13. Regression / PSM Made Simple for NEET PG and FMGE

📌𝗝𝗼𝗶𝗻 𝗢𝘂𝗿 𝗧𝗲𝗹𝗲𝗴𝗿𝗮𝗺 𝗖𝗵𝗮𝗻𝗻𝗲𝗹 𝗛𝗲𝗿𝗲:-https://t.me/conceptualmedicine009 📌 𝐅𝐨𝐥𝐥𝐨𝐰 𝐨𝐧 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦:- https://www.instagram.com/conceptual_... Regression 🇮🇳 PSM Made Simple for NEET PG and FMGE | Linear vs Logistic, Poisson, Adjusted Effects, Diagnostics 📊 Regression estimates how an outcome changes with predictors while holding other factors constant, which is essential for Indian PSM analyses and viva. Linear regression predicts a continuous outcome like birth weight or BP using beta coefficients interpreted as mean change per unit predictor; key assumptions are linearity, independence, normality of residuals, and constant variance homoscedasticity checked by residual plots and Q Q plots; report beta with 95 percent confidence interval, R squared for variance explained, and use transformations or robust or weighted regression when assumptions fail. Logistic regression models binary outcomes like hypertension yes or no using log odds; exponentiated coefficients give odds ratios adjusted for other variables, with 95 percent confidence intervals and p values; assess discrimination by ROC AUC and calibration by calibration plot or Hosmer Lemeshow aware of limits; avoid interpreting odds ratio as risk ratio when outcome is common, or use log binomial or modified Poisson with robust standard errors for adjusted risk ratios in cohort or program evaluations. Count outcomes like cases per ward often use Poisson regression with log link and offset for population at risk; handle overdispersion with quasi Poisson or negative binomial models; zero inflation can require zero inflated models. Time to event uses Cox proportional hazards regression with hazard ratios and proportionality checks via Schoenfeld residuals and log log plots; add time varying covariates when proportional hazards is violated. Model building follows a causal diagram or prior knowledge rather than p value fishing; include confounders that change the effect estimate, use interaction terms for effect modification for example sex by exposure, and code categorical predictors with dummy indicators and a reference group; centre or standardise continuous predictors to aid interpretation. Guard against multicollinearity high variance inflation factor over 5 to 10, sparse data bias, separation in logistic use Firth or penalised likelihood, influential points Cook’s distance, and missing data apply multiple imputation rather than complete case if missing at random. Present adjusted effects with clarity for example adjusted odds ratio 1.8 for tobacco with 95 percent CI 1.3 to 2.5 and specify the adjustment set; report absolute effects when possible predicted probabilities or marginal effects to aid policy. With clustered or survey data NFHS, NSS, HMIS facilities use mixed effects multilevel models random intercepts or cluster robust standard errors and apply survey weights, strata, and PSU to get correct standard errors; for panel or repeated measures consider generalized estimating equations or random effects; for ecological comparisons remember ecological fallacy and prefer multilevel approaches. Good practice pre register the model, check assumptions, test sensitivity alternative specifications, and translate coefficients into program terms for India NTEP TB outcomes, NCD clinics, IDSP incidence, and RMNCH plus A indicators. In one line for viva regression turns associations into adjusted, interpretable effects when assumptions are checked, confounding is controlled, and diagnostics are transparent. 🧠📈✨ #PSM #SPM #CommunityMedicine #Biostatistics #Regression #LinearRegression #LogisticRegression #Poisson #NegativeBinomial #ModifiedPoisson #CoxModel #HazardRatio #OddsRatio #AdjustedEffects #Confounding #Interaction #Multicollinearity #ROC #AUC #Calibration #GoodnessOfFit #Residuals #RobustSE #MixedEffects #GEE #SurveyWeights #NFHS #IDSP #HMIS #NTEP #RMNCH #NEETPG #FMGE #FMG #MBBS #PublicHealthIndia #MedicalEducationIndia #ExamPrep #ConceptualMedicine #MedicalConcepts #NEETPGPrep #FMGE2025 #USMLE2025 #ClinicalMedicine #MBBSConcepts #NextExamPrep #MedSchoolMadeEasy #MedStudentLife #HighYieldMedicine #PathophysiologySimplified #LearnMedicineFast #VisualMedicine #MedicalMnemonics #CrackNEETPG #USMLEStep1Prep #MedEducationRevolution #MBBSShorts #DoctorInTheMaking