2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Cigarette pack size and consumption: an adaptive randomised controlled trial

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Observational evidence suggests that cigarette pack size – the number of cigarettes in a single pack – is associated with consumption but experimental evidence of a causal relationship is lacking. The tobacco industry is introducing increasingly large packs, in the absence of maximum cigarette pack size regulation. In Australia, the minimum pack size is 20 but packs of up to 50 cigarettes are available. We aimed to estimate the impact on smoking of reducing cigarette pack sizes from ≥25 to 20 cigarettes per pack.

          Method

          A two-stage adaptive parallel group RCT in which Australian smokers who usually purchase packs containing ≥25 cigarettes were randomised to use only packs containing either 20 (intervention) or their usual packs (control) for four weeks. The primary outcome, the average number of cigarettes smoked per day, was measured through collecting all finished cigarette packs, labelled with the number of cigarettes participants smoked. An interim sample size re-estimation was used to evaluate the possibility of detecting a meaningful difference in the primary outcome.

          Results

          The interim analysis, conducted when 124 participants had been randomised, suggested 1122 additional participants needed to be randomised for sufficient power to detect a meaningful effect. This exceeded pre-specified criteria for feasible recruitment, and data collection was terminated accordingly. Analysis of complete data ( n = 79) indicated that the mean cigarettes smoked per day was 15.9 (SD = 8.5) in the intervention arm and 16.8 (SD = 6.7) among controls (difference − 0.9: 95%CI = − 4.3, 2.6).

          Conclusion

          It remains unclear whether reducing cigarette pack sizes from ≥25 to 20 cigarettes reduces cigarette consumption. Importantly, the results of this study provide no evidence that capping cigarette pack sizes would be ineffective at reducing smoking. The limitations identified in this study can inform a more efficient RCT, which is urgently required to address the dearth of experimental evidence on the impact of large cigarette pack sizes on smoking.

          Trial registration

          10.1186/ISRCTN34202533

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12889-021-11413-4.

          Related collections

          Most cited references23

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

          Summary Background The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of risk factor exposure and attributable burden of disease. By providing estimates over a long time series, this study can monitor risk exposure trends critical to health surveillance and inform policy debates on the importance of addressing risks in context. Methods We used the comparative risk assessment framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2016. This study included 481 risk-outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk (RR) and exposure estimates from 22 717 randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources, according to the GBD 2016 source counting methods. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. Finally, we explored four drivers of trends in attributable burden: population growth, population ageing, trends in risk exposure, and all other factors combined. Findings Since 1990, exposure increased significantly for 30 risks, did not change significantly for four risks, and decreased significantly for 31 risks. Among risks that are leading causes of burden of disease, child growth failure and household air pollution showed the most significant declines, while metabolic risks, such as body-mass index and high fasting plasma glucose, showed significant increases. In 2016, at Level 3 of the hierarchy, the three leading risk factors in terms of attributable DALYs at the global level for men were smoking (124·1 million DALYs [95% UI 111·2 million to 137·0 million]), high systolic blood pressure (122·2 million DALYs [110·3 million to 133·3 million], and low birthweight and short gestation (83·0 million DALYs [78·3 million to 87·7 million]), and for women, were high systolic blood pressure (89·9 million DALYs [80·9 million to 98·2 million]), high body-mass index (64·8 million DALYs [44·4 million to 87·6 million]), and high fasting plasma glucose (63·8 million DALYs [53·2 million to 76·3 million]). In 2016 in 113 countries, the leading risk factor in terms of attributable DALYs was a metabolic risk factor. Smoking remained among the leading five risk factors for DALYs for 109 countries, while low birthweight and short gestation was the leading risk factor for DALYs in 38 countries, particularly in sub-Saharan Africa and South Asia. In terms of important drivers of change in trends of burden attributable to risk factors, between 2006 and 2016 exposure to risks explains an 9·3% (6·9–11·6) decline in deaths and a 10·8% (8·3–13·1) decrease in DALYs at the global level, while population ageing accounts for 14·9% (12·7–17·5) of deaths and 6·2% (3·9–8·7) of DALYs, and population growth for 12·4% (10·1–14·9) of deaths and 12·4% (10·1–14·9) of DALYs. The largest contribution of trends in risk exposure to disease burden is seen between ages 1 year and 4 years, where a decline of 27·3% (24·9–29·7) of the change in DALYs between 2006 and 2016 can be attributed to declines in exposure to risks. Interpretation Increasingly detailed understanding of the trends in risk exposure and the RRs for each risk-outcome pair provide insights into both the magnitude of health loss attributable to risks and how modification of risk exposure has contributed to health trends. Metabolic risks warrant particular policy attention, due to their large contribution to global disease burden, increasing trends, and variable patterns across countries at the same level of development. GBD 2016 findings show that, while it has huge potential to improve health, risk modification has played a relatively small part in the past decade. Funding The Bill & Melinda Gates Foundation, Bloomberg Philanthropies.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Estimating the sample size for a pilot randomised trial to minimise the overall trial sample size for the external pilot and main trial for a continuous outcome variable

            Sample size justification is an important consideration when planning a clinical trial, not only for the main trial but also for any preliminary pilot trial. When the outcome is a continuous variable, the sample size calculation requires an accurate estimate of the standard deviation of the outcome measure. A pilot trial can be used to get an estimate of the standard deviation, which could then be used to anticipate what may be observed in the main trial. However, an important consideration is that pilot trials often estimate the standard deviation parameter imprecisely. This paper looks at how we can choose an external pilot trial sample size in order to minimise the sample size of the overall clinical trial programme, that is, the pilot and the main trial together. We produce a method of calculating the optimal solution to the required pilot trial sample size when the standardised effect size for the main trial is known. However, as it may not be possible to know the standardised effect size to be used prior to the pilot trial, approximate rules are also presented. For a main trial designed with 90% power and two-sided 5% significance, we recommend pilot trial sample sizes per treatment arm of 75, 25, 15 and 10 for standardised effect sizes that are extra small (≤0.1), small (0.2), medium (0.5) or large (0.8), respectively.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Adaptive designs in clinical trials: why use them, and how to run and report them

              Adaptive designs can make clinical trials more flexible by utilising results accumulating in the trial to modify the trial’s course in accordance with pre-specified rules. Trials with an adaptive design are often more efficient, informative and ethical than trials with a traditional fixed design since they often make better use of resources such as time and money, and might require fewer participants. Adaptive designs can be applied across all phases of clinical research, from early-phase dose escalation to confirmatory trials. The pace of the uptake of adaptive designs in clinical research, however, has remained well behind that of the statistical literature introducing new methods and highlighting their potential advantages. We speculate that one factor contributing to this is that the full range of adaptations available to trial designs, as well as their goals, advantages and limitations, remains unfamiliar to many parts of the clinical community. Additionally, the term adaptive design has been misleadingly used as an all-encompassing label to refer to certain methods that could be deemed controversial or that have been inadequately implemented. We believe that even if the planning and analysis of a trial is undertaken by an expert statistician, it is essential that the investigators understand the implications of using an adaptive design, for example, what the practical challenges are, what can (and cannot) be inferred from the results of such a trial, and how to report and communicate the results. This tutorial paper provides guidance on key aspects of adaptive designs that are relevant to clinical triallists. We explain the basic rationale behind adaptive designs, clarify ambiguous terminology and summarise the utility and pitfalls of adaptive designs. We discuss practical aspects around funding, ethical approval, treatment supply and communication with stakeholders and trial participants. Our focus, however, is on the interpretation and reporting of results from adaptive design trials, which we consider vital for anyone involved in medical research. We emphasise the general principles of transparency and reproducibility and suggest how best to put them into practice.
                Bookmark

                Author and article information

                Contributors
                tm388@medschl.cam.ac.uk
                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central (London )
                1471-2458
                18 July 2021
                18 July 2021
                2021
                : 21
                : 1420
                Affiliations
                [1 ]GRID grid.5335.0, ISNI 0000000121885934, Behaviour and Health Research Unit, , University of Cambridge, ; Cambridge, CB2 0SR UK
                [2 ]GRID grid.5337.2, ISNI 0000 0004 1936 7603, School of Psychological Science, University of Bristol, ; 12a Priory Road, Bristol, BS8 1TU UK
                [3 ]GRID grid.3263.4, ISNI 0000 0001 1482 3639, Centre for Behavioural Research in Cancer, ; Cancer Council Victoria 615 St Kilda Rd, Melbourne, Vic 3004 Australia
                [4 ]GRID grid.5337.2, ISNI 0000 0004 1936 7603, Department of Population Health Sciences, , Bristol Medical School, University of Bristol, ; Bristol, UK
                Author information
                http://orcid.org/0000-0003-3025-1129
                Article
                11413
                10.1186/s12889-021-11413-4
                8286601
                34275444
                69cb12e2-198f-4fbc-84dc-125a87e391c9
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 30 October 2020
                : 29 June 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: 206853/Z/17/Z
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2021

                Public health
                tobacco control,adaptive design,cigarette packaging,pack size
                Public health
                tobacco control, adaptive design, cigarette packaging, pack size

                Comments

                Comment on this article