scholarly journals SB4 A METHODOLOGY FOR ASSESSING TREATMENT EFFECT IN THE PRESENCE OF DISEASE SEVERITY AND COMORBIDITY IN RETROSPECTIVE OBSERVATIONAL STUDIES

2011 ◽  
Vol 14 (3) ◽  
pp. A5
Author(s):  
V.A. Kiri
Keyword(s):  
Disease Severity ◽  
Treatment Effect ◽  
Observational Studies

scholarly journals Assessing Treatment Effect Variation in Observational Studies: Results from a Data Challenge

2019 ◽  
Vol 5 (2) ◽  
pp. 21-35
Author(s):  
Carlos Carvalho ◽  
Avi Feller ◽  
Jared Murray ◽  
Spencer Woody ◽  
David Yeager
Keyword(s):  
Treatment Effect ◽  
Observational Studies

Inference for treatment effect parameters in potentially misspecified high-dimensional models

Biometrika ◽  
2020 ◽  
Author(s):  
Oliver Dukes ◽  
Stijn Vansteelandt
Keyword(s):  
Sample Size ◽  
Confidence Intervals ◽  
Treatment Effect ◽  
Observational Studies ◽  
Treatment Effects ◽  
Treatment Selection ◽  
High Dimensional ◽  
Selection Mechanism ◽  
Dimensional Models ◽  
Effect Parameter

Summary Eliminating the effect of confounding in observational studies typically involves fitting a model for an outcome adjusted for covariates. When, as often, these covariates are high-dimensional, this necessitates the use of sparse estimators, such as the lasso, or other regularization approaches. Naïve use of such estimators yields confidence intervals for the conditional treatment effect parameter that are not uniformly valid. Moreover, as the number of covariates grows with the sample size, correctly specifying a model for the outcome is nontrivial. In this article we deal with both of these concerns simultaneously, obtaining confidence intervals for conditional treatment effects that are uniformly valid, regardless of whether the outcome model is correct. This is done by incorporating an additional model for the treatment selection mechanism. When both models are correctly specified, we can weaken the standard conditions on model sparsity. Our procedure extends to multivariate treatment effect parameters and complex longitudinal settings.

scholarly journals Duration of Treatment Effect Should Be Considered in the Design and Interpretation of Clinical Trials: Results of a Discrete Choice Experiment

2019 ◽  
Vol 39 (4) ◽  
pp. 461-473 ◽  
Author(s):  
Bethan Copsey ◽  
James Buchanan ◽  
Raymond Fitzpatrick ◽  
Sarah E. Lamb ◽  
Susan J. Dutton ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Disease Severity ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Treatment Effect ◽  
Heart Attack ◽  
Choice Experiment ◽  
Ulcer Bleeding ◽  
Duration Of Treatment ◽  
Duration Of Effect

Objective. This study examined whether duration of treatment effect should be considered in a benefit-risk assessment using a case study of osteoarthritis medications. Study Design and Setting. A discrete choice experiment was completed by 300 residents of the United Kingdom with hip and/or knee osteoarthritis. In 16 choice tasks, participants selected their preferred option from 2 medications. Medications were described in terms of effect on pain, stiffness, and function; duration of treatment effect; and risk of heart attack and stomach ulcer bleeding. The analysis used mixed-effects logistic regression. Results. Pain, disease severity, and duration of treatment effect had the greatest influence on medication preferences, whereas stiffness did not significantly affect medication choice. Participants were willing to accept an increase in the risk of heart attack of 2.6% (95% confidence interval: 2.0% to 3.2%) to increase the duration of treatment effect from 1 month to 12 months. Reducing pain from moderate to mild was valued the same as increasing duration of effect from 1 month to 3 months; both were seen as equivalent to an absolute reduction of 1.2% in the risk of heart attack in the next year. Subgroup analysis suggested disease severity influenced patient preferences. Conclusions. Along with treatment benefits and risks, the results suggest that duration of treatment effect is an important factor in the medication choices of people with osteoarthritis. This could have implications for the design and interpretation of clinical trials, for example, incorporating longer-term surveillance of trial participants and accounting for duration of treatment effect in risk-benefit assessments. Future research is needed to assess whether these findings are generalizable to other samples, disease areas, and levels of duration of effect.

P46 Assessing the effects of treatment in observational studies: Adjusting for disease severity

1997 ◽  
Vol 18 (3) ◽  
pp. S138-S139
Author(s):  
Caroline A. Sabin ◽  
Amanda Mocroft ◽  
Andrew N. Phillips
Keyword(s):  
Disease Severity ◽  
Observational Studies

Trial designs, including observational to interventional

2020 ◽  
pp. 57-64
Author(s):  
Edo Richard
Keyword(s):  
Study Design ◽  
Treatment Effect ◽  
Clinical Relevance ◽  
Observational Studies ◽  
Research Question ◽  
Sample Size Calculation ◽  
Target Population ◽  
Ethical Considerations ◽  
Neuropsychiatric Diseases ◽  
The Right

Observational studies have taught us a lot about the origin of neurological and neuropsychiatric diseases. This chapter describes how we can translate this knowledge into action. Before engaging in a large RCT, several steps have to be taken. First, the potential for a treatment effect has to be compelling. The target population in the RCT has to resemble the population in which observational studies described an association. Second, the outcome of an RCT has to be chosen, and has to have clinical relevance or at least have the potential of clinical relevance in the future. Third, the right study design has to be decided on. Each research question will require a specific study design with accompanying sample size calculation. Lastly, specific ethical considerations have to be taken into account when designing and executing an intervention study. This chapter presents an overview of these issues.

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