Vithal K Dhulkhed|Thrivikrama Padur Tantry|Madhuri S Kurdi
Minimising statistical errors in the research domain: Time to work harder and dig deeper!
Institute of Medical Sciences|Krishna Institute of Medical Sciences Deemed University|Karnataka Institute of Medical Sciences|Karnataka Institute of Medical Sciences|Institute of Medical Sciences|Krishna Institute of Medical Sciences Deemed University|Krishna Institute of Medical Sciences Deemed University|Karnataka Institute of Medical Sciences|Institute of Medical Sciences
advancement science essential identify correct errors occur process investigation surprising know significant number articles even topranking journals continue errors study design methodology statistics1 interesting note early years original articles submitted publication indian journal anaesthesia ija lacked sufficient details data analysis except mention mean p value 005however years significant improvement quality23456789101112 research process undergone continual evolution proper application sound research ethics study methodology ways interpretation observations presentation statistics cockpit research understanding design layout components along ability analyse understand output great influence maintaining quality strength research juncture time ask questions right approach followed design analysis clinical trials research question clearly framed methodologies appropriately designed robust statistical techniques answer research question much magnitude statistical errors present times efforts made elevate level literacy research methodology statistical techniques minimise errors research process presentation results answers questions highly variable diversity articles submitted ija even editorial board committed encouraging robust methods research publish highquality articles statistical mistakes commonly encountered peer review statistical errors lead misleading conclusions could unsafe use clinical practice errors occur various levels study errors frequently encountered peer review process errors study design methodology analysis data governed study design peer review improper inadequate study designs resonate aims study incorrectly formulatedweak research hypothesis unclear primarysecondary objectives unexplained measurement variables imprecise inclusion exclusion criteria define population characteristics sample derived lack randomisation blinding often observed sampling sample size errors sampling techniques methods often mentioned sampling errors occur researcher select sample represents entire population interest majority times sample size study based previous literature clinical knowledge consultation experts field pilot study13 scientifically speaking calculated primary well important secondary objectives many studies mention sufficient details sample size calculation14 lack determination sample size start study common observation many authors cook false information incorrect formulae regarding sample size estimation questioned review process errors analysis analytical test sometimes match type data wrongweak tests applied statistical inference commonly observed parameters like mean standard deviation sd calculated pain scores nonparametric data ttest erroneously applied instead nonparametric statistical tests often investigators specify whether test paired unpaired tests ttest wilcoxons test whether one twotailed authors sheer ignorance mention standard error wrongly place sd application independent ttest meant twogroup comparison multiple group study application place analysis variance anova unpaired ttest paired data incorrect frequently observed moreover power statistical test confidence intervals described adequately15 authors quite often define priori sample size study give adequate power sufficiently reduce type ii error post hoc analysis power suggestion peer reviewer situation advisable nevertheless case authors explanation provided outcome measures defined effect sizes post hoc analysis done common error checking parametricity data choosing test applied andor application parametric test cases data skewed variable data nonnormal distribution16 failure follow first step datadistributionanalysis may lead serious errors application wellknown tests like shapirowilk test kolmogorovsmirnov test juncture help reduce errors application statistical significance tests baseline characteristics like gender height weight etc study groups commonly observed practice advisable17 case confounding influence study findings adjusted including variables multivariate analysis advent modern computers fast computational power development statistical software packages made possible progress multivariate data analysis failure use multivariate techniques adjust confounding factors frequent observation longitudinal repeatedly measured data consecutive time points frequently erroneously analysed wrong assumption level measurement independent ttest applied two levels appropriate multivariate techniques available generalised linear model repeated measurements produce robust results however authors need aware fact multivariable analysis different multivariate analysis two terms interchangeably used multivariate analysis multiple outcomes analysed whereas multivariable analysis single outcome variable multiple independent predictor variables person verifying results make use software program used author may choose another suitable software package analysis robust statistical techniques however many articles fail give detailed information computer software program used analysis insufficient knowledge mathematical concepts underlying statistical technique statistical principles lead improper use packages errors presentations interpretations results disparities statistical results analysis abstracts main body text values text tablesfigures found many articles published frontline journals researchers describing statistical methods used mention tests used analysis appropriate correct tests distinct data sets variables tests applied analysis elaborated except abstract part research articles commonly use p values 005 001 indicating significance test results ideally exact calculated p values mentioned poor p value interpretation another observation many researchers unaware difference clinical significance statistical significance even small difference means two groups lead statistically significant result sample size unethically large small value difference may clinically relevant significant hence sample size calculated clinically useful effect size18 clinically useful confidence intervals cis outcome measures like mean odds ratios mentioned cis clinically useful provide range values around parameter expected application intervention clinical scenario missing data wrong use cis conducting subgroup analysis inadequate graphicalnumerical description basic data add fuel fire statistical errors glimpses statistics original articles published ija ninety per cent articles ija mention basic statistical tests viz students ttest fishers exact test occasionally correlation statements analysis19 articles utilise anova oneway factorial receiver operating characteristic roc curve bland altman plots interclass correlation coefficients202122 value additional basic advanced tests best decided dedicated statistician basic tests like multivariate anova linear regression multiple regression absolutely essential often applied researchers utilisation univariate analysis multiple regression authors however noteworthy232425 constitutes additional advanced statistics matter subjective opinion multiple regression least three predictors factor analysis cluster analysis cluster observations kmeans clustering discriminant analysis timeseries analysis constitute additional tests logistic regression coxlongitudinal regression survival methods mixed effect modelsdata transformation bootstrapping propensity score matching sensitivity analysis considered advanced methods statistical tests seldom reported ija need incorporated analytical methodology data collection stage2627 additionally conclusions based correlation statistics lag behind regression statistics due inclusion single predictor rather multiple ones28 nonetheless wide variety statistical tests including students ttest chisquare test fishers exact test anova mannwhitney test repeated multivariate anova wilcoxons signedrank test roc curve univariate forward stepwise multivariate logistic regression analysis applied studies published issue ija2930313233343536 importance graphic representation data text format cannot ignored37 nonreporting missing data failure suggest alternate hypotheses frequent avoidable errors research authors arriving conclusions entirely divergent suggested statistical analysis unfortunately common meets eye tables timeseries data pleasing comprehensive graphic format often eliminate need lengthy explanations38 common mistake absence error bars representing sd interquartile ranges graphs irrespective whether bar charts line graphs error bar shown one side line graph keep simple many authors fail mention number subjects sample time point xaxis insertion asterisk data comparisons statistically significant p value graph makes selfexplanatory39 reputed journals including ija welcome systematic reviews metaanalyses registered prospero actually published lack enthusiasm complexity statistical techniques involved discourage authors venturing pursuit nevertheless presently researchers luxury access free software help statistical analysis40 common errors observed metaanalysis nonrecognition heterogeneity involved included studies failure evaluate metaregressions ignoring publication bias performing sensitivity analysis derive meaningful conclusions several cochrane articles guidelines available guide researchers conducting metaanalysis special issue ija dedicated metaanalysis become reality coming days high impact factor journals like anesthesia analgesia anesthesiology publish articles conclusions made aggressive advanced statistical methods nonetheless use tests categorical tests anova original articles published journals showing declining trend favour important regression multivariate analytical techniques41 improve quality statistics original research articles important study data verifiable analysis results reproducible instance may contested suspicion suppression misreporting trial data study might led false conclusions regarding safety efficacy drug desirable provide raw data individual participant data help reanalyse authenticate results42 involvement statistician early phase designing study help strengthening study methodology producing reliable resultsthe study investigators authors follow principles essential statistical methods description results per revised guidelines international committee medical journal editors43 inclusion statistical experts editorial team publishing statisticsrelated topics frequently journal statistical reviews certainly help improving quality research national panel expert biostatisticians formed researchers reviewers contact statistical aspects research studies statistics invisible messenger quality research one tries understand language poetry one learn understand language statistics use appropriately minimal errors draw distinct meaningful clear conclusions financial support sponsorship nil conflicts interest conflicts interest