explain the use of IV methods and principal stratification to evaluate the role of putative treatment effect mediators and therapeutic process measures. These results are extended to the analysis of longitudinal data structures. We consider the design of EME trials. We focus on designs to create convincing IVs, bearing in mind assumptions needed to attain model identifiability. A key area of application that has become apparent during this work is the potential role of treatment moderators (predictive markers) in the evaluation of treatment effect mechanisms for personalised therapies (stratified medicine). We consider the role of targeted therapies and multiarm trials and the use of parallel trials to help elucidate the evaluation of mediators working in parallel.ConclusionsIn order to demonstrate both efficacy and mechanism, it is necessary to (1) demonstrate a treatment effect on the primary (clinical) outcome, (2) demonstrate a treatment effect on the putative mediator (mechanism) and (3) demonstrate a causal effect from the mediator to the outcome. Appropriate regression models should be applied for (3) or alternative IV procedures, which account for unmeasured confounding, provided that a valid instrument can be identified. Stratified medicine may provide a setting where such instruments can be designed into the trial. This work could be extended by considering improved trial designs, sample size considerations and measurement properties.FundingThe project presents independent research funded under the MRC–NIHR Methodology Research Programme (grant reference G0900678).

Increasing Precision without Altering Treatment Effects: Repeated Measures Designs in Survey Experiments

2021 ◽  
pp. 1-18
Author(s):  
SCOTT CLIFFORD ◽  
GEOFFREY SHEAGLEY ◽  
SPENCER PISTON
Keyword(s):  
Treatment Effect ◽  
Repeated Measures ◽  
Treatment Effects ◽  
Repeated Measurements ◽  
Experimental Designs ◽  
Treatment Effect Size ◽  
Standard Design ◽  
Survey Experiments ◽  
Repeated Measures Designs ◽  
Alternative Designs

The use of survey experiments has surged in political science. The most common design is the between-subjects design in which the outcome is only measured posttreatment. This design relies heavily on recruiting a large number of subjects to precisely estimate treatment effects. Alternative designs that involve repeated measurements of the dependent variable promise greater precision, but they are rarely used out of fears that these designs will yield different results than a standard design (e.g., due to consistency pressures). Across six studies, we assess this conventional wisdom by testing experimental designs against each other. Contrary to common fears, repeated measures designs tend to yield the same results as more common designs while substantially increasing precision. These designs also offer new insights into treatment effect size and heterogeneity. We conclude by encouraging researchers to adopt repeated measures designs and providing guidelines for when and how to use them.

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 Statistical analysis of two arm randomized pre-post designs with one post-treatment measurement

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fei Wan
Keyword(s):  
Treatment Effect ◽  
Repeated Measures ◽  
Informed Choice ◽  
Clinical Effectiveness ◽  
Change Score ◽  
Post Treatment ◽  
Analysis Of Covariance ◽  
Alternative Methods ◽  
Main Effect ◽  
Comparable Performance

Abstract Background Randomized pre-post designs, with outcomes measured at baseline and after treatment, have been commonly used to compare the clinical effectiveness of two competing treatments. There are vast, but often conflicting, amount of information in current literature about the best analytic methods for pre-post designs. It is challenging for applied researchers to make an informed choice. Methods We discuss six methods commonly used in literature: one way analysis of variance (“ANOVA”), analysis of covariance main effect and interaction models on the post-treatment score (“ANCOVAI” and “ANCOVAII”), ANOVA on the change score between the baseline and post-treatment scores (“ANOVA-Change”), repeated measures (“RM”) and constrained repeated measures (“cRM”) models on the baseline and post-treatment scores as joint outcomes. We review a number of study endpoints in randomized pre-post designs and identify the mean difference in the post-treatment score as the common treatment effect that all six methods target. We delineate the underlying differences and connections between these competing methods in homogeneous and heterogeneous study populations. Results ANCOVA and cRM outperform other alternative methods because their treatment effect estimators have the smallest variances. cRM has comparable performance to ANCOVAI in the homogeneous scenario and to ANCOVAII in the heterogeneous scenario. In spite of that, ANCOVA has several advantages over cRM: i) the baseline score is adjusted as covariate because it is not an outcome by definition; ii) it is very convenient to incorporate other baseline variables and easy to handle complex heteroscedasticity patterns in a linear regression framework. Conclusions ANCOVA is a simple and the most efficient approach for analyzing pre-post randomized designs.

scholarly journals Osteoarthritis: Insights Offered by the Study of Bone Mass Genetics

2021 ◽  
Vol 19 (2) ◽  
pp. 115-122
Author(s):  
A. Hartley ◽  
C. L. Gregson ◽  
L. Paternoster ◽  
J. H. Tobias
Keyword(s):  
Bone Mass ◽  
Causal Effect ◽  
Mendelian Randomisation ◽  
Mineral Density ◽  
High Bone Mass ◽  
Increased Risk ◽  
High Bone ◽  
Genetic Mechanisms ◽  
Additional Support

Abstract Purpose of Review This paper reviews how bone genetics has contributed to our understanding of the pathogenesis of osteoarthritis. As well as identifying specific genetic mechanisms involved in osteoporosis which also contribute to osteoarthritis, we review whether bone mineral density (BMD) plays a causal role in OA development. Recent Findings We examined whether those genetically predisposed to elevated BMD are at increased risk of developing OA, using our high bone mass (HBM) cohort. HBM individuals were found to have a greater prevalence of OA compared with family controls and greater development of radiographic features of OA over 8 years, with predominantly osteophytic OA. Initial Mendelian randomisation analysis provided additional support for a causal effect of increased BMD on increased OA risk. In contrast, more recent investigation estimates this relationship to be bi-directional. However, both these findings could be explained instead by shared biological pathways. Summary Pathways which contribute to BMD appear to play an important role in OA development, likely reflecting shared common mechanisms as opposed to a causal effect of raised BMD on OA. Studies in HBM individuals suggest this reflects an important role of mechanisms involved in bone formation in OA development; however further work is required to establish whether the same applies to more common forms of OA within the general population.

scholarly journals CLINICAL OUTCOMES AND THE ROLE OF OBSERVATION IN EARLY‐STAGE FOLLICULAR LYMPHOMA

2021 ◽  
Vol 39 (S2) ◽  
Author(s):  
F. Sha ◽  
M. Okwali ◽  
A. Alperovich ◽  
P. C. Caron ◽  
L. Falchi ◽  
...  
Keyword(s):  
Follicular Lymphoma ◽  
Clinical Outcomes ◽  
Early Stage

Clinical outcomes from a prospective study evaluating the role of ambulation during medical termination of pregnancy

Contraception ◽  
2012 ◽  
Vol 85 (4) ◽  
pp. 398-401 ◽  
Author(s):  
Kamal Ojha ◽  
David J. Gillott ◽  
Patricia Wood ◽  
Elizabeth Valcarcel ◽  
Arti Matah ◽  
...  
Keyword(s):  
Prospective Study ◽  
Clinical Outcomes ◽  
Termination Of Pregnancy ◽  
Medical Termination Of Pregnancy ◽  
Medical Termination ◽  
A Prospective Study

Co-operation as a Conditional Strategy among Peers: Influence of Social Ecology and Kin Relations

1996 ◽  
Vol 19 (1) ◽  
pp. 39-52 ◽  
Author(s):  
Peter J. LaFreniere
Keyword(s):  
Peer Relations ◽  
Research Programme ◽  
Conditional Strategy ◽  
Social Unit ◽  
Kin Relations ◽  
Sources Of Variation ◽  
The Individual ◽  
Developmental Significance ◽  
The Relationship

The goal of this study is to analyse sources of variation, residing within the individual or within the relationship, in the ability to balance co-operative and competitive behaviours in a dyadic context. The ability to balance these two tendencies can be considered fundamental to successful adaptation within a social unit because co-operation may be essential in raising offspring, competing with other groups or in generating resources, whereas egoistic behaviour may protect the individual from exploitation or otherwise enhance reproductive success. Research is reviewed on the influence of social structures and relationships on co-operation in peer groups, and the origin and developmental significance of individual differences in co-operative abilities. Finally, a research programme investigating the conjunction of kin and peer relations is described, emphasising the role of affective synchrony, behavioural contingency, and reciprocity in shaping and sustaining co-operative behaviour as a conditional strategy.

scholarly journals Using genetic variants to evaluate the causal effect of serum vitamin D concentration on COVID-19 susceptibility, severity and hospitalization traits: a Mendelian randomization study

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Zhiyong Cui ◽  
Yun Tian
Keyword(s):  
Vitamin D ◽  
Fixed Effects ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Serum Vitamin ◽  
Vitamin D Supplementation ◽  
Sensitivity Analyses ◽  
Global Test ◽  
Serum Vitamin D

Abstract Background The coronavirus disease 2019 (COVID-19) pandemic has struck globally and is exerting a devastating toll on humans. The pandemic has led to calls for widespread vitamin D supplementation in public. However, evidence supporting the role of vitamin D in the COVID-19 pandemic remains controversial. Methods We performed a two-sample Mendelian randomization (MR) analysis to analyze the causal effect of the 25-hydroxyvitamin D [25(OH)D] concentration on COVID-19 susceptibility, severity and hospitalization traits by using summary-level GWAS data. The causal associations were estimated with inverse variance weighted (IVW) with fixed effects (IVW-fixed) and random effects (IVW-random), MR-Egger, weighted edian and MR Robust Adjusted Profile Score (MR.RAPS) methods. We further applied the MR Steiger filtering method, MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) global test and PhenoScanner tool to check and remove single nucleotide polymorphisms (SNPs) that were horizontally pleiotropic. Results We found no evidence to support the causal associations between the serum 25(OH)D concentration and the risk of COVID-19 susceptibility [IVW-fixed: odds ratio (OR) = 0.9049, 95% confidence interval (CI) 0.8197–0.9988, p = 0.0473], severity (IVW-fixed: OR = 1.0298, 95% CI 0.7699–1.3775, p = 0.8432) and hospitalized traits (IVW-fixed: OR = 1.0713, 95% CI 0.8819–1.3013, p = 0.4878) using outlier removed sets at a Bonferroni-corrected p threshold of 0.0167. Sensitivity analyses did not reveal any sign of horizontal pleiotropy. Conclusions Our MR analysis provided precise evidence that genetically lowered serum 25(OH)D concentrations were not causally associated with COVID-19 susceptibility, severity or hospitalized traits. Our study did not provide evidence assessing the role of vitamin D supplementation during the COVID-19 pandemic. High-quality randomized controlled trials are necessary to explore and define the role of vitamin D supplementation in the prevention and treatment of COVID-19.

scholarly journals The Causal Effect of Education on Health: What is the Role of Health Behaviors?

2012 ◽  
Author(s):  
Giorgio Brunello ◽  
Margherita Fort ◽  
Nicole E. Schneeweis ◽  
Rudolf Winter-Ebmer
Keyword(s):  
Health Behaviors ◽  
Causal Effect
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