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calculate_adherence = function(users = NULL, adherence_duration=100000, adherence_start=0){
filtered=baseline_adherence
  
  if (!is.null(users)) {
    filtered = filtered[id %in% users]
  }
  filtered = filtered[t1 < adherence_duration]
  
  adherence_dt = filtered[, .(id, t1=pmax(t1, adherence_start), t2=pmin(t2, adherence_duration), ace, bb, statin, age, gender, region, diabetes, 
                              baseline.condition,
                              ace_adherence = (pmin(t2, adherence_duration) - pmax(t1, adherence_start))*ace,
                              bb_adherence = (pmin(t2, adherence_duration) - pmax(t1, adherence_start))*bb,
                              statin_adherence = (pmin(t2, adherence_duration) - pmax(t1, adherence_start))*statin,
                              overall_adherence = ifelse(ace == 1 & bb == 1 & statin == 1, pmin(t2, adherence_duration) - pmax(t1, adherence_start), 0))]
  
  max_t2_by_id = adherence_dt[, .(max_t2 = max(t2-adherence_start)), by = id]

summed_adherence = adherence_dt[, .(sum_ace_adherence = sum(ace_adherence),
                                    sum_bb_adherence = sum(bb_adherence),
                                    sum_statin_adherence = sum(statin_adherence),
                                    sum_overall_adherence = sum(overall_adherence)),
                                by = .(id,age, gender, region, diabetes, baseline.condition)]
  joined_adherence = summed_adherence[max_t2_by_id, on = "id"]
  calculate_adherence = joined_adherence[, .(id, age, gender, region, diabetes, baseline.condition,
                                         ace_adherence_pct = sum_ace_adherence / max_t2,
                                         bb_adherence_pct = sum_bb_adherence / max_t2,
                                         statin_adherence_pct = sum_statin_adherence / max_t2,
                                         overall_adherence_pct = sum_overall_adherence / max_t2)]

  return(calculate_adherence)
}

#Data Creation
statin_initiated <- dat[statin == 1 & t1 < two_weeks, unique(id)]
bb_initiated <- dat[bb == 1 & t1 < two_weeks, unique(id)]
ace_initiated <- dat[ace == 1 & t1 < two_weeks, unique(id)]

q5_patients = dat[, ace_initiated_14 := ifelse(id %in% ace_initiated, 1, 0)]
q5_patients = dat[, bb_initiated_14 := ifelse(id %in% bb_initiated, 1, 0)]
q5_patients= dat[, statin_initiated_14 := ifelse(id %in% statin_initiated, 1, 0)]

q6_patients = merge(q5_patients, baseline, by="id")
q6_patients <- q6_patients[,.SD[1], id]

q9_initiated = q6_patients
q9_patients = dat[t2 >= followup_days, unique(id)]
q9_total_adherence = calculate_adherence(users=q9_patients, adherence_duration = followup_days, adherence_start=14)

# Left join based on 'id'
q9_data <- merge(q9_total_adherence, q9_initiated, by = "id", all.x = TRUE,suffixes = c("", "_y"))

# Drop redundant columns from the result
q9_data <- q9_data[, .(id, 
                     age, 
                     gender, 
                     region, 
                     diabetes, 
                     t1, 
                     t2, 
                     ace, 
                     bb, 
                     statin,
                     ace_initiated_14, 
                     bb_initiated_14, 
                     statin_initiated_14, 
                     baseline.condition, 
                     ace_adherence_pct, 
                     bb_adherence_pct, 
                     statin_adherence_pct, 
                     overall_adherence_pct)]

model_ace <- lm(ace_adherence_pct ~ age + gender + region + diabetes + baseline.condition + ace_initiated_14, data = q9_data)
coef_ace <- summary(model_ace)$coefficients
coef_ace_rounded <- apply(coef_ace, 2, round, digits = 2)
coef_ace_df <- as.data.frame(coef_ace_rounded )
datatable(coef_ace_df)