Paper 48: Smoking and Quality of Life - Is there really an association? Evidence from a Nepalese sample

Author

Lee Jones - Senior Biostatistician - Statistical Review

Published

March 29, 2026

References

Sagtani RA, Thapa S, Sagtani A (2019) Smoking and Quality of Life - Is there really an association? Evidence from a Nepalese sample. PLoS ONE 14(9): e0221799. https://doi.org/10.1371/journal.pone.0221799

Disclosure

This reproducibility project was conducted to the best of our ability, with careful attention to statistical methods and assumptions. The research team comprises four senior biostatisticians (three of whom are accredited), with 20 to 30 years of experience in statistical modelling and analysis of healthcare data. While statistical assumptions play a crucial role in analysis, their evaluation is inherently subjective, and contextual knowledge can influence judgements about the importance of assumption violations. Differences in interpretation may arise among statisticians and researchers, leading to reasonable disagreements about methodological choices.

Our approach aimed to reproduce published analyses as faithfully as possible, using the details provided in the original papers. We acknowledge that other statisticians may have differing success in reproducing results due to variations in data handling and implicit methodological choices not fully described in publications. However, we maintain that research articles should contain sufficient detail for any qualified statistician to reproduce the analyses independently.

Methods used in our reproducibility analyses

There were two parts to our study. First, 100 articles published in PLOS ONE were randomly selected from the health domain and sent for post-publication peer review by statisticians. Of these, 95 included linear regression analyses and were therefore assessed for reporting quality. The statisticians evaluated what was reported, including regression coefficients, 95% confidence intervals, and p-values, as well as whether model assumptions were described and how those assumptions were evaluated. This report provides a brief summary of the initial statistical review.

The second part of the study involved reproducing linear regression analyses for papers with available data to assess both computational and inferential reproducibility. All papers were initially assessed for data availability, and the statistical software used. From those with accessible data, the first 20 papers (from the original random sample) were evaluated for computational reproducibility. Within each paper, individual linear regression models were identified and assigned a unique number. A maximum of three models per paper were selected for assessment. When more than three models were reported, priority was given to the final model or the primary models of interest as identified by the authors; any remaining models were selected at random.

To assess computational reproducibility, differences between the original and reproduced results were evaluated using absolute discrepancies and rounding error thresholds, tailored to the number of decimal places reported in each paper. Results for each reported statistic, e.g., regression coefficient, were categorised as Reproduced, Incorrect Rounding, or Not Reproduced, depending on how closely they matched the original values. Each paper was then classified as Reproduced, Mostly Reproduced, Partially Reproduced, or Not Reproduced. The mostly reproduced category included cases with minor rounding or typographical errors, whereas partially reproduced indicated substantial errors were observed, but some results were reproduced.

For models deemed at least partially computationally reproducible, inferential reproducibility was further assessed by examining whether statistical assumptions were met and by conducting sensitivity analyses, including bootstrapping where appropriate. We examined changes in standardized regression coefficients, which reflect the change in the outcome (in standard deviation units) for a one standard deviation increase in the predictor. Meaningful differences were defined as a relative change of 10% or more, or absolute differences of 0.1 (moderate) and 0.2 (substantial). When non-linear relationships were identified, inferential reproducibility was assessed by comparing model fit measures, including R², Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). When the Gaussian distribution was not appropriate for the dependent variable, alternative distributions were considered, and model fit was evaluated using AIC and BIC.

Summary from statistical review

This observational study examined the association between smoking status and quality of life among patients attending a Nepalese dental clinic. Although multivariable regression analyses were reportedly conducted, the results were interpreted largely using univariate ANOVA and t-tests, creating an inconsistency between the stated methods and the inferential conclusions. The authors further performed stratified regression analyses for smokers and non-smokers without adequate justification or explanation. Given that the study title implies a direct comparison between smoking groups, this should have been explicitly tested, for example, by assessing and reporting interaction effects.

Collinearity was discussed; however, other key regression assumptions, including assessment of residual behaviour and influential observations, were not reported. A sample size calculation was provided, but the assumed effect size was not clearly specified, limiting the ability to determine whether the study was appropriately powered. Overall interpretation appeared to rely primarily on statistical significance, with little consideration of effect magnitude or clinical relevance. Linearity was not a concern, as predictors were categorised.

Several issues were identified in the reporting of regression results. Regression estimates were labelled as “AOR”, a term typically referring to adjusted odds ratios, yet the methods and tables indicate linear regression, and the reported values do not resemble odds ratios. Accordingly, these estimates are assumed to be linear regression coefficients. However, many confidence intervals were asymmetric, and in some cases, bounds appeared inconsistent with the reported estimates, suggesting reporting or transcription errors.

Data availability and software used

Data were provided in the supporting information in a wide-format Excel file with a limited data dictionary. Details of the statistical package used were not provided.

Regression sample

Data were potentially available but were not assessed for reproducibility, as reproduction was limited to the first 20 papers with accessible datasets.

Computational reproducibility results

Data were potentially available but were not assessed for computational reproducibility, as reproduction was limited to the first 20 papers with accessible datasets.

Inferential reproducibility results

Data were potentially available but were not assessed for inferential reproducibility, as reproduction was limited to the first 20 papers with accessible datasets.