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Real-World Evidence Generation
Published in Kelly H. Zou, Lobna A. Salem, Amrit Ray, Real-World Evidence in a Patient-Centric Digital Era, 2023
Joseph S. Imperato, Joseph P. Cook, Diana Morgenstern, Kim Gilchrist, Tarek A. Hassan, Jorge Saenz, Danute Ducinskiene
One of the factors that increases the internal validity of the study results, beside the randomization, is the blinding as part of the clinical trial design. Blinding in pRCTs is usually not feasible as a comparison of the new intervention versus the standard care will break this kind of blinding. Possible strategies to compensate for the unblinding is to focus on hard end points, use adjudicated committee and statisticians who are blinded during the trial conduct (Gamerman et al. 2019).
Study Limitations to Consider
Published in Lisa Chasan-Taber, Writing Grant Proposals in Epidemiology, Preventive Medicine, and Biostatistics, 2022
Do not limit the generalizability of your anticipated observed associations between exposure and outcome according to the representativeness of your study sample. Study populations should be selected to maximize internal validity and not to maximize representativeness. The issue of representativeness does not impact the generalizability of study findings. Instead, the generalizability of findings is based on the question of whether the physiologic or psychological mechanism between your exposure and outcome would be the same in other groups. In terms of our above example with the US nurses: Even though the nurses who volunteered to participate may not be representative of women who live in other parts of the country, and even if they are less likely to use hair dye, there is little basis for believing that the physiological relation between hair dye and breast cancer observed in this study population of nurses would be substantially different from that in most American women. Therefore, you can still generalize your findings of the association between hair dye and breast cancer to US women.
Introduction and Brief History of Structural Equation Modeling for Health and Medical Research
Published in Douglas D. Gunzler, Adam T. Perzynski, Adam C. Carle, Structural Equation Modeling for Health and Medicine, 2021
Douglas D. Gunzler, Adam T. Perzynski, Adam C. Carle
In observational research (and quasi-experiments), multiple strategies are available that attempt to mitigate the bias of the non-random sample in a comparison study. For example, propensity matching [22], an approach to balance treatment and control group observations across covariates, can be used for this purpose. Observational studies, due to the high possibility of confounding, often have poor internal validity. Similarly, due to possible confounding and lack of control over the study conditions, researchers conducting observational studies must confront challenges in establishing causality and poor external validity.
Perceived stress among Hispanic young adults: Impact of the coping with work and family stress program
Published in Journal of American College Health, 2023
Marivic B. Torregosa, Marcus Antonius Ynalvez, Maria del Rosario Benavides, Nandita Chaudhuri, Christopher Craddock
Study limitations limit the generalizability of our findings. Participants were lost to attrition due to participants’ changing priorities, academic demands, graduation, and transfer to another university. To minimize attrition, we delivered two modules per week instead of one module per week than was originally planned to contain the curriculum delivery within the regular semester while keeping intervention fidelity intact. Despite this change, participant attrition still occurred. In addition, there may have been other threats to internal validity such as other university-sponsored support group activities that occurred concurrently with this study. The study participants may have taken part in these activities that were unknown and unreported to the researchers. These potential threats to internal validity may have influenced our results.
The effects of chair intervention on lower back pain, discomfort and trunk muscle activation in office workers: a systematic review
Published in International Journal of Occupational Safety and Ergonomics, 2022
Sirinant Channak, Thaniya Klinsophon, Prawit Janwantanakul
All included studies failed to blind both care providers who administered the intervention and outcome assessors. Only one study was able to successfully blind participants. Blinding of all participants, care providers and outcome assessors is important for the internal validity of the study [43]. One of the most effective research tools to control for a placebo effect or Hawthorne effect is blinding [44]. Participants in the control group would have had no expectations, but the intervention group was prone to expectations. Blinding of care providers who administered the intervention or outcome assessors is also important to guarantee that the apparent effect of treatment is not due to the provider’s or assessor’s enthusiasm or lack of enthusiasm for the intervention or control condition [45]. One strategy that could be conducted to minimize the expectation bias of participants and outcome assessors is to set a trial in which two chair types are compared and ensure that the chairs are equally credible and acceptable to participants and outcome assessors, and that both participants and outcome assessors have limited experience or expectations regarding either chair type.
A spotlight on cross-sectional research: Addressing the issues of confounding and adjustment
Published in International Journal of Healthcare Management, 2021
Nestor Asiamah, Edwin Mends-Brew, Benjamin Kojo Teye Boison
To reiterate, previous researchers have considered one or two lurking variables in their effort to define and illustrate confounding, but more than two variables can confound a relationship in any cross-sectional research. This possibility is demonstrated later in this study, but it is incumbent on cross-sectional researchers to be prepared to identify an exhaustive set of alternative explanatory variables in their studies, without which they would risk the realization of an infiltrated effect. Similarly, cross-sectional researchers must treat multivariate confounding (i.e. the confounding influence of two or more background variables as shown in Figure 5) as one of the threats to internal validity and should be able to identify and adjust for all potential confounders. Furthermore, hypotheses are formulated and tested based on a theoretical framework, and the outcome of a hypothesis testing process can lead to the adaptation of existing theories through the study’s theoretical framework [19]. If so, it is ideal for confounders to be identified through a lens represented by the theoretical framework, and this lens can magnify the path towards using findings to adjust the relevant theories. We are keen to explain how theories can be integrated to select potential confounders for a cross-sectional study, but we need a comprehensive scenario to put forth this explanation.