Statistics, Research and Governance
Manit Arya, Taimur T. Shah, Jas S. Kalsi, Herman S. Fernando, Iqbal S. Shergill, Asif Muneer, Hashim U. Ahmed in MCQs for the FRCS(Urol) and Postgraduate Urology Examinations, 2020
A Kaplan-Meier curve is a commonly used analysis to calculate survival after a treatment using lifetime data. Censoring correctly describes the process of removing subjects from a survival analysis if they have not completed follow-up at the stipulated time-point and can be incorporated in a Kaplan-Meier analysis. Intention-to-treat analysis describes an analysis methodology that, instead of removing subjects that deviate from an agreed protocol, includes them all in the final analysis based on their initial treatment intent. Listwise deletion describes removal of a record if a single data item is missing. It is a generic method for handling missing data in statistics and is not particular to Kaplan-Meier analysis. Rounding up or rounding down a figure is commonly used in mathematics to simplify interpretation.
Epidemiology
John C Watkinson, Raymond W Clarke, Louise Jayne Clark, Adam J Donne, R James A England, Hisham M Mehanna, Gerald William McGarry, Sean Carrie in Basic Sciences Endocrine Surgery Rhinology, 2018
The results of trials can be analyzed in two ways. First, the comparison of the intervention can be carried out according to the intervention to which the patients were randomized (‘intention-to-treat’ analysis) or according to the treatment they actually received (‘per protocol’ analysis). The advantage of an intention-to-treat analysis is that the question that is being addressed corresponds exactly with the one clinicians and patients try to answer in clinical practice. The disadvantage is that if many patients do not receive the treatment they were randomized to, this would obscure the difference between the trial arms. Per protocol analysis, on the other hand, addresses which intervention is better more directly. With this form of analysis, the treatments are being compared according to the treatments that the patients actually received. The problem with this approach is that if many patients do not receive the treatment to which they were randomized, the study no longer represents an experimental study.
Development of palliative medicine in the United Kingdom and Ireland
Eduardo Bruera, Irene Higginson, Charles F von Gunten, Tatsuya Morita in Textbook of Palliative Medicine and Supportive Care, 2015
Another major source of bias in palliative care studies derives from the patients effectively analyzed for the outcome. In randomized clinical trials, the principle of "intention to treat analysis" is recommended to preserve the internal validity. This approach should be extended, whenever is possible, to all effectiveness studies. According to the "intention to treat principle," all randomized (or registered) patients should be included in the analyses of results, independently of their effective eligibility, of the received treatment, of the adherence and compliance to the treatments, and of the compliance to the assessments procedures. In palliative care research, the attrition rate is usually high and the contamination of exposure (especially in health services research) frequent.
What makes a great clinical trial in physiotherapy?
Published in Physiotherapy Theory and Practice, 2022
Joshua R. Zadro, Anita B. Amorim, Giovanni Ferreira, Xiaocong Hu, Rosa E. Becerra, Broti S. Reza, Samar Khan, Anne M. Moseley
The PEDro top five trials and physiotherapy trials published in the top medical journals have characteristics that may inform the design, conduct, and reporting of future trials. These trials often use concealed allocation and intention to treat analyses and have a low loss to follow-up. Concealed allocation reduces selection bias as the researcher randomizing participants does not know what the next treatment allocation will be (Elkins, 2013). Use of sealed, opaque, and consecutively numbered envelopes or an off-site investigator who randomizes participants are simple, low-cost ways to achieve this. Intention to treat analysis involves analyzing participants in the groups they were initially allocated to, regardless of whether they stopped treatment or crossed over to another group (Elkins and Moseley, 2015). This preserves the prognostic balance afforded by randomization and makes the effect estimates more realistic (McCoy, 2017). The loss to follow-up that exceeds 15% poses serious threats to the validity of a trial (Dettori, 2011; Maher et al., 2003). Strategies to reduce loss to follow-up include obtaining funding to employ research assistants who will rigorously implement trial follow-up procedures, offering participants in the control group the intervention at the end of the trial (Zadro et al., 2019), offering incentives for recruitment, or to complete assessments (e.g., payment and prize draw), tailoring the communication strategy with participants (e.g., text or e-mail reminders), and reducing the burden of follow-up assessments (Brueton et al., 2014).
A randomized controlled trial evaluating the effectiveness of an acceptance and commitment therapy–based bibliotherapy intervention among adults living with chronic pain
Published in Canadian Journal of Pain, 2019
Josée Veillette, Marie-Eve Martel, Frédérick Dionne
To reduce the potential bias of the effects of the treatment stemming from missing data, all participants who completed measures at T1 were considered in the data analysis following the intention-to-treat principle. This analysis strategy helps to maintain the integrity of the randomization process and provides a more realistic estimate of the effects of the intervention,46 given that dropout risk and lack of adherence to treatment protocol are relatively common as part of self-help psychological interventions. Furthermore, the intention-to-treat analysis helps to maintain the size of the sample in order to prevent a reduction in statistical power.47 To do this, the missing data were processed using the last observation carried forward method. This method assumes that the score of participants who only completed the pretest evaluations (T1) would remain at least equivalent during the posttest (T2). As such, for the statistical analyses, the last measure available for each individual was transposed to the second measurement time to avoid overestimating the results.
Effectiveness of music therapy on improving treatment motivation and emotion in female patients with methamphetamine use disorder: A randomized controlled trial
Published in Substance Abuse, 2020
Qianying Wu, Tianzhen Chen, Zhe Wang, Shujuan Chen, Jingying Zhang, Jiayi Bao, Hang Su, Haoye Tan, Haifeng Jiang, Jiang Du, Min Zhao
IBM SPSS version 22 was used to analyze all data obtained. Baseline characteristics were compared through the Chi squared test or Student’s t-test between the GMT + TAU group and TAU group. Each outcome was analyzed by Intention-to treat analysis methods. The multiple imputation method was performed with 50 imputations and the fully conditional specification method was used to impute missing data for all participants who completed the baseline test but neither the post 13-week intervention evaluation nor the 3-month follow-up evaluation. Repeated measures analysis of variance (ANOVA) was used to calculate the main effect of time, group, and group-time interaction, on the respective outcomes. Immediate posttreatment effects were calculated by performing the ANOVA with intergroup variable (condition: GMT + TAU vs. TAU) × intragroup variable (time: baseline vs. post 13-week intervention). Follow-up effects were calculated by performing the ANOVA with intergroup variable (condition: GMT + TAU vs. TAU) × intragroup variable (time: baseline vs. 3-month follow-up). Spearman’s rank correlation coefficient was used to assess the relationship between the changes of treatment motivation (from baseline to 3-month follow-up) and the total scores of the emotion-related scale (ie, SDS, SAS, ERQ, and IRI). The Bonferroni correlation for multiple testing was applied.
Related Knowledge Centers
- Clinical Trial
- Crossover Study
- Randomized Controlled Trial
- Type I & Type II Errors
- Adherence
- Random Assignment
- Lost to Follow-Up
- Type I & Type II Errors
- Analysis of Clinical Trials
- Protocol