Common Errors in Statistical Analysis of Clinical Research Data and How to Avoid Them

This article systematically reviews common misuses of statistical methods during data analysis based on the paper "Ten Common Statistical Errors and How to Avoid Them" published in the American Journal of Gastroenterology (Impact Factor: 9.566). It combines clinical research examples to detail the causes of errors and correct solutions, providing comprehensive methodological guidance for clinical researchers.

Error 1: Inferring Group Differences from Within-Group Comparisons

In randomized controlled trial (RCT) design, researchers often assess intervention effects by measuring changes at baseline levels and follow-up time points. A typical scenario is that the treatment group shows statistically significant differences before and after intervention (e.g., p<0.01), while the placebo group does not show significant changes. Many researchers directly conclude that "the treatment is effective," which has fundamental flaws.

For example, a study comparing antihypertensive drug efficacy shows an effect size of 25±10 (p<0.01) for Drug A within-group comparison, while Drug B shows an effect size of 10±10 (p>0.05). Although it seems Drug A is superior, inter-group comparison reveals no statistical difference with an effect size difference of 15±14 (p>0.05), indicating no real efficacy difference between both drugs. This error arises from confusing two different dimensions: "within-group effects" versus "between-group differences." Within-group comparisons only indicate longitudinal changes pre-and post-intervention; only between-group comparisons can truly reflect relative effectiveness among different interventions.

Solution: Interaction tests or mixed-effects models must be used for analyzing between-group differences. For repeated measures designs, linear mixed models (LMM) are recommended as they consider time effects along with group interaction terms; their advantage lies in handling missing data while accounting for intra-individual measurement correlations. Additionally, research protocols should clearly define whether primary analysis indicators focus on within or between variations to avoid selective reporting post hoc.

Error 2: Ignoring Correlation Structures in Data

Repeated measures designs are common in clinical studies such as multiple biopsies during endoscopic examinations or long-term monitoring physiological indicators over time. Treating these hierarchical structured data as independent samples leads to overestimation of statistical power manifested through underestimating standard errors, narrowing confidence intervals, and increasing Type I error risks.

A typical case compares two biopsy sampling methods: Method A takes one sample from each patient across different patients (100 people × 1 sample), whereas Method B takes ten samples from ten patients each (10 people × 10 samples). Despite having equal total sample sizes, Method B's actual effective sample size is much lower due to intra-patient correlation among samples compared with Method A’s approach where all samples are independent—applying conventional t-tests or ANOVA on Method B would erroneously yield more “significant” results.

Solution: Choose multilevel models (MLM) or generalized estimating equations (GEE) according to data structure requirements; construct three-level models for tissue biopsy data considering patient-sampling site-measurement index hierarchies; use autoregressive covariance structures when processing longitudinal follow-up data particularly regarding temporal correlations—reporting ICCs quantifying dependency degree becomes crucial when ICC >0.1 necessitating mixed-effects model usage.

Error 3: Failure To Use Corresponding Analytical Methods In Matched Designs

In case-control studies employing a matched design ratio like (1 :1), controlling confounding factors such as age & gender enhances efficiency but a frequent mistake occurs where collected matching datasets subsequently undergo ordinary logistic regression analyses leading towards loss advantages gained via matching itself causing biased OR estimates especially if matching variables correlate with exposure factors resulting biases exceeding above (20%). For instance, in gastric cancer risk factor investigations using unconditional logistic regression following age-gender match could inflate its OR values since matches alter original population distributions thereby regular regressions fail reflecting artificially designed conditional probability structures accordingly thus affecting validity significantly! n Solution: Conditional logistic regression modeling needs adoption treating every matched pair akin layers hence specific operational considerations include ensuring:(i)) Matching variables do not appear independently amongst predictors(ii)) Weighted likelihood functions utilized whenever applying (m:n)-matching strategies(iii)) Breslow method applied addressing tied-data situations(iv)); report ratios alongside balance checks confirming matched variable distributions! n ### Error Four - Incorrect Survival Data Analyses Methods Occurring During Cohort Studies Time-event dataset instances commonly exhibit mistakes comprising simple exclusion upon censoring records fixed-time truncation approaches erroneous employment substituting survival analyses yielding information losses/biases become pronounced particularly beyond tenth percentile censored cases traditional methodologies introduce unacceptable inaccuracies! n Taking colon cancer screening assessments illustrates high-risk participants prematurely exiting will underestimate incidence rates ignoring lost contacts treated per se 'unaffected' yields inflated screening efficiencies failing regard temporal discrepancies ultimately discarding prognostic insights! n Solutions: Systematic utilization incorporating survival analytic techniques includes:(i)): Descriptive phases employ Kaplan-Meier estimations calculating cumulative incidences adopting Greenwood formulas determining standard deviations;(ii)): Intergroup comparative evaluations suggest log-rank tests small-sized cohorts ((<50)) whilst larger groups leverage Wilcoxon enhancing early divergence sensitivity;(iii)): Multivariate assessments necessitate Cox proportional hazards modeling verifying assumptions surrounding proportionate risks e.g., Schoenfeld residual testing! (iv): Competing risks exist e.g., mortality interfering detection apply Fine-Gray frameworks instead! n ### Fifth Mistake - Insufficient Validation Of Assumptions Underpinning Statistical Tests Numerous inquiries directly implement parametric tests(e.g.t-tests/ANOVAs )without validating normality/homogeneity variances conditions neglect occurring notably smaller sampled scenarios((<30)) result conclusions faltering inaccurately assessing true significances observed wherein blood pressure analytics utilizing skewed diastolic pressures might mistakenly identify actual outcomes exhibiting probabilities nearing zero-point-zero-eight deemed falsely concluded below threshold value point-zero-four respectively.! Solutions involve establishing complete hypothesis verification processes including continuous variable scrutiny initially engaging Shapiro-Wilk Normality Assessments(small sets)/Kolmogorov-Smirnov Testings(larger datasets);(ii)); Levene’s methodology preferred evaluating variance homogeneities over Bartlett's approaches being sturdier recommendations applicable here ; non-compliance dictates alternative routes embracing Wilcoxon rank-sum/Kruskal-Wallis non-parametric alternatives meanwhile transformations necessary(including logarithmic adjustments!)must heed reversals correcting interpretative biases accurately . !### Sixth Pitfall – Uncorrected Multiple Comparisons In exploratory investigations simultaneously scrutinizing various outcome metrics/subgroups without managing multiplicities drastically elevates false-positive occurrences illustrating examining twenty biomarkers linked diseases even absent any genuine influences retains sixty-four percent chances encountering single significance markers emerging under p-value thresholds crossing point-zero-five criteria!. Solutions entail selecting adjustment strategies correlating respective investigation types:[a]]: Confirmatory inquiries adhere strictly Bonferroni corrections ((alpha=0 .05/n ));[b]]: Exploratory ones favor False Discovery Rate(FDR )control mechanisms[c]]; Gene-centric high-dimensional explorations utilize Storey's q-values systems[d]]; Graphical aids depicting Q-Q plots assist identifying anomalous divergences promptly! Notably adjustments occur uniformly across similar tier hypothesis verifications primarily separating main endpoints secondary ones rather than aggregating overall counts indiscriminately!.### References  * Original citations omitted * Additional methodological literature omitted *(Note:this document expands upon original literature extending error classifications supplement modern statistics applications detailing practical operation suggestions.New content derives entirely evidence-based guidelines authoritative texts.)